0s autopkgtest [04:42:28]: starting date and time: 2026-02-05 04:42:28+0000
  0s autopkgtest [04:42:28]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op
  0s autopkgtest [04:42:28]: host juju-7f2275-prod-proposed-migration-environment-15; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work._hy97meq/out --timeout-copy=6000 --needs-internet=try --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:glibc,src:chiark-tcl --apt-upgrade octave-statistics --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 '--env=ADT_TEST_TRIGGERS=glibc/2.42-2ubuntu5 chiark-tcl/1.3.7build1' -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-cpu2-ram4-disk20-arm64 --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-15@sto01-arm64-29.secgroup --name adt-resolute-arm64-octave-statistics-20260205-044228-juju-7f2275-prod-proposed-migration-environment-15-48ab01e4-4d4f-477a-86da-6fe1c8ee65d5 --image adt/ubuntu-resolute-arm64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-15 --net-id=net_prod-autopkgtest-workers-arm64 -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/
  3s Creating nova instance adt-resolute-arm64-octave-statistics-20260205-044228-juju-7f2275-prod-proposed-migration-environment-15-48ab01e4-4d4f-477a-86da-6fe1c8ee65d5 from image adt/ubuntu-resolute-arm64-server-20260204.img (UUID f58d981d-b271-4157-b9b1-fd704695563c)...
 65s autopkgtest [04:43:33]: testbed dpkg architecture: arm64
 65s autopkgtest [04:43:33]: testbed apt version: 3.1.14
 65s autopkgtest [04:43:33]: @@@@@@@@@@@@@@@@@@@@ test bed setup
 66s autopkgtest [04:43:33]: testbed release detected to be: None
 66s autopkgtest [04:43:34]: updating testbed package index (apt update)
 67s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB]
 67s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease
 67s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease
 67s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease
 67s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [227 kB]
 67s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1719 kB]
 67s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [5260 B]
 67s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [35.4 kB]
 67s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 Packages [265 kB]
 67s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 c-n-f Metadata [7328 B]
 68s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 Packages [52.9 kB]
 68s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 c-n-f Metadata [328 B]
 68s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 Packages [1481 kB]
 68s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 c-n-f Metadata [40.2 kB]
 68s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 Packages [26.0 kB]
 68s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 c-n-f Metadata [776 B]
 71s Fetched 3984 kB in 1s (3448 kB/s)
 72s Reading package lists...
 73s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease
 73s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease
 73s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease
 73s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease
 74s Reading package lists...
 74s Reading package lists...
 74s Building dependency tree...
 74s Reading state information...
 75s Calculating upgrade...
 75s The following packages will be upgraded:
 75s   libc-bin libc-gconv-modules-extra libc6 locales pollinate python3-linkify-it
 75s   python3-referencing sed
 75s 8 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
 75s Need to get 8110 kB of archives.
 75s After this operation, 0 B of additional disk space will be used.
 75s Get:1 http://ftpmaster.internal/ubuntu resolute/main arm64 sed arm64 4.9-2build3 [193 kB]
 75s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libc-gconv-modules-extra arm64 2.42-2ubuntu5 [1413 kB]
 76s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libc6 arm64 2.42-2ubuntu5 [1594 kB]
 78s Get:4 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libc-bin arm64 2.42-2ubuntu5 [599 kB]
 78s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 locales all 2.42-2ubuntu5 [4255 kB]
 81s Get:6 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-linkify-it all 2.0.3-1ubuntu3 [19.4 kB]
 81s Get:7 http://ftpmaster.internal/ubuntu resolute/main arm64 pollinate all 4.33-4ubuntu5 [14.0 kB]
 81s Get:8 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-referencing all 0.36.2-1ubuntu2 [22.2 kB]
 82s dpkg-preconfigure: unable to re-open stdin: No such file or directory
 82s Fetched 8110 kB in 6s (1278 kB/s)
 82s (Reading database ... 
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(Reading database ... 89360 files and directories currently installed.)
 82s Preparing to unpack .../sed_4.9-2build3_arm64.deb ...
 82s Unpacking sed (4.9-2build3) over (4.9-2build2) ...
 82s Setting up sed (4.9-2build3) ...
 82s (Reading database ... 
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(Reading database ... 89360 files and directories currently installed.)
 82s Preparing to unpack .../libc-gconv-modules-extra_2.42-2ubuntu5_arm64.deb ...
 82s Unpacking libc-gconv-modules-extra:arm64 (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 82s Setting up libc-gconv-modules-extra:arm64 (2.42-2ubuntu5) ...
 82s (Reading database ... 
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(Reading database ... 89360 files and directories currently installed.)
 82s Preparing to unpack .../libc6_2.42-2ubuntu5_arm64.deb ...
 82s Unpacking libc6:arm64 (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 83s Setting up libc6:arm64 (2.42-2ubuntu5) ...
 83s (Reading database ... 
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(Reading database ... 89360 files and directories currently installed.)
 83s Preparing to unpack .../libc-bin_2.42-2ubuntu5_arm64.deb ...
 83s Unpacking libc-bin (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 83s Setting up libc-bin (2.42-2ubuntu5) ...
 83s (Reading database ... 
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(Reading database ... 89360 files and directories currently installed.)
 83s Preparing to unpack .../locales_2.42-2ubuntu5_all.deb ...
 83s Unpacking locales (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 83s Preparing to unpack .../python3-linkify-it_2.0.3-1ubuntu3_all.deb ...
 83s Unpacking python3-linkify-it (2.0.3-1ubuntu3) over (2.0.3-1ubuntu2) ...
 83s Preparing to unpack .../pollinate_4.33-4ubuntu5_all.deb ...
 84s Unpacking pollinate (4.33-4ubuntu5) over (4.33-4ubuntu4) ...
 84s Preparing to unpack .../python3-referencing_0.36.2-1ubuntu2_all.deb ...
 84s Unpacking python3-referencing (0.36.2-1ubuntu2) over (0.36.2-1ubuntu1) ...
 84s Setting up locales (2.42-2ubuntu5) ...
 84s Generating locales (this might take a while)...
 86s   en_US.UTF-8... done
 86s Generation complete.
 86s Setting up pollinate (4.33-4ubuntu5) ...
 97s Setting up python3-linkify-it (2.0.3-1ubuntu3) ...
 97s Setting up python3-referencing (0.36.2-1ubuntu2) ...
 97s Processing triggers for man-db (2.13.1-1) ...
 98s Processing triggers for install-info (7.2-5) ...
 99s Processing triggers for systemd (259-1ubuntu3) ...
100s autopkgtest [04:44:08]: upgrading testbed (apt dist-upgrade and autopurge)
104s Reading package lists...
104s Building dependency tree...
104s Reading state information...
104s Calculating upgrade...
105s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
111s Reading package lists...
111s Building dependency tree...
111s Reading state information...
111s Solving dependencies...
112s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
113s autopkgtest [04:44:21]: rebooting testbed after setup commands that affected boot
173s autopkgtest [04:45:21]: testbed running kernel: Linux 6.18.0-9-generic #9-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 12 16:41:39 UTC 2026
176s autopkgtest [04:45:24]: @@@@@@@@@@@@@@@@@@@@ apt-source octave-statistics
178s Get:1 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (dsc) [2289 B]
178s Get:2 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (tar) [1424 kB]
178s Get:3 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (diff) [10.3 kB]
178s gpgv: Signature made Mon Oct 27 09:10:08 2025 UTC
178s gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
178s gpgv: Can't check signature: No public key
178s dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.6-2.dsc: no acceptable signature found
178s autopkgtest [04:45:26]: testing package octave-statistics version 1.7.6-2
179s autopkgtest [04:45:27]: build not needed
180s autopkgtest [04:45:28]: test command1: preparing testbed
180s Reading package lists...
180s Building dependency tree...
180s Reading state information...
180s Solving dependencies...
181s The following NEW packages will be installed:
181s   aglfn appstream autoconf automake autopoint autotools-dev cme comerr-dev cpp
181s   cpp-15 cpp-15-aarch64-linux-gnu cpp-aarch64-linux-gnu debhelper debugedit
181s   dh-autoreconf dh-octave dh-octave-autopkgtest dh-strip-nondeterminism
181s   diffstat dwz fontconfig fontconfig-config fonts-freefont-otf g++ g++-15
181s   g++-15-aarch64-linux-gnu g++-aarch64-linux-gnu gcc gcc-15
181s   gcc-15-aarch64-linux-gnu gcc-aarch64-linux-gnu gettext gfortran gfortran-15
181s   gfortran-15-aarch64-linux-gnu gfortran-aarch64-linux-gnu gnuplot-data
181s   gnuplot-nox hdf5-helpers intltool-debian krb5-multidev libaec-dev libaec0
181s   libalgorithm-c3-perl libaliased-perl libamd3 libaom3 libapp-cmd-perl
181s   libappstream5 libapt-pkg-perl libarchive-zip-perl libarpack2t64
181s   libarray-intspan-perl libasan8 libasound2-data libasound2t64 libasyncns0
181s   libavahi-client3 libavahi-common-data libavahi-common3
181s   libb-hooks-endofscope-perl libb-hooks-op-check-perl libb-keywords-perl
181s   libb2-1 libberkeleydb-perl libblas-dev libblas3 libboolean-perl
181s   libbrotli-dev libc-dev-bin libc6-dev libcairo2 libcamd3 libcapture-tiny-perl
181s   libcarp-assert-more-perl libcc1-0 libccolamd3 libcgi-pm-perl libcholmod5
181s   libclass-c3-perl libclass-data-inheritable-perl libclass-inspector-perl
181s   libclass-load-perl libclass-method-modifiers-perl libclass-tiny-perl
181s   libclass-xsaccessor-perl libclone-choose-perl libclone-perl libcolamd3
181s   libconfig-inifiles-perl libconfig-model-backend-yaml-perl
181s   libconfig-model-dpkg-perl libconfig-model-perl libconfig-tiny-perl
181s   libconst-fast-perl libconvert-binhex-perl libcpanel-json-xs-perl
181s   libcrypt-dev libcups2t64 libcurl4-openssl-dev libcxsparse4
181s   libdata-dpath-perl libdata-messagepack-perl libdata-optlist-perl
181s   libdata-section-perl libdata-validate-domain-perl libdata-validate-ip-perl
181s   libdata-validate-uri-perl libdatrie1 libde265-0 libdebhelper-perl
181s   libdeflate0 libdevel-callchecker-perl libdevel-size-perl
181s   libdevel-stacktrace-perl libdouble-conversion3 libduktape207
181s   libdynaloader-functions-perl libegl-mesa0 libegl1 libemail-address-xs-perl
181s   libencode-locale-perl liberror-perl libevent-2.1-7t64
181s   libexception-class-perl libexporter-lite-perl libexporter-tiny-perl
181s   libfftw3-bin libfftw3-dev libfftw3-double3 libfftw3-long3 libfftw3-single3
181s   libfile-basedir-perl libfile-find-rule-perl libfile-homedir-perl
181s   libfile-listing-perl libfile-sharedir-perl libfile-stripnondeterminism-perl
181s   libfile-which-perl libflac14 libfltk-gl1.3t64 libfltk1.3t64 libfont-ttf-perl
181s   libfontconfig1 libfontenc1 libfyaml0 libgbm1 libgcc-15-dev libgd3
181s   libgetopt-long-descriptive-perl libgfortran-15-dev libgfortran5 libgl-dev
181s   libgl1 libgl1-mesa-dri libgl2ps1.4 libglpk40 libglu1-mesa libglvnd0
181s   libglx-dev libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0t64
181s   libgnutls-openssl27t64 libgnutls28-dev libgomp1
181s   libgraphicsmagick++-q16-12t64 libgraphicsmagick-q16-3t64 libgraphite2-3
181s   libgssrpc4t64 libharfbuzz0b libhash-merge-perl libhdf5-310 libhdf5-cpp-310
181s   libhdf5-dev libhdf5-fortran-310 libhdf5-hl-310 libhdf5-hl-cpp-310
181s   libhdf5-hl-fortran-310 libheif-plugin-aomdec libheif-plugin-libde265
181s   libheif1 libhtml-form-perl libhtml-html5-entities-perl libhtml-parser-perl
181s   libhtml-tagset-perl libhtml-tokeparser-simple-perl libhtml-tree-perl
181s   libhttp-cookies-perl libhttp-date-perl libhttp-message-perl
181s   libhttp-negotiate-perl libhwasan0 libhwy1t64 libice6 libidn2-dev
181s   libimagequant0 libimport-into-perl libindirect-perl libinput-bin libinput10
181s   libintl-perl libio-html-perl libio-interactive-perl libio-socket-ssl-perl
181s   libio-string-perl libio-stringy-perl libio-tiecombine-perl libipc-run3-perl
181s   libipc-system-simple-perl libisl23 libiterator-perl libiterator-util-perl
181s   libitm1 libjack-jackd2-0 libjbig0 libjpeg-dev libjpeg-turbo8
181s   libjpeg-turbo8-dev libjpeg8 libjpeg8-dev libjson-maybexs-perl libjson-perl
181s   libjxl0.11 libkadm5clnt-mit12 libkadm5srv-mit12 libkdb5-10t64 libkrb5-dev
181s   liblapack-dev liblapack3 liblcms2-2 libldap-dev liblerc4
181s   liblingua-en-inflect-perl liblist-compare-perl liblist-moreutils-perl
181s   liblist-moreutils-xs-perl liblist-someutils-perl liblist-utilsby-perl
181s   liblog-any-adapter-screen-perl liblog-any-perl liblog-log4perl-perl liblsan0
181s   libltdl7 liblua5.4-0 liblwp-mediatypes-perl liblwp-protocol-https-perl
181s   libmailtools-perl libmarkdown2 libmd4c0 libmime-tools-perl libmldbm-perl
181s   libmodule-implementation-perl libmodule-pluggable-perl
181s   libmodule-runtime-perl libmoo-perl libmoox-aliases-perl libmouse-perl
181s   libmousex-nativetraits-perl libmousex-strictconstructor-perl libmp3lame0
181s   libmpc3 libmpg123-0t64 libmro-compat-perl libmtdev1t64
181s   libnamespace-clean-perl libncurses-dev libnet-domain-tld-perl
181s   libnet-http-perl libnet-ipv6addr-perl libnet-netmask-perl
181s   libnet-smtp-ssl-perl libnet-ssleay-perl libnetaddr-ip-perl libnghttp2-dev
181s   libnumber-compare-perl libobject-pad-perl libogg0 libopengl0 libopus0
181s   libp11-kit-dev libpackage-stash-perl libpango-1.0-0 libpangocairo-1.0-0
181s   libpangoft2-1.0-0 libparams-classify-perl libparams-util-perl
181s   libparams-validate-perl libparse-debcontrol-perl libparse-recdescent-perl
181s   libpath-iterator-rule-perl libpath-tiny-perl libpcre2-16-0
181s   libperl-critic-perl libperlio-gzip-perl libperlio-utf8-strict-perl
181s   libpixman-1-0 libpkgconf3 libpod-constants-perl libpod-parser-perl
181s   libpod-pom-perl libpod-spell-perl libportaudio2 libppi-perl
181s   libppix-quotelike-perl libppix-regexp-perl libppix-utils-perl
181s   libproc-processtable-perl libproxy1v5 libpsl-dev libpulse0 libqhull-r8.0
181s   libqrupdate1 libqscintilla2-qt6-15 libqscintilla2-qt6-l10n
181s   libqt6core5compat6 libqt6core6t64 libqt6dbus6 libqt6gui6 libqt6help6
181s   libqt6network6 libqt6opengl6 libqt6openglwidgets6 libqt6printsupport6
181s   libqt6sql6 libqt6widgets6 libqt6xml6 libreadline-dev libreadonly-perl
181s   libregexp-common-perl libregexp-pattern-license-perl libregexp-pattern-perl
181s   libregexp-wildcards-perl librole-tiny-perl librtmp-dev libsafe-isa-perl
181s   libsamplerate0 libsereal-decoder-perl libsereal-encoder-perl
181s   libset-intspan-perl libsharpyuv0 libsm6 libsndfile1
181s   libsoftware-copyright-perl libsoftware-license-perl
181s   libsoftware-licensemoreutils-perl libsort-versions-perl libspqr4
181s   libssh2-1-dev libssl-dev libstdc++-15-dev libstemmer0d libstrictures-perl
181s   libstring-copyright-perl libstring-escape-perl libstring-format-perl
181s   libstring-license-perl libstring-rewriteprefix-perl libsub-exporter-perl
181s   libsub-exporter-progressive-perl libsub-identify-perl libsub-install-perl
181s   libsub-name-perl libsub-quote-perl libsub-uplevel-perl libsuitesparseconfig7
181s   libsyntax-keyword-try-perl libsz2 libtask-weaken-perl libtasn1-6-dev
181s   libterm-readkey-perl libtest-exception-perl libtext-autoformat-perl
181s   libtext-glob-perl libtext-levenshtein-damerau-perl
181s   libtext-levenshteinxs-perl libtext-markdown-discount-perl
181s   libtext-reform-perl libtext-template-perl libtext-unidecode-perl
181s   libtext-wrapper-perl libtext-xslate-perl libthai-data libthai0 libtiff6
181s   libtime-duration-perl libtime-moment-perl libtimedate-perl libtoml-tiny-perl
181s   libtool libtry-tiny-perl libts0t64 libtsan2 libubsan1 libumfpack6
181s   libunbound8 libunicode-utf8-perl libunwind8 liburi-perl
181s   libvariable-magic-perl libvorbis0a libvorbisenc2 libvulkan1 libwacom-common
181s   libwacom9 libwayland-client0 libwebp7 libwebpmux3 libwmflite-0.2-7
181s   libwww-mechanize-perl libwww-perl libwww-robotrules-perl libx11-dev
181s   libx11-xcb1 libxau-dev libxaw7 libxcb-cursor0 libxcb-dri3-0 libxcb-glx0
181s   libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present0 libxcb-randr0
181s   libxcb-render-util0 libxcb-render0 libxcb-shape0 libxcb-shm0 libxcb-sync1
181s   libxcb-util1 libxcb-xfixes0 libxcb-xinput0 libxcb-xkb1 libxcb1-dev
181s   libxcursor1 libxdmcp-dev libxfixes3 libxfont2 libxft2 libxinerama1
181s   libxkbcommon-x11-0 libxkbfile1 libxml-libxml-perl
181s   libxml-namespacesupport-perl libxml-sax-base-perl libxml-sax-perl libxmu6
181s   libxpm4 libxrandr2 libxrender1 libxs-parse-keyword-perl
181s   libxs-parse-sublike-perl libxshmfence1 libxt6t64 libxxf86vm1
181s   libyaml-libyaml-perl libyaml-pp-perl libyaml-tiny-perl libzstd-dev
181s   licensecheck lintian linux-libc-dev lzip lzop m4 mesa-libgallium nettle-dev
181s   octave octave-common octave-dev octave-io octave-statistics
181s   octave-statistics-common patchutils perl-openssl-defaults perltidy pkgconf
181s   pkgconf-bin po-debconf rpcsvc-proto t1utils tex-common texinfo texinfo-lib
181s   unzip x11-common x11-xkb-utils x11proto-dev xorg-sgml-doctools
181s   xserver-common xtrans-dev xvfb zlib1g-dev
181s 0 upgraded, 495 newly installed, 0 to remove and 0 not upgraded.
181s Need to get 199 MB of archives.
181s After this operation, 734 MB of additional disk space will be used.
181s Get:1 http://ftpmaster.internal/ubuntu resolute/main arm64 libfyaml0 arm64 0.9.3-1 [246 kB]
181s Get:2 http://ftpmaster.internal/ubuntu resolute/main arm64 libstemmer0d arm64 3.0.1-1 [171 kB]
181s Get:3 http://ftpmaster.internal/ubuntu resolute/main arm64 libappstream5 arm64 1.1.1-1 [237 kB]
181s Get:4 http://ftpmaster.internal/ubuntu resolute/main arm64 appstream arm64 1.1.1-1 [71.1 kB]
181s Get:5 http://ftpmaster.internal/ubuntu resolute/main arm64 m4 arm64 1.4.20-2 [213 kB]
181s Get:6 http://ftpmaster.internal/ubuntu resolute/main arm64 autoconf all 2.72-3.1ubuntu1 [384 kB]
181s Get:7 http://ftpmaster.internal/ubuntu resolute/main arm64 autotools-dev all 20240727.1 [43.4 kB]
181s Get:8 http://ftpmaster.internal/ubuntu resolute/main arm64 automake all 1:1.18.1-3build1 [582 kB]
182s Get:9 http://ftpmaster.internal/ubuntu resolute/main arm64 autopoint all 0.23.2-1 [620 kB]
182s Get:10 http://ftpmaster.internal/ubuntu resolute/main arm64 libcapture-tiny-perl all 0.50-1 [20.7 kB]
182s Get:11 http://ftpmaster.internal/ubuntu resolute/main arm64 libparams-util-perl arm64 1.102-3build1 [20.6 kB]
182s Get:12 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-install-perl all 0.929-1 [9764 B]
182s Get:13 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-optlist-perl all 0.114-1 [9708 B]
182s Get:14 http://ftpmaster.internal/ubuntu resolute/main arm64 libb-hooks-op-check-perl arm64 0.22-3build2 [9348 B]
182s Get:15 http://ftpmaster.internal/ubuntu resolute/main arm64 libdynaloader-functions-perl all 0.004-2 [11.5 kB]
182s Get:16 http://ftpmaster.internal/ubuntu resolute/main arm64 libdevel-callchecker-perl arm64 0.009-2 [14.0 kB]
182s Get:17 http://ftpmaster.internal/ubuntu resolute/main arm64 libparams-classify-perl arm64 0.015-2build6 [19.8 kB]
182s Get:18 http://ftpmaster.internal/ubuntu resolute/main arm64 libmodule-runtime-perl all 0.018-1 [15.2 kB]
182s Get:19 http://ftpmaster.internal/ubuntu resolute/main arm64 libtry-tiny-perl all 0.32-1 [21.2 kB]
182s Get:20 http://ftpmaster.internal/ubuntu resolute/main arm64 libmodule-implementation-perl all 0.09-2 [12.0 kB]
182s Get:21 http://ftpmaster.internal/ubuntu resolute/main arm64 libpackage-stash-perl all 0.40-1 [19.5 kB]
182s Get:22 http://ftpmaster.internal/ubuntu resolute/universe arm64 libclass-load-perl all 0.25-2 [12.7 kB]
182s Get:23 http://ftpmaster.internal/ubuntu resolute/main arm64 libio-stringy-perl all 2.113-2 [45.3 kB]
182s Get:24 http://ftpmaster.internal/ubuntu resolute/universe arm64 libparams-validate-perl arm64 1.31-2build4 [52.1 kB]
182s Get:25 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-exporter-perl all 0.990-1 [49.0 kB]
182s Get:26 http://ftpmaster.internal/ubuntu resolute/universe arm64 libgetopt-long-descriptive-perl all 0.116-2 [25.0 kB]
182s Get:27 http://ftpmaster.internal/ubuntu resolute/universe arm64 libio-tiecombine-perl all 1.005-3 [9464 B]
182s Get:28 http://ftpmaster.internal/ubuntu resolute/universe arm64 libmodule-pluggable-perl all 5.2-5 [19.5 kB]
182s Get:29 http://ftpmaster.internal/ubuntu resolute/universe arm64 libstring-rewriteprefix-perl all 0.009-1 [6310 B]
182s Get:30 http://ftpmaster.internal/ubuntu resolute/universe arm64 libapp-cmd-perl all 0.338-1 [58.5 kB]
182s Get:31 http://ftpmaster.internal/ubuntu resolute/universe arm64 libboolean-perl all 0.46-3 [8430 B]
182s Get:32 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsub-uplevel-perl all 0.2800-3 [11.6 kB]
182s Get:33 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtest-exception-perl all 0.43-3 [13.4 kB]
182s Get:34 http://ftpmaster.internal/ubuntu resolute/universe arm64 libcarp-assert-more-perl all 2.9.0-1 [19.4 kB]
182s Get:35 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-which-perl all 1.27-2 [12.5 kB]
182s Get:36 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-homedir-perl all 1.006-2 [37.0 kB]
182s Get:37 http://ftpmaster.internal/ubuntu resolute/universe arm64 libclone-choose-perl all 0.010-2 [7738 B]
182s Get:38 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhash-merge-perl all 0.302-1 [13.0 kB]
182s Get:39 http://ftpmaster.internal/ubuntu resolute/main arm64 libjson-perl all 4.10000-1 [81.9 kB]
182s Get:40 http://ftpmaster.internal/ubuntu resolute/main arm64 libexporter-tiny-perl all 1.006003-1 [35.5 kB]
182s Get:41 http://ftpmaster.internal/ubuntu resolute/universe arm64 liblist-moreutils-xs-perl arm64 0.430-4build1 [39.9 kB]
182s Get:42 http://ftpmaster.internal/ubuntu resolute/universe arm64 liblist-moreutils-perl all 0.430-2 [38.2 kB]
182s Get:43 http://ftpmaster.internal/ubuntu resolute/universe arm64 liblog-log4perl-perl all 1.57-1 [345 kB]
182s Get:44 http://ftpmaster.internal/ubuntu resolute/main arm64 libmouse-perl arm64 2.6.1-1 [132 kB]
182s Get:45 http://ftpmaster.internal/ubuntu resolute/universe arm64 libmousex-nativetraits-perl all 1.09-3 [53.2 kB]
182s Get:46 http://ftpmaster.internal/ubuntu resolute/universe arm64 libmousex-strictconstructor-perl all 0.02-3 [4582 B]
182s Get:47 http://ftpmaster.internal/ubuntu resolute/universe arm64 libparse-recdescent-perl all 1.967015+dfsg-4 [139 kB]
182s Get:48 http://ftpmaster.internal/ubuntu resolute/main arm64 libpath-tiny-perl all 0.148-1 [47.9 kB]
182s Get:49 http://ftpmaster.internal/ubuntu resolute/universe arm64 libpod-pom-perl all 2.01-4 [61.3 kB]
182s Get:50 http://ftpmaster.internal/ubuntu resolute/main arm64 libregexp-common-perl all 2024080801-1 [162 kB]
182s Get:51 http://ftpmaster.internal/ubuntu resolute/main arm64 libyaml-tiny-perl all 1.76-1 [24.2 kB]
182s Get:52 http://ftpmaster.internal/ubuntu resolute/universe arm64 libconfig-model-perl all 2.155-1 [356 kB]
182s Get:53 http://ftpmaster.internal/ubuntu resolute/universe arm64 libyaml-pp-perl all 0.39.0-1 [107 kB]
182s Get:54 http://ftpmaster.internal/ubuntu resolute/universe arm64 cme all 1.043-2 [67.6 kB]
182s Get:55 http://ftpmaster.internal/ubuntu resolute/main arm64 libisl23 arm64 0.27-1build1 [676 kB]
182s Get:56 http://ftpmaster.internal/ubuntu resolute/main arm64 libmpc3 arm64 1.3.1-2 [55.6 kB]
182s Get:57 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-15-aarch64-linux-gnu arm64 15.2.0-12ubuntu1 [11.7 MB]
185s Get:58 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-15 arm64 15.2.0-12ubuntu1 [1030 B]
185s Get:59 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [5736 B]
185s Get:60 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp arm64 4:15.2.0-4ubuntu1 [22.4 kB]
185s Get:61 http://ftpmaster.internal/ubuntu resolute/main arm64 libdebhelper-perl all 13.28ubuntu1 [97.4 kB]
185s Get:62 http://ftpmaster.internal/ubuntu resolute/main arm64 libcc1-0 arm64 15.2.0-12ubuntu1 [49.0 kB]
185s Get:63 http://ftpmaster.internal/ubuntu resolute/main arm64 libgomp1 arm64 15.2.0-12ubuntu1 [147 kB]
185s Get:64 http://ftpmaster.internal/ubuntu resolute/main arm64 libitm1 arm64 15.2.0-12ubuntu1 [27.8 kB]
185s Get:65 http://ftpmaster.internal/ubuntu resolute/main arm64 libasan8 arm64 15.2.0-12ubuntu1 [2920 kB]
186s Get:66 http://ftpmaster.internal/ubuntu resolute/main arm64 liblsan0 arm64 15.2.0-12ubuntu1 [1316 kB]
186s Get:67 http://ftpmaster.internal/ubuntu resolute/main arm64 libtsan2 arm64 15.2.0-12ubuntu1 [2688 kB]
187s Get:68 http://ftpmaster.internal/ubuntu resolute/main arm64 libubsan1 arm64 15.2.0-12ubuntu1 [1175 kB]
188s Get:69 http://ftpmaster.internal/ubuntu resolute/main arm64 libhwasan0 arm64 15.2.0-12ubuntu1 [1638 kB]
188s Get:70 http://ftpmaster.internal/ubuntu resolute/main arm64 libgcc-15-dev arm64 15.2.0-12ubuntu1 [2600 kB]
189s Get:71 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-15-aarch64-linux-gnu arm64 15.2.0-12ubuntu1 [23.1 MB]
192s Get:72 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-15 arm64 15.2.0-12ubuntu1 [519 kB]
192s Get:73 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [1206 B]
192s Get:74 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc arm64 4:15.2.0-4ubuntu1 [5016 B]
192s Get:75 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libc-dev-bin arm64 2.42-2ubuntu5 [22.5 kB]
192s Get:76 http://ftpmaster.internal/ubuntu resolute/main arm64 linux-libc-dev arm64 6.18.0-9.9 [1834 kB]
192s Get:77 http://ftpmaster.internal/ubuntu resolute/main arm64 libcrypt-dev arm64 1:4.5.1-1 [123 kB]
192s Get:78 http://ftpmaster.internal/ubuntu resolute/main arm64 rpcsvc-proto arm64 1.4.3-1build1 [65.6 kB]
192s Get:79 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libc6-dev arm64 2.42-2ubuntu5 [1765 kB]
192s Get:80 http://ftpmaster.internal/ubuntu resolute/main arm64 libtool all 2.5.4-9 [169 kB]
192s Get:81 http://ftpmaster.internal/ubuntu resolute/main arm64 dh-autoreconf all 21 [12.5 kB]
192s Get:82 http://ftpmaster.internal/ubuntu resolute/main arm64 libarchive-zip-perl all 1.68-1 [90.2 kB]
192s Get:83 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-stripnondeterminism-perl all 1.15.0-1build1 [20.6 kB]
192s Get:84 http://ftpmaster.internal/ubuntu resolute/main arm64 dh-strip-nondeterminism all 1.15.0-1build1 [5110 B]
192s Get:85 http://ftpmaster.internal/ubuntu resolute/main arm64 debugedit arm64 1:5.2-3build1 [47.9 kB]
192s Get:86 http://ftpmaster.internal/ubuntu resolute/main arm64 dwz arm64 0.16-2 [113 kB]
192s Get:87 http://ftpmaster.internal/ubuntu resolute/main arm64 gettext arm64 0.23.2-1 [998 kB]
192s Get:88 http://ftpmaster.internal/ubuntu resolute/main arm64 intltool-debian all 0.35.0+20060710.6build1 [24.1 kB]
192s Get:89 http://ftpmaster.internal/ubuntu resolute/main arm64 po-debconf all 1.0.22 [215 kB]
192s Get:90 http://ftpmaster.internal/ubuntu resolute/main arm64 debhelper all 13.28ubuntu1 [916 kB]
192s Get:91 http://ftpmaster.internal/ubuntu resolute/universe arm64 aglfn all 1.7+git20191031.4036a9c-2build1 [33.2 kB]
192s Get:92 http://ftpmaster.internal/ubuntu resolute/universe arm64 gnuplot-data all 6.0.2+dfsg1-2ubuntu1 [76.0 kB]
192s Get:93 http://ftpmaster.internal/ubuntu resolute/universe arm64 fonts-freefont-otf all 20211204+svn4273-4build1 [4594 kB]
193s Get:94 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig-config arm64 2.17.1-3ubuntu1 [38.5 kB]
193s Get:95 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontconfig1 arm64 2.17.1-3ubuntu1 [144 kB]
193s Get:96 http://ftpmaster.internal/ubuntu resolute/main arm64 libpixman-1-0 arm64 0.46.4-1 [204 kB]
193s Get:97 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-render0 arm64 1.17.0-2ubuntu1 [16.4 kB]
193s Get:98 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-shm0 arm64 1.17.0-2ubuntu1 [5938 B]
193s Get:99 http://ftpmaster.internal/ubuntu resolute/main arm64 libxrender1 arm64 1:0.9.12-1 [19.5 kB]
193s Get:100 http://ftpmaster.internal/ubuntu resolute/main arm64 libcairo2 arm64 1.18.4-3 [556 kB]
193s Get:101 http://ftpmaster.internal/ubuntu resolute/main arm64 libsharpyuv0 arm64 1.5.0-0.1build1 [16.7 kB]
193s Get:102 http://ftpmaster.internal/ubuntu resolute/main arm64 libaom3 arm64 3.13.1-2 [1773 kB]
193s Get:103 http://ftpmaster.internal/ubuntu resolute/main arm64 libheif-plugin-aomdec arm64 1.21.2-1 [13.9 kB]
193s Get:104 http://ftpmaster.internal/ubuntu resolute/main arm64 libde265-0 arm64 1.0.16-1build1 [148 kB]
193s Get:105 http://ftpmaster.internal/ubuntu resolute/main arm64 libheif-plugin-libde265 arm64 1.21.2-1 [9696 B]
193s Get:106 http://ftpmaster.internal/ubuntu resolute/main arm64 libheif1 arm64 1.21.2-1 [502 kB]
193s Get:107 http://ftpmaster.internal/ubuntu resolute/main arm64 libimagequant0 arm64 2.18.0-1build1 [37.1 kB]
193s Get:108 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg-turbo8 arm64 2.1.5-4ubuntu2 [165 kB]
193s Get:109 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg8 arm64 8c-2ubuntu11 [2148 B]
193s Get:110 http://ftpmaster.internal/ubuntu resolute/main arm64 libdeflate0 arm64 1.23-2 [46.4 kB]
193s Get:111 http://ftpmaster.internal/ubuntu resolute/main arm64 libjbig0 arm64 2.1-6.1ubuntu3 [29.2 kB]
193s Get:112 http://ftpmaster.internal/ubuntu resolute/main arm64 liblerc4 arm64 4.0.0+ds-5ubuntu2 [174 kB]
193s Get:113 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebp7 arm64 1.5.0-0.1build1 [205 kB]
193s Get:114 http://ftpmaster.internal/ubuntu resolute/main arm64 libtiff6 arm64 4.7.0-3ubuntu3 [196 kB]
193s Get:115 http://ftpmaster.internal/ubuntu resolute/main arm64 libxpm4 arm64 1:3.5.17-1build3 [35.5 kB]
193s Get:116 http://ftpmaster.internal/ubuntu resolute/main arm64 libgd3 arm64 2.3.3-13ubuntu1 [124 kB]
193s Get:117 http://ftpmaster.internal/ubuntu resolute/main arm64 liblua5.4-0 arm64 5.4.8-1 [171 kB]
193s Get:118 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig arm64 2.17.1-3ubuntu1 [181 kB]
193s Get:119 http://ftpmaster.internal/ubuntu resolute/main arm64 libgraphite2-3 arm64 1.3.14-11ubuntu1 [72.1 kB]
193s Get:120 http://ftpmaster.internal/ubuntu resolute/main arm64 libharfbuzz0b arm64 12.3.2-1 [510 kB]
193s Get:121 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai-data all 0.1.30-1 [155 kB]
193s Get:122 http://ftpmaster.internal/ubuntu resolute/main arm64 libdatrie1 arm64 0.2.14-1 [19.6 kB]
193s Get:123 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai0 arm64 0.1.30-1 [18.3 kB]
193s Get:124 http://ftpmaster.internal/ubuntu resolute/main arm64 libpango-1.0-0 arm64 1.57.0-1 [238 kB]
193s Get:125 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangoft2-1.0-0 arm64 1.57.0-1 [51.5 kB]
193s Get:126 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangocairo-1.0-0 arm64 1.57.0-1 [27.9 kB]
193s Get:127 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebpmux3 arm64 1.5.0-0.1build1 [25.2 kB]
193s Get:128 http://ftpmaster.internal/ubuntu resolute/universe arm64 gnuplot-nox arm64 6.0.2+dfsg1-2ubuntu1 [971 kB]
193s Get:129 http://ftpmaster.internal/ubuntu resolute/universe arm64 dh-octave-autopkgtest all 1.14.0 [11.9 kB]
193s Get:130 http://ftpmaster.internal/ubuntu resolute/main arm64 libapt-pkg-perl arm64 0.1.43 [68.1 kB]
193s Get:131 http://ftpmaster.internal/ubuntu resolute/main arm64 libarray-intspan-perl all 2.004-2 [25.0 kB]
193s Get:132 http://ftpmaster.internal/ubuntu resolute/main arm64 libconfig-inifiles-perl all 3.000003-4 [38.5 kB]
193s Get:133 http://ftpmaster.internal/ubuntu resolute/main arm64 libyaml-libyaml-perl arm64 0.904.0+ds-1 [41.1 kB]
193s Get:134 http://ftpmaster.internal/ubuntu resolute/universe arm64 libconfig-model-backend-yaml-perl all 2.134-2 [10.5 kB]
193s Get:135 http://ftpmaster.internal/ubuntu resolute/universe arm64 libexporter-lite-perl all 0.09-2 [9748 B]
193s Get:136 http://ftpmaster.internal/ubuntu resolute/main arm64 libencode-locale-perl all 1.05-3 [11.6 kB]
193s Get:137 http://ftpmaster.internal/ubuntu resolute/main arm64 libtimedate-perl all 2.3300-2 [34.0 kB]
193s Get:138 http://ftpmaster.internal/ubuntu resolute/main arm64 libhttp-date-perl all 6.06-1 [10.2 kB]
193s Get:139 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-listing-perl all 6.16-1 [11.3 kB]
193s Get:140 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-tagset-perl all 3.24-1 [14.1 kB]
193s Get:141 http://ftpmaster.internal/ubuntu resolute/main arm64 liburi-perl all 5.34-2build1 [100 kB]
193s Get:142 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-parser-perl arm64 3.83-1build1 [85.3 kB]
193s Get:143 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-tree-perl all 5.07-3 [200 kB]
193s Get:144 http://ftpmaster.internal/ubuntu resolute/main arm64 libclone-perl arm64 0.47-1 [10.4 kB]
193s Get:145 http://ftpmaster.internal/ubuntu resolute/main arm64 libio-html-perl all 1.004-3 [15.9 kB]
193s Get:146 http://ftpmaster.internal/ubuntu resolute/main arm64 liblwp-mediatypes-perl all 6.04-2 [20.1 kB]
193s Get:147 http://ftpmaster.internal/ubuntu resolute/main arm64 libhttp-message-perl all 7.01-1ubuntu1 [76.1 kB]
193s Get:148 http://ftpmaster.internal/ubuntu resolute/main arm64 libhttp-cookies-perl all 6.11-1 [18.2 kB]
193s Get:149 http://ftpmaster.internal/ubuntu resolute/main arm64 libhttp-negotiate-perl all 6.01-2 [12.4 kB]
193s Get:150 http://ftpmaster.internal/ubuntu resolute/main arm64 perl-openssl-defaults arm64 7build4 [6710 B]
193s Get:151 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-ssleay-perl arm64 1.94-3 [307 kB]
193s Get:152 http://ftpmaster.internal/ubuntu resolute/main arm64 libio-socket-ssl-perl all 2.098-1 [205 kB]
193s Get:153 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-http-perl all 6.24-1build1 [21.7 kB]
193s Get:154 http://ftpmaster.internal/ubuntu resolute/main arm64 liblwp-protocol-https-perl all 6.14-1 [9040 B]
193s Get:155 http://ftpmaster.internal/ubuntu resolute/main arm64 libwww-robotrules-perl all 6.02-1build1 [12.4 kB]
193s Get:156 http://ftpmaster.internal/ubuntu resolute/main arm64 libwww-perl all 6.81-1build1 [141 kB]
193s Get:157 http://ftpmaster.internal/ubuntu resolute/main arm64 liberror-perl all 0.17030-1 [23.5 kB]
193s Get:158 http://ftpmaster.internal/ubuntu resolute/universe arm64 libparse-debcontrol-perl all 2.005-6 [20.4 kB]
193s Get:159 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsoftware-copyright-perl all 0.015-1 [14.4 kB]
193s Get:160 http://ftpmaster.internal/ubuntu resolute/universe arm64 libalgorithm-c3-perl all 0.11-2 [10.2 kB]
193s Get:161 http://ftpmaster.internal/ubuntu resolute/universe arm64 libclass-c3-perl all 0.35-2 [18.4 kB]
193s Get:162 http://ftpmaster.internal/ubuntu resolute/universe arm64 libmro-compat-perl all 0.15-2 [10.1 kB]
193s Get:163 http://ftpmaster.internal/ubuntu resolute/universe arm64 libdata-section-perl all 0.200008-1 [11.6 kB]
193s Get:164 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-template-perl all 1.61-1 [48.5 kB]
193s Get:165 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsoftware-license-perl all 0.104007-1 [123 kB]
193s Get:166 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsoftware-licensemoreutils-perl all 1.009-1 [21.5 kB]
193s Get:167 http://ftpmaster.internal/ubuntu resolute/main arm64 libsort-versions-perl all 1.62-3 [7378 B]
193s Get:168 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-reform-perl all 1.20-5 [35.4 kB]
193s Get:169 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-autoformat-perl all 1.750000-2 [29.8 kB]
193s Get:170 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-levenshtein-damerau-perl all 0.41-3 [10.8 kB]
193s Get:171 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtoml-tiny-perl all 0.20-1 [21.8 kB]
193s Get:172 http://ftpmaster.internal/ubuntu resolute/main arm64 libclass-inspector-perl all 1.36-3 [15.4 kB]
193s Get:173 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-sharedir-perl all 1.118-3 [14.0 kB]
193s Get:174 http://ftpmaster.internal/ubuntu resolute/main arm64 libindirect-perl arm64 0.39-2build5 [21.7 kB]
193s Get:175 http://ftpmaster.internal/ubuntu resolute/main arm64 libxs-parse-keyword-perl arm64 0.49-1 [61.1 kB]
193s Get:176 http://ftpmaster.internal/ubuntu resolute/main arm64 libxs-parse-sublike-perl arm64 0.41-1 [44.8 kB]
193s Get:177 http://ftpmaster.internal/ubuntu resolute/main arm64 libobject-pad-perl arm64 0.823-2 [129 kB]
193s Get:178 http://ftpmaster.internal/ubuntu resolute/main arm64 libsyntax-keyword-try-perl arm64 0.31-1 [24.4 kB]
193s Get:179 http://ftpmaster.internal/ubuntu resolute/main arm64 libio-interactive-perl all 1.027-1 [10.8 kB]
193s Get:180 http://ftpmaster.internal/ubuntu resolute/main arm64 liblog-any-perl all 1.718-1build1 [69.6 kB]
193s Get:181 http://ftpmaster.internal/ubuntu resolute/main arm64 liblog-any-adapter-screen-perl all 0.141-1 [12.9 kB]
193s Get:182 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-exporter-progressive-perl all 0.001013-3 [6718 B]
193s Get:183 http://ftpmaster.internal/ubuntu resolute/main arm64 libvariable-magic-perl arm64 0.64-1build1 [35.3 kB]
193s Get:184 http://ftpmaster.internal/ubuntu resolute/main arm64 libb-hooks-endofscope-perl all 0.28-2 [15.8 kB]
193s Get:185 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-identify-perl arm64 0.14-4 [9940 B]
193s Get:186 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-name-perl arm64 0.28-1 [10.5 kB]
193s Get:187 http://ftpmaster.internal/ubuntu resolute/main arm64 libnamespace-clean-perl all 0.27-2 [14.0 kB]
193s Get:188 http://ftpmaster.internal/ubuntu resolute/main arm64 libnumber-compare-perl all 0.03-3 [5974 B]
193s Get:189 http://ftpmaster.internal/ubuntu resolute/main arm64 libtext-glob-perl all 0.11-3 [6780 B]
193s Get:190 http://ftpmaster.internal/ubuntu resolute/main arm64 libpath-iterator-rule-perl all 1.015-2 [39.9 kB]
193s Get:191 http://ftpmaster.internal/ubuntu resolute/main arm64 libpod-parser-perl all 1.67-1 [80.6 kB]
193s Get:192 http://ftpmaster.internal/ubuntu resolute/main arm64 libpod-constants-perl all 0.19-2 [16.3 kB]
193s Get:193 http://ftpmaster.internal/ubuntu resolute/main arm64 libset-intspan-perl all 1.19-3 [24.8 kB]
193s Get:194 http://ftpmaster.internal/ubuntu resolute/main arm64 libstring-copyright-perl all 0.003014-1 [20.5 kB]
193s Get:195 http://ftpmaster.internal/ubuntu resolute/main arm64 libstring-escape-perl all 2010.002-3 [16.1 kB]
193s Get:196 http://ftpmaster.internal/ubuntu resolute/main arm64 libregexp-pattern-license-perl all 3.11.2-1 [85.3 kB]
193s Get:197 http://ftpmaster.internal/ubuntu resolute/main arm64 libregexp-pattern-perl all 0.2.14-2 [17.6 kB]
193s Get:198 http://ftpmaster.internal/ubuntu resolute/main arm64 libstring-license-perl all 0.0.11-1ubuntu1 [34.3 kB]
193s Get:199 http://ftpmaster.internal/ubuntu resolute/main arm64 licensecheck all 3.3.9-1ubuntu2 [37.2 kB]
193s Get:200 http://ftpmaster.internal/ubuntu resolute/main arm64 diffstat arm64 1.68-1 [29.1 kB]
193s Get:201 http://ftpmaster.internal/ubuntu resolute/main arm64 libberkeleydb-perl arm64 0.66-2 [118 kB]
193s Get:202 http://ftpmaster.internal/ubuntu resolute/main arm64 libclass-xsaccessor-perl arm64 1.19-4build6 [32.8 kB]
193s Get:203 http://ftpmaster.internal/ubuntu resolute/main arm64 libconfig-tiny-perl all 2.30-1 [14.7 kB]
193s Get:204 http://ftpmaster.internal/ubuntu resolute/main arm64 libconst-fast-perl all 0.014-2 [8034 B]
193s Get:205 http://ftpmaster.internal/ubuntu resolute/main arm64 libcpanel-json-xs-perl arm64 4.40-1 [115 kB]
193s Get:206 http://ftpmaster.internal/ubuntu resolute/main arm64 libaliased-perl all 0.34-3 [12.8 kB]
193s Get:207 http://ftpmaster.internal/ubuntu resolute/main arm64 libclass-data-inheritable-perl all 0.10-1 [8038 B]
193s Get:208 http://ftpmaster.internal/ubuntu resolute/main arm64 libdevel-stacktrace-perl all 2.0500-1 [22.1 kB]
193s Get:209 http://ftpmaster.internal/ubuntu resolute/main arm64 libexception-class-perl all 1.45-1 [28.6 kB]
193s Get:210 http://ftpmaster.internal/ubuntu resolute/main arm64 libiterator-perl all 0.03+ds1-2 [18.8 kB]
193s Get:211 http://ftpmaster.internal/ubuntu resolute/main arm64 libiterator-util-perl all 0.02+ds1-2 [14.1 kB]
193s Get:212 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-dpath-perl all 0.60-1 [37.3 kB]
193s Get:213 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-domain-tld-perl all 1.75-4 [29.0 kB]
193s Get:214 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-validate-domain-perl all 0.15-1 [10.4 kB]
193s Get:215 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-ipv6addr-perl all 1.02-1 [21.0 kB]
193s Get:216 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-netmask-perl all 2.0003-1build1 [24.8 kB]
193s Get:217 http://ftpmaster.internal/ubuntu resolute/main arm64 libnetaddr-ip-perl arm64 4.079+dfsg-2build5 [79.9 kB]
193s Get:218 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-validate-ip-perl all 0.31-1 [17.2 kB]
193s Get:219 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-validate-uri-perl all 0.07-3 [10.8 kB]
193s Get:220 http://ftpmaster.internal/ubuntu resolute/main arm64 libdevel-size-perl arm64 0.85-1 [19.1 kB]
193s Get:221 http://ftpmaster.internal/ubuntu resolute/main arm64 libemail-address-xs-perl arm64 1.05-1build5 [29.0 kB]
193s Get:222 http://ftpmaster.internal/ubuntu resolute/main arm64 libipc-system-simple-perl all 1.30-2 [22.3 kB]
193s Get:223 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-basedir-perl all 0.09-2 [14.4 kB]
193s Get:224 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-find-rule-perl all 0.35-1build1 [24.0 kB]
193s Get:225 http://ftpmaster.internal/ubuntu resolute/main arm64 libio-string-perl all 1.08-4 [11.1 kB]
193s Get:226 http://ftpmaster.internal/ubuntu resolute/main arm64 libfont-ttf-perl all 1.06-2 [323 kB]
193s Get:227 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-html5-entities-perl all 0.004-3 [21.6 kB]
193s Get:228 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-tokeparser-simple-perl all 3.16-4 [38.0 kB]
193s Get:229 http://ftpmaster.internal/ubuntu resolute/main arm64 libipc-run3-perl all 0.049-1 [28.8 kB]
193s Get:230 http://ftpmaster.internal/ubuntu resolute/main arm64 libjson-maybexs-perl all 1.004008-1 [11.1 kB]
193s Get:231 http://ftpmaster.internal/ubuntu resolute/main arm64 liblist-compare-perl all 0.55-2 [62.9 kB]
193s Get:232 http://ftpmaster.internal/ubuntu resolute/main arm64 liblist-someutils-perl all 0.59-1 [30.4 kB]
193s Get:233 http://ftpmaster.internal/ubuntu resolute/main arm64 liblist-utilsby-perl all 0.12-2 [14.9 kB]
193s Get:234 http://ftpmaster.internal/ubuntu resolute/main arm64 libmldbm-perl all 2.05-4 [16.0 kB]
193s Get:235 http://ftpmaster.internal/ubuntu resolute/main arm64 libclass-method-modifiers-perl all 2.15-1 [16.1 kB]
193s Get:236 http://ftpmaster.internal/ubuntu resolute/main arm64 libimport-into-perl all 1.002005-2 [10.7 kB]
193s Get:237 http://ftpmaster.internal/ubuntu resolute/main arm64 librole-tiny-perl all 2.002004-1 [16.3 kB]
193s Get:238 http://ftpmaster.internal/ubuntu resolute/main arm64 libsub-quote-perl all 2.006009-1ubuntu1 [20.3 kB]
193s Get:239 http://ftpmaster.internal/ubuntu resolute/main arm64 libmoo-perl all 2.005005-1 [47.4 kB]
193s Get:240 http://ftpmaster.internal/ubuntu resolute/main arm64 libstrictures-perl all 2.000006-1build1 [15.2 kB]
193s Get:241 http://ftpmaster.internal/ubuntu resolute/main arm64 libmoox-aliases-perl all 0.001006-2 [6796 B]
193s Get:242 http://ftpmaster.internal/ubuntu resolute/main arm64 libperlio-gzip-perl arm64 0.20-1build5 [14.6 kB]
193s Get:243 http://ftpmaster.internal/ubuntu resolute/main arm64 libperlio-utf8-strict-perl arm64 0.010-1build4 [11.1 kB]
193s Get:244 http://ftpmaster.internal/ubuntu resolute/main arm64 libproc-processtable-perl arm64 0.637-1 [35.1 kB]
193s Get:245 http://ftpmaster.internal/ubuntu resolute/main arm64 libregexp-wildcards-perl all 1.05-3 [12.9 kB]
193s Get:246 http://ftpmaster.internal/ubuntu resolute/main arm64 libsereal-decoder-perl arm64 5.004+ds-1build5 [102 kB]
193s Get:247 http://ftpmaster.internal/ubuntu resolute/main arm64 libsereal-encoder-perl arm64 5.004+ds-1build4 [104 kB]
193s Get:248 http://ftpmaster.internal/ubuntu resolute/main arm64 libterm-readkey-perl arm64 2.38-2build5 [23.2 kB]
193s Get:249 http://ftpmaster.internal/ubuntu resolute/main arm64 libtext-levenshteinxs-perl arm64 0.03-5build5 [8052 B]
193s Get:250 http://ftpmaster.internal/ubuntu resolute/main arm64 libmarkdown2 arm64 2.2.7-2.1build1 [37.6 kB]
193s Get:251 http://ftpmaster.internal/ubuntu resolute/main arm64 libtext-markdown-discount-perl arm64 0.18-1 [12.4 kB]
193s Get:252 http://ftpmaster.internal/ubuntu resolute/main arm64 libdata-messagepack-perl arm64 1.02-3 [31.0 kB]
193s Get:253 http://ftpmaster.internal/ubuntu resolute/main arm64 libtext-xslate-perl arm64 3.5.9-2build1 [161 kB]
193s Get:254 http://ftpmaster.internal/ubuntu resolute/main arm64 libtime-duration-perl all 1.21-2 [12.3 kB]
193s Get:255 http://ftpmaster.internal/ubuntu resolute/main arm64 libtime-moment-perl arm64 0.46-1 [76.7 kB]
193s Get:256 http://ftpmaster.internal/ubuntu resolute/main arm64 libunicode-utf8-perl arm64 0.63-1 [18.6 kB]
193s Get:257 http://ftpmaster.internal/ubuntu resolute/main arm64 libcgi-pm-perl all 4.71-1build1 [185 kB]
193s Get:258 http://ftpmaster.internal/ubuntu resolute/main arm64 libhtml-form-perl all 6.13-1build1 [31.3 kB]
193s Get:259 http://ftpmaster.internal/ubuntu resolute/main arm64 libwww-mechanize-perl all 2.20-1ubuntu1 [95.5 kB]
193s Get:260 http://ftpmaster.internal/ubuntu resolute/main arm64 libxml-namespacesupport-perl all 1.12-2 [13.5 kB]
193s Get:261 http://ftpmaster.internal/ubuntu resolute/main arm64 libxml-sax-base-perl all 1.09-3 [18.9 kB]
193s Get:262 http://ftpmaster.internal/ubuntu resolute/main arm64 libxml-sax-perl all 1.02+dfsg-4 [52.4 kB]
193s Get:263 http://ftpmaster.internal/ubuntu resolute/main arm64 libxml-libxml-perl arm64 2.0207+dfsg+really+2.0207-0ubuntu7 [305 kB]
193s Get:264 http://ftpmaster.internal/ubuntu resolute/main arm64 lzip arm64 1.25-4 [84.2 kB]
193s Get:265 http://ftpmaster.internal/ubuntu resolute/main arm64 lzop arm64 1.04-2build4 [82.6 kB]
193s Get:266 http://ftpmaster.internal/ubuntu resolute/main arm64 patchutils arm64 0.4.3-1 [79.7 kB]
193s Get:267 http://ftpmaster.internal/ubuntu resolute/main arm64 t1utils arm64 1.41-4build4 [59.8 kB]
193s Get:268 http://ftpmaster.internal/ubuntu resolute/main arm64 unzip arm64 6.0-29ubuntu1 [176 kB]
193s Get:269 http://ftpmaster.internal/ubuntu resolute/main arm64 lintian all 2.127.0ubuntu1 [1079 kB]
193s Get:270 http://ftpmaster.internal/ubuntu resolute/universe arm64 libconfig-model-dpkg-perl all 3.016 [194 kB]
193s Get:271 http://ftpmaster.internal/ubuntu resolute/main arm64 libconvert-binhex-perl all 1.125-3 [27.1 kB]
193s Get:272 http://ftpmaster.internal/ubuntu resolute/main arm64 libnet-smtp-ssl-perl all 1.04-2 [6218 B]
193s Get:273 http://ftpmaster.internal/ubuntu resolute/main arm64 libmailtools-perl all 2.22-1 [77.1 kB]
193s Get:274 http://ftpmaster.internal/ubuntu resolute/main arm64 libmime-tools-perl all 5.515-1 [187 kB]
193s Get:275 http://ftpmaster.internal/ubuntu resolute/universe arm64 libb-keywords-perl all 1.29-1 [10.4 kB]
193s Get:276 http://ftpmaster.internal/ubuntu resolute/universe arm64 libclass-tiny-perl all 1.008-2 [16.4 kB]
193s Get:277 http://ftpmaster.internal/ubuntu resolute/universe arm64 liblingua-en-inflect-perl all 1.905-2 [50.8 kB]
193s Get:278 http://ftpmaster.internal/ubuntu resolute/universe arm64 libpod-spell-perl all 1.27-1 [30.4 kB]
193s Get:279 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsafe-isa-perl all 1.000010-1build1 [7334 B]
193s Get:280 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtask-weaken-perl all 1.06-2 [7924 B]
193s Get:281 http://ftpmaster.internal/ubuntu resolute/universe arm64 libppi-perl all 1.284-1 [281 kB]
193s Get:282 http://ftpmaster.internal/ubuntu resolute/universe arm64 libreadonly-perl all 2.050-3 [19.9 kB]
193s Get:283 http://ftpmaster.internal/ubuntu resolute/universe arm64 libppix-quotelike-perl all 0.023-1 [71.6 kB]
193s Get:284 http://ftpmaster.internal/ubuntu resolute/universe arm64 libppix-regexp-perl all 0.091-1 [234 kB]
193s Get:285 http://ftpmaster.internal/ubuntu resolute/universe arm64 libppix-utils-perl all 0.003-2 [28.4 kB]
193s Get:286 http://ftpmaster.internal/ubuntu resolute/universe arm64 libstring-format-perl all 1.18-1build1 [8824 B]
193s Get:287 http://ftpmaster.internal/ubuntu resolute/universe arm64 perltidy all 20250105-1build1 [645 kB]
193s Get:288 http://ftpmaster.internal/ubuntu resolute/universe arm64 libperl-critic-perl all 1.156-1 [654 kB]
193s Get:289 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-wrapper-perl all 1.05-4 [9820 B]
193s Get:290 http://ftpmaster.internal/ubuntu resolute/main arm64 libsuitesparseconfig7 arm64 1:7.12.1+dfsg-1 [22.0 kB]
193s Get:291 http://ftpmaster.internal/ubuntu resolute/universe arm64 libamd3 arm64 1:7.12.1+dfsg-1 [34.6 kB]
193s Get:292 http://ftpmaster.internal/ubuntu resolute/main arm64 libblas3 arm64 3.12.1-7ubuntu1 [181 kB]
193s Get:293 http://ftpmaster.internal/ubuntu resolute/main arm64 libgfortran5 arm64 15.2.0-12ubuntu1 [451 kB]
193s Get:294 http://ftpmaster.internal/ubuntu resolute/main arm64 liblapack3 arm64 3.12.1-7ubuntu1 [2299 kB]
194s Get:295 http://ftpmaster.internal/ubuntu resolute/universe arm64 libarpack2t64 arm64 3.9.1-6 [94.9 kB]
194s Get:296 http://ftpmaster.internal/ubuntu resolute/universe arm64 libccolamd3 arm64 1:7.12.1+dfsg-1 [33.5 kB]
194s Get:297 http://ftpmaster.internal/ubuntu resolute/universe arm64 libcamd3 arm64 1:7.12.1+dfsg-1 [30.8 kB]
194s Get:298 http://ftpmaster.internal/ubuntu resolute/main arm64 libcolamd3 arm64 1:7.12.1+dfsg-1 [27.1 kB]
194s Get:299 http://ftpmaster.internal/ubuntu resolute/universe arm64 libcholmod5 arm64 1:7.12.1+dfsg-1 [626 kB]
194s Get:300 http://ftpmaster.internal/ubuntu resolute/universe arm64 libcxsparse4 arm64 1:7.12.1+dfsg-1 [78.1 kB]
194s Get:301 http://ftpmaster.internal/ubuntu resolute/main arm64 libfftw3-double3 arm64 3.3.10-2fakesync1build2 [399 kB]
194s Get:302 http://ftpmaster.internal/ubuntu resolute/main arm64 libfftw3-single3 arm64 3.3.10-2fakesync1build2 [611 kB]
194s Get:303 http://ftpmaster.internal/ubuntu resolute/main arm64 libxfixes3 arm64 1:6.0.0-2build2 [11.4 kB]
194s Get:304 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcursor1 arm64 1:1.2.3-1build1 [22.0 kB]
194s Get:305 http://ftpmaster.internal/ubuntu resolute/main arm64 libxft2 arm64 2.3.6-1build2 [43.2 kB]
194s Get:306 http://ftpmaster.internal/ubuntu resolute/main arm64 libxinerama1 arm64 2:1.1.4-3build2 [6486 B]
194s Get:307 http://ftpmaster.internal/ubuntu resolute/universe arm64 libfltk1.3t64 arm64 1.3.11-3 [599 kB]
194s Get:308 http://ftpmaster.internal/ubuntu resolute/main arm64 libglvnd0 arm64 1.7.0-3 [57.9 kB]
194s Get:309 http://ftpmaster.internal/ubuntu resolute/main arm64 libx11-xcb1 arm64 2:1.8.12-1build1 [8216 B]
194s Get:310 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-dri3-0 arm64 1.17.0-2ubuntu1 [7624 B]
194s Get:311 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-present0 arm64 1.17.0-2ubuntu1 [6198 B]
194s Get:312 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-randr0 arm64 1.17.0-2ubuntu1 [18.1 kB]
194s Get:313 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-sync1 arm64 1.17.0-2ubuntu1 [9620 B]
194s Get:314 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-xfixes0 arm64 1.17.0-2ubuntu1 [10.4 kB]
194s Get:315 http://ftpmaster.internal/ubuntu resolute/main arm64 libxshmfence1 arm64 1.3.3-1build1 [5482 B]
194s Get:316 http://ftpmaster.internal/ubuntu resolute/main arm64 mesa-libgallium arm64 25.3.3-1ubuntu1 [12.1 MB]
194s Get:317 http://ftpmaster.internal/ubuntu resolute/main arm64 libgbm1 arm64 25.3.3-1ubuntu1 [34.4 kB]
194s Get:318 http://ftpmaster.internal/ubuntu resolute/main arm64 libvulkan1 arm64 1.4.335.0-1 [171 kB]
194s Get:319 http://ftpmaster.internal/ubuntu resolute/main arm64 libgl1-mesa-dri arm64 25.3.3-1ubuntu1 [36.4 kB]
194s Get:320 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-glx0 arm64 1.17.0-2ubuntu1 [25.1 kB]
194s Get:321 http://ftpmaster.internal/ubuntu resolute/main arm64 libxxf86vm1 arm64 1:1.1.4-2 [10.2 kB]
194s Get:322 http://ftpmaster.internal/ubuntu resolute/main arm64 libglx-mesa0 arm64 25.3.3-1ubuntu1 [110 kB]
194s Get:323 http://ftpmaster.internal/ubuntu resolute/main arm64 libglx0 arm64 1.7.0-3 [33.0 kB]
194s Get:324 http://ftpmaster.internal/ubuntu resolute/main arm64 libgl1 arm64 1.7.0-3 [102 kB]
194s Get:325 http://ftpmaster.internal/ubuntu resolute/universe arm64 libfltk-gl1.3t64 arm64 1.3.11-3 [40.6 kB]
194s Get:326 http://ftpmaster.internal/ubuntu resolute/universe arm64 libgl2ps1.4 arm64 1.4.2+dfsg1-4 [44.7 kB]
194s Get:327 http://ftpmaster.internal/ubuntu resolute/main arm64 libltdl7 arm64 2.5.4-9 [43.3 kB]
194s Get:328 http://ftpmaster.internal/ubuntu resolute/universe arm64 libglpk40 arm64 5.0-2 [347 kB]
194s Get:329 http://ftpmaster.internal/ubuntu resolute/main arm64 libopengl0 arm64 1.7.0-3 [34.4 kB]
194s Get:330 http://ftpmaster.internal/ubuntu resolute/universe arm64 libglu1-mesa arm64 9.0.2-1.1build2 [145 kB]
194s Get:331 http://ftpmaster.internal/ubuntu resolute/main arm64 libhwy1t64 arm64 1.3.0-2 [513 kB]
194s Get:332 http://ftpmaster.internal/ubuntu resolute/main arm64 liblcms2-2 arm64 2.17-1 [170 kB]
194s Get:333 http://ftpmaster.internal/ubuntu resolute/main arm64 libjxl0.11 arm64 0.11.1-6ubuntu1 [909 kB]
194s Get:334 http://ftpmaster.internal/ubuntu resolute/main arm64 libwmflite-0.2-7 arm64 0.2.13-2 [67.2 kB]
194s Get:335 http://ftpmaster.internal/ubuntu resolute/universe arm64 libgraphicsmagick-q16-3t64 arm64 1.4+really1.3.45+hg17696-1build1 [1228 kB]
195s Get:336 http://ftpmaster.internal/ubuntu resolute/universe arm64 libgraphicsmagick++-q16-12t64 arm64 1.4+really1.3.45+hg17696-1build1 [120 kB]
195s Get:337 http://ftpmaster.internal/ubuntu resolute/universe arm64 libsz2 arm64 1.1.5-1 [17.0 kB]
195s Get:338 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-310 arm64 1.14.6+repack-2 [1287 kB]
195s Get:339 http://ftpmaster.internal/ubuntu resolute/main arm64 libasound2-data all 1.2.15.3-1ubuntu1 [21.4 kB]
195s Get:340 http://ftpmaster.internal/ubuntu resolute/main arm64 libasound2t64 arm64 1.2.15.3-1ubuntu1 [402 kB]
195s Get:341 http://ftpmaster.internal/ubuntu resolute/main arm64 libopus0 arm64 1.6.1-1 [3538 kB]
195s Get:342 http://ftpmaster.internal/ubuntu resolute/main arm64 libsamplerate0 arm64 0.2.2-4build2 [1341 kB]
195s Get:343 http://ftpmaster.internal/ubuntu resolute/main arm64 libjack-jackd2-0 arm64 1.9.22~dfsg-5 [309 kB]
195s Get:344 http://ftpmaster.internal/ubuntu resolute/main arm64 libasyncns0 arm64 0.8-7 [11.7 kB]
195s Get:345 http://ftpmaster.internal/ubuntu resolute/main arm64 libogg0 arm64 1.3.6-2 [23.0 kB]
195s Get:346 http://ftpmaster.internal/ubuntu resolute/main arm64 libflac14 arm64 1.5.0+ds-5 [141 kB]
195s Get:347 http://ftpmaster.internal/ubuntu resolute/main arm64 libmp3lame0 arm64 3.100-6build2 [144 kB]
195s Get:348 http://ftpmaster.internal/ubuntu resolute/main arm64 libmpg123-0t64 arm64 1.33.3-2 [177 kB]
195s Get:349 http://ftpmaster.internal/ubuntu resolute/main arm64 libvorbis0a arm64 1.3.7-3build1 [99.3 kB]
195s Get:350 http://ftpmaster.internal/ubuntu resolute/main arm64 libvorbisenc2 arm64 1.3.7-3build1 [79.8 kB]
195s Get:351 http://ftpmaster.internal/ubuntu resolute/main arm64 libsndfile1 arm64 1.2.2-4 [213 kB]
195s Get:352 http://ftpmaster.internal/ubuntu resolute/main arm64 libpulse0 arm64 1:17.0+dfsg1-2ubuntu4 [271 kB]
195s Get:353 http://ftpmaster.internal/ubuntu resolute/universe arm64 libportaudio2 arm64 19.7.0+git20251227.3270c9ae-0ubuntu1 [74.6 kB]
195s Get:354 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqhull-r8.0 arm64 2020.2-8 [191 kB]
195s Get:355 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqrupdate1 arm64 1.1.5-3 [36.9 kB]
195s Get:356 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqscintilla2-qt6-l10n all 2.14.1+dfsg-2 [55.5 kB]
195s Get:357 http://ftpmaster.internal/ubuntu resolute/universe arm64 libb2-1 arm64 0.98.1-1.1build2 [18.9 kB]
195s Get:358 http://ftpmaster.internal/ubuntu resolute/universe arm64 libdouble-conversion3 arm64 3.4.0-1 [39.1 kB]
195s Get:359 http://ftpmaster.internal/ubuntu resolute/main arm64 libpcre2-16-0 arm64 10.46-1 [225 kB]
195s Get:360 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6core6t64 arm64 6.9.2+dfsg-3ubuntu2 [1944 kB]
195s Get:361 http://ftpmaster.internal/ubuntu resolute/main arm64 libwayland-client0 arm64 1.24.0-2 [27.6 kB]
195s Get:362 http://ftpmaster.internal/ubuntu resolute/main arm64 libegl-mesa0 arm64 25.3.3-1ubuntu1 [115 kB]
195s Get:363 http://ftpmaster.internal/ubuntu resolute/main arm64 libegl1 arm64 1.7.0-3 [29.7 kB]
195s Get:364 http://ftpmaster.internal/ubuntu resolute/main arm64 x11-common all 1:7.7+24ubuntu1 [22.4 kB]
195s Get:365 http://ftpmaster.internal/ubuntu resolute/main arm64 libice6 arm64 2:1.1.1-1build1 [43.0 kB]
195s Get:366 http://ftpmaster.internal/ubuntu resolute/main arm64 libmtdev1t64 arm64 1.1.7-1build1 [14.9 kB]
195s Get:367 http://ftpmaster.internal/ubuntu resolute/main arm64 libwacom-common all 2.16.1-1 [113 kB]
195s Get:368 http://ftpmaster.internal/ubuntu resolute/main arm64 libwacom9 arm64 2.16.1-1 [28.2 kB]
195s Get:369 http://ftpmaster.internal/ubuntu resolute/main arm64 libinput-bin arm64 1.30.1-1 [24.9 kB]
195s Get:370 http://ftpmaster.internal/ubuntu resolute/main arm64 libinput10 arm64 1.30.1-1 [153 kB]
195s Get:371 http://ftpmaster.internal/ubuntu resolute/universe arm64 libmd4c0 arm64 0.5.2-2build1 [42.4 kB]
195s Get:372 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6dbus6 arm64 6.9.2+dfsg-3ubuntu2 [270 kB]
195s Get:373 http://ftpmaster.internal/ubuntu resolute/main arm64 libsm6 arm64 2:1.2.6-1 [16.6 kB]
195s Get:374 http://ftpmaster.internal/ubuntu resolute/universe arm64 libts0t64 arm64 1.22-1.1build2 [64.9 kB]
195s Get:375 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-util1 arm64 0.4.1-1build1 [10.7 kB]
195s Get:376 http://ftpmaster.internal/ubuntu resolute/universe arm64 libxcb-image0 arm64 0.4.0-2build2 [10.8 kB]
195s Get:377 http://ftpmaster.internal/ubuntu resolute/universe arm64 libxcb-render-util0 arm64 0.3.10-1build1 [10.4 kB]
195s Get:378 http://ftpmaster.internal/ubuntu resolute/universe arm64 libxcb-cursor0 arm64 0.1.5-1build1 [10.6 kB]
195s Get:379 http://ftpmaster.internal/ubuntu resolute/universe arm64 libxcb-icccm4 arm64 0.4.2-1build1 [10.9 kB]
195s Get:380 http://ftpmaster.internal/ubuntu resolute/universe arm64 libxcb-keysyms1 arm64 0.4.1-1build1 [8790 B]
195s Get:381 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-shape0 arm64 1.17.0-2ubuntu1 [6360 B]
195s Get:382 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-xinput0 arm64 1.17.0-2ubuntu1 [33.3 kB]
195s Get:383 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-xkb1 arm64 1.17.0-2ubuntu1 [32.2 kB]
195s Get:384 http://ftpmaster.internal/ubuntu resolute/main arm64 libxkbcommon-x11-0 arm64 1.12.3-1 [13.8 kB]
195s Get:385 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6gui6 arm64 6.9.2+dfsg-3ubuntu2 [3314 kB]
195s Get:386 http://ftpmaster.internal/ubuntu resolute/main arm64 libavahi-common-data arm64 0.8-17ubuntu2 [31.5 kB]
195s Get:387 http://ftpmaster.internal/ubuntu resolute/main arm64 libavahi-common3 arm64 0.8-17ubuntu2 [22.6 kB]
195s Get:388 http://ftpmaster.internal/ubuntu resolute/main arm64 libavahi-client3 arm64 0.8-17ubuntu2 [26.7 kB]
195s Get:389 http://ftpmaster.internal/ubuntu resolute/main arm64 libcups2t64 arm64 2.4.16-1ubuntu1 [271 kB]
195s Get:390 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6widgets6 arm64 6.9.2+dfsg-3ubuntu2 [2783 kB]
196s Get:391 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6printsupport6 arm64 6.9.2+dfsg-3ubuntu2 [222 kB]
196s Get:392 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqscintilla2-qt6-15 arm64 2.14.1+dfsg-2 [1160 kB]
196s Get:393 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6core5compat6 arm64 6.9.2-3build1 [144 kB]
196s Get:394 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6sql6 arm64 6.9.2+dfsg-3ubuntu2 [142 kB]
196s Get:395 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6help6 arm64 6.9.2-5 [192 kB]
196s Get:396 http://ftpmaster.internal/ubuntu resolute/main arm64 libduktape207 arm64 2.7.0+tests-0ubuntu4 [144 kB]
196s Get:397 http://ftpmaster.internal/ubuntu resolute/main arm64 libproxy1v5 arm64 0.5.12-1 [27.7 kB]
196s Get:398 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6network6 arm64 6.9.2+dfsg-3ubuntu2 [834 kB]
196s Get:399 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6opengl6 arm64 6.9.2+dfsg-3ubuntu2 [425 kB]
196s Get:400 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6openglwidgets6 arm64 6.9.2+dfsg-3ubuntu2 [43.8 kB]
196s Get:401 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqt6xml6 arm64 6.9.2+dfsg-3ubuntu2 [81.6 kB]
196s Get:402 http://ftpmaster.internal/ubuntu resolute/universe arm64 libspqr4 arm64 1:7.12.1+dfsg-1 [137 kB]
196s Get:403 http://ftpmaster.internal/ubuntu resolute/universe arm64 libumfpack6 arm64 1:7.12.1+dfsg-1 [254 kB]
196s Get:404 http://ftpmaster.internal/ubuntu resolute/universe arm64 libtext-unidecode-perl all 1.30-3 [105 kB]
196s Get:405 http://ftpmaster.internal/ubuntu resolute/main arm64 libintl-perl all 1.35-1 [701 kB]
196s Get:406 http://ftpmaster.internal/ubuntu resolute/universe arm64 texinfo-lib arm64 7.2-5 [454 kB]
196s Get:407 http://ftpmaster.internal/ubuntu resolute/universe arm64 tex-common all 6.20 [30.0 kB]
196s Get:408 http://ftpmaster.internal/ubuntu resolute/universe arm64 texinfo all 7.2-5 [1217 kB]
196s Get:409 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave-common all 10.3.0-3 [6088 kB]
197s Get:410 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave arm64 10.3.0-3 [9068 kB]
197s Get:411 http://ftpmaster.internal/ubuntu resolute/main arm64 libncurses-dev arm64 6.6+20251231-1 [391 kB]
197s Get:412 http://ftpmaster.internal/ubuntu resolute/main arm64 libreadline-dev arm64 8.3-3 [199 kB]
197s Get:413 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-fortran-310 arm64 1.14.6+repack-2 [104 kB]
197s Get:414 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-hl-310 arm64 1.14.6+repack-2 [59.1 kB]
197s Get:415 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-hl-fortran-310 arm64 1.14.6+repack-2 [31.1 kB]
197s Get:416 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-cpp-310 arm64 1.14.6+repack-2 [117 kB]
197s Get:417 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-hl-cpp-310 arm64 1.14.6+repack-2 [11.7 kB]
197s Get:418 http://ftpmaster.internal/ubuntu resolute/main arm64 zlib1g-dev arm64 1:1.3.dfsg+really1.3.1-1ubuntu2 [899 kB]
197s Get:419 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg-turbo8-dev arm64 2.1.5-4ubuntu2 [306 kB]
197s Get:420 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg8-dev arm64 8c-2ubuntu11 [1484 B]
197s Get:421 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg-dev arm64 8c-2ubuntu11 [1482 B]
197s Get:422 http://ftpmaster.internal/ubuntu resolute/universe arm64 libaec0 arm64 1.1.5-1 [21.8 kB]
197s Get:423 http://ftpmaster.internal/ubuntu resolute/universe arm64 libaec-dev arm64 1.1.5-1 [22.7 kB]
197s Get:424 http://ftpmaster.internal/ubuntu resolute/main arm64 libbrotli-dev arm64 1.1.0-2build6 [355 kB]
197s Get:425 http://ftpmaster.internal/ubuntu resolute/main arm64 libidn2-dev arm64 2.3.8-4 [123 kB]
197s Get:426 http://ftpmaster.internal/ubuntu resolute/main arm64 comerr-dev arm64 2.1-1.47.2-3ubuntu2 [45.6 kB]
197s Get:427 http://ftpmaster.internal/ubuntu resolute/main arm64 libgssrpc4t64 arm64 1.22.1-2 [57.9 kB]
197s Get:428 http://ftpmaster.internal/ubuntu resolute/main arm64 libkadm5clnt-mit12 arm64 1.22.1-2 [39.9 kB]
197s Get:429 http://ftpmaster.internal/ubuntu resolute/main arm64 libkdb5-10t64 arm64 1.22.1-2 [41.4 kB]
197s Get:430 http://ftpmaster.internal/ubuntu resolute/main arm64 libkadm5srv-mit12 arm64 1.22.1-2 [54.8 kB]
197s Get:431 http://ftpmaster.internal/ubuntu resolute/main arm64 krb5-multidev arm64 1.22.1-2 [126 kB]
197s Get:432 http://ftpmaster.internal/ubuntu resolute/main arm64 libkrb5-dev arm64 1.22.1-2 [11.9 kB]
197s Get:433 http://ftpmaster.internal/ubuntu resolute/main arm64 libldap-dev arm64 2.6.10+dfsg-1ubuntu5 [317 kB]
197s Get:434 http://ftpmaster.internal/ubuntu resolute/main arm64 libpkgconf3 arm64 1.8.1-4build1 [33.7 kB]
197s Get:435 http://ftpmaster.internal/ubuntu resolute/main arm64 pkgconf-bin arm64 1.8.1-4build1 [21.7 kB]
197s Get:436 http://ftpmaster.internal/ubuntu resolute/main arm64 pkgconf arm64 1.8.1-4build1 [16.8 kB]
197s Get:437 http://ftpmaster.internal/ubuntu resolute/main arm64 libnghttp2-dev arm64 1.64.0-1.1ubuntu1 [119 kB]
197s Get:438 http://ftpmaster.internal/ubuntu resolute/main arm64 libpsl-dev arm64 0.21.2-1.1build2 [79.1 kB]
197s Get:439 http://ftpmaster.internal/ubuntu resolute/main arm64 libgmpxx4ldbl arm64 2:6.3.0+dfsg-5ubuntu1 [9944 B]
197s Get:440 http://ftpmaster.internal/ubuntu resolute/main arm64 libgmp-dev arm64 2:6.3.0+dfsg-5ubuntu1 [348 kB]
197s Get:441 http://ftpmaster.internal/ubuntu resolute/main arm64 libevent-2.1-7t64 arm64 2.1.12-stable-10build1 [152 kB]
197s Get:442 http://ftpmaster.internal/ubuntu resolute/main arm64 libunbound8 arm64 1.24.2-1ubuntu1 [436 kB]
197s Get:443 http://ftpmaster.internal/ubuntu resolute/main arm64 libgnutls-dane0t64 arm64 3.8.10-3ubuntu1 [24.5 kB]
197s Get:444 http://ftpmaster.internal/ubuntu resolute/main arm64 libgnutls-openssl27t64 arm64 3.8.10-3ubuntu1 [24.4 kB]
197s Get:445 http://ftpmaster.internal/ubuntu resolute/main arm64 libp11-kit-dev arm64 0.25.10-1 [29.3 kB]
197s Get:446 http://ftpmaster.internal/ubuntu resolute/main arm64 libtasn1-6-dev arm64 4.21.0-2 [91.3 kB]
197s Get:447 http://ftpmaster.internal/ubuntu resolute/main arm64 nettle-dev arm64 3.10.2-1 [1183 kB]
197s Get:448 http://ftpmaster.internal/ubuntu resolute/main arm64 libgnutls28-dev arm64 3.8.10-3ubuntu1 [1126 kB]
197s Get:449 http://ftpmaster.internal/ubuntu resolute/main arm64 librtmp-dev arm64 2.4+20151223.gitfa8646d.1-3 [70.6 kB]
197s Get:450 http://ftpmaster.internal/ubuntu resolute/main arm64 libssl-dev arm64 3.5.3-1ubuntu2 [3448 kB]
198s Get:451 http://ftpmaster.internal/ubuntu resolute/main arm64 libssh2-1-dev arm64 1.11.1-1build1 [292 kB]
198s Get:452 http://ftpmaster.internal/ubuntu resolute/main arm64 libzstd-dev arm64 1.5.7+dfsg-3 [349 kB]
198s Get:453 http://ftpmaster.internal/ubuntu resolute/main arm64 libcurl4-openssl-dev arm64 8.18.0-1ubuntu1 [548 kB]
198s Get:454 http://ftpmaster.internal/ubuntu resolute/universe arm64 hdf5-helpers arm64 1.14.6+repack-2 [17.3 kB]
198s Get:455 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhdf5-dev arm64 1.14.6+repack-2 [3483 kB]
198s Get:456 http://ftpmaster.internal/ubuntu resolute/main arm64 xorg-sgml-doctools all 1:1.11-1.1build1 [10.3 kB]
198s Get:457 http://ftpmaster.internal/ubuntu resolute/main arm64 x11proto-dev all 2025.1-1 [608 kB]
198s Get:458 http://ftpmaster.internal/ubuntu resolute/main arm64 libxau-dev arm64 1:1.0.11-1build1 [10.6 kB]
198s Get:459 http://ftpmaster.internal/ubuntu resolute/main arm64 libxdmcp-dev arm64 1:1.1.5-2 [25.9 kB]
198s Get:460 http://ftpmaster.internal/ubuntu resolute/main arm64 xtrans-dev all 1.6.0-1build1 [66.2 kB]
198s Get:461 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb1-dev arm64 1.17.0-2ubuntu1 [90.0 kB]
198s Get:462 http://ftpmaster.internal/ubuntu resolute/main arm64 libx11-dev arm64 2:1.8.12-1build1 [776 kB]
198s Get:463 http://ftpmaster.internal/ubuntu resolute/main arm64 libglx-dev arm64 1.7.0-3 [14.1 kB]
198s Get:464 http://ftpmaster.internal/ubuntu resolute/main arm64 libgl-dev arm64 1.7.0-3 [103 kB]
198s Get:465 http://ftpmaster.internal/ubuntu resolute/main arm64 libblas-dev arm64 3.12.1-7ubuntu1 [160 kB]
198s Get:466 http://ftpmaster.internal/ubuntu resolute/main arm64 liblapack-dev arm64 3.12.1-7ubuntu1 [4456 kB]
198s Get:467 http://ftpmaster.internal/ubuntu resolute/main arm64 libfftw3-long3 arm64 3.3.10-2fakesync1build2 [636 kB]
198s Get:468 http://ftpmaster.internal/ubuntu resolute/main arm64 libfftw3-bin arm64 3.3.10-2fakesync1build2 [34.5 kB]
198s Get:469 http://ftpmaster.internal/ubuntu resolute/main arm64 libfftw3-dev arm64 3.3.10-2fakesync1build2 [1500 kB]
198s Get:470 http://ftpmaster.internal/ubuntu resolute/main arm64 libgfortran-15-dev arm64 15.2.0-12ubuntu1 [490 kB]
198s Get:471 http://ftpmaster.internal/ubuntu resolute/main arm64 gfortran-15-aarch64-linux-gnu arm64 15.2.0-12ubuntu1 [12.5 MB]
199s Get:472 http://ftpmaster.internal/ubuntu resolute/main arm64 gfortran-15 arm64 15.2.0-12ubuntu1 [18.1 kB]
199s Get:473 http://ftpmaster.internal/ubuntu resolute/main arm64 gfortran-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [1022 B]
199s Get:474 http://ftpmaster.internal/ubuntu resolute/main arm64 gfortran arm64 4:15.2.0-4ubuntu1 [1160 B]
199s Get:475 http://ftpmaster.internal/ubuntu resolute/main arm64 libstdc++-15-dev arm64 15.2.0-12ubuntu1 [2549 kB]
199s Get:476 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-15-aarch64-linux-gnu arm64 15.2.0-12ubuntu1 [13.2 MB]
199s Get:477 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-15 arm64 15.2.0-12ubuntu1 [25.3 kB]
199s Get:478 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [956 B]
199s Get:479 http://ftpmaster.internal/ubuntu resolute/main arm64 g++ arm64 4:15.2.0-4ubuntu1 [1080 B]
199s Get:480 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave-dev arm64 10.3.0-3 [464 kB]
199s Get:481 http://ftpmaster.internal/ubuntu resolute/universe arm64 dh-octave all 1.14.0 [21.1 kB]
199s Get:482 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontenc1 arm64 1:1.1.8-1build2 [13.9 kB]
199s Get:483 http://ftpmaster.internal/ubuntu resolute/main arm64 libunwind8 arm64 1.8.3-0ubuntu1 [60.8 kB]
199s Get:484 http://ftpmaster.internal/ubuntu resolute/main arm64 libxt6t64 arm64 1:1.2.1-1.3 [168 kB]
199s Get:485 http://ftpmaster.internal/ubuntu resolute/main arm64 libxmu6 arm64 2:1.1.3-4 [47.6 kB]
199s Get:486 http://ftpmaster.internal/ubuntu resolute/main arm64 libxaw7 arm64 2:1.0.16-1build1 [183 kB]
199s Get:487 http://ftpmaster.internal/ubuntu resolute/main arm64 libxfont2 arm64 1:2.0.6-2 [90.8 kB]
199s Get:488 http://ftpmaster.internal/ubuntu resolute/main arm64 libxkbfile1 arm64 1:1.1.0-1build5 [69.0 kB]
199s Get:489 http://ftpmaster.internal/ubuntu resolute/main arm64 libxrandr2 arm64 2:1.5.4-1build1 [19.1 kB]
199s Get:490 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave-io arm64 2.7.0-3 [254 kB]
199s Get:491 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave-statistics-common all 1.7.6-2 [1008 kB]
199s Get:492 http://ftpmaster.internal/ubuntu resolute/universe arm64 octave-statistics arm64 1.7.6-2 [157 kB]
199s Get:493 http://ftpmaster.internal/ubuntu resolute/main arm64 x11-xkb-utils arm64 7.7+9build1 [165 kB]
199s Get:494 http://ftpmaster.internal/ubuntu resolute/main arm64 xserver-common all 2:21.1.21-1ubuntu1 [34.8 kB]
199s Get:495 http://ftpmaster.internal/ubuntu resolute/universe arm64 xvfb arm64 2:21.1.21-1ubuntu1 [872 kB]
200s Fetched 199 MB in 18s (11.0 MB/s)
200s Selecting previously unselected package libfyaml0:arm64.
200s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 89360 files and directories currently installed.)
200s Preparing to unpack .../000-libfyaml0_0.9.3-1_arm64.deb ...
200s Unpacking libfyaml0:arm64 (0.9.3-1) ...
200s Selecting previously unselected package libstemmer0d:arm64.
200s Preparing to unpack .../001-libstemmer0d_3.0.1-1_arm64.deb ...
200s Unpacking libstemmer0d:arm64 (3.0.1-1) ...
200s Selecting previously unselected package libappstream5:arm64.
200s Preparing to unpack .../002-libappstream5_1.1.1-1_arm64.deb ...
200s Unpacking libappstream5:arm64 (1.1.1-1) ...
200s Selecting previously unselected package appstream.
200s Preparing to unpack .../003-appstream_1.1.1-1_arm64.deb ...
200s Unpacking appstream (1.1.1-1) ...
200s Selecting previously unselected package m4.
200s Preparing to unpack .../004-m4_1.4.20-2_arm64.deb ...
200s Unpacking m4 (1.4.20-2) ...
200s Selecting previously unselected package autoconf.
200s Preparing to unpack .../005-autoconf_2.72-3.1ubuntu1_all.deb ...
200s Unpacking autoconf (2.72-3.1ubuntu1) ...
200s Selecting previously unselected package autotools-dev.
200s Preparing to unpack .../006-autotools-dev_20240727.1_all.deb ...
200s Unpacking autotools-dev (20240727.1) ...
200s Selecting previously unselected package automake.
200s Preparing to unpack .../007-automake_1%3a1.18.1-3build1_all.deb ...
200s Unpacking automake (1:1.18.1-3build1) ...
200s Selecting previously unselected package autopoint.
200s Preparing to unpack .../008-autopoint_0.23.2-1_all.deb ...
200s Unpacking autopoint (0.23.2-1) ...
200s Selecting previously unselected package libcapture-tiny-perl.
200s Preparing to unpack .../009-libcapture-tiny-perl_0.50-1_all.deb ...
200s Unpacking libcapture-tiny-perl (0.50-1) ...
200s Selecting previously unselected package libparams-util-perl.
201s Preparing to unpack .../010-libparams-util-perl_1.102-3build1_arm64.deb ...
201s Unpacking libparams-util-perl (1.102-3build1) ...
201s Selecting previously unselected package libsub-install-perl.
201s Preparing to unpack .../011-libsub-install-perl_0.929-1_all.deb ...
201s Unpacking libsub-install-perl (0.929-1) ...
201s Selecting previously unselected package libdata-optlist-perl.
201s Preparing to unpack .../012-libdata-optlist-perl_0.114-1_all.deb ...
201s Unpacking libdata-optlist-perl (0.114-1) ...
201s Selecting previously unselected package libb-hooks-op-check-perl:arm64.
201s Preparing to unpack .../013-libb-hooks-op-check-perl_0.22-3build2_arm64.deb ...
201s Unpacking libb-hooks-op-check-perl:arm64 (0.22-3build2) ...
201s Selecting previously unselected package libdynaloader-functions-perl.
201s Preparing to unpack .../014-libdynaloader-functions-perl_0.004-2_all.deb ...
201s Unpacking libdynaloader-functions-perl (0.004-2) ...
201s Selecting previously unselected package libdevel-callchecker-perl:arm64.
201s Preparing to unpack .../015-libdevel-callchecker-perl_0.009-2_arm64.deb ...
201s Unpacking libdevel-callchecker-perl:arm64 (0.009-2) ...
201s Selecting previously unselected package libparams-classify-perl:arm64.
201s Preparing to unpack .../016-libparams-classify-perl_0.015-2build6_arm64.deb ...
201s Unpacking libparams-classify-perl:arm64 (0.015-2build6) ...
201s Selecting previously unselected package libmodule-runtime-perl.
201s Preparing to unpack .../017-libmodule-runtime-perl_0.018-1_all.deb ...
201s Unpacking libmodule-runtime-perl (0.018-1) ...
201s Selecting previously unselected package libtry-tiny-perl.
201s Preparing to unpack .../018-libtry-tiny-perl_0.32-1_all.deb ...
201s Unpacking libtry-tiny-perl (0.32-1) ...
201s Selecting previously unselected package libmodule-implementation-perl.
201s Preparing to unpack .../019-libmodule-implementation-perl_0.09-2_all.deb ...
201s Unpacking libmodule-implementation-perl (0.09-2) ...
201s Selecting previously unselected package libpackage-stash-perl.
201s Preparing to unpack .../020-libpackage-stash-perl_0.40-1_all.deb ...
201s Unpacking libpackage-stash-perl (0.40-1) ...
201s Selecting previously unselected package libclass-load-perl.
201s Preparing to unpack .../021-libclass-load-perl_0.25-2_all.deb ...
201s Unpacking libclass-load-perl (0.25-2) ...
201s Selecting previously unselected package libio-stringy-perl.
201s Preparing to unpack .../022-libio-stringy-perl_2.113-2_all.deb ...
201s Unpacking libio-stringy-perl (2.113-2) ...
201s Selecting previously unselected package libparams-validate-perl:arm64.
201s Preparing to unpack .../023-libparams-validate-perl_1.31-2build4_arm64.deb ...
201s Unpacking libparams-validate-perl:arm64 (1.31-2build4) ...
201s Selecting previously unselected package libsub-exporter-perl.
201s Preparing to unpack .../024-libsub-exporter-perl_0.990-1_all.deb ...
201s Unpacking libsub-exporter-perl (0.990-1) ...
201s Selecting previously unselected package libgetopt-long-descriptive-perl.
201s Preparing to unpack .../025-libgetopt-long-descriptive-perl_0.116-2_all.deb ...
201s Unpacking libgetopt-long-descriptive-perl (0.116-2) ...
201s Selecting previously unselected package libio-tiecombine-perl.
201s Preparing to unpack .../026-libio-tiecombine-perl_1.005-3_all.deb ...
201s Unpacking libio-tiecombine-perl (1.005-3) ...
201s Selecting previously unselected package libmodule-pluggable-perl.
201s Preparing to unpack .../027-libmodule-pluggable-perl_5.2-5_all.deb ...
201s Unpacking libmodule-pluggable-perl (5.2-5) ...
201s Selecting previously unselected package libstring-rewriteprefix-perl.
201s Preparing to unpack .../028-libstring-rewriteprefix-perl_0.009-1_all.deb ...
201s Unpacking libstring-rewriteprefix-perl (0.009-1) ...
201s Selecting previously unselected package libapp-cmd-perl.
201s Preparing to unpack .../029-libapp-cmd-perl_0.338-1_all.deb ...
201s Unpacking libapp-cmd-perl (0.338-1) ...
201s Selecting previously unselected package libboolean-perl.
201s Preparing to unpack .../030-libboolean-perl_0.46-3_all.deb ...
201s Unpacking libboolean-perl (0.46-3) ...
201s Selecting previously unselected package libsub-uplevel-perl.
201s Preparing to unpack .../031-libsub-uplevel-perl_0.2800-3_all.deb ...
201s Unpacking libsub-uplevel-perl (0.2800-3) ...
201s Selecting previously unselected package libtest-exception-perl.
201s Preparing to unpack .../032-libtest-exception-perl_0.43-3_all.deb ...
201s Unpacking libtest-exception-perl (0.43-3) ...
201s Selecting previously unselected package libcarp-assert-more-perl.
201s Preparing to unpack .../033-libcarp-assert-more-perl_2.9.0-1_all.deb ...
201s Unpacking libcarp-assert-more-perl (2.9.0-1) ...
201s Selecting previously unselected package libfile-which-perl.
201s Preparing to unpack .../034-libfile-which-perl_1.27-2_all.deb ...
201s Unpacking libfile-which-perl (1.27-2) ...
201s Selecting previously unselected package libfile-homedir-perl.
201s Preparing to unpack .../035-libfile-homedir-perl_1.006-2_all.deb ...
201s Unpacking libfile-homedir-perl (1.006-2) ...
201s Selecting previously unselected package libclone-choose-perl.
201s Preparing to unpack .../036-libclone-choose-perl_0.010-2_all.deb ...
201s Unpacking libclone-choose-perl (0.010-2) ...
201s Selecting previously unselected package libhash-merge-perl.
201s Preparing to unpack .../037-libhash-merge-perl_0.302-1_all.deb ...
201s Unpacking libhash-merge-perl (0.302-1) ...
201s Selecting previously unselected package libjson-perl.
202s Preparing to unpack .../038-libjson-perl_4.10000-1_all.deb ...
202s Unpacking libjson-perl (4.10000-1) ...
202s Selecting previously unselected package libexporter-tiny-perl.
202s Preparing to unpack .../039-libexporter-tiny-perl_1.006003-1_all.deb ...
202s Unpacking libexporter-tiny-perl (1.006003-1) ...
202s Selecting previously unselected package liblist-moreutils-xs-perl.
202s Preparing to unpack .../040-liblist-moreutils-xs-perl_0.430-4build1_arm64.deb ...
202s Unpacking liblist-moreutils-xs-perl (0.430-4build1) ...
202s Selecting previously unselected package liblist-moreutils-perl.
202s Preparing to unpack .../041-liblist-moreutils-perl_0.430-2_all.deb ...
202s Unpacking liblist-moreutils-perl (0.430-2) ...
202s Selecting previously unselected package liblog-log4perl-perl.
202s Preparing to unpack .../042-liblog-log4perl-perl_1.57-1_all.deb ...
202s Unpacking liblog-log4perl-perl (1.57-1) ...
202s Selecting previously unselected package libmouse-perl:arm64.
202s Preparing to unpack .../043-libmouse-perl_2.6.1-1_arm64.deb ...
202s Unpacking libmouse-perl:arm64 (2.6.1-1) ...
202s Selecting previously unselected package libmousex-nativetraits-perl.
202s Preparing to unpack .../044-libmousex-nativetraits-perl_1.09-3_all.deb ...
202s Unpacking libmousex-nativetraits-perl (1.09-3) ...
202s Selecting previously unselected package libmousex-strictconstructor-perl.
202s Preparing to unpack .../045-libmousex-strictconstructor-perl_0.02-3_all.deb ...
202s Unpacking libmousex-strictconstructor-perl (0.02-3) ...
202s Selecting previously unselected package libparse-recdescent-perl.
202s Preparing to unpack .../046-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
202s Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
202s Selecting previously unselected package libpath-tiny-perl.
202s Preparing to unpack .../047-libpath-tiny-perl_0.148-1_all.deb ...
202s Unpacking libpath-tiny-perl (0.148-1) ...
202s Selecting previously unselected package libpod-pom-perl.
202s Preparing to unpack .../048-libpod-pom-perl_2.01-4_all.deb ...
202s Unpacking libpod-pom-perl (2.01-4) ...
202s Selecting previously unselected package libregexp-common-perl.
202s Preparing to unpack .../049-libregexp-common-perl_2024080801-1_all.deb ...
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202s Selecting previously unselected package libyaml-tiny-perl.
202s Preparing to unpack .../050-libyaml-tiny-perl_1.76-1_all.deb ...
202s Unpacking libyaml-tiny-perl (1.76-1) ...
202s Selecting previously unselected package libconfig-model-perl.
202s Preparing to unpack .../051-libconfig-model-perl_2.155-1_all.deb ...
202s Unpacking libconfig-model-perl (2.155-1) ...
202s Selecting previously unselected package libyaml-pp-perl.
202s Preparing to unpack .../052-libyaml-pp-perl_0.39.0-1_all.deb ...
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202s Selecting previously unselected package cme.
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202s Selecting previously unselected package libisl23:arm64.
202s Preparing to unpack .../054-libisl23_0.27-1build1_arm64.deb ...
202s Unpacking libisl23:arm64 (0.27-1build1) ...
202s Selecting previously unselected package libmpc3:arm64.
202s Preparing to unpack .../055-libmpc3_1.3.1-2_arm64.deb ...
202s Unpacking libmpc3:arm64 (1.3.1-2) ...
202s Selecting previously unselected package cpp-15-aarch64-linux-gnu.
202s Preparing to unpack .../056-cpp-15-aarch64-linux-gnu_15.2.0-12ubuntu1_arm64.deb ...
202s Unpacking cpp-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
202s Selecting previously unselected package cpp-15.
203s Preparing to unpack .../057-cpp-15_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking cpp-15 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package cpp-aarch64-linux-gnu.
203s Preparing to unpack .../058-cpp-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ...
203s Unpacking cpp-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
203s Selecting previously unselected package cpp.
203s Preparing to unpack .../059-cpp_4%3a15.2.0-4ubuntu1_arm64.deb ...
203s Unpacking cpp (4:15.2.0-4ubuntu1) ...
203s Selecting previously unselected package libdebhelper-perl.
203s Preparing to unpack .../060-libdebhelper-perl_13.28ubuntu1_all.deb ...
203s Unpacking libdebhelper-perl (13.28ubuntu1) ...
203s Selecting previously unselected package libcc1-0:arm64.
203s Preparing to unpack .../061-libcc1-0_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libcc1-0:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libgomp1:arm64.
203s Preparing to unpack .../062-libgomp1_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libgomp1:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libitm1:arm64.
203s Preparing to unpack .../063-libitm1_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libitm1:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libasan8:arm64.
203s Preparing to unpack .../064-libasan8_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libasan8:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package liblsan0:arm64.
203s Preparing to unpack .../065-liblsan0_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking liblsan0:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libtsan2:arm64.
203s Preparing to unpack .../066-libtsan2_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libtsan2:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libubsan1:arm64.
203s Preparing to unpack .../067-libubsan1_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libubsan1:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libhwasan0:arm64.
203s Preparing to unpack .../068-libhwasan0_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libhwasan0:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package libgcc-15-dev:arm64.
203s Preparing to unpack .../069-libgcc-15-dev_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking libgcc-15-dev:arm64 (15.2.0-12ubuntu1) ...
203s Selecting previously unselected package gcc-15-aarch64-linux-gnu.
203s Preparing to unpack .../070-gcc-15-aarch64-linux-gnu_15.2.0-12ubuntu1_arm64.deb ...
203s Unpacking gcc-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package gcc-15.
204s Preparing to unpack .../071-gcc-15_15.2.0-12ubuntu1_arm64.deb ...
204s Unpacking gcc-15 (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package gcc-aarch64-linux-gnu.
204s Preparing to unpack .../072-gcc-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ...
204s Unpacking gcc-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
204s Selecting previously unselected package gcc.
204s Preparing to unpack .../073-gcc_4%3a15.2.0-4ubuntu1_arm64.deb ...
204s Unpacking gcc (4:15.2.0-4ubuntu1) ...
204s Selecting previously unselected package libc-dev-bin.
204s Preparing to unpack .../074-libc-dev-bin_2.42-2ubuntu5_arm64.deb ...
204s Unpacking libc-dev-bin (2.42-2ubuntu5) ...
204s Selecting previously unselected package linux-libc-dev:arm64.
204s Preparing to unpack .../075-linux-libc-dev_6.18.0-9.9_arm64.deb ...
204s Unpacking linux-libc-dev:arm64 (6.18.0-9.9) ...
204s Selecting previously unselected package libcrypt-dev:arm64.
204s Preparing to unpack .../076-libcrypt-dev_1%3a4.5.1-1_arm64.deb ...
204s Unpacking libcrypt-dev:arm64 (1:4.5.1-1) ...
204s Selecting previously unselected package rpcsvc-proto.
204s Preparing to unpack .../077-rpcsvc-proto_1.4.3-1build1_arm64.deb ...
204s Unpacking rpcsvc-proto (1.4.3-1build1) ...
204s Selecting previously unselected package libc6-dev:arm64.
204s Preparing to unpack .../078-libc6-dev_2.42-2ubuntu5_arm64.deb ...
204s Unpacking libc6-dev:arm64 (2.42-2ubuntu5) ...
204s Selecting previously unselected package libtool.
204s Preparing to unpack .../079-libtool_2.5.4-9_all.deb ...
204s Unpacking libtool (2.5.4-9) ...
204s Selecting previously unselected package dh-autoreconf.
204s Preparing to unpack .../080-dh-autoreconf_21_all.deb ...
204s Unpacking dh-autoreconf (21) ...
204s Selecting previously unselected package libarchive-zip-perl.
204s Preparing to unpack .../081-libarchive-zip-perl_1.68-1_all.deb ...
204s Unpacking libarchive-zip-perl (1.68-1) ...
204s Selecting previously unselected package libfile-stripnondeterminism-perl.
204s Preparing to unpack .../082-libfile-stripnondeterminism-perl_1.15.0-1build1_all.deb ...
204s Unpacking libfile-stripnondeterminism-perl (1.15.0-1build1) ...
204s Selecting previously unselected package dh-strip-nondeterminism.
204s Preparing to unpack .../083-dh-strip-nondeterminism_1.15.0-1build1_all.deb ...
204s Unpacking dh-strip-nondeterminism (1.15.0-1build1) ...
204s Selecting previously unselected package debugedit.
204s Preparing to unpack .../084-debugedit_1%3a5.2-3build1_arm64.deb ...
204s Unpacking debugedit (1:5.2-3build1) ...
204s Selecting previously unselected package dwz.
204s Preparing to unpack .../085-dwz_0.16-2_arm64.deb ...
204s Unpacking dwz (0.16-2) ...
204s Selecting previously unselected package gettext.
204s Preparing to unpack .../086-gettext_0.23.2-1_arm64.deb ...
204s Unpacking gettext (0.23.2-1) ...
204s Selecting previously unselected package intltool-debian.
204s Preparing to unpack .../087-intltool-debian_0.35.0+20060710.6build1_all.deb ...
204s Unpacking intltool-debian (0.35.0+20060710.6build1) ...
204s Selecting previously unselected package po-debconf.
205s Preparing to unpack .../088-po-debconf_1.0.22_all.deb ...
205s Unpacking po-debconf (1.0.22) ...
205s Selecting previously unselected package debhelper.
205s Preparing to unpack .../089-debhelper_13.28ubuntu1_all.deb ...
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205s Selecting previously unselected package aglfn.
205s Preparing to unpack .../090-aglfn_1.7+git20191031.4036a9c-2build1_all.deb ...
205s Unpacking aglfn (1.7+git20191031.4036a9c-2build1) ...
205s Selecting previously unselected package gnuplot-data.
205s Preparing to unpack .../091-gnuplot-data_6.0.2+dfsg1-2ubuntu1_all.deb ...
205s Unpacking gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
205s Selecting previously unselected package fonts-freefont-otf.
205s Preparing to unpack .../092-fonts-freefont-otf_20211204+svn4273-4build1_all.deb ...
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205s Selecting previously unselected package fontconfig-config.
205s Preparing to unpack .../093-fontconfig-config_2.17.1-3ubuntu1_arm64.deb ...
205s Unpacking fontconfig-config (2.17.1-3ubuntu1) ...
205s Selecting previously unselected package libfontconfig1:arm64.
205s Preparing to unpack .../094-libfontconfig1_2.17.1-3ubuntu1_arm64.deb ...
205s Unpacking libfontconfig1:arm64 (2.17.1-3ubuntu1) ...
205s Selecting previously unselected package libpixman-1-0:arm64.
205s Preparing to unpack .../095-libpixman-1-0_0.46.4-1_arm64.deb ...
205s Unpacking libpixman-1-0:arm64 (0.46.4-1) ...
205s Selecting previously unselected package libxcb-render0:arm64.
205s Preparing to unpack .../096-libxcb-render0_1.17.0-2ubuntu1_arm64.deb ...
205s Unpacking libxcb-render0:arm64 (1.17.0-2ubuntu1) ...
205s Selecting previously unselected package libxcb-shm0:arm64.
205s Preparing to unpack .../097-libxcb-shm0_1.17.0-2ubuntu1_arm64.deb ...
205s Unpacking libxcb-shm0:arm64 (1.17.0-2ubuntu1) ...
205s Selecting previously unselected package libxrender1:arm64.
205s Preparing to unpack .../098-libxrender1_1%3a0.9.12-1_arm64.deb ...
205s Unpacking libxrender1:arm64 (1:0.9.12-1) ...
205s Selecting previously unselected package libcairo2:arm64.
205s Preparing to unpack .../099-libcairo2_1.18.4-3_arm64.deb ...
205s Unpacking libcairo2:arm64 (1.18.4-3) ...
205s Selecting previously unselected package libsharpyuv0:arm64.
205s Preparing to unpack .../100-libsharpyuv0_1.5.0-0.1build1_arm64.deb ...
205s Unpacking libsharpyuv0:arm64 (1.5.0-0.1build1) ...
205s Selecting previously unselected package libaom3:arm64.
205s Preparing to unpack .../101-libaom3_3.13.1-2_arm64.deb ...
205s Unpacking libaom3:arm64 (3.13.1-2) ...
205s Selecting previously unselected package libheif-plugin-aomdec:arm64.
205s Preparing to unpack .../102-libheif-plugin-aomdec_1.21.2-1_arm64.deb ...
205s Unpacking libheif-plugin-aomdec:arm64 (1.21.2-1) ...
205s Selecting previously unselected package libde265-0:arm64.
205s Preparing to unpack .../103-libde265-0_1.0.16-1build1_arm64.deb ...
205s Unpacking libde265-0:arm64 (1.0.16-1build1) ...
205s Selecting previously unselected package libheif-plugin-libde265:arm64.
205s Preparing to unpack .../104-libheif-plugin-libde265_1.21.2-1_arm64.deb ...
205s Unpacking libheif-plugin-libde265:arm64 (1.21.2-1) ...
206s Selecting previously unselected package libheif1:arm64.
206s Preparing to unpack .../105-libheif1_1.21.2-1_arm64.deb ...
206s Unpacking libheif1:arm64 (1.21.2-1) ...
206s Selecting previously unselected package libimagequant0:arm64.
206s Preparing to unpack .../106-libimagequant0_2.18.0-1build1_arm64.deb ...
206s Unpacking libimagequant0:arm64 (2.18.0-1build1) ...
206s Selecting previously unselected package libjpeg-turbo8:arm64.
206s Preparing to unpack .../107-libjpeg-turbo8_2.1.5-4ubuntu2_arm64.deb ...
206s Unpacking libjpeg-turbo8:arm64 (2.1.5-4ubuntu2) ...
206s Selecting previously unselected package libjpeg8:arm64.
206s Preparing to unpack .../108-libjpeg8_8c-2ubuntu11_arm64.deb ...
206s Unpacking libjpeg8:arm64 (8c-2ubuntu11) ...
206s Selecting previously unselected package libdeflate0:arm64.
206s Preparing to unpack .../109-libdeflate0_1.23-2_arm64.deb ...
206s Unpacking libdeflate0:arm64 (1.23-2) ...
206s Selecting previously unselected package libjbig0:arm64.
206s Preparing to unpack .../110-libjbig0_2.1-6.1ubuntu3_arm64.deb ...
206s Unpacking libjbig0:arm64 (2.1-6.1ubuntu3) ...
206s Selecting previously unselected package liblerc4:arm64.
206s Preparing to unpack .../111-liblerc4_4.0.0+ds-5ubuntu2_arm64.deb ...
206s Unpacking liblerc4:arm64 (4.0.0+ds-5ubuntu2) ...
206s Selecting previously unselected package libwebp7:arm64.
206s Preparing to unpack .../112-libwebp7_1.5.0-0.1build1_arm64.deb ...
206s Unpacking libwebp7:arm64 (1.5.0-0.1build1) ...
206s Selecting previously unselected package libtiff6:arm64.
206s Preparing to unpack .../113-libtiff6_4.7.0-3ubuntu3_arm64.deb ...
206s Unpacking libtiff6:arm64 (4.7.0-3ubuntu3) ...
206s Selecting previously unselected package libxpm4:arm64.
206s Preparing to unpack .../114-libxpm4_1%3a3.5.17-1build3_arm64.deb ...
206s Unpacking libxpm4:arm64 (1:3.5.17-1build3) ...
206s Selecting previously unselected package libgd3:arm64.
206s Preparing to unpack .../115-libgd3_2.3.3-13ubuntu1_arm64.deb ...
206s Unpacking libgd3:arm64 (2.3.3-13ubuntu1) ...
206s Selecting previously unselected package liblua5.4-0:arm64.
206s Preparing to unpack .../116-liblua5.4-0_5.4.8-1_arm64.deb ...
206s Unpacking liblua5.4-0:arm64 (5.4.8-1) ...
206s Selecting previously unselected package fontconfig.
206s Preparing to unpack .../117-fontconfig_2.17.1-3ubuntu1_arm64.deb ...
206s Unpacking fontconfig (2.17.1-3ubuntu1) ...
206s Selecting previously unselected package libgraphite2-3:arm64.
206s Preparing to unpack .../118-libgraphite2-3_1.3.14-11ubuntu1_arm64.deb ...
206s Unpacking libgraphite2-3:arm64 (1.3.14-11ubuntu1) ...
206s Selecting previously unselected package libharfbuzz0b:arm64.
206s Preparing to unpack .../119-libharfbuzz0b_12.3.2-1_arm64.deb ...
206s Unpacking libharfbuzz0b:arm64 (12.3.2-1) ...
206s Selecting previously unselected package libthai-data.
206s Preparing to unpack .../120-libthai-data_0.1.30-1_all.deb ...
206s Unpacking libthai-data (0.1.30-1) ...
206s Selecting previously unselected package libdatrie1:arm64.
206s Preparing to unpack .../121-libdatrie1_0.2.14-1_arm64.deb ...
206s Unpacking libdatrie1:arm64 (0.2.14-1) ...
206s Selecting previously unselected package libthai0:arm64.
206s Preparing to unpack .../122-libthai0_0.1.30-1_arm64.deb ...
206s Unpacking libthai0:arm64 (0.1.30-1) ...
206s Selecting previously unselected package libpango-1.0-0:arm64.
206s Preparing to unpack .../123-libpango-1.0-0_1.57.0-1_arm64.deb ...
206s Unpacking libpango-1.0-0:arm64 (1.57.0-1) ...
206s Selecting previously unselected package libpangoft2-1.0-0:arm64.
206s Preparing to unpack .../124-libpangoft2-1.0-0_1.57.0-1_arm64.deb ...
206s Unpacking libpangoft2-1.0-0:arm64 (1.57.0-1) ...
206s Selecting previously unselected package libpangocairo-1.0-0:arm64.
206s Preparing to unpack .../125-libpangocairo-1.0-0_1.57.0-1_arm64.deb ...
206s Unpacking libpangocairo-1.0-0:arm64 (1.57.0-1) ...
206s Selecting previously unselected package libwebpmux3:arm64.
206s Preparing to unpack .../126-libwebpmux3_1.5.0-0.1build1_arm64.deb ...
206s Unpacking libwebpmux3:arm64 (1.5.0-0.1build1) ...
206s Selecting previously unselected package gnuplot-nox.
206s Preparing to unpack .../127-gnuplot-nox_6.0.2+dfsg1-2ubuntu1_arm64.deb ...
206s Unpacking gnuplot-nox (6.0.2+dfsg1-2ubuntu1) ...
206s Selecting previously unselected package dh-octave-autopkgtest.
206s Preparing to unpack .../128-dh-octave-autopkgtest_1.14.0_all.deb ...
206s Unpacking dh-octave-autopkgtest (1.14.0) ...
206s Selecting previously unselected package libapt-pkg-perl.
206s Preparing to unpack .../129-libapt-pkg-perl_0.1.43_arm64.deb ...
206s Unpacking libapt-pkg-perl (0.1.43) ...
206s Selecting previously unselected package libarray-intspan-perl.
206s Preparing to unpack .../130-libarray-intspan-perl_2.004-2_all.deb ...
206s Unpacking libarray-intspan-perl (2.004-2) ...
206s Selecting previously unselected package libconfig-inifiles-perl.
206s Preparing to unpack .../131-libconfig-inifiles-perl_3.000003-4_all.deb ...
206s Unpacking libconfig-inifiles-perl (3.000003-4) ...
207s Selecting previously unselected package libyaml-libyaml-perl.
207s Preparing to unpack .../132-libyaml-libyaml-perl_0.904.0+ds-1_arm64.deb ...
207s Unpacking libyaml-libyaml-perl (0.904.0+ds-1) ...
207s Selecting previously unselected package libconfig-model-backend-yaml-perl.
207s Preparing to unpack .../133-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
207s Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
207s Selecting previously unselected package libexporter-lite-perl.
207s Preparing to unpack .../134-libexporter-lite-perl_0.09-2_all.deb ...
207s Unpacking libexporter-lite-perl (0.09-2) ...
207s Selecting previously unselected package libencode-locale-perl.
207s Preparing to unpack .../135-libencode-locale-perl_1.05-3_all.deb ...
207s Unpacking libencode-locale-perl (1.05-3) ...
207s Selecting previously unselected package libtimedate-perl.
207s Preparing to unpack .../136-libtimedate-perl_2.3300-2_all.deb ...
207s Unpacking libtimedate-perl (2.3300-2) ...
207s Selecting previously unselected package libhttp-date-perl.
207s Preparing to unpack .../137-libhttp-date-perl_6.06-1_all.deb ...
207s Unpacking libhttp-date-perl (6.06-1) ...
207s Selecting previously unselected package libfile-listing-perl.
207s Preparing to unpack .../138-libfile-listing-perl_6.16-1_all.deb ...
207s Unpacking libfile-listing-perl (6.16-1) ...
207s Selecting previously unselected package libhtml-tagset-perl.
207s Preparing to unpack .../139-libhtml-tagset-perl_3.24-1_all.deb ...
207s Unpacking libhtml-tagset-perl (3.24-1) ...
207s Selecting previously unselected package liburi-perl.
207s Preparing to unpack .../140-liburi-perl_5.34-2build1_all.deb ...
207s Unpacking liburi-perl (5.34-2build1) ...
207s Selecting previously unselected package libhtml-parser-perl:arm64.
207s Preparing to unpack .../141-libhtml-parser-perl_3.83-1build1_arm64.deb ...
207s Unpacking libhtml-parser-perl:arm64 (3.83-1build1) ...
207s Selecting previously unselected package libhtml-tree-perl.
207s Preparing to unpack .../142-libhtml-tree-perl_5.07-3_all.deb ...
207s Unpacking libhtml-tree-perl (5.07-3) ...
207s Selecting previously unselected package libclone-perl:arm64.
207s Preparing to unpack .../143-libclone-perl_0.47-1_arm64.deb ...
207s Unpacking libclone-perl:arm64 (0.47-1) ...
207s Selecting previously unselected package libio-html-perl.
207s Preparing to unpack .../144-libio-html-perl_1.004-3_all.deb ...
207s Unpacking libio-html-perl (1.004-3) ...
207s Selecting previously unselected package liblwp-mediatypes-perl.
207s Preparing to unpack .../145-liblwp-mediatypes-perl_6.04-2_all.deb ...
207s Unpacking liblwp-mediatypes-perl (6.04-2) ...
207s Selecting previously unselected package libhttp-message-perl.
207s Preparing to unpack .../146-libhttp-message-perl_7.01-1ubuntu1_all.deb ...
207s Unpacking libhttp-message-perl (7.01-1ubuntu1) ...
207s Selecting previously unselected package libhttp-cookies-perl.
207s Preparing to unpack .../147-libhttp-cookies-perl_6.11-1_all.deb ...
207s Unpacking libhttp-cookies-perl (6.11-1) ...
207s Selecting previously unselected package libhttp-negotiate-perl.
207s Preparing to unpack .../148-libhttp-negotiate-perl_6.01-2_all.deb ...
207s Unpacking libhttp-negotiate-perl (6.01-2) ...
207s Selecting previously unselected package perl-openssl-defaults:arm64.
207s Preparing to unpack .../149-perl-openssl-defaults_7build4_arm64.deb ...
207s Unpacking perl-openssl-defaults:arm64 (7build4) ...
207s Selecting previously unselected package libnet-ssleay-perl:arm64.
207s Preparing to unpack .../150-libnet-ssleay-perl_1.94-3_arm64.deb ...
207s Unpacking libnet-ssleay-perl:arm64 (1.94-3) ...
207s Selecting previously unselected package libio-socket-ssl-perl.
207s Preparing to unpack .../151-libio-socket-ssl-perl_2.098-1_all.deb ...
207s Unpacking libio-socket-ssl-perl (2.098-1) ...
207s Selecting previously unselected package libnet-http-perl.
207s Preparing to unpack .../152-libnet-http-perl_6.24-1build1_all.deb ...
207s Unpacking libnet-http-perl (6.24-1build1) ...
207s Selecting previously unselected package liblwp-protocol-https-perl.
207s Preparing to unpack .../153-liblwp-protocol-https-perl_6.14-1_all.deb ...
207s Unpacking liblwp-protocol-https-perl (6.14-1) ...
207s Selecting previously unselected package libwww-robotrules-perl.
207s Preparing to unpack .../154-libwww-robotrules-perl_6.02-1build1_all.deb ...
207s Unpacking libwww-robotrules-perl (6.02-1build1) ...
207s Selecting previously unselected package libwww-perl.
207s Preparing to unpack .../155-libwww-perl_6.81-1build1_all.deb ...
207s Unpacking libwww-perl (6.81-1build1) ...
207s Selecting previously unselected package liberror-perl.
207s Preparing to unpack .../156-liberror-perl_0.17030-1_all.deb ...
207s Unpacking liberror-perl (0.17030-1) ...
207s Selecting previously unselected package libparse-debcontrol-perl.
207s Preparing to unpack .../157-libparse-debcontrol-perl_2.005-6_all.deb ...
207s Unpacking libparse-debcontrol-perl (2.005-6) ...
207s Selecting previously unselected package libsoftware-copyright-perl.
207s Preparing to unpack .../158-libsoftware-copyright-perl_0.015-1_all.deb ...
207s Unpacking libsoftware-copyright-perl (0.015-1) ...
207s Selecting previously unselected package libalgorithm-c3-perl.
208s Preparing to unpack .../159-libalgorithm-c3-perl_0.11-2_all.deb ...
208s Unpacking libalgorithm-c3-perl (0.11-2) ...
208s Selecting previously unselected package libclass-c3-perl.
208s Preparing to unpack .../160-libclass-c3-perl_0.35-2_all.deb ...
208s Unpacking libclass-c3-perl (0.35-2) ...
208s Selecting previously unselected package libmro-compat-perl.
208s Preparing to unpack .../161-libmro-compat-perl_0.15-2_all.deb ...
208s Unpacking libmro-compat-perl (0.15-2) ...
208s Selecting previously unselected package libdata-section-perl.
208s Preparing to unpack .../162-libdata-section-perl_0.200008-1_all.deb ...
208s Unpacking libdata-section-perl (0.200008-1) ...
208s Selecting previously unselected package libtext-template-perl.
208s Preparing to unpack .../163-libtext-template-perl_1.61-1_all.deb ...
208s Unpacking libtext-template-perl (1.61-1) ...
208s Selecting previously unselected package libsoftware-license-perl.
208s Preparing to unpack .../164-libsoftware-license-perl_0.104007-1_all.deb ...
208s Unpacking libsoftware-license-perl (0.104007-1) ...
208s Selecting previously unselected package libsoftware-licensemoreutils-perl.
208s Preparing to unpack .../165-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
208s Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
208s Selecting previously unselected package libsort-versions-perl.
208s Preparing to unpack .../166-libsort-versions-perl_1.62-3_all.deb ...
208s Unpacking libsort-versions-perl (1.62-3) ...
208s Selecting previously unselected package libtext-reform-perl.
208s Preparing to unpack .../167-libtext-reform-perl_1.20-5_all.deb ...
208s Unpacking libtext-reform-perl (1.20-5) ...
208s Selecting previously unselected package libtext-autoformat-perl.
208s Preparing to unpack .../168-libtext-autoformat-perl_1.750000-2_all.deb ...
208s Unpacking libtext-autoformat-perl (1.750000-2) ...
208s Selecting previously unselected package libtext-levenshtein-damerau-perl.
208s Preparing to unpack .../169-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
208s Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
208s Selecting previously unselected package libtoml-tiny-perl.
208s Preparing to unpack .../170-libtoml-tiny-perl_0.20-1_all.deb ...
208s Unpacking libtoml-tiny-perl (0.20-1) ...
208s Selecting previously unselected package libclass-inspector-perl.
208s Preparing to unpack .../171-libclass-inspector-perl_1.36-3_all.deb ...
208s Unpacking libclass-inspector-perl (1.36-3) ...
208s Selecting previously unselected package libfile-sharedir-perl.
208s Preparing to unpack .../172-libfile-sharedir-perl_1.118-3_all.deb ...
208s Unpacking libfile-sharedir-perl (1.118-3) ...
208s Selecting previously unselected package libindirect-perl.
208s Preparing to unpack .../173-libindirect-perl_0.39-2build5_arm64.deb ...
208s Unpacking libindirect-perl (0.39-2build5) ...
208s Selecting previously unselected package libxs-parse-keyword-perl.
208s Preparing to unpack .../174-libxs-parse-keyword-perl_0.49-1_arm64.deb ...
208s Unpacking libxs-parse-keyword-perl (0.49-1) ...
208s Selecting previously unselected package libxs-parse-sublike-perl:arm64.
208s Preparing to unpack .../175-libxs-parse-sublike-perl_0.41-1_arm64.deb ...
208s Unpacking libxs-parse-sublike-perl:arm64 (0.41-1) ...
208s Selecting previously unselected package libobject-pad-perl.
208s Preparing to unpack .../176-libobject-pad-perl_0.823-2_arm64.deb ...
208s Unpacking libobject-pad-perl (0.823-2) ...
208s Selecting previously unselected package libsyntax-keyword-try-perl.
208s Preparing to unpack .../177-libsyntax-keyword-try-perl_0.31-1_arm64.deb ...
208s Unpacking libsyntax-keyword-try-perl (0.31-1) ...
208s Selecting previously unselected package libio-interactive-perl.
208s Preparing to unpack .../178-libio-interactive-perl_1.027-1_all.deb ...
208s Unpacking libio-interactive-perl (1.027-1) ...
208s Selecting previously unselected package liblog-any-perl.
208s Preparing to unpack .../179-liblog-any-perl_1.718-1build1_all.deb ...
208s Unpacking liblog-any-perl (1.718-1build1) ...
208s Selecting previously unselected package liblog-any-adapter-screen-perl.
208s Preparing to unpack .../180-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
208s Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
208s Selecting previously unselected package libsub-exporter-progressive-perl.
208s Preparing to unpack .../181-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
208s Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
208s Selecting previously unselected package libvariable-magic-perl.
208s Preparing to unpack .../182-libvariable-magic-perl_0.64-1build1_arm64.deb ...
208s Unpacking libvariable-magic-perl (0.64-1build1) ...
208s Selecting previously unselected package libb-hooks-endofscope-perl.
208s Preparing to unpack .../183-libb-hooks-endofscope-perl_0.28-2_all.deb ...
208s Unpacking libb-hooks-endofscope-perl (0.28-2) ...
208s Selecting previously unselected package libsub-identify-perl.
209s Preparing to unpack .../184-libsub-identify-perl_0.14-4_arm64.deb ...
209s Unpacking libsub-identify-perl (0.14-4) ...
209s Selecting previously unselected package libsub-name-perl:arm64.
209s Preparing to unpack .../185-libsub-name-perl_0.28-1_arm64.deb ...
209s Unpacking libsub-name-perl:arm64 (0.28-1) ...
209s Selecting previously unselected package libnamespace-clean-perl.
209s Preparing to unpack .../186-libnamespace-clean-perl_0.27-2_all.deb ...
209s Unpacking libnamespace-clean-perl (0.27-2) ...
209s Selecting previously unselected package libnumber-compare-perl.
209s Preparing to unpack .../187-libnumber-compare-perl_0.03-3_all.deb ...
209s Unpacking libnumber-compare-perl (0.03-3) ...
209s Selecting previously unselected package libtext-glob-perl.
209s Preparing to unpack .../188-libtext-glob-perl_0.11-3_all.deb ...
209s Unpacking libtext-glob-perl (0.11-3) ...
209s Selecting previously unselected package libpath-iterator-rule-perl.
209s Preparing to unpack .../189-libpath-iterator-rule-perl_1.015-2_all.deb ...
209s Unpacking libpath-iterator-rule-perl (1.015-2) ...
209s Selecting previously unselected package libpod-parser-perl.
209s Preparing to unpack .../190-libpod-parser-perl_1.67-1_all.deb ...
209s Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
209s Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
209s Unpacking libpod-parser-perl (1.67-1) ...
209s Selecting previously unselected package libpod-constants-perl.
209s Preparing to unpack .../191-libpod-constants-perl_0.19-2_all.deb ...
209s Unpacking libpod-constants-perl (0.19-2) ...
209s Selecting previously unselected package libset-intspan-perl.
209s Preparing to unpack .../192-libset-intspan-perl_1.19-3_all.deb ...
209s Unpacking libset-intspan-perl (1.19-3) ...
209s Selecting previously unselected package libstring-copyright-perl.
209s Preparing to unpack .../193-libstring-copyright-perl_0.003014-1_all.deb ...
209s Unpacking libstring-copyright-perl (0.003014-1) ...
209s Selecting previously unselected package libstring-escape-perl.
209s Preparing to unpack .../194-libstring-escape-perl_2010.002-3_all.deb ...
209s Unpacking libstring-escape-perl (2010.002-3) ...
209s Selecting previously unselected package libregexp-pattern-license-perl.
209s Preparing to unpack .../195-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
209s Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
209s Selecting previously unselected package libregexp-pattern-perl.
209s Preparing to unpack .../196-libregexp-pattern-perl_0.2.14-2_all.deb ...
209s Unpacking libregexp-pattern-perl (0.2.14-2) ...
209s Selecting previously unselected package libstring-license-perl.
209s Preparing to unpack .../197-libstring-license-perl_0.0.11-1ubuntu1_all.deb ...
209s Unpacking libstring-license-perl (0.0.11-1ubuntu1) ...
209s Selecting previously unselected package licensecheck.
209s Preparing to unpack .../198-licensecheck_3.3.9-1ubuntu2_all.deb ...
209s Unpacking licensecheck (3.3.9-1ubuntu2) ...
209s Selecting previously unselected package diffstat.
209s Preparing to unpack .../199-diffstat_1.68-1_arm64.deb ...
209s Unpacking diffstat (1.68-1) ...
209s Selecting previously unselected package libberkeleydb-perl:arm64.
209s Preparing to unpack .../200-libberkeleydb-perl_0.66-2_arm64.deb ...
209s Unpacking libberkeleydb-perl:arm64 (0.66-2) ...
209s Selecting previously unselected package libclass-xsaccessor-perl.
209s Preparing to unpack .../201-libclass-xsaccessor-perl_1.19-4build6_arm64.deb ...
209s Unpacking libclass-xsaccessor-perl (1.19-4build6) ...
209s Selecting previously unselected package libconfig-tiny-perl.
209s Preparing to unpack .../202-libconfig-tiny-perl_2.30-1_all.deb ...
209s Unpacking libconfig-tiny-perl (2.30-1) ...
209s Selecting previously unselected package libconst-fast-perl.
209s Preparing to unpack .../203-libconst-fast-perl_0.014-2_all.deb ...
209s Unpacking libconst-fast-perl (0.014-2) ...
209s Selecting previously unselected package libcpanel-json-xs-perl:arm64.
209s Preparing to unpack .../204-libcpanel-json-xs-perl_4.40-1_arm64.deb ...
209s Unpacking libcpanel-json-xs-perl:arm64 (4.40-1) ...
209s Selecting previously unselected package libaliased-perl.
209s Preparing to unpack .../205-libaliased-perl_0.34-3_all.deb ...
209s Unpacking libaliased-perl (0.34-3) ...
209s Selecting previously unselected package libclass-data-inheritable-perl.
209s Preparing to unpack .../206-libclass-data-inheritable-perl_0.10-1_all.deb ...
209s Unpacking libclass-data-inheritable-perl (0.10-1) ...
209s Selecting previously unselected package libdevel-stacktrace-perl.
209s Preparing to unpack .../207-libdevel-stacktrace-perl_2.0500-1_all.deb ...
209s Unpacking libdevel-stacktrace-perl (2.0500-1) ...
209s Selecting previously unselected package libexception-class-perl.
209s Preparing to unpack .../208-libexception-class-perl_1.45-1_all.deb ...
209s Unpacking libexception-class-perl (1.45-1) ...
209s Selecting previously unselected package libiterator-perl.
209s Preparing to unpack .../209-libiterator-perl_0.03+ds1-2_all.deb ...
209s Unpacking libiterator-perl (0.03+ds1-2) ...
210s Selecting previously unselected package libiterator-util-perl.
210s Preparing to unpack .../210-libiterator-util-perl_0.02+ds1-2_all.deb ...
210s Unpacking libiterator-util-perl (0.02+ds1-2) ...
210s Selecting previously unselected package libdata-dpath-perl.
210s Preparing to unpack .../211-libdata-dpath-perl_0.60-1_all.deb ...
210s Unpacking libdata-dpath-perl (0.60-1) ...
210s Selecting previously unselected package libnet-domain-tld-perl.
210s Preparing to unpack .../212-libnet-domain-tld-perl_1.75-4_all.deb ...
210s Unpacking libnet-domain-tld-perl (1.75-4) ...
210s Selecting previously unselected package libdata-validate-domain-perl.
210s Preparing to unpack .../213-libdata-validate-domain-perl_0.15-1_all.deb ...
210s Unpacking libdata-validate-domain-perl (0.15-1) ...
210s Selecting previously unselected package libnet-ipv6addr-perl.
210s Preparing to unpack .../214-libnet-ipv6addr-perl_1.02-1_all.deb ...
210s Unpacking libnet-ipv6addr-perl (1.02-1) ...
210s Selecting previously unselected package libnet-netmask-perl.
210s Preparing to unpack .../215-libnet-netmask-perl_2.0003-1build1_all.deb ...
210s Unpacking libnet-netmask-perl (2.0003-1build1) ...
210s Selecting previously unselected package libnetaddr-ip-perl.
210s Preparing to unpack .../216-libnetaddr-ip-perl_4.079+dfsg-2build5_arm64.deb ...
210s Unpacking libnetaddr-ip-perl (4.079+dfsg-2build5) ...
210s Selecting previously unselected package libdata-validate-ip-perl.
210s Preparing to unpack .../217-libdata-validate-ip-perl_0.31-1_all.deb ...
210s Unpacking libdata-validate-ip-perl (0.31-1) ...
210s Selecting previously unselected package libdata-validate-uri-perl.
210s Preparing to unpack .../218-libdata-validate-uri-perl_0.07-3_all.deb ...
210s Unpacking libdata-validate-uri-perl (0.07-3) ...
210s Selecting previously unselected package libdevel-size-perl.
210s Preparing to unpack .../219-libdevel-size-perl_0.85-1_arm64.deb ...
210s Unpacking libdevel-size-perl (0.85-1) ...
210s Selecting previously unselected package libemail-address-xs-perl.
210s Preparing to unpack .../220-libemail-address-xs-perl_1.05-1build5_arm64.deb ...
210s Unpacking libemail-address-xs-perl (1.05-1build5) ...
210s Selecting previously unselected package libipc-system-simple-perl.
210s Preparing to unpack .../221-libipc-system-simple-perl_1.30-2_all.deb ...
210s Unpacking libipc-system-simple-perl (1.30-2) ...
210s Selecting previously unselected package libfile-basedir-perl.
210s Preparing to unpack .../222-libfile-basedir-perl_0.09-2_all.deb ...
210s Unpacking libfile-basedir-perl (0.09-2) ...
210s Selecting previously unselected package libfile-find-rule-perl.
210s Preparing to unpack .../223-libfile-find-rule-perl_0.35-1build1_all.deb ...
210s Unpacking libfile-find-rule-perl (0.35-1build1) ...
210s Selecting previously unselected package libio-string-perl.
210s Preparing to unpack .../224-libio-string-perl_1.08-4_all.deb ...
210s Unpacking libio-string-perl (1.08-4) ...
210s Selecting previously unselected package libfont-ttf-perl.
210s Preparing to unpack .../225-libfont-ttf-perl_1.06-2_all.deb ...
210s Unpacking libfont-ttf-perl (1.06-2) ...
210s Selecting previously unselected package libhtml-html5-entities-perl.
210s Preparing to unpack .../226-libhtml-html5-entities-perl_0.004-3_all.deb ...
210s Unpacking libhtml-html5-entities-perl (0.004-3) ...
210s Selecting previously unselected package libhtml-tokeparser-simple-perl.
210s Preparing to unpack .../227-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
210s Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
210s Selecting previously unselected package libipc-run3-perl.
210s Preparing to unpack .../228-libipc-run3-perl_0.049-1_all.deb ...
210s Unpacking libipc-run3-perl (0.049-1) ...
210s Selecting previously unselected package libjson-maybexs-perl.
210s Preparing to unpack .../229-libjson-maybexs-perl_1.004008-1_all.deb ...
210s Unpacking libjson-maybexs-perl (1.004008-1) ...
210s Selecting previously unselected package liblist-compare-perl.
210s Preparing to unpack .../230-liblist-compare-perl_0.55-2_all.deb ...
210s Unpacking liblist-compare-perl (0.55-2) ...
210s Selecting previously unselected package liblist-someutils-perl.
210s Preparing to unpack .../231-liblist-someutils-perl_0.59-1_all.deb ...
210s Unpacking liblist-someutils-perl (0.59-1) ...
210s Selecting previously unselected package liblist-utilsby-perl.
210s Preparing to unpack .../232-liblist-utilsby-perl_0.12-2_all.deb ...
210s Unpacking liblist-utilsby-perl (0.12-2) ...
210s Selecting previously unselected package libmldbm-perl.
210s Preparing to unpack .../233-libmldbm-perl_2.05-4_all.deb ...
210s Unpacking libmldbm-perl (2.05-4) ...
210s Selecting previously unselected package libclass-method-modifiers-perl.
210s Preparing to unpack .../234-libclass-method-modifiers-perl_2.15-1_all.deb ...
210s Unpacking libclass-method-modifiers-perl (2.15-1) ...
210s Selecting previously unselected package libimport-into-perl.
210s Preparing to unpack .../235-libimport-into-perl_1.002005-2_all.deb ...
210s Unpacking libimport-into-perl (1.002005-2) ...
211s Selecting previously unselected package librole-tiny-perl.
211s Preparing to unpack .../236-librole-tiny-perl_2.002004-1_all.deb ...
211s Unpacking librole-tiny-perl (2.002004-1) ...
211s Selecting previously unselected package libsub-quote-perl.
211s Preparing to unpack .../237-libsub-quote-perl_2.006009-1ubuntu1_all.deb ...
211s Unpacking libsub-quote-perl (2.006009-1ubuntu1) ...
211s Selecting previously unselected package libmoo-perl.
211s Preparing to unpack .../238-libmoo-perl_2.005005-1_all.deb ...
211s Unpacking libmoo-perl (2.005005-1) ...
211s Selecting previously unselected package libstrictures-perl.
211s Preparing to unpack .../239-libstrictures-perl_2.000006-1build1_all.deb ...
211s Unpacking libstrictures-perl (2.000006-1build1) ...
211s Selecting previously unselected package libmoox-aliases-perl.
211s Preparing to unpack .../240-libmoox-aliases-perl_0.001006-2_all.deb ...
211s Unpacking libmoox-aliases-perl (0.001006-2) ...
211s Selecting previously unselected package libperlio-gzip-perl.
211s Preparing to unpack .../241-libperlio-gzip-perl_0.20-1build5_arm64.deb ...
211s Unpacking libperlio-gzip-perl (0.20-1build5) ...
211s Selecting previously unselected package libperlio-utf8-strict-perl.
211s Preparing to unpack .../242-libperlio-utf8-strict-perl_0.010-1build4_arm64.deb ...
211s Unpacking libperlio-utf8-strict-perl (0.010-1build4) ...
211s Selecting previously unselected package libproc-processtable-perl:arm64.
211s Preparing to unpack .../243-libproc-processtable-perl_0.637-1_arm64.deb ...
211s Unpacking libproc-processtable-perl:arm64 (0.637-1) ...
211s Selecting previously unselected package libregexp-wildcards-perl.
211s Preparing to unpack .../244-libregexp-wildcards-perl_1.05-3_all.deb ...
211s Unpacking libregexp-wildcards-perl (1.05-3) ...
211s Selecting previously unselected package libsereal-decoder-perl.
211s Preparing to unpack .../245-libsereal-decoder-perl_5.004+ds-1build5_arm64.deb ...
211s Unpacking libsereal-decoder-perl (5.004+ds-1build5) ...
211s Selecting previously unselected package libsereal-encoder-perl.
211s Preparing to unpack .../246-libsereal-encoder-perl_5.004+ds-1build4_arm64.deb ...
211s Unpacking libsereal-encoder-perl (5.004+ds-1build4) ...
211s Selecting previously unselected package libterm-readkey-perl.
211s Preparing to unpack .../247-libterm-readkey-perl_2.38-2build5_arm64.deb ...
211s Unpacking libterm-readkey-perl (2.38-2build5) ...
211s Selecting previously unselected package libtext-levenshteinxs-perl.
211s Preparing to unpack .../248-libtext-levenshteinxs-perl_0.03-5build5_arm64.deb ...
211s Unpacking libtext-levenshteinxs-perl (0.03-5build5) ...
211s Selecting previously unselected package libmarkdown2:arm64.
211s Preparing to unpack .../249-libmarkdown2_2.2.7-2.1build1_arm64.deb ...
211s Unpacking libmarkdown2:arm64 (2.2.7-2.1build1) ...
211s Selecting previously unselected package libtext-markdown-discount-perl.
211s Preparing to unpack .../250-libtext-markdown-discount-perl_0.18-1_arm64.deb ...
211s Unpacking libtext-markdown-discount-perl (0.18-1) ...
211s Selecting previously unselected package libdata-messagepack-perl.
211s Preparing to unpack .../251-libdata-messagepack-perl_1.02-3_arm64.deb ...
211s Unpacking libdata-messagepack-perl (1.02-3) ...
211s Selecting previously unselected package libtext-xslate-perl:arm64.
211s Preparing to unpack .../252-libtext-xslate-perl_3.5.9-2build1_arm64.deb ...
211s Unpacking libtext-xslate-perl:arm64 (3.5.9-2build1) ...
211s Selecting previously unselected package libtime-duration-perl.
211s Preparing to unpack .../253-libtime-duration-perl_1.21-2_all.deb ...
211s Unpacking libtime-duration-perl (1.21-2) ...
211s Selecting previously unselected package libtime-moment-perl.
211s Preparing to unpack .../254-libtime-moment-perl_0.46-1_arm64.deb ...
211s Unpacking libtime-moment-perl (0.46-1) ...
211s Selecting previously unselected package libunicode-utf8-perl.
211s Preparing to unpack .../255-libunicode-utf8-perl_0.63-1_arm64.deb ...
211s Unpacking libunicode-utf8-perl (0.63-1) ...
211s Selecting previously unselected package libcgi-pm-perl.
211s Preparing to unpack .../256-libcgi-pm-perl_4.71-1build1_all.deb ...
211s Unpacking libcgi-pm-perl (4.71-1build1) ...
211s Selecting previously unselected package libhtml-form-perl.
211s Preparing to unpack .../257-libhtml-form-perl_6.13-1build1_all.deb ...
211s Unpacking libhtml-form-perl (6.13-1build1) ...
211s Selecting previously unselected package libwww-mechanize-perl.
211s Preparing to unpack .../258-libwww-mechanize-perl_2.20-1ubuntu1_all.deb ...
211s Unpacking libwww-mechanize-perl (2.20-1ubuntu1) ...
211s Selecting previously unselected package libxml-namespacesupport-perl.
211s Preparing to unpack .../259-libxml-namespacesupport-perl_1.12-2_all.deb ...
211s Unpacking libxml-namespacesupport-perl (1.12-2) ...
211s Selecting previously unselected package libxml-sax-base-perl.
212s Preparing to unpack .../260-libxml-sax-base-perl_1.09-3_all.deb ...
212s Unpacking libxml-sax-base-perl (1.09-3) ...
212s Selecting previously unselected package libxml-sax-perl.
212s Preparing to unpack .../261-libxml-sax-perl_1.02+dfsg-4_all.deb ...
212s Unpacking libxml-sax-perl (1.02+dfsg-4) ...
212s Selecting previously unselected package libxml-libxml-perl.
212s Preparing to unpack .../262-libxml-libxml-perl_2.0207+dfsg+really+2.0207-0ubuntu7_arm64.deb ...
212s Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0207-0ubuntu7) ...
212s Selecting previously unselected package lzip.
212s Preparing to unpack .../263-lzip_1.25-4_arm64.deb ...
212s Unpacking lzip (1.25-4) ...
212s Selecting previously unselected package lzop.
212s Preparing to unpack .../264-lzop_1.04-2build4_arm64.deb ...
212s Unpacking lzop (1.04-2build4) ...
212s Selecting previously unselected package patchutils.
212s Preparing to unpack .../265-patchutils_0.4.3-1_arm64.deb ...
212s Unpacking patchutils (0.4.3-1) ...
212s Selecting previously unselected package t1utils.
212s Preparing to unpack .../266-t1utils_1.41-4build4_arm64.deb ...
212s Unpacking t1utils (1.41-4build4) ...
212s Selecting previously unselected package unzip.
212s Preparing to unpack .../267-unzip_6.0-29ubuntu1_arm64.deb ...
212s Unpacking unzip (6.0-29ubuntu1) ...
212s Selecting previously unselected package lintian.
212s Preparing to unpack .../268-lintian_2.127.0ubuntu1_all.deb ...
212s Unpacking lintian (2.127.0ubuntu1) ...
212s Selecting previously unselected package libconfig-model-dpkg-perl.
212s Preparing to unpack .../269-libconfig-model-dpkg-perl_3.016_all.deb ...
212s Unpacking libconfig-model-dpkg-perl (3.016) ...
212s Selecting previously unselected package libconvert-binhex-perl.
212s Preparing to unpack .../270-libconvert-binhex-perl_1.125-3_all.deb ...
212s Unpacking libconvert-binhex-perl (1.125-3) ...
212s Selecting previously unselected package libnet-smtp-ssl-perl.
212s Preparing to unpack .../271-libnet-smtp-ssl-perl_1.04-2_all.deb ...
212s Unpacking libnet-smtp-ssl-perl (1.04-2) ...
212s Selecting previously unselected package libmailtools-perl.
212s Preparing to unpack .../272-libmailtools-perl_2.22-1_all.deb ...
212s Unpacking libmailtools-perl (2.22-1) ...
212s Selecting previously unselected package libmime-tools-perl.
212s Preparing to unpack .../273-libmime-tools-perl_5.515-1_all.deb ...
212s Unpacking libmime-tools-perl (5.515-1) ...
212s Selecting previously unselected package libb-keywords-perl.
212s Preparing to unpack .../274-libb-keywords-perl_1.29-1_all.deb ...
212s Unpacking libb-keywords-perl (1.29-1) ...
212s Selecting previously unselected package libclass-tiny-perl.
212s Preparing to unpack .../275-libclass-tiny-perl_1.008-2_all.deb ...
212s Unpacking libclass-tiny-perl (1.008-2) ...
212s Selecting previously unselected package liblingua-en-inflect-perl.
212s Preparing to unpack .../276-liblingua-en-inflect-perl_1.905-2_all.deb ...
212s Unpacking liblingua-en-inflect-perl (1.905-2) ...
212s Selecting previously unselected package libpod-spell-perl.
212s Preparing to unpack .../277-libpod-spell-perl_1.27-1_all.deb ...
212s Unpacking libpod-spell-perl (1.27-1) ...
212s Selecting previously unselected package libsafe-isa-perl.
212s Preparing to unpack .../278-libsafe-isa-perl_1.000010-1build1_all.deb ...
212s Unpacking libsafe-isa-perl (1.000010-1build1) ...
212s Selecting previously unselected package libtask-weaken-perl.
212s Preparing to unpack .../279-libtask-weaken-perl_1.06-2_all.deb ...
212s Unpacking libtask-weaken-perl (1.06-2) ...
212s Selecting previously unselected package libppi-perl.
213s Preparing to unpack .../280-libppi-perl_1.284-1_all.deb ...
213s Unpacking libppi-perl (1.284-1) ...
213s Selecting previously unselected package libreadonly-perl.
213s Preparing to unpack .../281-libreadonly-perl_2.050-3_all.deb ...
213s Unpacking libreadonly-perl (2.050-3) ...
213s Selecting previously unselected package libppix-quotelike-perl.
213s Preparing to unpack .../282-libppix-quotelike-perl_0.023-1_all.deb ...
213s Unpacking libppix-quotelike-perl (0.023-1) ...
213s Selecting previously unselected package libppix-regexp-perl.
213s Preparing to unpack .../283-libppix-regexp-perl_0.091-1_all.deb ...
213s Unpacking libppix-regexp-perl (0.091-1) ...
213s Selecting previously unselected package libppix-utils-perl.
213s Preparing to unpack .../284-libppix-utils-perl_0.003-2_all.deb ...
213s Unpacking libppix-utils-perl (0.003-2) ...
213s Selecting previously unselected package libstring-format-perl.
213s Preparing to unpack .../285-libstring-format-perl_1.18-1build1_all.deb ...
213s Unpacking libstring-format-perl (1.18-1build1) ...
213s Selecting previously unselected package perltidy.
213s Preparing to unpack .../286-perltidy_20250105-1build1_all.deb ...
213s Unpacking perltidy (20250105-1build1) ...
213s Selecting previously unselected package libperl-critic-perl.
213s Preparing to unpack .../287-libperl-critic-perl_1.156-1_all.deb ...
213s Unpacking libperl-critic-perl (1.156-1) ...
213s Selecting previously unselected package libtext-wrapper-perl.
213s Preparing to unpack .../288-libtext-wrapper-perl_1.05-4_all.deb ...
213s Unpacking libtext-wrapper-perl (1.05-4) ...
213s Selecting previously unselected package libsuitesparseconfig7:arm64.
213s Preparing to unpack .../289-libsuitesparseconfig7_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libsuitesparseconfig7:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libamd3:arm64.
213s Preparing to unpack .../290-libamd3_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libamd3:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libblas3:arm64.
213s Preparing to unpack .../291-libblas3_3.12.1-7ubuntu1_arm64.deb ...
213s Unpacking libblas3:arm64 (3.12.1-7ubuntu1) ...
213s Selecting previously unselected package libgfortran5:arm64.
213s Preparing to unpack .../292-libgfortran5_15.2.0-12ubuntu1_arm64.deb ...
213s Unpacking libgfortran5:arm64 (15.2.0-12ubuntu1) ...
213s Selecting previously unselected package liblapack3:arm64.
213s Preparing to unpack .../293-liblapack3_3.12.1-7ubuntu1_arm64.deb ...
213s Unpacking liblapack3:arm64 (3.12.1-7ubuntu1) ...
213s Selecting previously unselected package libarpack2t64:arm64.
213s Preparing to unpack .../294-libarpack2t64_3.9.1-6_arm64.deb ...
213s Unpacking libarpack2t64:arm64 (3.9.1-6) ...
213s Selecting previously unselected package libccolamd3:arm64.
213s Preparing to unpack .../295-libccolamd3_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libccolamd3:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libcamd3:arm64.
213s Preparing to unpack .../296-libcamd3_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libcamd3:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libcolamd3:arm64.
213s Preparing to unpack .../297-libcolamd3_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libcolamd3:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libcholmod5:arm64.
213s Preparing to unpack .../298-libcholmod5_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libcholmod5:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libcxsparse4:arm64.
213s Preparing to unpack .../299-libcxsparse4_1%3a7.12.1+dfsg-1_arm64.deb ...
213s Unpacking libcxsparse4:arm64 (1:7.12.1+dfsg-1) ...
213s Selecting previously unselected package libfftw3-double3:arm64.
213s Preparing to unpack .../300-libfftw3-double3_3.3.10-2fakesync1build2_arm64.deb ...
213s Unpacking libfftw3-double3:arm64 (3.3.10-2fakesync1build2) ...
213s Selecting previously unselected package libfftw3-single3:arm64.
214s Preparing to unpack .../301-libfftw3-single3_3.3.10-2fakesync1build2_arm64.deb ...
214s Unpacking libfftw3-single3:arm64 (3.3.10-2fakesync1build2) ...
214s Selecting previously unselected package libxfixes3:arm64.
214s Preparing to unpack .../302-libxfixes3_1%3a6.0.0-2build2_arm64.deb ...
214s Unpacking libxfixes3:arm64 (1:6.0.0-2build2) ...
214s Selecting previously unselected package libxcursor1:arm64.
214s Preparing to unpack .../303-libxcursor1_1%3a1.2.3-1build1_arm64.deb ...
214s Unpacking libxcursor1:arm64 (1:1.2.3-1build1) ...
214s Selecting previously unselected package libxft2:arm64.
214s Preparing to unpack .../304-libxft2_2.3.6-1build2_arm64.deb ...
214s Unpacking libxft2:arm64 (2.3.6-1build2) ...
214s Selecting previously unselected package libxinerama1:arm64.
214s Preparing to unpack .../305-libxinerama1_2%3a1.1.4-3build2_arm64.deb ...
214s Unpacking libxinerama1:arm64 (2:1.1.4-3build2) ...
214s Selecting previously unselected package libfltk1.3t64:arm64.
214s Preparing to unpack .../306-libfltk1.3t64_1.3.11-3_arm64.deb ...
214s Unpacking libfltk1.3t64:arm64 (1.3.11-3) ...
214s Selecting previously unselected package libglvnd0:arm64.
214s Preparing to unpack .../307-libglvnd0_1.7.0-3_arm64.deb ...
214s Unpacking libglvnd0:arm64 (1.7.0-3) ...
214s Selecting previously unselected package libx11-xcb1:arm64.
214s Preparing to unpack .../308-libx11-xcb1_2%3a1.8.12-1build1_arm64.deb ...
214s Unpacking libx11-xcb1:arm64 (2:1.8.12-1build1) ...
214s Selecting previously unselected package libxcb-dri3-0:arm64.
214s Preparing to unpack .../309-libxcb-dri3-0_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-dri3-0:arm64 (1.17.0-2ubuntu1) ...
214s Selecting previously unselected package libxcb-present0:arm64.
214s Preparing to unpack .../310-libxcb-present0_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-present0:arm64 (1.17.0-2ubuntu1) ...
214s Selecting previously unselected package libxcb-randr0:arm64.
214s Preparing to unpack .../311-libxcb-randr0_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-randr0:arm64 (1.17.0-2ubuntu1) ...
214s Selecting previously unselected package libxcb-sync1:arm64.
214s Preparing to unpack .../312-libxcb-sync1_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-sync1:arm64 (1.17.0-2ubuntu1) ...
214s Selecting previously unselected package libxcb-xfixes0:arm64.
214s Preparing to unpack .../313-libxcb-xfixes0_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-xfixes0:arm64 (1.17.0-2ubuntu1) ...
214s Selecting previously unselected package libxshmfence1:arm64.
214s Preparing to unpack .../314-libxshmfence1_1.3.3-1build1_arm64.deb ...
214s Unpacking libxshmfence1:arm64 (1.3.3-1build1) ...
214s Selecting previously unselected package mesa-libgallium:arm64.
214s Preparing to unpack .../315-mesa-libgallium_25.3.3-1ubuntu1_arm64.deb ...
214s Unpacking mesa-libgallium:arm64 (25.3.3-1ubuntu1) ...
214s Selecting previously unselected package libgbm1:arm64.
214s Preparing to unpack .../316-libgbm1_25.3.3-1ubuntu1_arm64.deb ...
214s Unpacking libgbm1:arm64 (25.3.3-1ubuntu1) ...
214s Selecting previously unselected package libvulkan1:arm64.
214s Preparing to unpack .../317-libvulkan1_1.4.335.0-1_arm64.deb ...
214s Unpacking libvulkan1:arm64 (1.4.335.0-1) ...
214s Selecting previously unselected package libgl1-mesa-dri:arm64.
214s Preparing to unpack .../318-libgl1-mesa-dri_25.3.3-1ubuntu1_arm64.deb ...
214s Unpacking libgl1-mesa-dri:arm64 (25.3.3-1ubuntu1) ...
214s Selecting previously unselected package libxcb-glx0:arm64.
214s Preparing to unpack .../319-libxcb-glx0_1.17.0-2ubuntu1_arm64.deb ...
214s Unpacking libxcb-glx0:arm64 (1.17.0-2ubuntu1) ...
215s Selecting previously unselected package libxxf86vm1:arm64.
215s Preparing to unpack .../320-libxxf86vm1_1%3a1.1.4-2_arm64.deb ...
215s Unpacking libxxf86vm1:arm64 (1:1.1.4-2) ...
215s Selecting previously unselected package libglx-mesa0:arm64.
215s Preparing to unpack .../321-libglx-mesa0_25.3.3-1ubuntu1_arm64.deb ...
215s Unpacking libglx-mesa0:arm64 (25.3.3-1ubuntu1) ...
215s Selecting previously unselected package libglx0:arm64.
215s Preparing to unpack .../322-libglx0_1.7.0-3_arm64.deb ...
215s Unpacking libglx0:arm64 (1.7.0-3) ...
215s Selecting previously unselected package libgl1:arm64.
215s Preparing to unpack .../323-libgl1_1.7.0-3_arm64.deb ...
215s Unpacking libgl1:arm64 (1.7.0-3) ...
215s Selecting previously unselected package libfltk-gl1.3t64:arm64.
215s Preparing to unpack .../324-libfltk-gl1.3t64_1.3.11-3_arm64.deb ...
215s Unpacking libfltk-gl1.3t64:arm64 (1.3.11-3) ...
215s Selecting previously unselected package libgl2ps1.4:arm64.
215s Preparing to unpack .../325-libgl2ps1.4_1.4.2+dfsg1-4_arm64.deb ...
215s Unpacking libgl2ps1.4:arm64 (1.4.2+dfsg1-4) ...
215s Selecting previously unselected package libltdl7:arm64.
215s Preparing to unpack .../326-libltdl7_2.5.4-9_arm64.deb ...
215s Unpacking libltdl7:arm64 (2.5.4-9) ...
215s Selecting previously unselected package libglpk40:arm64.
215s Preparing to unpack .../327-libglpk40_5.0-2_arm64.deb ...
215s Unpacking libglpk40:arm64 (5.0-2) ...
215s Selecting previously unselected package libopengl0:arm64.
215s Preparing to unpack .../328-libopengl0_1.7.0-3_arm64.deb ...
215s Unpacking libopengl0:arm64 (1.7.0-3) ...
215s Selecting previously unselected package libglu1-mesa:arm64.
215s Preparing to unpack .../329-libglu1-mesa_9.0.2-1.1build2_arm64.deb ...
215s Unpacking libglu1-mesa:arm64 (9.0.2-1.1build2) ...
215s Selecting previously unselected package libhwy1t64:arm64.
215s Preparing to unpack .../330-libhwy1t64_1.3.0-2_arm64.deb ...
215s Unpacking libhwy1t64:arm64 (1.3.0-2) ...
215s Selecting previously unselected package liblcms2-2:arm64.
215s Preparing to unpack .../331-liblcms2-2_2.17-1_arm64.deb ...
215s Unpacking liblcms2-2:arm64 (2.17-1) ...
215s Selecting previously unselected package libjxl0.11:arm64.
215s Preparing to unpack .../332-libjxl0.11_0.11.1-6ubuntu1_arm64.deb ...
215s Unpacking libjxl0.11:arm64 (0.11.1-6ubuntu1) ...
215s Selecting previously unselected package libwmflite-0.2-7:arm64.
215s Preparing to unpack .../333-libwmflite-0.2-7_0.2.13-2_arm64.deb ...
215s Unpacking libwmflite-0.2-7:arm64 (0.2.13-2) ...
215s Selecting previously unselected package libgraphicsmagick-q16-3t64.
215s Preparing to unpack .../334-libgraphicsmagick-q16-3t64_1.4+really1.3.45+hg17696-1build1_arm64.deb ...
215s Unpacking libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17696-1build1) ...
215s Selecting previously unselected package libgraphicsmagick++-q16-12t64.
215s Preparing to unpack .../335-libgraphicsmagick++-q16-12t64_1.4+really1.3.45+hg17696-1build1_arm64.deb ...
215s Unpacking libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17696-1build1) ...
215s Selecting previously unselected package libsz2:arm64.
215s Preparing to unpack .../336-libsz2_1.1.5-1_arm64.deb ...
215s Unpacking libsz2:arm64 (1.1.5-1) ...
215s Selecting previously unselected package libhdf5-310:arm64.
215s Preparing to unpack .../337-libhdf5-310_1.14.6+repack-2_arm64.deb ...
215s Unpacking libhdf5-310:arm64 (1.14.6+repack-2) ...
215s Selecting previously unselected package libasound2-data.
215s Preparing to unpack .../338-libasound2-data_1.2.15.3-1ubuntu1_all.deb ...
215s Unpacking libasound2-data (1.2.15.3-1ubuntu1) ...
215s Selecting previously unselected package libasound2t64:arm64.
215s Preparing to unpack .../339-libasound2t64_1.2.15.3-1ubuntu1_arm64.deb ...
215s Unpacking libasound2t64:arm64 (1.2.15.3-1ubuntu1) ...
215s Selecting previously unselected package libopus0:arm64.
215s Preparing to unpack .../340-libopus0_1.6.1-1_arm64.deb ...
215s Unpacking libopus0:arm64 (1.6.1-1) ...
215s Selecting previously unselected package libsamplerate0:arm64.
215s Preparing to unpack .../341-libsamplerate0_0.2.2-4build2_arm64.deb ...
215s Unpacking libsamplerate0:arm64 (0.2.2-4build2) ...
216s Selecting previously unselected package libjack-jackd2-0:arm64.
216s Preparing to unpack .../342-libjack-jackd2-0_1.9.22~dfsg-5_arm64.deb ...
216s Unpacking libjack-jackd2-0:arm64 (1.9.22~dfsg-5) ...
216s Selecting previously unselected package libasyncns0:arm64.
216s Preparing to unpack .../343-libasyncns0_0.8-7_arm64.deb ...
216s Unpacking libasyncns0:arm64 (0.8-7) ...
216s Selecting previously unselected package libogg0:arm64.
216s Preparing to unpack .../344-libogg0_1.3.6-2_arm64.deb ...
216s Unpacking libogg0:arm64 (1.3.6-2) ...
216s Selecting previously unselected package libflac14:arm64.
216s Preparing to unpack .../345-libflac14_1.5.0+ds-5_arm64.deb ...
216s Unpacking libflac14:arm64 (1.5.0+ds-5) ...
216s Selecting previously unselected package libmp3lame0:arm64.
216s Preparing to unpack .../346-libmp3lame0_3.100-6build2_arm64.deb ...
216s Unpacking libmp3lame0:arm64 (3.100-6build2) ...
216s Selecting previously unselected package libmpg123-0t64:arm64.
216s Preparing to unpack .../347-libmpg123-0t64_1.33.3-2_arm64.deb ...
216s Unpacking libmpg123-0t64:arm64 (1.33.3-2) ...
216s Selecting previously unselected package libvorbis0a:arm64.
216s Preparing to unpack .../348-libvorbis0a_1.3.7-3build1_arm64.deb ...
216s Unpacking libvorbis0a:arm64 (1.3.7-3build1) ...
216s Selecting previously unselected package libvorbisenc2:arm64.
216s Preparing to unpack .../349-libvorbisenc2_1.3.7-3build1_arm64.deb ...
216s Unpacking libvorbisenc2:arm64 (1.3.7-3build1) ...
216s Selecting previously unselected package libsndfile1:arm64.
216s Preparing to unpack .../350-libsndfile1_1.2.2-4_arm64.deb ...
216s Unpacking libsndfile1:arm64 (1.2.2-4) ...
216s Selecting previously unselected package libpulse0:arm64.
216s Preparing to unpack .../351-libpulse0_1%3a17.0+dfsg1-2ubuntu4_arm64.deb ...
216s Unpacking libpulse0:arm64 (1:17.0+dfsg1-2ubuntu4) ...
216s Selecting previously unselected package libportaudio2:arm64.
216s Preparing to unpack .../352-libportaudio2_19.7.0+git20251227.3270c9ae-0ubuntu1_arm64.deb ...
216s Unpacking libportaudio2:arm64 (19.7.0+git20251227.3270c9ae-0ubuntu1) ...
216s Selecting previously unselected package libqhull-r8.0:arm64.
216s Preparing to unpack .../353-libqhull-r8.0_2020.2-8_arm64.deb ...
216s Unpacking libqhull-r8.0:arm64 (2020.2-8) ...
216s Selecting previously unselected package libqrupdate1:arm64.
216s Preparing to unpack .../354-libqrupdate1_1.1.5-3_arm64.deb ...
216s Unpacking libqrupdate1:arm64 (1.1.5-3) ...
216s Selecting previously unselected package libqscintilla2-qt6-l10n.
216s Preparing to unpack .../355-libqscintilla2-qt6-l10n_2.14.1+dfsg-2_all.deb ...
216s Unpacking libqscintilla2-qt6-l10n (2.14.1+dfsg-2) ...
216s Selecting previously unselected package libb2-1:arm64.
216s Preparing to unpack .../356-libb2-1_0.98.1-1.1build2_arm64.deb ...
216s Unpacking libb2-1:arm64 (0.98.1-1.1build2) ...
216s Selecting previously unselected package libdouble-conversion3:arm64.
216s Preparing to unpack .../357-libdouble-conversion3_3.4.0-1_arm64.deb ...
216s Unpacking libdouble-conversion3:arm64 (3.4.0-1) ...
216s Selecting previously unselected package libpcre2-16-0:arm64.
216s Preparing to unpack .../358-libpcre2-16-0_10.46-1_arm64.deb ...
216s Unpacking libpcre2-16-0:arm64 (10.46-1) ...
216s Selecting previously unselected package libqt6core6t64:arm64.
216s Preparing to unpack .../359-libqt6core6t64_6.9.2+dfsg-3ubuntu2_arm64.deb ...
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216s Selecting previously unselected package libwayland-client0:arm64.
216s Preparing to unpack .../360-libwayland-client0_1.24.0-2_arm64.deb ...
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216s Selecting previously unselected package libegl-mesa0:arm64.
216s Preparing to unpack .../361-libegl-mesa0_25.3.3-1ubuntu1_arm64.deb ...
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216s Selecting previously unselected package libegl1:arm64.
216s Preparing to unpack .../362-libegl1_1.7.0-3_arm64.deb ...
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216s Selecting previously unselected package x11-common.
216s Preparing to unpack .../363-x11-common_1%3a7.7+24ubuntu1_all.deb ...
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216s Selecting previously unselected package libice6:arm64.
216s Preparing to unpack .../364-libice6_2%3a1.1.1-1build1_arm64.deb ...
216s Unpacking libice6:arm64 (2:1.1.1-1build1) ...
216s Selecting previously unselected package libmtdev1t64:arm64.
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217s Selecting previously unselected package libwacom-common.
217s Preparing to unpack .../366-libwacom-common_2.16.1-1_all.deb ...
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217s Selecting previously unselected package libwacom9:arm64.
217s Preparing to unpack .../367-libwacom9_2.16.1-1_arm64.deb ...
217s Unpacking libwacom9:arm64 (2.16.1-1) ...
217s Selecting previously unselected package libinput-bin.
217s Preparing to unpack .../368-libinput-bin_1.30.1-1_arm64.deb ...
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217s Selecting previously unselected package libinput10:arm64.
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217s Selecting previously unselected package libmd4c0:arm64.
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217s Selecting previously unselected package libqt6dbus6:arm64.
217s Preparing to unpack .../371-libqt6dbus6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
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217s Selecting previously unselected package libsm6:arm64.
217s Preparing to unpack .../372-libsm6_2%3a1.2.6-1_arm64.deb ...
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217s Selecting previously unselected package libts0t64:arm64.
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217s Selecting previously unselected package libxcb-util1:arm64.
217s Preparing to unpack .../374-libxcb-util1_0.4.1-1build1_arm64.deb ...
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217s Selecting previously unselected package libxcb-image0:arm64.
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217s Selecting previously unselected package libxcb-render-util0:arm64.
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217s Selecting previously unselected package libxcb-cursor0:arm64.
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217s Selecting previously unselected package libxcb-icccm4:arm64.
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217s Selecting previously unselected package libxcb-keysyms1:arm64.
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217s Selecting previously unselected package libxcb-shape0:arm64.
217s Preparing to unpack .../380-libxcb-shape0_1.17.0-2ubuntu1_arm64.deb ...
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217s Selecting previously unselected package libxcb-xinput0:arm64.
217s Preparing to unpack .../381-libxcb-xinput0_1.17.0-2ubuntu1_arm64.deb ...
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217s Selecting previously unselected package libxcb-xkb1:arm64.
217s Preparing to unpack .../382-libxcb-xkb1_1.17.0-2ubuntu1_arm64.deb ...
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217s Selecting previously unselected package libxkbcommon-x11-0:arm64.
217s Preparing to unpack .../383-libxkbcommon-x11-0_1.12.3-1_arm64.deb ...
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218s Selecting previously unselected package libqt6gui6:arm64.
218s Preparing to unpack .../384-libqt6gui6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libavahi-common-data:arm64.
218s Preparing to unpack .../385-libavahi-common-data_0.8-17ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libavahi-common3:arm64.
218s Preparing to unpack .../386-libavahi-common3_0.8-17ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libavahi-client3:arm64.
218s Preparing to unpack .../387-libavahi-client3_0.8-17ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libcups2t64:arm64.
218s Preparing to unpack .../388-libcups2t64_2.4.16-1ubuntu1_arm64.deb ...
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218s Selecting previously unselected package libqt6widgets6:arm64.
218s Preparing to unpack .../389-libqt6widgets6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libqt6printsupport6:arm64.
218s Preparing to unpack .../390-libqt6printsupport6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
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218s Selecting previously unselected package libqscintilla2-qt6-15:arm64.
218s Preparing to unpack .../391-libqscintilla2-qt6-15_2.14.1+dfsg-2_arm64.deb ...
218s Unpacking libqscintilla2-qt6-15:arm64 (2.14.1+dfsg-2) ...
218s Selecting previously unselected package libqt6core5compat6:arm64.
218s Preparing to unpack .../392-libqt6core5compat6_6.9.2-3build1_arm64.deb ...
218s Unpacking libqt6core5compat6:arm64 (6.9.2-3build1) ...
218s Selecting previously unselected package libqt6sql6:arm64.
218s Preparing to unpack .../393-libqt6sql6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
218s Unpacking libqt6sql6:arm64 (6.9.2+dfsg-3ubuntu2) ...
218s Selecting previously unselected package libqt6help6:arm64.
218s Preparing to unpack .../394-libqt6help6_6.9.2-5_arm64.deb ...
218s Unpacking libqt6help6:arm64 (6.9.2-5) ...
218s Selecting previously unselected package libduktape207:arm64.
218s Preparing to unpack .../395-libduktape207_2.7.0+tests-0ubuntu4_arm64.deb ...
218s Unpacking libduktape207:arm64 (2.7.0+tests-0ubuntu4) ...
218s Selecting previously unselected package libproxy1v5:arm64.
218s Preparing to unpack .../396-libproxy1v5_0.5.12-1_arm64.deb ...
218s Unpacking libproxy1v5:arm64 (0.5.12-1) ...
218s Selecting previously unselected package libqt6network6:arm64.
218s Preparing to unpack .../397-libqt6network6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
218s Unpacking libqt6network6:arm64 (6.9.2+dfsg-3ubuntu2) ...
218s Selecting previously unselected package libqt6opengl6:arm64.
218s Preparing to unpack .../398-libqt6opengl6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
218s Unpacking libqt6opengl6:arm64 (6.9.2+dfsg-3ubuntu2) ...
218s Selecting previously unselected package libqt6openglwidgets6:arm64.
218s Preparing to unpack .../399-libqt6openglwidgets6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
218s Unpacking libqt6openglwidgets6:arm64 (6.9.2+dfsg-3ubuntu2) ...
218s Selecting previously unselected package libqt6xml6:arm64.
218s Preparing to unpack .../400-libqt6xml6_6.9.2+dfsg-3ubuntu2_arm64.deb ...
218s Unpacking libqt6xml6:arm64 (6.9.2+dfsg-3ubuntu2) ...
218s Selecting previously unselected package libspqr4:arm64.
218s Preparing to unpack .../401-libspqr4_1%3a7.12.1+dfsg-1_arm64.deb ...
218s Unpacking libspqr4:arm64 (1:7.12.1+dfsg-1) ...
218s Selecting previously unselected package libumfpack6:arm64.
218s Preparing to unpack .../402-libumfpack6_1%3a7.12.1+dfsg-1_arm64.deb ...
218s Unpacking libumfpack6:arm64 (1:7.12.1+dfsg-1) ...
218s Selecting previously unselected package libtext-unidecode-perl.
218s Preparing to unpack .../403-libtext-unidecode-perl_1.30-3_all.deb ...
218s Unpacking libtext-unidecode-perl (1.30-3) ...
218s Selecting previously unselected package libintl-perl.
218s Preparing to unpack .../404-libintl-perl_1.35-1_all.deb ...
218s Unpacking libintl-perl (1.35-1) ...
218s Selecting previously unselected package texinfo-lib.
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218s Selecting previously unselected package tex-common.
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218s Selecting previously unselected package texinfo.
218s Preparing to unpack .../407-texinfo_7.2-5_all.deb ...
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219s Selecting previously unselected package octave-common.
219s Preparing to unpack .../408-octave-common_10.3.0-3_all.deb ...
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219s Selecting previously unselected package octave.
219s Preparing to unpack .../409-octave_10.3.0-3_arm64.deb ...
219s Unpacking octave (10.3.0-3) ...
219s Selecting previously unselected package libncurses-dev:arm64.
219s Preparing to unpack .../410-libncurses-dev_6.6+20251231-1_arm64.deb ...
219s Unpacking libncurses-dev:arm64 (6.6+20251231-1) ...
219s Selecting previously unselected package libreadline-dev:arm64.
219s Preparing to unpack .../411-libreadline-dev_8.3-3_arm64.deb ...
219s Unpacking libreadline-dev:arm64 (8.3-3) ...
219s Selecting previously unselected package libhdf5-fortran-310:arm64.
219s Preparing to unpack .../412-libhdf5-fortran-310_1.14.6+repack-2_arm64.deb ...
219s Unpacking libhdf5-fortran-310:arm64 (1.14.6+repack-2) ...
219s Selecting previously unselected package libhdf5-hl-310:arm64.
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219s Selecting previously unselected package libhdf5-hl-fortran-310:arm64.
219s Preparing to unpack .../414-libhdf5-hl-fortran-310_1.14.6+repack-2_arm64.deb ...
219s Unpacking libhdf5-hl-fortran-310:arm64 (1.14.6+repack-2) ...
219s Selecting previously unselected package libhdf5-cpp-310:arm64.
219s Preparing to unpack .../415-libhdf5-cpp-310_1.14.6+repack-2_arm64.deb ...
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219s Selecting previously unselected package libhdf5-hl-cpp-310:arm64.
219s Preparing to unpack .../416-libhdf5-hl-cpp-310_1.14.6+repack-2_arm64.deb ...
219s Unpacking libhdf5-hl-cpp-310:arm64 (1.14.6+repack-2) ...
219s Selecting previously unselected package zlib1g-dev:arm64.
219s Preparing to unpack .../417-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu2_arm64.deb ...
219s Unpacking zlib1g-dev:arm64 (1:1.3.dfsg+really1.3.1-1ubuntu2) ...
219s Selecting previously unselected package libjpeg-turbo8-dev:arm64.
219s Preparing to unpack .../418-libjpeg-turbo8-dev_2.1.5-4ubuntu2_arm64.deb ...
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219s Selecting previously unselected package libjpeg8-dev:arm64.
219s Preparing to unpack .../419-libjpeg8-dev_8c-2ubuntu11_arm64.deb ...
219s Unpacking libjpeg8-dev:arm64 (8c-2ubuntu11) ...
220s Selecting previously unselected package libjpeg-dev:arm64.
220s Preparing to unpack .../420-libjpeg-dev_8c-2ubuntu11_arm64.deb ...
220s Unpacking libjpeg-dev:arm64 (8c-2ubuntu11) ...
220s Selecting previously unselected package libaec0:arm64.
220s Preparing to unpack .../421-libaec0_1.1.5-1_arm64.deb ...
220s Unpacking libaec0:arm64 (1.1.5-1) ...
220s Selecting previously unselected package libaec-dev:arm64.
220s Preparing to unpack .../422-libaec-dev_1.1.5-1_arm64.deb ...
220s Unpacking libaec-dev:arm64 (1.1.5-1) ...
220s Selecting previously unselected package libbrotli-dev:arm64.
220s Preparing to unpack .../423-libbrotli-dev_1.1.0-2build6_arm64.deb ...
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220s Selecting previously unselected package libidn2-dev:arm64.
220s Preparing to unpack .../424-libidn2-dev_2.3.8-4_arm64.deb ...
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220s Selecting previously unselected package comerr-dev:arm64.
220s Preparing to unpack .../425-comerr-dev_2.1-1.47.2-3ubuntu2_arm64.deb ...
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220s Selecting previously unselected package libgssrpc4t64:arm64.
220s Preparing to unpack .../426-libgssrpc4t64_1.22.1-2_arm64.deb ...
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220s Selecting previously unselected package libkadm5clnt-mit12:arm64.
220s Preparing to unpack .../427-libkadm5clnt-mit12_1.22.1-2_arm64.deb ...
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220s Selecting previously unselected package libkdb5-10t64:arm64.
220s Preparing to unpack .../428-libkdb5-10t64_1.22.1-2_arm64.deb ...
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220s Selecting previously unselected package libkadm5srv-mit12:arm64.
220s Preparing to unpack .../429-libkadm5srv-mit12_1.22.1-2_arm64.deb ...
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220s Selecting previously unselected package krb5-multidev:arm64.
220s Preparing to unpack .../430-krb5-multidev_1.22.1-2_arm64.deb ...
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220s Selecting previously unselected package libkrb5-dev:arm64.
220s Preparing to unpack .../431-libkrb5-dev_1.22.1-2_arm64.deb ...
220s Unpacking libkrb5-dev:arm64 (1.22.1-2) ...
220s Selecting previously unselected package libldap-dev:arm64.
220s Preparing to unpack .../432-libldap-dev_2.6.10+dfsg-1ubuntu5_arm64.deb ...
220s Unpacking libldap-dev:arm64 (2.6.10+dfsg-1ubuntu5) ...
220s Selecting previously unselected package libpkgconf3:arm64.
220s Preparing to unpack .../433-libpkgconf3_1.8.1-4build1_arm64.deb ...
220s Unpacking libpkgconf3:arm64 (1.8.1-4build1) ...
220s Selecting previously unselected package pkgconf-bin.
220s Preparing to unpack .../434-pkgconf-bin_1.8.1-4build1_arm64.deb ...
220s Unpacking pkgconf-bin (1.8.1-4build1) ...
220s Selecting previously unselected package pkgconf:arm64.
220s Preparing to unpack .../435-pkgconf_1.8.1-4build1_arm64.deb ...
220s Unpacking pkgconf:arm64 (1.8.1-4build1) ...
220s Selecting previously unselected package libnghttp2-dev:arm64.
220s Preparing to unpack .../436-libnghttp2-dev_1.64.0-1.1ubuntu1_arm64.deb ...
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220s Selecting previously unselected package libpsl-dev:arm64.
220s Preparing to unpack .../437-libpsl-dev_0.21.2-1.1build2_arm64.deb ...
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220s Selecting previously unselected package libgmpxx4ldbl:arm64.
220s Preparing to unpack .../438-libgmpxx4ldbl_2%3a6.3.0+dfsg-5ubuntu1_arm64.deb ...
220s Unpacking libgmpxx4ldbl:arm64 (2:6.3.0+dfsg-5ubuntu1) ...
220s Selecting previously unselected package libgmp-dev:arm64.
220s Preparing to unpack .../439-libgmp-dev_2%3a6.3.0+dfsg-5ubuntu1_arm64.deb ...
220s Unpacking libgmp-dev:arm64 (2:6.3.0+dfsg-5ubuntu1) ...
220s Selecting previously unselected package libevent-2.1-7t64:arm64.
220s Preparing to unpack .../440-libevent-2.1-7t64_2.1.12-stable-10build1_arm64.deb ...
220s Unpacking libevent-2.1-7t64:arm64 (2.1.12-stable-10build1) ...
220s Selecting previously unselected package libunbound8:arm64.
220s Preparing to unpack .../441-libunbound8_1.24.2-1ubuntu1_arm64.deb ...
220s Unpacking libunbound8:arm64 (1.24.2-1ubuntu1) ...
220s Selecting previously unselected package libgnutls-dane0t64:arm64.
220s Preparing to unpack .../442-libgnutls-dane0t64_3.8.10-3ubuntu1_arm64.deb ...
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221s Selecting previously unselected package libgnutls-openssl27t64:arm64.
221s Preparing to unpack .../443-libgnutls-openssl27t64_3.8.10-3ubuntu1_arm64.deb ...
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221s Selecting previously unselected package libp11-kit-dev:arm64.
221s Preparing to unpack .../444-libp11-kit-dev_0.25.10-1_arm64.deb ...
221s Unpacking libp11-kit-dev:arm64 (0.25.10-1) ...
221s Selecting previously unselected package libtasn1-6-dev:arm64.
221s Preparing to unpack .../445-libtasn1-6-dev_4.21.0-2_arm64.deb ...
221s Unpacking libtasn1-6-dev:arm64 (4.21.0-2) ...
221s Selecting previously unselected package nettle-dev:arm64.
221s Preparing to unpack .../446-nettle-dev_3.10.2-1_arm64.deb ...
221s Unpacking nettle-dev:arm64 (3.10.2-1) ...
221s Selecting previously unselected package libgnutls28-dev:arm64.
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221s Unpacking libgnutls28-dev:arm64 (3.8.10-3ubuntu1) ...
221s Selecting previously unselected package librtmp-dev:arm64.
221s Preparing to unpack .../448-librtmp-dev_2.4+20151223.gitfa8646d.1-3_arm64.deb ...
221s Unpacking librtmp-dev:arm64 (2.4+20151223.gitfa8646d.1-3) ...
221s Selecting previously unselected package libssl-dev:arm64.
221s Preparing to unpack .../449-libssl-dev_3.5.3-1ubuntu2_arm64.deb ...
221s Unpacking libssl-dev:arm64 (3.5.3-1ubuntu2) ...
221s Selecting previously unselected package libssh2-1-dev:arm64.
221s Preparing to unpack .../450-libssh2-1-dev_1.11.1-1build1_arm64.deb ...
221s Unpacking libssh2-1-dev:arm64 (1.11.1-1build1) ...
221s Selecting previously unselected package libzstd-dev:arm64.
221s Preparing to unpack .../451-libzstd-dev_1.5.7+dfsg-3_arm64.deb ...
221s Unpacking libzstd-dev:arm64 (1.5.7+dfsg-3) ...
221s Selecting previously unselected package libcurl4-openssl-dev:arm64.
221s Preparing to unpack .../452-libcurl4-openssl-dev_8.18.0-1ubuntu1_arm64.deb ...
221s Unpacking libcurl4-openssl-dev:arm64 (8.18.0-1ubuntu1) ...
221s Selecting previously unselected package hdf5-helpers.
221s Preparing to unpack .../453-hdf5-helpers_1.14.6+repack-2_arm64.deb ...
221s Unpacking hdf5-helpers (1.14.6+repack-2) ...
221s Selecting previously unselected package libhdf5-dev.
221s Preparing to unpack .../454-libhdf5-dev_1.14.6+repack-2_arm64.deb ...
221s Unpacking libhdf5-dev (1.14.6+repack-2) ...
221s Selecting previously unselected package xorg-sgml-doctools.
221s Preparing to unpack .../455-xorg-sgml-doctools_1%3a1.11-1.1build1_all.deb ...
221s Unpacking xorg-sgml-doctools (1:1.11-1.1build1) ...
221s Selecting previously unselected package x11proto-dev.
221s Preparing to unpack .../456-x11proto-dev_2025.1-1_all.deb ...
221s Unpacking x11proto-dev (2025.1-1) ...
221s Selecting previously unselected package libxau-dev:arm64.
221s Preparing to unpack .../457-libxau-dev_1%3a1.0.11-1build1_arm64.deb ...
221s Unpacking libxau-dev:arm64 (1:1.0.11-1build1) ...
221s Selecting previously unselected package libxdmcp-dev:arm64.
221s Preparing to unpack .../458-libxdmcp-dev_1%3a1.1.5-2_arm64.deb ...
221s Unpacking libxdmcp-dev:arm64 (1:1.1.5-2) ...
221s Selecting previously unselected package xtrans-dev.
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222s Unpacking xtrans-dev (1.6.0-1build1) ...
222s Selecting previously unselected package libxcb1-dev:arm64.
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222s Unpacking libxcb1-dev:arm64 (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libx11-dev:arm64.
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222s Unpacking libx11-dev:arm64 (2:1.8.12-1build1) ...
222s Selecting previously unselected package libglx-dev:arm64.
222s Preparing to unpack .../462-libglx-dev_1.7.0-3_arm64.deb ...
222s Unpacking libglx-dev:arm64 (1.7.0-3) ...
222s Selecting previously unselected package libgl-dev:arm64.
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222s Unpacking libgl-dev:arm64 (1.7.0-3) ...
222s Selecting previously unselected package libblas-dev:arm64.
222s Preparing to unpack .../464-libblas-dev_3.12.1-7ubuntu1_arm64.deb ...
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222s Selecting previously unselected package liblapack-dev:arm64.
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222s Unpacking liblapack-dev:arm64 (3.12.1-7ubuntu1) ...
222s Selecting previously unselected package libfftw3-long3:arm64.
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222s Selecting previously unselected package libfftw3-bin.
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222s Unpacking libfftw3-bin (3.3.10-2fakesync1build2) ...
222s Selecting previously unselected package libfftw3-dev:arm64.
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222s Selecting previously unselected package libgfortran-15-dev:arm64.
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222s Unpacking libgfortran-15-dev:arm64 (15.2.0-12ubuntu1) ...
222s Selecting previously unselected package gfortran-15-aarch64-linux-gnu.
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222s Unpacking gfortran-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
222s Selecting previously unselected package gfortran-15.
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222s Unpacking gfortran-15 (15.2.0-12ubuntu1) ...
222s Selecting previously unselected package gfortran-aarch64-linux-gnu.
223s Preparing to unpack .../472-gfortran-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ...
223s Unpacking gfortran-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
223s Selecting previously unselected package gfortran.
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223s Unpacking gfortran (4:15.2.0-4ubuntu1) ...
223s Selecting previously unselected package libstdc++-15-dev:arm64.
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223s Selecting previously unselected package g++-15-aarch64-linux-gnu.
223s Preparing to unpack .../475-g++-15-aarch64-linux-gnu_15.2.0-12ubuntu1_arm64.deb ...
223s Unpacking g++-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
223s Selecting previously unselected package g++-15.
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223s Selecting previously unselected package g++-aarch64-linux-gnu.
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223s Selecting previously unselected package octave-dev.
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223s Selecting previously unselected package dh-octave.
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223s Selecting previously unselected package libfontenc1:arm64.
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223s Selecting previously unselected package libunwind8:arm64.
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223s Selecting previously unselected package libxaw7:arm64.
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224s Selecting previously unselected package libxfont2:arm64.
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224s Setting up libconfig-inifiles-perl (3.000003-4) ...
224s Setting up libapt-pkg-perl (0.1.43) ...
224s Setting up libhwy1t64:arm64 (1.3.0-2) ...
224s Setting up libmodule-pluggable-perl (5.2-5) ...
224s Setting up libgraphite2-3:arm64 (1.3.14-11ubuntu1) ...
224s Setting up libstring-escape-perl (2010.002-3) ...
224s Setting up libgnutls-openssl27t64:arm64 (3.8.10-3ubuntu1) ...
224s Setting up libxcb-dri3-0:arm64 (1.17.0-2ubuntu1) ...
224s Setting up liblcms2-2:arm64 (2.17-1) ...
224s Setting up libberkeleydb-perl:arm64 (0.66-2) ...
224s Setting up libpixman-1-0:arm64 (0.46.4-1) ...
224s Setting up libsharpyuv0:arm64 (1.5.0-0.1build1) ...
224s Setting up libaom3:arm64 (3.13.1-2) ...
224s Setting up liblingua-en-inflect-perl (1.905-2) ...
224s Setting up libx11-xcb1:arm64 (2:1.8.12-1build1) ...
224s Setting up libfile-which-perl (1.27-2) ...
224s Setting up libdouble-conversion3:arm64 (3.4.0-1) ...
224s Setting up libsafe-isa-perl (1.000010-1build1) ...
224s Setting up libtask-weaken-perl (1.06-2) ...
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224s Setting up libxcb-xfixes0:arm64 (1.17.0-2ubuntu1) ...
224s Setting up libogg0:arm64 (1.3.6-2) ...
224s Setting up libmouse-perl:arm64 (2.6.1-1) ...
224s Setting up libzstd-dev:arm64 (1.5.7+dfsg-3) ...
224s Setting up liblerc4:arm64 (4.0.0+ds-5ubuntu2) ...
224s Setting up libpod-pom-perl (2.01-4) ...
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224s Setting up hdf5-helpers (1.14.6+repack-2) ...
224s Setting up libwmflite-0.2-7:arm64 (0.2.13-2) ...
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224s Setting up libxcb-xinput0:arm64 (1.17.0-2ubuntu1) ...
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224s Setting up libdatrie1:arm64 (0.2.14-1) ...
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224s Setting up libclone-perl:arm64 (0.47-1) ...
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224s Setting up libglvnd0:arm64 (1.7.0-3) ...
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224s Setting up perl-openssl-defaults:arm64 (7build4) ...
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224s Setting up libopengl0:arm64 (1.7.0-3) ...
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224s Setting up libpsl-dev:arm64 (0.21.2-1.1build2) ...
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224s Setting up libindirect-perl (0.39-2build5) ...
224s Setting up libfyaml0:arm64 (0.9.3-1) ...
224s Setting up libxcb-xkb1:arm64 (1.17.0-2ubuntu1) ...
224s Setting up libxcb-image0:arm64 (0.4.0-2build2) ...
224s Setting up libnumber-compare-perl (0.03-3) ...
224s Setting up libxcb-present0:arm64 (1.17.0-2ubuntu1) ...
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224s Setting up libasound2-data (1.2.15.3-1ubuntu1) ...
224s Setting up patchutils (0.4.3-1) ...
224s Setting up tex-common (6.20) ...
225s update-language: texlive-base not installed and configured, doing nothing!
225s Setting up libjson-maybexs-perl (1.004008-1) ...
225s Setting up libxml-sax-base-perl (1.09-3) ...
225s Setting up libio-string-perl (1.08-4) ...
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225s Setting up libnetaddr-ip-perl (4.079+dfsg-2build5) ...
225s Setting up xtrans-dev (1.6.0-1build1) ...
225s Setting up libfontenc1:arm64 (1:1.1.8-1build2) ...
225s Setting up autotools-dev (20240727.1) ...
225s Setting up libblas3:arm64 (3.12.1-7ubuntu1) ...
225s update-alternatives: using /usr/lib/aarch64-linux-gnu/blas/libblas.so.3 to provide /usr/lib/aarch64-linux-gnu/libblas.so.3 (libblas.so.3-aarch64-linux-gnu) in auto mode
225s Setting up libclass-data-inheritable-perl (0.10-1) ...
225s Setting up libunbound8:arm64 (1.24.2-1ubuntu1) ...
225s Setting up libpkgconf3:arm64 (1.8.1-4build1) ...
225s Setting up libgmpxx4ldbl:arm64 (2:6.3.0+dfsg-5ubuntu1) ...
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225s Setting up rpcsvc-proto (1.4.3-1build1) ...
225s Setting up libasound2t64:arm64 (1.2.15.3-1ubuntu1) ...
225s Setting up liblog-log4perl-perl (1.57-1) ...
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225s Setting up libfile-find-rule-perl (0.35-1build1) ...
225s Setting up libxfixes3:arm64 (1:6.0.0-2build2) ...
225s Setting up libxcb-sync1:arm64 (1.17.0-2ubuntu1) ...
225s Setting up libipc-system-simple-perl (1.30-2) ...
225s Setting up libio-tiecombine-perl (1.005-3) ...
225s Setting up libnet-domain-tld-perl (1.75-4) ...
225s Setting up libgssrpc4t64:arm64 (1.22.1-2) ...
225s Setting up libperlio-utf8-strict-perl (0.010-1build4) ...
225s Setting up libldap-dev:arm64 (2.6.10+dfsg-1ubuntu5) ...
225s Setting up aglfn (1.7+git20191031.4036a9c-2build1) ...
225s Setting up libstring-format-perl (1.18-1build1) ...
225s Setting up libxcb-cursor0:arm64 (0.1.5-1build1) ...
225s Setting up lzip (1.25-4) ...
225s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip (lzip) in auto mode
225s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-compressor (lzip-compressor) in auto mode
225s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
225s Setting up libavahi-common-data:arm64 (0.8-17ubuntu2) ...
225s Setting up libopus0:arm64 (1.6.1-1) ...
225s Setting up t1utils (1.41-4build4) ...
225s Setting up libxinerama1:arm64 (2:1.1.4-3build2) ...
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225s Setting up libimagequant0:arm64 (2.18.0-1build1) ...
225s Setting up libxkbcommon-x11-0:arm64 (1.12.3-1) ...
225s Setting up libssl-dev:arm64 (3.5.3-1ubuntu2) ...
225s Setting up libmpc3:arm64 (1.3.1-2) ...
225s Setting up libvorbis0a:arm64 (1.3.7-3build1) ...
225s Setting up libvariable-magic-perl (0.64-1build1) ...
225s Setting up libio-html-perl (1.004-3) ...
225s Setting up libxrandr2:arm64 (2:1.5.4-1build1) ...
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225s Setting up autopoint (0.23.2-1) ...
225s Setting up libb-hooks-op-check-perl:arm64 (0.22-3build2) ...
225s Setting up libflac14:arm64 (1.5.0+ds-5) ...
225s Setting up liblist-moreutils-xs-perl (0.430-4build1) ...
225s Setting up pkgconf-bin (1.8.1-4build1) ...
225s Setting up libjpeg-turbo8:arm64 (2.1.5-4ubuntu2) ...
225s Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-2) ...
225s Setting up libltdl7:arm64 (2.5.4-9) ...
225s Setting up libidn2-dev:arm64 (2.3.8-4) ...
225s Setting up libfftw3-double3:arm64 (3.3.10-2fakesync1build2) ...
225s Setting up libb-keywords-perl (1.29-1) ...
225s Setting up libparams-util-perl (1.102-3build1) ...
225s Setting up libgfortran5:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libvulkan1:arm64 (1.4.335.0-1) ...
225s Setting up libtime-duration-perl (1.21-2) ...
225s Setting up autoconf (2.72-3.1ubuntu1) ...
225s Setting up libtext-xslate-perl:arm64 (3.5.9-2build1) ...
225s Setting up libsub-exporter-progressive-perl (0.001013-3) ...
225s Setting up libwebp7:arm64 (1.5.0-0.1build1) ...
225s Setting up libarray-intspan-perl (2.004-2) ...
225s Setting up libcapture-tiny-perl (0.50-1) ...
225s Setting up libtimedate-perl (2.3300-2) ...
225s Setting up libexporter-lite-perl (0.09-2) ...
225s Setting up libubsan1:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libsub-name-perl:arm64 (0.28-1) ...
225s Setting up dwz (0.16-2) ...
225s Setting up libdata-validate-domain-perl (0.15-1) ...
225s Setting up libproc-processtable-perl:arm64 (0.637-1) ...
225s Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
225s Setting up libmtdev1t64:arm64 (1.1.7-1build1) ...
225s Setting up libduktape207:arm64 (2.7.0+tests-0ubuntu4) ...
225s Setting up libasyncns0:arm64 (0.8-7) ...
225s Setting up libxshmfence1:arm64 (1.3.3-1build1) ...
225s Setting up libhwasan0:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libcrypt-dev:arm64 (1:4.5.1-1) ...
225s Setting up libxcb-randr0:arm64 (1.17.0-2ubuntu1) ...
225s Setting up libpath-tiny-perl (0.148-1) ...
225s Setting up libasan8:arm64 (15.2.0-12ubuntu1) ...
225s Setting up lzop (1.04-2build4) ...
225s Setting up libjson-perl (4.10000-1) ...
225s Setting up liblog-any-adapter-screen-perl (0.141-1) ...
225s Setting up librole-tiny-perl (2.002004-1) ...
225s Setting up libintl-perl (1.35-1) ...
225s Setting up debugedit (1:5.2-3build1) ...
225s Setting up libipc-run3-perl (0.049-1) ...
225s Setting up libmd4c0:arm64 (0.5.2-2build1) ...
225s Setting up libregexp-wildcards-perl (1.05-3) ...
225s Setting up libmousex-strictconstructor-perl (0.02-3) ...
225s Setting up libfile-sharedir-perl (1.118-3) ...
225s Setting up libsub-uplevel-perl (0.2800-3) ...
225s Setting up libsuitesparseconfig7:arm64 (1:7.12.1+dfsg-1) ...
225s Setting up liblua5.4-0:arm64 (5.4.8-1) ...
225s Setting up libaliased-perl (0.34-3) ...
225s Setting up libharfbuzz0b:arm64 (12.3.2-1) ...
225s Setting up libthai-data (0.1.30-1) ...
225s Setting up xorg-sgml-doctools (1:1.11-1.1build1) ...
225s Setting up libstrictures-perl (2.000006-1build1) ...
225s Setting up libsub-quote-perl (2.006009-1ubuntu1) ...
225s Setting up libdevel-stacktrace-perl (2.0500-1) ...
225s Setting up libclass-xsaccessor-perl (1.19-4build6) ...
225s Setting up libpod-spell-perl (1.27-1) ...
225s Setting up libtext-autoformat-perl (1.750000-2) ...
225s Setting up libglu1-mesa:arm64 (9.0.2-1.1build2) ...
225s Setting up libtoml-tiny-perl (0.20-1) ...
225s Setting up libstemmer0d:arm64 (3.0.1-1) ...
225s Setting up libxkbfile1:arm64 (1:1.1.0-1build5) ...
225s Setting up libsort-versions-perl (1.62-3) ...
225s Setting up libtsan2:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libexporter-tiny-perl (1.006003-1) ...
225s Setting up libterm-readkey-perl (2.38-2build5) ...
225s Setting up libisl23:arm64 (0.27-1build1) ...
225s Setting up libtext-unidecode-perl (1.30-3) ...
225s Setting up libde265-0:arm64 (1.0.16-1build1) ...
225s Setting up libc-dev-bin (2.42-2ubuntu5) ...
225s Setting up libfont-ttf-perl (1.06-2) ...
225s Setting up libfile-homedir-perl (1.006-2) ...
225s Setting up libsamplerate0:arm64 (0.2.2-4build2) ...
225s Setting up libtasn1-6-dev:arm64 (4.21.0-2) ...
225s Setting up libwebpmux3:arm64 (1.5.0-0.1build1) ...
225s Setting up libtext-levenshteinxs-perl (0.03-5build5) ...
225s Setting up libperlio-gzip-perl (0.20-1build5) ...
225s Setting up libjxl0.11:arm64 (0.11.1-6ubuntu1) ...
225s Setting up libxfont2:arm64 (1:2.0.6-2) ...
225s Setting up libhtml-html5-entities-perl (0.004-3) ...
225s Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
225s Setting up libsereal-decoder-perl (5.004+ds-1build5) ...
225s Setting up libmarkdown2:arm64 (2.2.7-2.1build1) ...
225s Setting up libcc1-0:arm64 (15.2.0-12ubuntu1) ...
225s Setting up liburi-perl (5.34-2build1) ...
225s Setting up libnet-ipv6addr-perl (1.02-1) ...
225s Setting up libbrotli-dev:arm64 (1.1.0-2build6) ...
225s Setting up liblsan0:arm64 (15.2.0-12ubuntu1) ...
225s Setting up perltidy (20250105-1build1) ...
225s Setting up libp11-kit-dev:arm64 (0.25.10-1) ...
225s Setting up libmp3lame0:arm64 (3.100-6build2) ...
225s Setting up libblas-dev:arm64 (3.12.1-7ubuntu1) ...
225s update-alternatives: using /usr/lib/aarch64-linux-gnu/blas/libblas.so to provide /usr/lib/aarch64-linux-gnu/libblas.so (libblas.so-aarch64-linux-gnu) in auto mode
225s Setting up libsz2:arm64 (1.1.5-1) ...
225s Setting up libitm1:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libvorbisenc2:arm64 (1.3.7-3build1) ...
225s Setting up libkadm5clnt-mit12:arm64 (1.22.1-2) ...
225s Setting up libdata-validate-ip-perl (0.31-1) ...
225s Setting up libwacom-common (2.16.1-1) ...
225s Setting up libmousex-nativetraits-perl (1.09-3) ...
225s Setting up libemail-address-xs-perl (1.05-1build5) ...
225s Setting up libwayland-client0:arm64 (1.24.0-2) ...
225s Setting up libnet-ssleay-perl:arm64 (1.94-3) ...
225s Setting up libjpeg8:arm64 (8c-2ubuntu11) ...
225s Setting up automake (1:1.18.1-3build1) ...
225s update-alternatives: using /usr/bin/automake-1.18 to provide /usr/bin/automake (automake) in auto mode
225s Setting up libb2-1:arm64 (0.98.1-1.1build2) ...
225s Setting up x11proto-dev (2025.1-1) ...
225s Setting up libfile-stripnondeterminism-perl (1.15.0-1build1) ...
225s Setting up gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
225s Setting up cpp-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
225s Setting up libice6:arm64 (2:1.1.1-1build1) ...
225s Setting up libqt6core6t64:arm64 (6.9.2+dfsg-3ubuntu2) ...
225s Setting up libhttp-date-perl (6.06-1) ...
225s Setting up mesa-libgallium:arm64 (25.3.3-1ubuntu1) ...
225s Setting up liblapack3:arm64 (3.12.1-7ubuntu1) ...
225s update-alternatives: using /usr/lib/aarch64-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/aarch64-linux-gnu/liblapack.so.3 (liblapack.so.3-aarch64-linux-gnu) in auto mode
225s Setting up libproxy1v5:arm64 (0.5.12-1) ...
225s Setting up libfile-basedir-perl (0.09-2) ...
225s Setting up gettext (0.23.2-1) ...
225s Setting up libarpack2t64:arm64 (3.9.1-6) ...
225s Setting up libfftw3-single3:arm64 (3.3.10-2fakesync1build2) ...
225s Setting up libgcc-15-dev:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libgmp-dev:arm64 (2:6.3.0+dfsg-5ubuntu1) ...
225s Setting up libamd3:arm64 (1:7.12.1+dfsg-1) ...
225s Setting up libfile-listing-perl (6.16-1) ...
225s Setting up libxau-dev:arm64 (1:1.0.11-1build1) ...
225s Setting up nettle-dev:arm64 (3.10.2-1) ...
225s Setting up libkdb5-10t64:arm64 (1.22.1-2) ...
225s Setting up libgbm1:arm64 (25.3.3-1ubuntu1) ...
225s Setting up libcolamd3:arm64 (1:7.12.1+dfsg-1) ...
225s Setting up libwacom9:arm64 (2.16.1-1) ...
225s Setting up fontconfig-config (2.17.1-3ubuntu1) ...
225s Setting up liblist-moreutils-perl (0.430-2) ...
225s Setting up libxcursor1:arm64 (1:1.2.3-1build1) ...
225s Setting up libpod-constants-perl (0.19-2) ...
225s Setting up libgl1-mesa-dri:arm64 (25.3.3-1ubuntu1) ...
225s Setting up libhash-merge-perl (0.302-1) ...
225s Setting up libsoftware-copyright-perl (0.015-1) ...
225s Setting up libaec-dev:arm64 (1.1.5-1) ...
225s Setting up libavahi-common3:arm64 (0.8-17ubuntu2) ...
225s Setting up libcxsparse4:arm64 (1:7.12.1+dfsg-1) ...
225s Setting up libfftw3-long3:arm64 (3.3.10-2fakesync1build2) ...
225s Setting up libnet-http-perl (6.24-1build1) ...
225s Setting up libpath-iterator-rule-perl (1.015-2) ...
225s Setting up libtext-markdown-discount-perl (0.18-1) ...
225s Setting up libappstream5:arm64 (1.1.1-1) ...
225s Setting up libexception-class-perl (1.45-1) ...
225s Setting up libclass-c3-perl (0.35-2) ...
225s Setting up libqrupdate1:arm64 (1.1.5-3) ...
225s Setting up libdevel-callchecker-perl:arm64 (0.009-2) ...
225s Setting up libgfortran-15-dev:arm64 (15.2.0-12ubuntu1) ...
225s Setting up libxml-sax-perl (1.02+dfsg-4) ...
225s update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
225s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
225s Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
226s Setting up libcamd3:arm64 (1:7.12.1+dfsg-1) ...
226s Setting up pkgconf:arm64 (1.8.1-4build1) ...
226s Setting up libinput-bin (1.30.1-1) ...
226s Setting up libxs-parse-sublike-perl:arm64 (0.41-1) ...
226s Setting up intltool-debian (0.35.0+20060710.6build1) ...
226s Setting up libthai0:arm64 (0.1.30-1) ...
226s Setting up libxdmcp-dev:arm64 (1:1.1.5-2) ...
226s Setting up cpp-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
226s Setting up libegl-mesa0:arm64 (25.3.3-1ubuntu1) ...
226s Setting up libdata-validate-uri-perl (0.07-3) ...
226s Setting up libxs-parse-keyword-perl (0.49-1) ...
226s Setting up libtest-exception-perl (0.43-3) ...
226s Setting up appstream (1.1.1-1) ...
226s ✔ Metadata cache was updated successfully.
226s Setting up libqt6xml6:arm64 (6.9.2+dfsg-3ubuntu2) ...
226s Setting up libglpk40:arm64 (5.0-2) ...
226s Setting up libqt6sql6:arm64 (6.9.2+dfsg-3ubuntu2) ...
226s Setting up libstring-copyright-perl (0.003014-1) ...
226s Setting up libppi-perl (1.284-1) ...
226s Setting up liblapack-dev:arm64 (3.12.1-7ubuntu1) ...
226s update-alternatives: using /usr/lib/aarch64-linux-gnu/lapack/liblapack.so to provide /usr/lib/aarch64-linux-gnu/liblapack.so (liblapack.so-aarch64-linux-gnu) in auto mode
226s Setting up libdata-optlist-perl (0.114-1) ...
226s Setting up libccolamd3:arm64 (1:7.12.1+dfsg-1) ...
226s Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0207-0ubuntu7) ...
226s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
226s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
226s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
226s Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
226s Setting up dh-strip-nondeterminism (1.15.0-1build1) ...
226s Setting up libwww-robotrules-perl (6.02-1build1) ...
226s Setting up libsyntax-keyword-try-perl (0.31-1) ...
226s Setting up libjack-jackd2-0:arm64 (1.9.22~dfsg-5) ...
226s Setting up libhdf5-310:arm64 (1.14.6+repack-2) ...
226s Setting up cpp-15 (15.2.0-12ubuntu1) ...
226s Setting up libtiff6:arm64 (4.7.0-3ubuntu3) ...
226s Setting up cpp (4:15.2.0-4ubuntu1) ...
226s Setting up libhtml-parser-perl:arm64 (3.83-1build1) ...
226s Setting up libppix-regexp-perl (0.091-1) ...
226s Setting up libkadm5srv-mit12:arm64 (1.22.1-2) ...
226s Setting up libegl1:arm64 (1.7.0-3) ...
226s Setting up libc6-dev:arm64 (2.42-2ubuntu5) ...
226s Setting up libqt6core5compat6:arm64 (6.9.2-3build1) ...
226s Setting up libfontconfig1:arm64 (2.17.1-3ubuntu1) ...
226s Setting up libsndfile1:arm64 (1.2.2-4) ...
226s Setting up libmro-compat-perl (0.15-2) ...
226s Setting up libhdf5-fortran-310:arm64 (1.14.6+repack-2) ...
226s Setting up libsm6:arm64 (2:1.2.6-1) ...
226s Setting up libavahi-client3:arm64 (0.8-17ubuntu2) ...
226s Setting up libio-socket-ssl-perl (2.098-1) ...
226s Setting up libsub-exporter-perl (0.990-1) ...
226s Setting up libqt6dbus6:arm64 (6.9.2+dfsg-3ubuntu2) ...
226s Setting up libppix-quotelike-perl (0.023-1) ...
226s Setting up libhttp-message-perl (7.01-1ubuntu1) ...
226s Setting up libhtml-form-perl (6.13-1build1) ...
226s Setting up libhdf5-cpp-310:arm64 (1.14.6+repack-2) ...
226s Setting up libiterator-perl (0.03+ds1-2) ...
226s Setting up libgnutls28-dev:arm64 (3.8.10-3ubuntu1) ...
226s Setting up libinput10:arm64 (1.30.1-1) ...
226s Setting up libnghttp2-dev:arm64 (1.64.0-1.1ubuntu1) ...
226s Setting up libhdf5-hl-310:arm64 (1.14.6+repack-2) ...
226s Setting up libhttp-negotiate-perl (6.01-2) ...
226s Setting up fontconfig (2.17.1-3ubuntu1) ...
228s Regenerating fonts cache... done.
228s Setting up libjpeg-turbo8-dev:arm64 (2.1.5-4ubuntu2) ...
228s Setting up libcarp-assert-more-perl (2.9.0-1) ...
228s Setting up libcholmod5:arm64 (1:7.12.1+dfsg-1) ...
228s Setting up libxft2:arm64 (2.3.6-1build2) ...
228s Setting up libncurses-dev:arm64 (6.6+20251231-1) ...
228s Setting up libppix-utils-perl (0.003-2) ...
228s Setting up libglx-mesa0:arm64 (25.3.3-1ubuntu1) ...
228s Setting up gcc-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
228s Setting up libxcb1-dev:arm64 (1.17.0-2ubuntu1) ...
228s Setting up libiterator-util-perl (0.02+ds1-2) ...
228s Setting up libglx0:arm64 (1.7.0-3) ...
228s Setting up libhttp-cookies-perl (6.11-1) ...
228s Setting up libspqr4:arm64 (1:7.12.1+dfsg-1) ...
228s Setting up libpulse0:arm64 (1:17.0+dfsg1-2ubuntu4) ...
228s Setting up libfftw3-bin (3.3.10-2fakesync1build2) ...
229s Setting up po-debconf (1.0.22) ...
229s Setting up libhtml-tree-perl (5.07-3) ...
229s Setting up libparams-classify-perl:arm64 (0.015-2build6) ...
229s Setting up libpango-1.0-0:arm64 (1.57.0-1) ...
229s Setting up libcgi-pm-perl (4.71-1build1) ...
229s Setting up libx11-dev:arm64 (2:1.8.12-1build1) ...
229s Setting up libreadline-dev:arm64 (8.3-3) ...
229s Setting up libcairo2:arm64 (1.18.4-3) ...
229s Setting up libobject-pad-perl (0.823-2) ...
229s Setting up gcc-15 (15.2.0-12ubuntu1) ...
229s Setting up libgl1:arm64 (1.7.0-3) ...
229s Setting up libstdc++-15-dev:arm64 (15.2.0-12ubuntu1) ...
229s Setting up libqt6gui6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up libnet-smtp-ssl-perl (1.04-2) ...
229s Setting up gfortran-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
229s Setting up libmodule-runtime-perl (0.018-1) ...
229s Setting up libmailtools-perl (2.22-1) ...
229s Setting up g++-15-aarch64-linux-gnu (15.2.0-12ubuntu1) ...
229s Setting up libconfig-model-perl (2.155-1) ...
229s Setting up libxt6t64:arm64 (1:1.2.1-1.3) ...
229s Setting up comerr-dev:arm64 (2.1-1.47.2-3ubuntu2) ...
229s Setting up texinfo (7.2-5) ...
229s Setting up zlib1g-dev:arm64 (1:1.3.dfsg+really1.3.1-1ubuntu2) ...
229s Setting up libumfpack6:arm64 (1:7.12.1+dfsg-1) ...
229s Setting up libconst-fast-perl (0.014-2) ...
229s Setting up libqt6network6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up libdata-section-perl (0.200008-1) ...
229s Setting up libglx-dev:arm64 (1.7.0-3) ...
229s Setting up libpangoft2-1.0-0:arm64 (1.57.0-1) ...
229s Setting up libjpeg8-dev:arm64 (8c-2ubuntu11) ...
229s Setting up libdata-dpath-perl (0.60-1) ...
229s Setting up g++-15 (15.2.0-12ubuntu1) ...
229s Setting up libfltk1.3t64:arm64 (1.3.11-3) ...
229s Setting up libfftw3-dev:arm64 (3.3.10-2fakesync1build2) ...
229s Setting up libcups2t64:arm64 (2.4.16-1ubuntu1) ...
229s Setting up libgl-dev:arm64 (1.7.0-3) ...
229s Setting up libstring-rewriteprefix-perl (0.009-1) ...
229s Setting up gfortran-15 (15.2.0-12ubuntu1) ...
229s Setting up libpangocairo-1.0-0:arm64 (1.57.0-1) ...
229s Setting up krb5-multidev:arm64 (1.22.1-2) ...
229s Setting up libhdf5-hl-cpp-310:arm64 (1.14.6+repack-2) ...
229s Setting up libconfig-model-backend-yaml-perl (2.134-2) ...
229s Setting up gcc-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
229s Setting up libhdf5-hl-fortran-310:arm64 (1.14.6+repack-2) ...
229s Setting up libportaudio2:arm64 (19.7.0+git20251227.3270c9ae-0ubuntu1) ...
229s Setting up libxmu6:arm64 (2:1.1.3-4) ...
229s Setting up libtool (2.5.4-9) ...
229s Setting up libmodule-implementation-perl (0.09-2) ...
229s Setting up libpackage-stash-perl (0.40-1) ...
229s Setting up libimport-into-perl (1.002005-2) ...
229s Setting up libmoo-perl (2.005005-1) ...
229s Setting up libqt6opengl6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up libjpeg-dev:arm64 (8c-2ubuntu11) ...
229s Setting up liblist-someutils-perl (0.59-1) ...
229s Setting up libxaw7:arm64 (2:1.0.16-1build1) ...
229s Setting up libmime-tools-perl (5.515-1) ...
229s Setting up libsoftware-license-perl (0.104007-1) ...
229s Setting up libclass-load-perl (0.25-2) ...
229s Setting up gcc (4:15.2.0-4ubuntu1) ...
229s Setting up libgl2ps1.4:arm64 (1.4.2+dfsg1-4) ...
229s Setting up libqt6widgets6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up dh-autoreconf (21) ...
229s Setting up libfltk-gl1.3t64:arm64 (1.3.11-3) ...
229s Setting up libkrb5-dev:arm64 (1.22.1-2) ...
229s Setting up librtmp-dev:arm64 (2.4+20151223.gitfa8646d.1-3) ...
229s Setting up libssh2-1-dev:arm64 (1.11.1-1build1) ...
229s Setting up g++-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
229s Setting up gfortran-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ...
229s Setting up libmoox-aliases-perl (0.001006-2) ...
229s Setting up libparams-validate-perl:arm64 (1.31-2build4) ...
229s Setting up libqt6openglwidgets6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up libb-hooks-endofscope-perl (0.28-2) ...
229s Setting up libperl-critic-perl (1.156-1) ...
229s Setting up libqt6printsupport6:arm64 (6.9.2+dfsg-3ubuntu2) ...
229s Setting up libsoftware-licensemoreutils-perl (1.009-1) ...
229s Setting up x11-xkb-utils (7.7+9build1) ...
229s Setting up libqt6help6:arm64 (6.9.2-5) ...
229s Setting up libqscintilla2-qt6-15:arm64 (2.14.1+dfsg-2) ...
229s Setting up debhelper (13.28ubuntu1) ...
229s Setting up gfortran (4:15.2.0-4ubuntu1) ...
229s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode
229s update-alternatives: warning: skip creation of /usr/share/man/man1/f95.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f95) doesn't exist
229s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode
229s update-alternatives: warning: skip creation of /usr/share/man/man1/f77.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f77) doesn't exist
229s Setting up libcurl4-openssl-dev:arm64 (8.18.0-1ubuntu1) ...
229s Setting up libhdf5-dev (1.14.6+repack-2) ...
229s update-alternatives: using /usr/lib/aarch64-linux-gnu/pkgconfig/hdf5-serial.pc to provide /usr/lib/aarch64-linux-gnu/pkgconfig/hdf5.pc (hdf5.pc) in auto mode
229s Setting up libnamespace-clean-perl (0.27-2) ...
229s Setting up libstring-license-perl (0.0.11-1ubuntu1) ...
229s Setting up libgetopt-long-descriptive-perl (0.116-2) ...
229s Setting up g++ (4:15.2.0-4ubuntu1) ...
229s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode
229s Setting up xserver-common (2:21.1.21-1ubuntu1) ...
229s Setting up licensecheck (3.3.9-1ubuntu2) ...
229s Setting up libapp-cmd-perl (0.338-1) ...
229s Setting up xvfb (2:21.1.21-1ubuntu1) ...
229s Setting up cme (1.043-2) ...
229s Setting up libheif-plugin-aomdec:arm64 (1.21.2-1) ...
229s Setting up libwww-perl (6.81-1build1) ...
229s Setting up libheif1:arm64 (1.21.2-1) ...
229s Setting up libparse-debcontrol-perl (2.005-6) ...
229s Setting up libhtml-tokeparser-simple-perl (3.16-4) ...
229s Setting up libwww-mechanize-perl (2.20-1ubuntu1) ...
229s Setting up libgd3:arm64 (2.3.3-13ubuntu1) ...
229s Setting up gnuplot-nox (6.0.2+dfsg1-2ubuntu1) ...
229s update-alternatives: using /usr/bin/gnuplot-nox to provide /usr/bin/gnuplot (gnuplot) in auto mode
229s Setting up liblwp-protocol-https-perl (6.14-1) ...
229s Setting up libheif-plugin-libde265:arm64 (1.21.2-1) ...
229s Setting up libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17696-1build1) ...
229s Setting up lintian (2.127.0ubuntu1) ...
229s Setting up libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17696-1build1) ...
229s Setting up libconfig-model-dpkg-perl (3.016) ...
229s Setting up dh-octave-autopkgtest (1.14.0) ...
229s Setting up octave (10.3.0-3) ...
229s Setting up octave-dev (10.3.0-3) ...
229s Setting up octave-io:arm64 (2.7.0-3) ...
229s Setting up octave-statistics-common (1.7.6-2) ...
229s Setting up octave-statistics (1.7.6-2) ...
229s Setting up dh-octave (1.14.0) ...
229s Processing triggers for libc-bin (2.42-2ubuntu5) ...
229s Processing triggers for man-db (2.13.1-1) ...
231s Processing triggers for udev (259-1ubuntu3) ...
231s Processing triggers for install-info (7.2-5) ...
233s autopkgtest [04:46:21]: test command1: DH_OCTAVE_TEST_ENV="xvfb-run -a" /usr/bin/dh_octave_check --use-installed-package
233s autopkgtest [04:46:21]: test command1: [-----------------------
233s Checking package...
234s Run the unit tests...
235s Checking m files ...
235s [inst/clusterdata.m]
235s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/clusterdata.m
235s ***** demo
235s  randn ("seed", 1)  # for reproducibility
235s  r1 = randn (10, 2) * 0.25 + 1;
235s  randn ("seed", 5)  # for reproducibility
235s  r2 = randn (20, 2) * 0.5 - 1;
235s  X = [r1; r2];
235s 
235s  wnl = warning ("off", "Octave:linkage_savemem", "local");
235s  T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
235s  scatter (X(:,1), X(:,2), 36, T, "filled");
236s ***** error<clusterdata: function called with too few input arguments.> ...
236s  clusterdata ()
236s ***** error<clusterdata: function called with too few input arguments.> ...
236s  clusterdata (1)
236s ***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
236s ***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
236s 4 tests, 4 passed, 0 known failure, 0 skipped
236s [inst/Clustering/KDTreeSearcher.m]
236s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/KDTreeSearcher.m
236s ***** demo
236s  ## Demo to verify implementation using fisheriris dataset
236s  load fisheriris
236s  numSamples = size (meas, 1);
236s  queryIndices = [1, 23, 46, 63, 109];
236s  dataIndices = ~ismember (1:numSamples, queryIndices);
236s  queryPoints = meas(queryIndices, :);
236s  dataPoints = meas(dataIndices, :);
236s  searchRadius = 0.3;
236s  kdTree = KDTreeSearcher (dataPoints, 'Distance', 'minkowski')
236s  nearestNeighbors = knnsearch (kdTree, queryPoints, "K", 2)
236s  neighborsInRange = rangesearch (kdTree, queryPoints, searchRadius)
236s ***** demo
236s  ## Create a KDTreeSearcher with Euclidean distance
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = KDTreeSearcher (X);
236s  ## Find the nearest neighbor to [2, 3]
236s  Y = [2, 3];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  disp ("Nearest neighbor index:");
236s  disp (idx);
236s  disp ("Distance:");
236s  disp (D);
236s  ## Find all points within radius 2
236s  [idx, D] = rangesearch (obj, Y, 2);
236s  disp ("Indices within radius:");
236s  disp (idx);
236s  disp ("Distances:");
236s  disp (D);
236s ***** demo
236s  ## Create a KDTreeSearcher with Minkowski distance (P=3)
236s  X = [0, 0; 1, 0; 2, 0];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  ## Find the nearest neighbor to [1, 0]
236s  Y = [1, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  disp ("Nearest neighbor index:");
236s  disp (idx);
236s  disp ("Distance:");
236s  disp (D);
236s ***** demo
236s  rng(42);
236s  disp('Demonstrating KDTreeSearcher');
236s 
236s  n = 100;
236s  mu1 = [0.3, 0.3];
236s  mu2 = [0.7, 0.7];
236s  sigma = 0.1;
236s  X1 = mu1 + sigma * randn (n / 2, 2);
236s  X2 = mu2 + sigma * randn (n / 2, 2);
236s  X = [X1; X2];
236s 
236s  obj = KDTreeSearcher(X);
236s 
236s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
236s 
236s  K = 5;
236s  [idx, D] = knnsearch (obj, Y, "K", K);
236s 
236s  disp ('For the first query point:');
236s  disp (['Query point: ', num2str(Y(1,:))]);
236s  disp ('Indices of nearest neighbors:');
236s  disp (idx(1,:));
236s  disp ('Distances:');
236s  disp (D(1,:));
236s 
236s  figure;
236s  scatter (X(:,1), X(:,2), 36, 'b', 'filled'); # Training points
236s  hold on;
236s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled'); # Query points
236s  for i = 1:size (Y, 1)
236s      query = Y(i,:);
236s      neighbors = X(idx(i,:), :);
236s      for j = 1:K
236s          plot ([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
236s      endfor
236s  endfor
236s  hold off;
236s  title ('K Nearest Neighbors with KDTreeSearcher');
236s  xlabel ('X1');
236s  ylabel ('X2');
236s 
236s  r = 0.15;
236s  [idx, D] = rangesearch (obj, Y, r);
236s 
236s  disp ('For the first query point in rangesearch:');
236s  disp (['Query point: ', num2str(Y(1,:))]);
236s  disp ('Indices of points within radius:');
236s  disp (idx{1});
236s  disp ('Distances:');
236s  disp (D{1});
236s 
236s  figure;
236s  scatter (X(:,1), X(:,2), 36, 'b', 'filled');
236s  hold on;
236s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled');
236s  theta = linspace (0, 2 * pi, 100);
236s  for i = 1:size (Y, 1)
236s      center = Y(i,:);
236s      x_circle = center(1) + r * cos (theta);
236s      y_circle = center(2) + r * sin (theta);
236s      plot (x_circle, y_circle, 'g-');
236s      ## Highlight points within radius
236s      if (! isempty (idx{i}))
236s        in_radius = X(idx{i}, :);
236s        scatter (in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
236s      endif
236s  endfor
236s  hold off
236s  title ('Points within Radius with KDTreeSearcher');
236s  xlabel ('X1');
236s  ylabel ('X2');
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X);
236s  Y = X(1:5,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 3);
236s  assert (idx, [[1, 18, 5]; [2, 35, 46]; [3, 48, 4]; [4, 48, 30]; [5, 38, 1]])
236s  assert (D, [[0, 0.1000, 0.1414]; [0,  0.1414,  0.1414]; [0, 0.1414, 0.2449];
236s              [0, 0.1414, 0.1732]; [0, 0.1414, 0.1414]], 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = X(10:15,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 2);
236s  assert (idx, [[10, 35]; [11, 49]; [12, 30]; [13, 2]; [14, 39]; [15, 34]])
236s  assert (D, [[0, 0.1000]; [0, 0.1000]; [0, 0.2080]; [0, 0.1260]; [0, 0.2154];
236s              [0, 0.3503]], 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = X(20:25,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, [20; 21; 22; 23; 24; 25])
236s  assert (D, [0; 0; 0; 0; 0; 0])
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = X(30:35,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 4);
236s  assert (idx, [[30, 31, 4, 12]; [31, 30, 10, 35]; [32, 21, 37, 28];
236s                [33, 20, 34, 47]; [34, 16, 15, 33]; [35, 10, 2, 26]])
236s  assert (D, [[0, 0.1000, 0.1000, 0.2000]; [0, 0.1000, 0.1000, 0.1000];
236s              [0, 0.2000, 0.2000, 0.2000]; [0, 0.3000, 0.3000, 0.3000];
236s              [0, 0.2000, 0.3000, 0.3000]; [0, 0.1000, 0.1000, 0.1000]], 5e-15)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "BucketSize", 20);
236s  Y = X(40:45,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 2);
236s  assert (idx, [[40, 8]; [41, 18]; [42, 9]; [43, 39]; [44, 27]; [45, 47]])
236s  assert (D, [[0, 0.1000]; [0, 0.1414]; [0, 0.6245]; [0, 0.2000]; [0, 0.2236];
236s              [0, 0.3606]], 4.7e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X);
236s  Y = X(50:55,:);
236s  [idx, D] = knnsearch (obj, Y, "K", 3, "IncludeTies", true);
236s  assert (idx, {[50; 8; 40]; [51; 53; 87]; [52; 57; 76]; [53; 51; 87]; [54; ...
236s                 90; 81]; [55; 59; 76]})
236s  assert (D, {[0; 0.1414; 0.1732]; [0; 0.2646; 0.3317]; [0; 0.2646; 0.3162];
236s              [0; 0.2646; 0.2828]; [0; 0.2000; 0.3000]; [0; 0.2449; 0.3162]}, 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X);
236s  Y = X(60:65,:);
236s  [idx, D] = rangesearch (obj, Y, 0.4);
236s  assert (idx, {[60; 90]; [61; 94]; [62; 97; 79; 96; 100; 89; 98; 72]; [63];
236s                [64; 92; 74; 79]; [65]})
236s  assert (D, {[0; 0.3873]; [0; 0.3606];
236s              [0; 0.3000; 0.3317; 0.3606; 0.3606; 0.3742; 0.3873; 0.4000]; [0];
236s              [0; 0.1414; 0.2236; 0.2449]; [0]}, 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = X(70:72,:);
236s  [idx, D] = rangesearch (obj, Y, 1.0);
236s  assert (idx, {[70; 81; 90; 82; 83; 93; 54; 68; 95; 80; 91; 100; 60; 65; ...
236s                 89; 63]; [71; 139; 128; 150; 127; 57; 86; 64; 79; 92; 124];
236s                [72; 100; 98; 83; 93; 97; 75; 68; 62; 89; 95; 74; 56; 90; ...
236s                 79; 92; 96; 64; 63; 65]})
236s  assert (D, {[0; 0.3000; 0.4000; 0.5000; 0.5000; 0.5000; 0.6000; 0.7000; ...
236s               0.7000; 0.7000; 0.8000; 0.8000; 0.9000; 0.9000; 0.9000; 0.9000];
236s              [0; 0.3000; 0.5000; 0.5000; 0.7000; 0.8000; 0.8000; 1.0000; ...
236s               1.0000; 1.0000; 1]; [0; 0.5000; 0.5000; 0.6000; 0.6000; ...
236s               0.7000; 0.7000; 0.8000; 0.8000; 0.8000; 0.8000; 0.8000; ...
236s               0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 1.0000; 1]}, 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = X(80:85,:);
236s  [idx, D] = rangesearch (obj, Y, 0.8);
236s  assert (idx, {[80; 82; 81; 65; 70; 83; 93; 90; 54; 63; 68; 72; 100; 60; ...
236s                 89; 99; 95; 94; 97; 96]; [81; 82; 70; 54; 90; 93; 80; 83; ...
236s                 60; 68; 95; 100; 65; 63; 97; 61; 91; 94; 89; 96; 72; 58; ...
236s                 62; 56]; [82; 81; 70; 80; 54; 90; 93; 83; 68; 60; 65; 63; ...
236s                 100; 95; 94; 61; 58; 97; 89; 72; 96; 91; 99]; [83; 93; ...
236s                 100; 68; 70; 72; 95; 90; 97; 65; 89; 96; 81; 82; 80; 62; ...
236s                 54; 98; 63; 91; 56; 60; 79; 67; 75; 88; 85; 92; 69]; [84; ...
236s                 134; 102; 143; 150; 124; 128; 73; 127; 139; 147; 64; 112; ...
236s                 114; 120; 74; 135; 122; 71; 92; 104; 138; 148; 117; 79; ...
236s                 55; 56; 57; 67; 111; 129; 69; 78; 59; 52; 133; 85; 88; ...
236s                 87]; [85; 67; 56; 97; 95; 89; 96; 91; 100; 62; 71; 122; ...
236s                 79; 60; 107; 90; 139; 93; 68; 86; 83; 92; 64; 150; 102; ...
236s                 143; 74; 114; 70; 128; 84; 54; 72]})
236s  assert (D, {[0; 0.2884; 0.3530; 0.3826; 0.4062; 0.4198; 0.5117; 0.5440; 0.5718;
236s               0.6000; 0.6018; 0.6073; 0.6308; 0.6333; 0.6753; 0.7000; 0.7192;
236s               0.7230; 0.7350; 0.7459];
236s              [0; 0.1260; 0.1442; 0.2571; 0.2571; 0.3530; 0.3530; 0.3826; 0.4344;
236s               0.4344; 0.4642; 0.4747; 0.5217; 0.5217; 0.5896; 0.6009; 0.6082;
236s               0.6316; 0.6316; 0.6611; 0.6664; 0.6993; 0.7417; 0.7507];
236s              [0; 0.1260; 0.2224; 0.2884; 0.3803; 0.3803; 0.4121; 0.4121; 0.4905;
236s               0.5013; 0.5360; 0.5429; 0.5463; 0.5646; 0.5749; 0.5819; 0.6542;
236s               0.6581; 0.6753; 0.6938; 0.7094; 0.7107; 0.7423];
236s              [0; 0.1260; 0.2224; 0.2520; 0.2571; 0.3107; 0.3302; 0.3332; 0.3332;
236s               0.3530; 0.3530; 0.3803; 0.3826; 0.4121; 0.4198; 0.4344; 0.4531;
236s               0.5155; 0.5217; 0.5348; 0.6028; 0.6073; 0.6374; 0.6527; 0.6611;
236s               0.6804; 0.6938; 0.7399; 0.7560];
236s              [0; 0.3072; 0.3271; 0.3271; 0.3302; 0.3503; 0.3530; 0.3530; 0.3530;
236s               0.3958; 0.3979; 0.4327; 0.4626; 0.4642; 0.5027; 0.5066; 0.5130;
236s               0.5155; 0.5440; 0.5440; 0.5518; 0.5848; 0.6009; 0.6073; 0.6082;
236s               0.6316; 0.6471; 0.6746; 0.6753; 0.6797; 0.6804; 0.7047; 0.7192;
236s               0.7218; 0.7405; 0.7405; 0.7719; 0.7725; 0.7786];
236s              [0; 0.2000; 0.3503; 0.3979; 0.4121; 0.4309; 0.4327; 0.4531; 0.4747;
236s               0.5337; 0.5718; 0.5896; 0.6009; 0.6316; 0.6366; 0.6374; 0.6463;
236s               0.6542; 0.6542; 0.6550; 0.6938; 0.7014; 0.7067; 0.7166; 0.7186;
236s               0.7186; 0.7281; 0.7380; 0.7447; 0.7571; 0.7719; 0.7813; 0.7851]}, 5e-5)
236s ***** test
236s  load fisheriris
236s  X = meas;
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = X(90,:);
236s  [idx, D] = rangesearch (obj, Y, 0.7);
236s  assert (idx, {[90; 70; 54; 81; 95; 60; 83; 93; 100; 68; 82; 65; 97; 91; ...
236s                 56; 61; 62; 63; 67; 79; 80; 85; 89; 96; 72; 92; 107]})
236s  assert (D, {[0; 0.2000; 0.2000; 0.2000; 0.2000; 0.3000; 0.3000; 0.3000; ...
236s              0.3000; 0.3000; 0.3000; 0.4000; 0.4000; 0.4000; 0.5000; ...
236s              0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; ...
236s              0.5000; 0.5000; 0.6000; 0.6000; 0.6000]}, 5e-16)
236s ***** test
236s  ## Constructor with single-point dataset
236s  X = [0, 0];
236s  obj = KDTreeSearcher (X);
236s  assert (obj.X, X);
236s  assert (obj.Distance, "euclidean");
236s  assert (isempty (obj.DistParameter));
236s  assert (obj.BucketSize, 50);
236s ***** test
236s  ## Constructor with duplicate points
236s  X = [0, 0; 0, 0; 1, 0];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  assert (obj.X, X);
236s  assert (obj.Distance, "cityblock");
236s ***** test
236s  ## Constructor with 3D data
236s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  assert (obj.X, X);
236s  assert (obj.DistParameter, 3);
236s ***** test
236s  ## knnsearch with grid, K = 1
236s  X = [0, 0; 0, 1; 1, 0; 1, 1];
236s  obj = KDTreeSearcher (X, "Distance", "euclidean");
236s  Y = [0.5, 0.5];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  D_true = pdist2 (X, Y, "euclidean");
236s  assert (D, min (D_true), 1e-10);
236s  assert (any (idx == find (D_true == min (D_true))));
236s ***** test
236s  ## knnsearch with IncludeTies, all points equidistant
236s  X = [0, 0; 0, 1; 1, 0; 1, 1];
236s  obj = KDTreeSearcher (X);
236s  Y = [0.5, 0.5];
236s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
236s  D_true = pdist2 (X, Y, "euclidean");
236s  expected_idx = find (D_true == min (D_true));
236s  assert (sort (idx{1}(:)), sort (expected_idx));
236s  assert (D{1}(:)', repmat (min (D_true), 1, 4), 1e-10);
236s ***** test
236s  ## rangesearch with line dataset
236s  X = [0, 0; 1, 0; 2, 0; 3, 0];
236s  obj = KDTreeSearcher (X);
236s  Y = [1.5, 0];
236s  r = 1;
236s  [idx, D] = rangesearch (obj, Y, r);
236s  D_true = pdist2 (X, Y, "euclidean");
236s  expected_idx = find (D_true <= r);
236s  assert (sort (idx{1}(:)), sort (expected_idx));
236s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
236s ***** test
236s  ## knnsearch with duplicates
236s  X = [0, 0; 0, 0; 1, 0];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = [0, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
236s  assert (sort (idx{1}(:))', [1, 2]);
236s  assert (D{1}', [0, 0], 1e-10);
236s ***** test
236s  ## rangesearch with 3D data
236s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = [0, 0, 0];
236s  r = 1;
236s  [idx, D] = rangesearch (obj, Y, r);
236s  assert (sort (idx{1}(:))', [1, 2, 3]);
236s  assert (D{1}', [0, 1, 1], 1e-10);
236s ***** test
236s  ## knnsearch with P = 2 (Euclidean equivalent)
236s  X = [0, 0; 1, 1];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 2);
236s  Y = [0, 1];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 1);
236s  assert (D, 1, 1e-10);
236s ***** test
236s  ## rangesearch with P = 3
236s  X = [0, 0; 1, 0; 0, 1];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = [0.5, 0.5];
236s  r = 0.8;
236s  [idx, D] = rangesearch (obj, Y, r);
236s  D_true = pdist2 (X, Y, "minkowski", 3);
236s  expected_idx = find (D_true <= r);
236s  assert (sort (idx{1}(:)), sort (expected_idx));
236s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
236s ***** test
236s  ## knnsearch with P = 4, random data
236s  X = rand (5, 2);
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 4);
236s  Y = rand (1, 2);
236s  [idx, D] = knnsearch (obj, Y, "K", 3);
236s  D_true = pdist2 (X, Y, "minkowski", 4);
236s  [sorted_D, sort_idx] = sort (D_true);
236s  assert (idx', sort_idx(1:3));
236s  assert (D', sorted_D(1:3), 1e-10);
236s ***** test
236s  ## knnsearch with all same points
236s  X = [1, 1; 1, 1; 1, 1];
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = [1, 1];
236s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
236s  assert (sort (idx{1}(:))', [1, 2, 3]);
236s  assert (D{1}', [0, 0, 0], 1e-10);
236s ***** test
236s  ## rangesearch with grid
236s  X = [0, 0; 0, 1; 1, 0; 1, 1];
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = [0.5, 0.5];
236s  r = 0.5;
236s  [idx, D] = rangesearch (obj, Y, r);
236s  D_true = pdist2 (X, Y, "chebychev");
236s  expected_idx = find (D_true <= r);
236s  assert (sort (idx{1}(:)), sort (expected_idx));
236s  assert (D{1}, D_true(expected_idx), 1e-10);
236s ***** test
236s  ## Changing Distance and verifying search
236s  X = [0,0; 1,0];
236s  obj = KDTreeSearcher(X, "Distance", "euclidean");
236s  Y = [0,1];
236s  [idx, D] = knnsearch(obj, Y, "K", 1);
236s  assert(D, 1, 1e-10);
236s  obj.Distance = "chebychev";
236s  [idx, D] = knnsearch(obj, Y, "K", 1);
236s  assert(D, 1, 1e-10);
236s ***** test
236s  ## Changing DistParameter for minkowski
236s  X = [0,0; 1,0];
236s  obj = KDTreeSearcher(X, "Distance", "minkowski", "P", 1);
236s  Y = [0,1];
236s  [idx, D] = knnsearch(obj, Y, "K", 1);
236s  assert(D, 1, 1e-10);
236s  obj.DistParameter = 3;
236s  [idx, D] = knnsearch(obj, Y, "K", 1);
236s  assert(D, 1, 1e-10);
236s ***** test
236s  ## Different BucketSize values
236s  X = rand(20,2);
236s  obj1 = KDTreeSearcher(X, "BucketSize", 5);
236s  obj2 = KDTreeSearcher(X, "BucketSize", 15);
236s  Y = rand(1,2);
236s  [idx1, D1] = knnsearch(obj1, Y, "K", 3);
236s  [idx2, D2] = knnsearch(obj2, Y, "K", 3);
236s  assert(idx1, idx2);
236s  assert(D1, D2, 1e-10);
236s ***** test
236s  ## Basic constructor with default Euclidean
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = KDTreeSearcher (X);
236s  assert (obj.X, X);
236s  assert (obj.Distance, "euclidean");
236s  assert (isempty (obj.DistParameter));
236s  assert (obj.BucketSize, 50);
236s ***** test
236s  ## Minkowski distance with custom P
236s  X = [0, 0; 1, 1; 2, 2];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  assert (obj.Distance, "minkowski");
236s  assert (obj.DistParameter, 3);
236s ***** test
236s  ## Cityblock distance
236s  X = [0, 0; 1, 0; 0, 1];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  assert (obj.Distance, "cityblock");
236s  assert (isempty (obj.DistParameter));
236s ***** test
236s  ## Chebychev distance
236s  X = [1, 1; 2, 3; 4, 2];
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  assert (obj.Distance, "chebychev");
236s  assert (isempty (obj.DistParameter));
236s ***** test
236s  ## knnsearch with Euclidean distance
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = KDTreeSearcher (X);
236s  Y = [2, 3];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 1);
236s  assert (D, sqrt(2), 1e-10);
236s ***** test
236s  ## knnsearch with Cityblock distance
236s  X = [0, 0; 1, 1; 2, 2];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = [1, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (ismember (idx, [1, 2]));
236s  assert (D, 1, 1e-10);
236s ***** test
236s  ## knnsearch with Chebychev distance
236s  X = [1, 1; 2, 3; 4, 2];
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = [2, 2];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (ismember (idx, [1, 2]));
236s  assert (D, 1, 1e-10);
236s ***** test
236s  ## knnsearch with Minkowski P=3
236s  X = [0, 0; 1, 0; 2, 0];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = [1, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 2);
236s  assert (D, 0, 1e-10);
236s ***** test
236s  ## knnsearch with IncludeTies
236s  X = [0, 0; 1, 0; 0, 1];
236s  obj = KDTreeSearcher (X);
236s  Y = [0.5, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
236s  assert (iscell (idx));
236s  assert (sort (idx{1}(:))', [1, 2]);
236s  assert (sort (D{1}(:)), [0.5; 0.5], 1e-10);
236s ***** test
236s  ## rangesearch with Euclidean
236s  X = [1, 1; 2, 2; 3, 3];
236s  obj = KDTreeSearcher (X);
236s  Y = [0, 0];
236s  [idx, D] = rangesearch (obj, Y, 2);
236s  assert (idx{1}, [1]);
236s  assert (D{1}, [sqrt(2)], 1e-10);
236s ***** test
236s  ## rangesearch with Cityblock
236s  X = [0, 0; 1, 1; 2, 2];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = [0, 0];
236s  [idx, D] = rangesearch (obj, Y, 1);
236s  assert (idx{1}, [1]);
236s  assert (D{1}, [0], 1e-10);
236s ***** test
236s  ## rangesearch with Chebychev
236s  X = [1, 1; 2, 3; 4, 2];
236s  obj = KDTreeSearcher (X, "Distance", "chebychev");
236s  Y = [2, 2];
236s  [idx, D] = rangesearch (obj, Y, 1);
236s  assert (sort (idx{1}(:))', [1, 2]);
236s  assert (sort (D{1}(:))', [1, 1], 1e-10);
236s ***** test
236s  ## rangesearch with Minkowski P=3
236s  X = [0, 0; 1, 0; 2, 0];
236s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = [1, 0];
236s  [idx, D] = rangesearch (obj, Y, 1);
236s  assert (sort (idx{1}(:))', [1, 2, 3]);
236s  assert (sort (D{1}(:))', [0, 1, 1], 1e-10);
236s ***** test
236s  ## Diverse dataset with Euclidean
236s  X = [0, 10; 5, 5; 10, 0];
236s  obj = KDTreeSearcher (X);
236s  Y = [5, 5];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 2);
236s  assert (D, 0, 1e-10);
236s ***** test
236s  ## High-dimensional data with Cityblock
236s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
236s  obj = KDTreeSearcher (X, "Distance", "cityblock");
236s  Y = [4, 5, 6];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 2);
236s  assert (D, 0, 1e-10);
236s ***** error<KDTreeSearcher: too few input arguments.> ...
236s  KDTreeSearcher ()
236s ***** error<KDTreeSearcher: Name-Value arguments must be in pairs.> ...
236s  KDTreeSearcher (ones(3,2), "Distance")
236s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
236s  KDTreeSearcher ("abc")
236s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
236s  KDTreeSearcher ([1; Inf; 3])
236s ***** error<KDTreeSearcher: invalid parameter name: 'foo'.> ...
236s  KDTreeSearcher (ones(3,2), "foo", "bar")
236s ***** error<KDTreeSearcher: unsupported distance metric 'invalid'.> ...
236s  KDTreeSearcher (ones(3,2), "Distance", "invalid")
236s ***** error<KDTreeSearcher: Distance must be a string.> ...
236s  KDTreeSearcher (ones(3,2), "Distance", 1)
236s ***** error<KDTreeSearcher: P must be a positive finite scalar.> ...
236s  KDTreeSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
236s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
236s  KDTreeSearcher (ones(3,2), "BucketSize", 0)
236s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
236s  KDTreeSearcher(ones(3,2), "BucketSize", -1)
236s ***** error<KDTreeSearcher.knnsearch: too few input arguments.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)))
236s ***** error<KDTreeSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies")
236s ***** error<KDTreeSearcher.knnsearch: Y must be a finite numeric matrix.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), "abc", "K", 1)
236s ***** error<KDTreeSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,3), "K", 1)
236s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 0)
236s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
236s  obj = KDTreeSearcher(ones(3,2)); knnsearch(obj, ones(1,2), "K", Inf)
236s ***** error<KDTreeSearcher.knnsearch: invalid parameter name: 'foo'.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "foo", "bar")
236s ***** error<KDTreeSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies", 1)
236s ***** error<KDTreeSearcher.knnsearch: SortIndices must be a logical scalar.> ...
236s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "SortIndices", 1)
236s ***** error<KDTreeSearcher.rangesearch: too few input arguments.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)))
236s ***** error<KDTreeSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
236s ***** error<KDTreeSearcher.rangesearch: Y must be a finite numeric matrix.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), "abc", 1)
236s ***** error<KDTreeSearcher.rangesearch: number of columns in X and Y must match.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,3), 1)
236s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), -1)
236s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
236s  obj = KDTreeSearcher(ones(3,2)); rangesearch(obj, ones(1,2), Inf)
236s ***** error<KDTreeSearcher.rangesearch: invalid parameter name: 'foo'.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
236s ***** error<KDTreeSearcher.rangesearch: SortIndices must be a logical scalar.> ...
236s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
236s ***** error<KDTreeSearcher.subsref: \(\) indexing not supported.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj(1)
236s ***** error<KDTreeSearcher.subsref: {} indexing not supported.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj{1}
236s ***** error<KDTreeSearcher.subsref: unrecognized property: 'invalid'.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.invalid
236s ***** error<KDTreeSearcher.subsasgn: \(\) indexing not supported.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj(1) = 1
236s ***** error<KDTreeSearcher.subsasgn: {} indexing not supported.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj{1} = 1
236s ***** error<KDTreeSearcher.subsasgn: chained subscripts not allowed.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.X.Y = 1
236s ***** error<KDTreeSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.X = 1
236s ***** error<KDTreeSearcher.subsasgn: 'KDTree' is read-only and cannot be modified.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.KDTree = 1
236s ***** error<KDTreeSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = "invalid"
236s ***** error<KDTreeSearcher.subsasgn: 'Distance' must be a string.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = 1
236s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be a positive finite scalar for Minkowski distance.> ...
236s  obj = KDTreeSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
236s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be empty for this distance metric.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.DistParameter = 1
236s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.BucketSize = 0
236s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
236s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = -1
236s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
236s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = 1.5
236s ***** error<KDTreeSearcher.subsasgn: unrecognized property: 'invalid'.> ...
236s  obj = KDTreeSearcher (ones(3,2)); obj.invalid = 1
236s 84 tests, 84 passed, 0 known failure, 0 skipped
236s [inst/Clustering/ClusterCriterion.m]
236s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/ClusterCriterion.m
236s ***** error <ClusterCriterion: X must be a numeric matrix.> ...
236s  ClusterCriterion ("1", "kmeans", [1:6])
236s ***** error <ClusterCriterion: unknown clustering algorithm 'k'.> ...
236s  ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
236s ***** error <ClusterCriterion: invalid matrix of clustering solutions.> ...
236s  ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
236s ***** error <ClusterCriterion: invalid argument.> ...
236s  ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
236s 4 tests, 4 passed, 0 known failure, 0 skipped
236s [inst/Clustering/CalinskiHarabaszEvaluation.m]
236s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/CalinskiHarabaszEvaluation.m
236s ***** test
236s  load fisheriris
236s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
236s  assert (class (eva), "CalinskiHarabaszEvaluation");
236s 1 test, 1 passed, 0 known failure, 0 skipped
236s [inst/Clustering/ExhaustiveSearcher.m]
236s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/ExhaustiveSearcher.m
236s ***** demo
236s  ## Demo to verify implementation using fisheriris dataset
236s  load fisheriris
236s  rng('default');
236s  numSamples = size (meas, 1);
236s  queryIndices = [20, 95, 123, 136, 138];
236s  dataPoints = meas(~ismember (1:numSamples, queryIndices), :);
236s  queryPoints = meas(queryIndices, :);
236s  searchModel = ExhaustiveSearcher (dataPoints, 'Distance', 'mahalanobis')
236s  mahalanobisParam = searchModel.DistParameter
236s  searchRadius = 3;
236s  nearestNeighbors = knnsearch (searchModel, queryPoints, "K", 2)
236s  neighborsInRange = rangesearch (searchModel, queryPoints, searchRadius)
236s ***** demo
236s  ## Create an ExhaustiveSearcher with Euclidean distance
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = ExhaustiveSearcher (X);
236s  ## Find the nearest neighbor to [2, 3]
236s  Y = [2, 3];
236s  [idx, D] = knnsearch (obj, Y);
236s  disp ("Nearest neighbor index:"); disp (idx);
236s  disp ("Distance:"); disp (D);
236s  ## Find all points within radius 2
236s  [idx, D] = rangesearch (obj, Y, 2);
236s  disp ("Indices within radius:"); disp (idx);
236s  disp ("Distances:"); disp (D);
236s ***** demo
236s  ## Create an ExhaustiveSearcher with Minkowski distance (P=1)
236s  X = [0, 0; 1, 0; 0, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
236s  ## Find the 2 nearest neighbors to [0.5, 0.5]
236s  Y = [0.5, 0.5];
236s  [idx, D] = knnsearch (obj, Y, "K", 2);
236s  disp ("Nearest neighbor indices:"); disp (idx);
236s  disp ("Distances:"); disp (D);
236s ***** demo
236s  rng(42);
236s  disp('Demonstrating ExhaustiveSearcher');
236s 
236s  n = 100;
236s  mu1 = [0.3, 0.3];
236s  mu2 = [0.7, 0.7];
236s  sigma = 0.1;
236s  X1 = mu1 + sigma * randn(n/2, 2);
236s  X2 = mu2 + sigma * randn(n/2, 2);
236s  X = [X1; X2];
236s 
236s  obj = ExhaustiveSearcher(X);
236s 
236s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
236s 
236s  K = 5;
236s  [idx, D] = knnsearch(obj, Y, "K", K);
236s 
236s  disp('For the first query point:');
236s  disp(['Query point: ', num2str(Y(1,:))]);
236s  disp('Indices of nearest neighbors:');
236s  disp(idx(1,:));
236s  disp('Distances:');
236s  disp(D(1,:));
236s 
236s  figure;
236s  scatter(X(:,1), X(:,2), 36, 'b', 'filled'); % Training points
236s  hold on;
236s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled'); % Query points
236s  for i = 1:size(Y,1)
236s      query = Y(i,:);
236s      neighbors = X(idx(i,:), :);
236s      for j = 1:K
236s          plot([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
236s      end
236s  end
236s  hold off;
236s  title('K Nearest Neighbors with ExhaustiveSearcher');
236s  xlabel('X1');
236s  ylabel('X2');
236s 
236s  r = 0.15;
236s  [idx, D] = rangesearch(obj, Y, r);
236s 
236s  disp('For the first query point in rangesearch:');
236s  disp(['Query point: ', num2str(Y(1,:))]);
236s  disp('Indices of points within radius:');
236s  disp(idx{1});
236s  disp('Distances:');
236s  disp(D{1});
236s 
236s  figure;
236s  scatter(X(:,1), X(:,2), 36, 'b', 'filled');
236s  hold on;
236s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled');
236s  theta = linspace(0, 2*pi, 100);
236s  for i = 1:size(Y,1)
236s      center = Y(i,:);
236s      x_circle = center(1) + r * cos(theta);
236s      y_circle = center(2) + r * sin(theta);
236s      plot(x_circle, y_circle, 'g-');
236s      % Highlight points within radius
236s      if ~isempty(idx{i})
236s          in_radius = X(idx{i}, :);
236s          scatter(in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
236s      end
236s  end
236s  hold off;
236s  title('Points within Radius with ExhaustiveSearcher');
236s  xlabel('X1');
236s  ylabel('X2');
236s ***** test
236s  ## Basic constructor with default Euclidean
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = ExhaustiveSearcher (X);
236s  assert (obj.X, X)
236s  assert (obj.Distance, "euclidean")
236s  assert (isempty (obj.DistParameter))
236s ***** test
236s  ## Minkowski distance with custom P
236s  X = [1, 2; 3, 4];
236s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
236s  assert (obj.Distance, "minkowski")
236s  assert (obj.DistParameter, 3)
236s ***** test
236s  ## Seuclidean distance with custom Scale
236s  X = [1, 2; 3, 4; 5, 6];
236s  S = [1, 2];
236s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
236s  assert (obj.Distance, "seuclidean")
236s  assert (obj.DistParameter, S)
236s ***** test
236s  ## Mahalanobis distance with custom Cov
236s  X = [1, 2; 3, 4; 5, 6];
236s  C = [1, 0; 0, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
236s  assert (obj.Distance, "mahalanobis")
236s  assert (obj.DistParameter, C)
236s ***** test
236s  ## knnsearch with Euclidean distance
236s  X = [1, 2; 3, 4; 5, 6];
236s  obj = ExhaustiveSearcher (X);
236s  Y = [2, 3];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 1)
236s  assert (D, sqrt(2), 1e-10)
236s ***** test
236s  ## knnsearch with Cityblock distance
236s  X = [0, 0; 1, 1; 2, 2];
236s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
236s  Y = [1, 0];
236s  [idx, D] = knnsearch (obj, Y, "K", 1);
236s  assert (idx, 1)
236s  assert (D, 1, 1e-10)
236s ***** test
236s  ## knnsearch with Chebychev distance
236s  X = [1, 1; 2, 3; 4, 2];
236s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
236s  Y = [2, 2];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D, 1, 1e-10)
236s ***** test
236s  ## knnsearch with Cosine distance
236s  X = [1, 0; 0, 1; 1, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
236s  Y = [1, 0.5];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 3)
236s  assert (D < 0.1, true)
236s ***** test
236s  ## knnsearch with Minkowski P=1 (Manhattan)
236s  X = [0, 0; 1, 0; 0, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
236s  Y = [0.5, 0.5];
236s  [idx, D] = knnsearch (obj, Y, "K", 2, "IncludeTies", true);
236s  assert (iscell (idx))
236s  assert (idx{1}, [1, 2, 3])
236s  assert (D{1}, [1, 1, 1], 1e-10)
236s ***** test
236s  ## rangesearch with Seuclidean
236s  X = [1, 1; 2, 2; 3, 3];
236s  S = [1, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
236s  Y = [0, 0];
236s  [idx, D] = rangesearch (obj, Y, 2);
236s  assert (idx{1}, [1])
236s  assert (D{1}, [sqrt(2)], 1e-10)
236s ***** test
236s  ## rangesearch with Mahalanobis
236s  X = [1, 1; 2, 2; 3, 3];
236s  C = [1, 0; 0, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
236s  Y = [0, 0];
236s  [idx, D] = rangesearch (obj, Y, 3, "SortIndices", false);
236s  assert (idx{1}, [1, 2])
236s  assert (D{1}, [sqrt(2), sqrt(8)], 1e-10)
236s ***** test
236s  ## rangesearch with Hamming distance
236s  X = [0, 1; 1, 0; 1, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "hamming");
236s  Y = [0, 0];
236s  [idx, D] = rangesearch (obj, Y, 0.5);
236s  assert (idx{1}, [1, 2])
236s  assert (D{1}, [0.5, 0.5], 1e-10)
236s ***** test
236s  ## Custom distance function
236s  X = [1, 2; 3, 4];
236s  custom_dist = @(x, y) sum(abs(x - y));
236s  obj = ExhaustiveSearcher (X, "Distance", custom_dist);
236s  Y = [2, 3];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D, 2, 1e-10)
236s ***** test
236s  ## Euclidean with high-dimensional data
236s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9; 10, 11, 12];
236s  obj = ExhaustiveSearcher (X);
236s  Y = [5, 6, 7];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 2)
236s  assert (D, sqrt(3), 1e-10)
236s ***** test
236s  ## Minkowski P=3 with scaled data
236s  X = [0, 1; 2, 3; 4, 5] * 10;
236s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
236s  Y = [20, 30];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 2)
236s  assert (D, 0, 1e-10)
236s ***** test
236s  ## Seuclidean with custom scales on diverse data
236s  X = [1, 10; 2, 20; 3, 30];
236s  S = [1, 5];
236s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
236s  Y = [1.5, 15];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D, sqrt((0.5/1)^2 + (5/5)^2), 1e-10)
236s ***** test
236s  ## Mahalanobis with correlated data
236s  X = [1, 1; 2, 1.5; 3, 2];
236s  C = [1, 0.5; 0.5, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
236s  Y = [2, 1.5];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 2)
236s  assert (D, 0, 1e-10)
236s ***** test
236s  ## Cityblock with sparse data
236s  X = [0, 0, 1; 1, 0, 0; 0, 1, 0];
236s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
236s  Y = [0, 0, 0];
236s  [idx, D] = rangesearch (obj, Y, 1);
236s  assert (idx{1}, [1, 2, 3])
236s  assert (D{1}, [1, 1, 1], 1e-10)
236s ***** test
236s  ## Chebychev with extreme values
236s  X = [0, 100; 50, 50; 100, 0];
236s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
236s  Y = [60, 60];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 2)
236s  assert (D, 10, 1e-10)
236s ***** test
236s  ## Cosine with normalized data
236s  X = [1, 0; 0, 1; 1/sqrt(2), 1/sqrt(2)];
236s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
236s  Y = [1, 1];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 3)
236s  assert (D < 0.1, true)
236s ***** test
236s  ## Correlation with time-series-like data
236s  X = [1, 2, 3; 2, 4, 6; 1, 1, 1];
236s  obj = ExhaustiveSearcher (X, "Distance", "correlation");
236s  Y = [1.5, 3, 4.5];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D < 0.1, true)
236s ***** test
236s  ## Spearman with ranked data
236s  X = [1, 2, 3; 3, 2, 1; 2, 1, 3];
236s  obj = ExhaustiveSearcher (X, "Distance", "spearman");
236s  Y = [1, 2, 3];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D, 0, 1e-10)
236s ***** test
236s  ## Jaccard with binary sparse data
236s  X = [1, 0, 0; 0, 1, 0; 1, 1, 0];
236s  obj = ExhaustiveSearcher (X, "Distance", "jaccard");
236s  Y = [1, 0, 0];
236s  [idx, D] = knnsearch (obj, Y);
236s  assert (idx, 1)
236s  assert (D, 0, 1e-10)
236s ***** test
236s  obj = ExhaustiveSearcher (ones(3,2));
236s  assert (obj.X, ones(3,2))
236s  assert (obj.Distance, "euclidean")
236s  assert (isempty (obj.DistParameter))
236s ***** test
236s  obj = ExhaustiveSearcher (ones(3,2));
236s  obj.Distance = "minkowski";
236s  assert (obj.Distance, "minkowski")
236s ***** test
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski");
236s  obj.DistParameter = 3;
236s  assert (obj.DistParameter, 3)
236s ***** test
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean");
236s  obj.DistParameter = [1, 2];
236s  assert (obj.DistParameter, [1, 2])
236s ***** test
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis");
236s  obj.DistParameter = eye(2);
236s  assert (obj.DistParameter, eye(2))
236s ***** error<ExhaustiveSearcher: too few input arguments.> ...
236s  ExhaustiveSearcher ()
236s ***** error<ExhaustiveSearcher: Name-Value arguments must be in pairs.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance")
236s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
236s  ExhaustiveSearcher ("abc")
236s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
236s  ExhaustiveSearcher ([1; Inf; 3])
236s ***** error<ExhaustiveSearcher: invalid parameter name: 'foo'.> ...
236s  ExhaustiveSearcher (ones(3,2), "foo", "bar")
236s ***** error<ExhaustiveSearcher: unsupported distance metric 'invalid'.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", "invalid")
236s ***** error<ExhaustiveSearcher: invalid distance function handle.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", @(x) x)
236s ***** error<ExhaustiveSearcher: Distance must be a string or function handle.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", 1)
236s ***** error<ExhaustiveSearcher: P must be a positive finite scalar.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
236s ***** error<ExhaustiveSearcher: Scale must be a nonnegative vector matching X columns.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
236s ***** error<ExhaustiveSearcher: Cov must be a square matrix matching X columns.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
236s ***** error<ExhaustiveSearcher: Cov must be positive definite.> ...
236s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
236s ***** error<ExhaustiveSearcher.knnsearch: too few input arguments.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)))
236s ***** error<ExhaustiveSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies")
236s ***** error<ExhaustiveSearcher.knnsearch: Y must be a finite numeric matrix.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), "abc")
236s ***** error<ExhaustiveSearcher.knnsearch: number of columns in X and Y must match.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,3))
236s ***** error<ExhaustiveSearcher.knnsearch: K must be a positive integer.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "K", 0)
236s ***** error<ExhaustiveSearcher.knnsearch: invalid parameter name: 'foo'.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "foo", "bar")
236s ***** error<ExhaustiveSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
236s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies", 1)
236s ***** error<ExhaustiveSearcher.rangesearch: too few input arguments.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)))
236s ***** error<ExhaustiveSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
236s ***** error<ExhaustiveSearcher.rangesearch: Y must be a finite numeric matrix.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), "abc", 1)
236s ***** error<ExhaustiveSearcher.rangesearch: number of columns in X and Y must match.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,3), 1)
236s ***** error<ExhaustiveSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), -1)
236s ***** error<ExhaustiveSearcher.rangesearch: invalid parameter name: 'foo'.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
236s ***** error<ExhaustiveSearcher.rangesearch: SortIndices must be a logical scalar.> ...
236s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
236s ***** error<ExhaustiveSearcher.subsref: \(\) indexing not supported.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj(1)
236s ***** error<ExhaustiveSearcher.subsref: {} indexing not supported.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj{1}
236s ***** error<dynamic structure field names must be strings> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1)
236s ***** error<ExhaustiveSearcher.subsref: unrecognized property: 'invalid'> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid
236s ***** error<ExhaustiveSearcher.subsasgn: \(\) indexing not supported.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj(1) = 1
236s ***** error<ExhaustiveSearcher.subsasgn: {} indexing not supported.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj{1} = 1
236s ***** error<ExhaustiveSearcher.subsasgn: chained subscripts not allowed.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.X.Y = 1
236s ***** error<dynamic structure field names must be strings> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1) = 1
236s ***** error<ExhaustiveSearcher.subsasgn: X is read-only and cannot be modified.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.X = 1
236s ***** error<ExhaustiveSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = "invalid"
236s ***** error<ExhaustiveSearcher.subsasgn: invalid distance function handle.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x) x
236s ***** error<ExhaustiveSearcher.subsasgn: custom distance function output invalid.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x, y) [1; 1]
236s ***** error<ExhaustiveSearcher.subsasgn: Distance must be a string or function handle.> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = 1
236s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a positive finite scalar for minkowski.> ...
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
236s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a nonnegative vector matching X columns.> ...
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
236s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a square matrix matching X columns.> ...
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
236s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be positive definite for mahalanobis.> ...
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
236s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be empty for this distance metric.> ...
236s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "euclidean"); obj.DistParameter = 1
236s ***** error<ExhaustiveSearcher.subsasgn: unrecognized property: 'invalid'> ...
236s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid = 1
236s 73 tests, 73 passed, 0 known failure, 0 skipped
236s [inst/Clustering/cvpartition.m]
236s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/cvpartition.m
236s ***** test
236s  custom = [1, 1, 1, 2, 2, 2, 1, 2, 3, 2, 3, 3, 2, 1, 3]';
236s  cv = cvpartition ('CustomPartition', custom);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 15);
236s  assert (cv.NumTestSets, 3);
236s  assert (cv.TrainSize, [10, 9, 11]);
236s  assert (cv.TestSize, [5, 6, 4]);
236s  assert (cv.IsCustom, true);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  idx = training (cv, 1);
236s  assert (idx, custom != 1);
236s  idx = test (cv, 1);
236s  assert (idx, custom == 1);
236s  idx = training (cv, 2);
236s  assert (idx, custom != 2);
236s  idx = test (cv, 2);
236s  assert (idx, custom == 2);
236s  idx = training (cv, 3);
236s  assert (idx, custom != 3);
236s  idx = test (cv, 3);
236s  assert (idx, custom == 3);
236s  idx1 = training (cv, 'all');
236s  idx2 = test (cv, 'all');
236s  assert (idx1, ! idx2);
236s ***** test
236s  custom = logical ([1, 1, 1, 0, 0, 0, 1, 0, 1, 1])';
236s  cv = cvpartition ('CustomPartition', custom);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 4);
236s  assert (cv.TestSize, 6);
236s  assert (cv.IsCustom, true);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  idx = training (cv, 1);
236s  assert (idx, custom != 1);
236s  assert (idx, training (cv, 'all'));
236s  idx = test (cv, 1);
236s  assert (idx, custom == 1);
236s  assert (idx, test (cv, 'all'));
236s ***** test
236s  custom = logical ([1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1]);
236s  cv = cvpartition ('CustomPartition', custom);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 4);
236s  assert (cv.NumTestSets, 3);
236s  assert (cv.TrainSize, [2, 3, 3]);
236s  assert (cv.TestSize, [2, 1, 1]);
236s  assert (cv.IsCustom, true);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  idx = training (cv, 1);
236s  assert (idx, custom(:,1) == false);
236s  idx = test (cv, 1);
236s  assert (idx, custom(:,1) == true);
236s  idx = training (cv, 2);
236s  assert (idx, custom(:,2) == false);
236s  idx = test (cv, 2);
236s  assert (idx, custom(:,2) == true);
236s  assert (! custom, training (cv, 'all'));
236s  assert (custom, test (cv, 'all'));
236s ***** test
236s  cv = cvpartition ('CustomPartition', [1:8]);
236s  assert (cv.Type, 'leaveout');
236s  assert (cv.NumObservations, 8);
236s  assert (cv.NumTestSets, 8);
236s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
236s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
236s  assert (cv.IsCustom, true);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 7);
236s  assert (sum (training (cv, 'all')), cv.TrainSize);
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 1);
236s  assert (sum (test (cv, 'all')), cv.TestSize);
236s  assert (! training (cv, 'all'), test (cv, 'all'));
236s ***** test
236s  cv = cvpartition ('CustomPartition', logical (eye (8)));
236s  assert (cv.Type, 'leaveout');
236s  assert (cv.NumObservations, 8);
236s  assert (cv.NumTestSets, 8);
236s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
236s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
236s  assert (cv.IsCustom, true);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 7);
236s  assert (sum (training (cv, 'all')), cv.TrainSize);
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 1);
236s  assert (sum (test (cv, 'all')), cv.TestSize);
236s  assert (! training (cv, 'all'), test (cv, 'all'));
236s ***** test
236s  cv = cvpartition (10, 'resubstitution');
236s  assert (cv.Type, 'resubstitution');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 10);
236s  assert (cv.TestSize, 10);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 10);
236s  assert (training (cv, 'all'), logical (ones (10, 1)));
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 10);
236s  assert (test (cv, 'all'), logical (ones (10, 1)));
236s  assert (test (cv), training (cv));
236s ***** test
236s  cv = cvpartition (10, 'leaveout');
236s  assert (cv.Type, 'leaveout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 10);
236s  assert (cv.TrainSize, ones (1, 10) * 9);
236s  assert (cv.TestSize, ones (1, 10));
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 9);
236s  assert (training (cv, 'all'), ! logical (eye (10)));
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 1);
236s  assert (test (cv, 'all'), logical (eye (10)));
236s  assert (test (cv), ! training (cv));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s ***** test
236s  rand ('seed', 5);  # for reproducibility
236s  cv = cvpartition (10, 'holdout', 0.3);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 7);
236s  assert (cv.TestSize, 3);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 7);
236s  assert (training (cv, 'all'), logical ([1, 0, 1, 1, 0, 1, 1, 1, 0, 1])');
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 3);
236s  assert (test (cv, 'all'), logical ([0, 1, 0, 0, 1, 0, 0, 0, 1, 0])');
236s  assert (test (cv), ! training (cv));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s ***** test
236s  cv = cvpartition (10, 'holdout', 4);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 6);
236s  assert (cv.TestSize, 4);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (class (training (cv, 1)), 'logical');
236s  assert (sum (training (cv, 1)), 6);
236s  assert (class (test (cv, 1)), 'logical');
236s  assert (sum (test (cv, 1)), 4);
236s  assert (test (cv), ! training (cv));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s ***** test
236s  cv = cvpartition (5, 'kfold');
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 5);
236s  assert (cv.NumTestSets, 5);
236s ***** test
236s  cv = cvpartition (20, 'kfold');
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 20);
236s  assert (cv.NumTestSets, 10);
236s ***** test
236s  cv = cvpartition (10, 'kfold', 5);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 5);
236s  assert (cv.TrainSize, [8, 8, 8, 8, 8]);
236s  assert (cv.TestSize, [2, 2, 2, 2, 2]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [10, 5]);
236s ***** test
236s  grpvar = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 5, 5];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 5, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 5);
236s  assert (cv.TrainSize, [10, 10, 10, 8, 10]);
236s  assert (cv.TestSize, [2, 2, 2, 4, 2]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 5]);
236s  assert (sum (test (cv, 'all')), [2, 2, 2, 4, 2]);
236s  assert (sum (training (cv, 'all')), [10, 10, 10, 8, 10]);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 3, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 3);
236s  assert (cv.TrainSize, [9, 10, 5]);
236s  assert (cv.TestSize, [3, 2, 7]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 3]);
236s  assert (sum (test (cv, 'all')), [3, 2, 7]);
236s  assert (sum (training (cv, 'all')), [9, 10, 5]);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [6, 6]);
236s  assert (cv.TestSize, [6, 6]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 2]);
236s  assert (sum (test (cv, 'all')), [6, 6]);
236s  assert (sum (training (cv, 'all')), [6, 6]);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, NaN, 2, 3, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 11);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [6, 5]);
236s  assert (cv.TestSize, [5, 6]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [11, 2]);
236s  assert (sum (test (cv, 'all')), [5, 6]);
236s  assert (sum (training (cv, 'all')), [6, 5]);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [5, 7]);
236s  assert (cv.TestSize, [7, 5]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 2]);
236s  assert (sum (test (cv, 'all')), [7, 5]);
236s  assert (sum (training (cv, 'all')), [5, 7]);
236s  assert (test (cv, 1)', grpvar == 2);
236s  assert (test (cv, 2)', grpvar != 2);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [7, 5]);
236s  assert (cv.TestSize, [5, 7]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 2]);
236s  assert (sum (test (cv, 'all')), [5, 7]);
236s  assert (sum (training (cv, 'all')), [7, 5]);
236s  assert (test (cv, 1)', grpvar == 2);
236s  assert (test (cv, 2)', grpvar != 2);
236s ***** test
236s  grpvar = [1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3];
236s  rand ('seed', 5);
236s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 12);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [7, 5]);
236s  assert (cv.TestSize, [5, 7]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, true);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (size (test (cv, 'all')), [12, 2]);
236s  assert (sum (test (cv, 'all')), [5, 7]);
236s  assert (sum (training (cv, 'all')), [7, 5]);
236s  assert (test (cv, 1)', grpvar == 3);
236s  assert (test (cv, 2)', grpvar != 3);
236s ***** test
236s  rand ('seed', 5);
236s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 3);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 7);
236s  assert (cv.TestSize, 3);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, true);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv), logical ([0, 0, 0, 0, 1, 0, 1, 0, 0, 1])');
236s ***** test
236s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 4);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 6);
236s  assert (cv.TestSize, 4);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, true);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (sum (test (cv)(1:5)), 2);
236s  assert (sum (test (cv)(6:10)), 2);
236s ***** test
236s  grpvar = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2];
236s  rand ('seed', 5);
236s  cv = cvpartition (grpvar, 'holdout', 4, 'Stratify', false);
236s  assert (cv.Type, 'holdout');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 1);
236s  assert (cv.TrainSize, 6);
236s  assert (cv.TestSize, 4);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (sum (test (cv)(1:5)), 3);
236s  assert (sum (test (cv)(6:10)), 1);
236s ***** test
236s  cv = cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [5, 5]);
236s  assert (cv.TestSize, [5, 5]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, true);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (sum (test (cv, 1)(1:5)), 3);
236s  assert (sum (test (cv, 2)(1:5)), 2);
236s  assert (sum (test (cv, 1)(6:10)), 2);
236s  assert (sum (test (cv, 2)(6:10)), 3);
236s ***** test
236s  grpvar = [1 1 1 1 1 2 2 2 2 1];
236s  rand ('seed', 5);
236s  cv = cvpartition (grpvar, 'kfold', 2, 'Stratify', false);
236s  assert (cv.Type, 'kfold');
236s  assert (cv.NumObservations, 10);
236s  assert (cv.NumTestSets, 2);
236s  assert (cv.TrainSize, [5, 5]);
236s  assert (cv.TestSize, [5, 5]);
236s  assert (cv.IsCustom, false);
236s  assert (cv.IsGrouped, false);
236s  assert (cv.IsStratified, false);
236s  assert (test (cv, 1), ! training (cv, 1));
236s  assert (test (cv, 'all'), ! training (cv, 'all'));
236s  assert (sum (test (cv, 1)(1:5)), 4);
236s  assert (sum (test (cv, 2)(1:5)), 1);
236s  assert (sum (test (cv, 1)(6:10)), 1);
236s  assert (sum (test (cv, 2)(6:10)), 4);
236s ***** error <cvpartition: too few input arguments.> cvpartition (2)
236s ***** error <cvpartition: too many input arguments.> cvpartition (1, 2, 3, 4, 5, 6)
236s ***** error <cvpartition: TESTSETS must be numeric of logical.> ...
236s  cvpartition ("CustomPartition", 'a')
236s ***** error <cvpartition: TESTSETS must be a numeric vector.> ...
236s  cvpartition ("CustomPartition", [2, 3; 2, 3])
236s ***** error <cvpartition: TESTSETS must be a logical vector or matrix.> ...
236s  cvpartition ("CustomPartition", false (3, 3, 3))
236s ***** error <cvpartition: each observation in TESTSETS must be exactly one in each row.> ...
236s  cvpartition ("CustomPartition", [false, true; true, true; true, false])
236s ***** error <cvpartition: a logical matrix in TESTSETS must not have more columns that rows.> ...
236s  cvpartition ("CustomPartition", false (3, 5))
236s ***** error <cvpartition: X must be a scalar positive integer value.> ...
236s  cvpartition (-20, "LeaveOut")
236s ***** error <cvpartition: X must be a scalar positive integer value.> ...
236s  cvpartition (20.5, "LeaveOut")
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition (20, "HoldOut", [0.2, 0.3])
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition (20, "HoldOut", 'a')
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition (20, "HoldOut", 0)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition (20, "HoldOut", -0.1)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition (20, "HoldOut", 21)
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition (20, "kfold", [2, 3])
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition (20, "kfold", 'a')
236s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
236s  cvpartition (20, "kfold", 2.5)
236s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
236s  cvpartition (20, "kfold", 21)
236s ***** error <cvpartition: invalid optional paired argument for 'GroupingVariables'.> ...
236s  cvpartition (10, "kfold", 3, "Group")
236s ***** error <cvpartition: missing value for optional paired argument 'GroupingVariables'.> ...
236s  cvpartition (10, "kfold", 3, "GroupingVariables")
236s ***** error <cvpartition: invalid value for optional paired argument 'GroupingVariables'.> ...
236s  cvpartition (10, "kfold", 3, "GroupingVariables", ones (3, 3, 3))
236s ***** error <cvpartition: grouping variable does not match the number of observations.> ...
236s  cvpartition (10, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'})
236s ***** warning <cvpartition: number of folds K in greater that the groups in 'GroupingVariables'.> ...
236s  cvpartition (5, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'});
236s ***** error <cvpartition: invalid optional paired argument.> ...
236s  cvpartition (20, "some")
236s ***** error <cvpartition: invalid optional paired argument for stratification.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "strat")
236s ***** error <cvpartition: missing value for optional paired argument 'stratify'.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify")
236s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", [true, true])
236s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", 'no')
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a')
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a', "stratify", true)
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3])
236s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3], "stratify", true)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0, "stratify", true)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1, "stratify", true)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2, "stratify", false)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6)
236s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
236s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6, "stratify", false)
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a')
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a', "stratify", true)
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3])
236s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
236s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3], "stratify", false)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0, "stratify", true)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5, "stratify", true)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5)
237s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
237s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5, "stratify", true)
237s ***** error <cvpartition: invalid optional paired argument.> ...
237s  cvpartition ([1, 1, 1, 2, 2], "leaveout")
237s ***** error <cvpartition: invalid optional paired argument.> ...
237s  cvpartition ([1, 1, 1, 2, 2], "resubstitution")
237s ***** error <cvpartition: invalid optional paired argument.> ...
237s  cvpartition ([1, 1, 1, 2, 2], "some")
237s ***** error <cvpartition: invalid first input argument.> ...
237s  cvpartition ({1, 1; 2, 2}, "kfold")
237s ***** error <cvpartition.repartition: cannot repartition a custom partition.> ...
237s  repartition (cvpartition ('CustomPartition', [1,1,2,2,3,3]))
237s ***** error <cvpartition.repartition: 'legacy' flag does not apply to stratified or grouped 'kfold' partitioned objects.> ...
237s  repartition (cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2, 'Stratify', true), 'legacy')
237s ***** error <cvpartition.repartition: 'legacy' flag is only valid for 'kfold' partitioned objects.> ...
237s  repartition (cvpartition (20, 'Leaveout', 0.2), 'legacy')
237s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
237s  repartition (cvpartition (20, 'Leaveout', 0.2), 'asd')
237s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
237s  repartition (cvpartition (20, 'Leaveout', 0.2), 2+i)
237s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
237s  repartition (cvpartition (20, 'KFold', 5), [34, 56; 2, 3])
237s ***** error <cvpartition.test: too many input arguments.> ...
237s  test (cvpartition (20, "kfold"), 2, 3)
237s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
237s  test (cvpartition (20, "kfold"), 0)
237s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
237s  test (cvpartition (20, "kfold"), 1.5)
237s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
237s  test (cvpartition (20, "kfold"), [1, 1.5])
237s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
237s  test (cvpartition (20, "kfold"), [2, 3; 2, 3])
237s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
237s  test (cvpartition (20, "kfold"), 21)
237s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
237s  test (cvpartition (20, "kfold"), [18, 21])
237s ***** error <cvpartition.training: too many input arguments.> ...
237s  training (cvpartition (20, "kfold"), 2, 3)
237s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
237s  training (cvpartition (20, "kfold"), 0)
237s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
237s  training (cvpartition (20, "kfold"), 1.5)
237s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
237s  training (cvpartition (20, "kfold"), [1, 1.5])
237s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
237s  training (cvpartition (20, "kfold"), [2, 3; 2, 3])
237s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
237s  training (cvpartition (20, "kfold"), 21)
237s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
237s  training (cvpartition (20, "kfold"), [18, 21])
237s 98 tests, 98 passed, 0 known failure, 0 skipped
237s [inst/Clustering/SilhouetteEvaluation.m]
237s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/SilhouetteEvaluation.m
237s ***** test
237s  load fisheriris
237s  eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
237s  assert (class (eva), "SilhouetteEvaluation");
237s 1 test, 1 passed, 0 known failure, 0 skipped
237s [inst/Clustering/GapEvaluation.m]
237s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/GapEvaluation.m
237s ***** test
237s  load fisheriris
237s  eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
237s                      "referencedistribution", "uniform");
237s  assert (class (eva), "GapEvaluation");
241s 1 test, 1 passed, 0 known failure, 0 skipped
241s [inst/Clustering/DaviesBouldinEvaluation.m]
241s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/DaviesBouldinEvaluation.m
241s ***** test
241s  load fisheriris
241s  eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
241s  assert (class (eva), "DaviesBouldinEvaluation");
242s 1 test, 1 passed, 0 known failure, 0 skipped
242s [inst/Clustering/hnswSearcher.m]
242s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Clustering/hnswSearcher.m
242s ***** demo
242s  ## Create an hnswSearcher with Euclidean distance
242s  X = [1, 2; 3, 4; 5, 6];
242s  obj = hnswSearcher (X);
242s  ## Find the nearest neighbor to [2, 3]
242s  Y = [2, 3];
242s  [idx, D] = knnsearch (obj, Y, "K", 1);
242s  disp ("Nearest neighbor index:");
242s  disp (idx);
242s  disp ("Distance:");
242s  disp (D);
242s ***** demo
242s  ## Create an hnswSearcher with Minkowski distance (P=3)
242s  X = [0, 0; 1, 0; 2, 0];
242s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
242s  ## Find the nearest neighbor to [1, 0]
242s  Y = [1, 0];
242s  [idx, D] = knnsearch (obj, Y, "K", 1);
242s  disp ("Nearest neighbor index:");
242s  disp (idx);
242s  disp ("Distance:");
242s  disp (D);
242s ***** test
242s  load fisheriris
242s  X = meas;
242s  obj = hnswSearcher (X, "Distance", "chebychev");
242s  Y = X(30:35,:);
242s  [idx, D] = knnsearch (obj, Y, "K", 4);
242s  assert (idx, [[30 31 4 12]; [31 30 10 35]; [32 21 37 28]; [33 47 20 34]; ...
242s                [34 16 33 15]; [35 10 26 2]])
242s  assert (D, [[0 0.1000 0.1000 0.2000]; [0 0.1000 0.1000 0.1000]; [0 0.2000 ...
242s               0.2000 0.2000]; [0 0.3000 0.3000 0.3000]; [0 0.2000 0.3000 ...
242s               0.3000]; [0 0.1000 0.1000 0.1000]], 5e-15)
248s ***** test
248s  load fisheriris
248s  X = meas;
248s  C = cov (X);
248s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
248s  Y = X(120:125,:);
248s  [idx, D] = knnsearch (obj, Y, "K", 2);
248s  assert (idx(1, :), [120 82])
248s  assert (idx(4, :), [123 106])
248s  assert (idx(5, :), [124 127])
248s  assert (idx(6, :), [125 57])
248s  assert (D(1, :), [0 0.7734], 1e-4)
248s  assert (D(4, :), [0 0.8452], 1e-4)
248s  assert (D(5, :), [0 0.4152], 1e-4)
248s  assert (D(6, :), [0 0.7322], 1e-4)
253s ***** test
253s  ## Basic constructor with default Euclidean
253s  X = [1, 2; 3, 4; 5, 6];
253s  obj = hnswSearcher (X);
253s  assert (obj.X, X);
253s  assert (obj.Distance, "euclidean");
253s  assert (isempty (obj.DistParameter));
253s ***** test
253s  ## Minkowski distance with custom P
253s  X = [0, 0; 1, 1; 2, 2];
253s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
253s  assert (obj.Distance, "minkowski");
253s  assert (obj.DistParameter, 3);
253s ***** test
253s  ## Seuclidean distance with custom Scale
253s  X = [1, 2; 3, 4; 5, 6];
253s  S = [1, 2];
253s  obj = hnswSearcher (X, "Distance", "seuclidean", "Scale", S);
253s  assert (obj.Distance, "seuclidean");
253s  assert (obj.DistParameter, S);
254s ***** test
254s  ## Mahalanobis distance with custom Cov
254s  X = [1, 2; 3, 4; 5, 6];
254s  C = [1, 0; 0, 1];
254s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
254s  assert (obj.Distance, "mahalanobis");
254s  assert (obj.DistParameter, C);
254s ***** test
254s  ## knnsearch with Euclidean distance
254s  X = [1, 2; 3, 4; 5, 6];
254s  obj = hnswSearcher (X);
254s  Y = [2, 3];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [2]));
254s  assert (abs (D - sqrt(2)) < 1e-2);
254s ***** test
254s  ## knnsearch with Cityblock distance
254s  X = [0, 0; 1, 1; 2, 2];
254s  obj = hnswSearcher (X, "Distance", "cityblock");
254s  Y = [1, 0];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [1, 2]));
254s  assert (abs (D - 1) < 1e-2);
254s ***** test
254s  ## knnsearch with Chebychev distance
254s  X = [1, 1; 2, 3; 4, 2];
254s  obj = hnswSearcher (X, "Distance", "chebychev");
254s  Y = [2, 2];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [1, 2]));
254s  assert (abs (D - 1) < 1e-2);
254s ***** test
254s  ## knnsearch with Minkowski P=3
254s  X = [0, 0; 1, 0; 2, 0];
254s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
254s  Y = [1, 0];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [2]));
254s  assert (abs (D - 0) < 1e-2);
254s ***** test
254s  ## Diverse dataset with Euclidean
254s  X = [0, 10; 5, 5; 10, 0];
254s  obj = hnswSearcher (X);
254s  Y = [5, 5];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [2]));
254s  assert (abs (D - 0) < 1e-2);
254s ***** test
254s  ## High-dimensional data with Cityblock
254s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
254s  obj = hnswSearcher (X, "Distance", "cityblock");
254s  Y = [4, 5, 6];
254s  [idx, D] = knnsearch (obj, Y, "K", 1);
254s  assert (ismember (idx, [2]));
254s  assert (abs (D - 0) < 1e-2);
254s ***** error<hnswSearcher: too few input arguments.> ...
254s  hnswSearcher ()
254s ***** error<hnswSearcher: Name-Value arguments must be in pairs.> ...
254s  hnswSearcher (ones(3,2), "Distance")
254s ***** error<hnswSearcher: X cannot be empty.> ...
254s  hnswSearcher ([])
254s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
254s  hnswSearcher ("abc")
254s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
254s  hnswSearcher ([1; Inf; 3])
254s ***** error<hnswSearcher: invalid parameter name: 'foo'.> ...
254s  hnswSearcher (ones(3,2), "foo", "bar")
254s ***** error<hnswSearcher: unsupported distance metric 'invalid'.> ...
254s  hnswSearcher (ones(3,2), "Distance", "invalid")
254s ***** error<hnswSearcher: 'Distance' must be a string.> ...
254s  hnswSearcher (ones(3,2), "Distance", 1)
254s ***** error<hnswSearcher: 'P' must be a positive finite scalar.> ...
254s  hnswSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
254s ***** error<hnswSearcher: 'Scale' must be a nonnegative vector matching X columns.> ...
254s  hnswSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
254s ***** error<hnswSearcher: 'Cov' must be a square matrix matching X columns.> ...
254s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
254s ***** error<hnswSearcher: 'Cov' must be symmetric for mahalanobis.> ...
254s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", [1, 2; 3, 4])
254s ***** error<hnswSearcher: 'Cov' must be positive definite for mahalanobis.> ...
254s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
254s ***** error<hnswSearcher: 'MaxNumLinksPerNode' must be a positive integer.> ...
254s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 0)
254s ***** error<hnswSearcher: 'TrainSetSize' must be a positive integer.> ...
254s  hnswSearcher (ones(3,2), "TrainSetSize", -1)
254s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
254s  hnswSearcher (ones(3,2), "TrainSetSize", 4)
254s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
254s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 200, "TrainSetSize", 100)
254s ***** error<hnswSearcher.knnsearch: too few input arguments.> ...
254s  knnsearch (hnswSearcher (ones(3,2)))
254s ***** error<hnswSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K")
254s ***** error<hnswSearcher.knnsearch: Y cannot be empty.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), [])
254s ***** error<hnswSearcher.knnsearch: Y must be a finite numeric matrix.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), "abc")
254s ***** error<hnswSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), ones(3,3))
254s ***** error<hnswSearcher.knnsearch: 'K' must be a positive integer.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K", 0)
254s ***** error<hnswSearcher.knnsearch: invalid parameter name: 'foo'.> ...
254s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "foo", "bar")
254s ***** error<hnswSearcher.subsref: \(\) indexing not supported.> ...
254s  obj = hnswSearcher (ones(3,2)); obj(1)
254s ***** error<hnswSearcher.subsref: {} indexing not supported.> ...
254s  obj = hnswSearcher (ones(3,2)); obj{1}
254s ***** error<hnswSearcher.subsref: unrecognized property: 'invalid'.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.invalid
254s ***** error<hnswSearcher.subsasgn: \(\) indexing not supported.> ...
254s  obj = hnswSearcher (ones(3,2)); obj(1) = 1
254s ***** error<hnswSearcher.subsasgn: {} indexing not supported.> ...
254s  obj = hnswSearcher (ones(3,2)); obj{1} = 1
254s ***** error<hnswSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.X = 1
254s ***** error<hnswSearcher.subsasgn: 'HNSWGraph' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.HNSWGraph = 1
254s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.Distance = "invalid"
254s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.Distance = 1
254s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
254s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
254s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
254s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
254s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
254s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
254s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.DistParameter = 1
254s ***** error<hnswSearcher.subsasgn: 'MaxNumLinksPerNode' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.MaxNumLinksPerNode = 0
254s ***** error<hnswSearcher.subsasgn: 'TrainSetSize' is read-only and cannot be modified.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.TrainSetSize = -1
254s ***** error<hnswSearcher.subsasgn: unrecognized property: 'efSearch'.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.efSearch = 1.5
254s ***** error<hnswSearcher.subsasgn: unrecognized property: 'invalid'.> ...
254s  obj = hnswSearcher (ones(3,2)); obj.invalid = 1
254s 54 tests, 54 passed, 0 known failure, 0 skipped
254s [inst/pca.m]
254s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/pca.m
254s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
254s ***** test
254s  x=[7 4 3
254s     4 1 8
254s     6 3 5
254s     8 6 1
254s     8 5 7
254s     7 2 9
254s     5 3 3
254s     9 5 8
254s     7 4 5
254s     8 2 2];
254s  R = corrcoef (x);
254s  [V, lambda] = eig (R);
254s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
254s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
254s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4); #contribution of first 2 PCs to each original variable
254s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
254s  F = zscore(x)*B;
254s  [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
254s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
254s ***** test
254s  x=[1,2,3;2,1,3]';
254s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
254s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
254s  m(:,1) = m(:,1)*sign(COEFF(1,1));
254s  m(:,2) = m(:,2)*sign(COEFF(1,2));
254s ***** assert(COEFF,m(1:2,:),10*eps);
254s ***** assert(SCORE,-m,10*eps);
254s ***** assert(latent,[1.5;.5],10*eps);
254s ***** assert(tsquare,[4;4;4]/3,10*eps);
254s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
254s ***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
254s ***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
254s ***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
254s ***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
254s !!!!! known failure
254s ASSERT errors for:  assert (tsquare,([1.5; 0.5; 1.5]),10 * eps)
254s 
254s   Location  |  Observed  |  Expected  |  Reason
254s     (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
254s     (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
254s     (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
254s ***** test
254s  x=x';
254s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
254s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
254s  m(:,1) = m(:,1)*sign(COEFF(1,1));
254s  m(:,2) = m(:,2)*sign(COEFF(1,2));
254s  m(:,3) = m(:,3)*sign(COEFF(3,3));
254s ***** assert(COEFF,m,10*eps);
254s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
254s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
254s ***** assert(latent,[1;0;0],10*eps);
254s ***** assert(tsquare,[0.5;0.5],10*eps)
254s ***** test
254s  [COEFF,SCORE,latent,tsquare] = pca(x);
254s ***** assert(COEFF,m(:, 1),10*eps);
254s ***** assert(SCORE,-m(1:2,1),10*eps);
254s ***** assert(latent,[1],10*eps);
254s ***** assert(tsquare,[0.5;0.5],10*eps)
254s ***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
254s ***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
254s ***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
254s ***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
254s ***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
254s ***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
254s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
254s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
254s ***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
254s 32 tests, 31 passed, 1 known failure, 0 skipped
254s [inst/dendrogram.m]
254s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dendrogram.m
254s ***** demo
254s  ## simple dendrogram
254s  y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
254s  y(:,3) = 1:5;
254s  dendrogram (y);
254s  title ("simple dendrogram");
254s ***** demo
254s  ## another simple dendrogram
254s  v = 2 * rand (30, 1) - 1;
254s  d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
254s  y = linkage (squareform (d, "tovector"));
254s  dendrogram (y);
254s  title ("another simple dendrogram");
254s ***** demo
254s  ## collapsed tree, find all the leaves of node 5
254s  X = randn (60, 2);
254s  D = pdist (X);
254s  y = linkage (D, "average");
254s  subplot (2, 1, 1);
254s  title ("original tree");
254s  dendrogram (y, 0);
254s  subplot (2, 1, 2);
254s  title ("collapsed tree");
254s  [~, t] = dendrogram (y, 20);
254s  find(t == 5)
254s ***** demo
254s  ## optimal leaf order
254s  X = randn (30, 2);
254s  D = pdist (X);
254s  y = linkage (D, "average");
254s  order = optimalleaforder (y, D);
254s  subplot (2, 1, 1);
254s  title ("original leaf order");
254s  dendrogram (y);
254s  subplot (2, 1, 2);
254s  title ("optimal leaf order");
254s  dendrogram (y, "Reorder", order);
254s ***** demo
254s  ## horizontal orientation and labels
254s  X = randn (8, 2);
254s  D = pdist (X);
254s  L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
254s       "Sneezy"; "Dopey"];
254s  y = linkage (D, "average");
254s  dendrogram (y, "Orientation", "left", "Labels", L);
254s  title ("horizontal orientation and labels");
254s ***** shared visibility_setting
254s  visibility_setting = get (0, "DefaultFigureVisible");
254s ***** test
254s  hf = figure ("visible", "off");
254s  unwind_protect
254s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
254s    y(:,3) = 1:5;
254s    dendrogram (y);
254s  unwind_protect_cleanup
254s    close (hf);
254s  end_unwind_protect
254s ***** test
254s  hf = figure ("visible", "off");
254s  unwind_protect
254s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
254s    y(:,3) = 1:5;
254s    dendrogram (y);
254s  unwind_protect_cleanup
254s    close (hf);
254s  end_unwind_protect
254s ***** test
254s  hf = figure ("visible", "off");
254s  unwind_protect
254s    v = 2 * rand (30, 1) - 1;
254s    d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
254s    y = linkage (squareform (d, "tovector"));
254s    dendrogram (y);
254s  unwind_protect_cleanup
254s    close (hf);
254s  end_unwind_protect
254s warning: using the gnuplot graphics toolkit is discouraged
254s 
254s The gnuplot graphics toolkit is not actively maintained and has a number
254s of limitations that are unlikely to be fixed.  Communication with gnuplot
254s uses a one-directional pipe and limited information is passed back to the
254s Octave interpreter so most changes made interactively in the plot window
254s will not be reflected in the graphics properties managed by Octave.  For
254s example, if the plot window is closed with a mouse click, Octave will not
254s be notified and will not update its internal list of open figure windows.
254s The qt toolkit is recommended instead.
254s ***** test
254s  hf = figure ("visible", "off");
254s  unwind_protect
254s    X = randn (30, 2);
254s    D = pdist (X);
254s    y = linkage (D, "average");
254s    order = optimalleaforder (y, D);
254s    subplot (2, 1, 1);
254s    title ("original leaf order");
254s    dendrogram (y);
254s    subplot (2, 1, 2);
254s    title ("optimal leaf order");
254s    dendrogram (y, "Reorder", order);
254s  unwind_protect_cleanup
254s    close (hf);
254s  end_unwind_protect
256s ***** error dendrogram ();
256s ***** error <tree must be .*> dendrogram (ones (2, 2), 1);
256s ***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
256s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
256s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
256s  fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
256s 9 tests, 9 passed, 0 known failure, 0 skipped
256s [inst/ranksum.m]
256s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ranksum.m
256s ***** test
256s  mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
256s             32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
256s             32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
256s  [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
256s  assert (p, 0.004329004329004329, 1e-14);
256s  assert (h, true);
256s  assert (stats.ranksum, 21.5);
256s ***** test
256s  year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
256s           49 47 50 60 59 60 62 61 71]';
256s  year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
256s           54 50 53 62 62 62 72 60 67]';
256s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
256s  assert (p, 0.1270832752950605, 1e-14);
256s  assert (h, false);
256s  assert (stats.ranksum, 837.5);
256s  assert (stats.zval, -1.140287483634606, 1e-14);
256s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
256s                        "method", "exact");
256s  assert (p, 0.127343916432862, 1e-14);
256s  assert (h, false);
256s  assert (stats.ranksum, 837.5);
265s 2 tests, 2 passed, 0 known failure, 0 skipped
265s [inst/factoran.m]
265s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/factoran.m
265s ***** demo
265s  x = [ 7    26     6    60;
265s        1    29    15    52;
265s       11    56     8    20;
265s       11    31     8    47;
265s        7    52     6    33;
265s       11    55     9    22;
265s        3    71    17     6;
265s        1    31    22    44;
265s        2    54    18    22;
265s       21    47     4    26;
265s        1    40    23    34;
265s       11    66     9    12;
265s       10    68     8    12
265s      ];
265s  [loadings, specvar, fscores] = factoran (x, 2);
265s ***** test
265s  x = [1, 2; 2, 1; 3, 3];
265s  [loadings, specvar, fscores] = factoran (x, 1);
265s  l_out = [0.7071; 0.7071];
265s  s_out = [0.5000; 0.5000];
265s  f_out = [-0.7071; -0.7071; 1.4142];
265s  assert (loadings, l_out, 1.3e-4);
265s  assert (specvar, s_out, 1.3e-4);
265s  assert (fscores, f_out, 1.3e-4);
265s ***** error factoran ()
265s ***** error factoran (ones (5,3), 0)
265s ***** error factoran (ones (5,3), 3)
265s ***** error<factoran: X must be a numeric matrix.> factoran ({1,2}, 1)
265s ***** error<factoran: X must be a numeric matrix.> factoran (ones (2,2,2), 1)
265s ***** error<EIG: matrix contains Inf or NaN values> x=ones (3,2); x(:,2)=0; factoran (x,1)
265s 7 tests, 7 passed, 0 known failure, 0 skipped
265s [inst/qqplot.m]
265s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/qqplot.m
265s ***** test
265s  hf = figure ("visible", "off");
265s  unwind_protect
265s    qqplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
265s  unwind_protect_cleanup
265s    close (hf);
265s  end_unwind_protect
265s ***** error qqplot ()
265s ***** error <qqplot: X must be a numeric vector> qqplot ({1})
265s ***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
265s ***** error <qqplot: X must be a numeric vector> qqplot (1, "foobar")
265s ***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
265s 6 tests, 6 passed, 0 known failure, 0 skipped
265s [inst/tabulate.m]
265s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/tabulate.m
265s ***** demo
265s  ## Generate a frequency table for a vector of data in a cell array
265s  load patients
265s 
265s  ## Display the first seven entries of the Gender variable
265s  gender = Gender(1:7)
265s 
265s  ## Compute the frequency table that shows the number and
265s  ## percentage of Male and Female patients
265s  tabulate (Gender)
265s ***** demo
265s  ## Create a frequency table for a vector of positive integers
265s  load patients
265s 
265s  ## Display the first seven entries of the Gender variable
265s  height = Height(1:7)
265s 
265s  ## Create a frequency table that shows, in its second and third columns,
265s  ## the number and percentage of patients with a particular height.
265s  table = tabulate (Height);
265s 
265s  ## Display the first and last seven entries of the frequency table
265s  first = table(1:7,:)
265s 
265s  last = table(end-6:end,:)
265s ***** demo
265s  ## Create a frequency table from a character array
265s  load carsmall
265s 
265s  ## Tabulate the data in the Origin variable, which shows the
265s  ## country of origin of each car in the data set
265s  tabulate (Origin)
265s ***** demo
265s  ## Create a frequency table from a numeric vector with NaN values
265s  load carsmall
265s 
265s  ## The carsmall dataset contains measurements of 100 cars
265s  total_cars = length (MPG)
265s  ## For six cars, the MPG value is missing
265s  missingMPG = length (MPG(isnan (MPG)))
265s 
265s  ## Create a frequency table using MPG
265s  tabulate (MPG)
265s  table = tabulate (MPG);
265s 
265s  ## Only 94 cars were used
265s  valid_cars = sum (table(:,2))
265s ***** test
265s  load patients
265s  table = tabulate (Gender);
265s  assert (table{1,1}, "Male");
265s  assert (table{2,1}, "Female");
265s  assert (table{1,2}, 47);
265s  assert (table{2,2}, 53);
265s ***** test
265s  load patients
265s  table = tabulate (Height);
265s  assert (table(end-4,:), [68, 15, 15]);
265s  assert (table(end-3,:), [69, 8, 8]);
265s  assert (table(end-2,:), [70, 11, 11]);
265s  assert (table(end-1,:), [71, 10, 10]);
265s  assert (table(end,:), [72, 4, 4]);
265s ***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
265s ***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
265s ***** error<tabulate: X must be either a numeric vector> ...
265s  tabulate ({"a", "b"; "a", "c"})
265s 5 tests, 5 passed, 0 known failure, 0 skipped
265s [inst/evalclusters.m]
265s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/evalclusters.m
265s ***** demo
265s  load fisheriris;
265s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
265s  plot (eva)
265s ***** error evalclusters ()
265s ***** error evalclusters ([1 1;0 1])
265s ***** error evalclusters ([1 1;0 1], "kmeans")
265s ***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
265s ***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
265s ***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
265s ***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
265s ***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
265s ***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
265s ***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
265s ***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
265s ***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
265s ***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
265s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
265s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
265s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
265s ***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
265s                                         "distance", "a")
265s ***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
265s                                        "distance", "a")
265s ***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
265s                                      "clusterpriors", "equal")
265s ***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
265s                                          "silhouette", "clusterpriors", "xxx")
265s ***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
265s ***** test
265s  load fisheriris;
265s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
265s  assert (isa (eva, "CalinskiHarabaszEvaluation"));
265s  assert (eva.NumObservations, 150);
265s  assert (eva.OptimalK, 3);
265s  assert (eva.InspectedK, [1 2 3 4 5 6]);
265s 22 tests, 22 passed, 0 known failure, 0 skipped
265s [inst/histfit.m]
265s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/histfit.m
265s ***** demo
265s  histfit (randn (100, 1))
265s ***** demo
265s  histfit (poissrnd (2, 1000, 1), 10, "Poisson")
265s ***** demo
265s  histfit (betarnd (3, 10, 1000, 1), 10, "beta")
265s ***** test
265s  hf = figure ("visible", "off");
265s  unwind_protect
265s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
265s    histfit (x);
265s  unwind_protect_cleanup
265s    close (hf);
265s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
266s    histfit (x);
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
266s    histfit (x, 3);
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    histfit (randn (100, 1));
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    histfit (poissrnd (2, 1000, 1), 10, "Poisson");
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    histfit (betarnd (3, 10, 1000, 1), 10, "beta");
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    ax = gca ();
266s    histfit (ax, randn (100, 1));
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    ax = gca ();
266s    histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    ax = gca ();
266s    histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** test
266s  hf = figure ("visible", "off");
266s  unwind_protect
266s    ax = axes ("parent", hf);
266s    fail ("histfit (ax)", "histfit: too few input arguments.");
266s  unwind_protect_cleanup
266s    close (hf);
266s  end_unwind_protect
266s ***** error<histfit: X must be a numeric vector of real numbers.> ...
266s  histfit ('wer')
266s ***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
266s ***** error<histfit: NBINS must be a real scalar integer value.> ...
266s  histfit (randn (100, 1), 5.6)
266s ***** error<histfit: DISTNAME must be a character vector.> ...
266s  histfit (randn (100, 1), 8, 5)
266s ***** error<histfit: DISTNAME must be a character vector.> ...
266s  histfit (randn (100, 1), 8, {'normal'})
266s ***** error<histfit: 'Kernel' distribution is not supported yet.> ...
266s  histfit (randn (100, 1), 8, 'Kernel')
266s ***** error<histfit: unrecognized distribution name.> ...
266s  histfit (randn (100, 1), 8, 'ASDASDASD')
266s 17 tests, 17 passed, 0 known failure, 0 skipped
266s [inst/regression_ftest.m]
266s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/regression_ftest.m
266s ***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
266s ***** error<Invalid call to regression_ftest.  Correct usage> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
266s ***** error<regression_ftest: Y must contain finite real numbers.> ...
266s  regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
266s ***** error<regression_ftest: Y must contain finite real numbers.> ...
266s  regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
266s ***** error<regression_ftest: Y must contain finite real numbers.> ...
266s  regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
266s ***** error<regression_ftest: X must contain finite real numbers.> ...
266s  regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
266s ***** error<regression_ftest: X must contain finite real numbers.> ...
266s  regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
266s ***** error<regression_ftest: X must contain finite real numbers.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
266s ***** error<regression_ftest: invalid value for alpha.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
266s ***** error<regression_ftest: invalid value for alpha.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
266s ***** error<regression_ftest: invalid value for alpha.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
266s ***** error<regression_ftest: invalid value for alpha.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
266s ***** error<regression_ftest: invalid Name argument.> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
266s ***** error<regression_ftest: Y must be a vector of length> ...
266s  regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
266s ***** error<regression_ftest: Y must be a vector of length> ...
266s  regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
266s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
266s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
266s ***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
266s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
266s 18 tests, 18 passed, 0 known failure, 0 skipped
266s [inst/x2fx.m]
266s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/x2fx.m
266s ***** test
266s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
266s  D = x2fx(X,'quadratic');
266s  assert (D(1,:), [1, 1, 10, 10, 1, 100]);
266s  assert (D(2,:), [1, 2, 20, 40, 4, 400]);
266s ***** test
266s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
266s  model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
266s  D = x2fx(X,model);
266s  assert (D(1,:), [1, 1, 10, 10, 1]);
266s  assert (D(2,:), [1, 2, 20, 40, 4]);
266s  assert (D(4,:), [1, 4, 20, 80, 16]);
266s ***** test
266s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
266s  D = x2fx (x, 'linear');
266s  assert (D, [1, 1, 2, 3; 1, 2, 3, 4;, 1, 3, 4, 5]);
266s  D = x2fx (x, 'interaction');
266s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6]);
266s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12]);
266s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20]);
266s  D = x2fx (x, 'quadratic');
266s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6, 1, 4, 9]);
266s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12, 4, 9, 16]);
266s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20, 9, 16, 25]);
266s  D = x2fx (x, 'purequadratic');
266s  assert (D(1,:), [1, 1, 2, 3, 1, 4, 9]);
266s  assert (D(2,:), [1, 2, 3, 4, 4, 9, 16]);
266s  assert (D(3,:), [1, 3, 4, 5, 9, 16, 25]);
266s ***** test
266s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
266s  D = x2fx (x, [0, 0, 1; 1, 0, 2]);
266s  assert (D, [3, 9; 4, 32; 5, 75]);
266s ***** test
266s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
266s  D = x2fx (x, 'linear', [1, 3]);
266s  assert (D, [1, 1, 0, 2, 1, 0; 1, 0, 1, 3, 0, 1; 1, 0, 0, 4, 0, 0]);
266s ***** test
266s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
266s  D = x2fx (x, 'quadratic', [1, 3]);
266s  assert (D(1,:), [1, 1, 0, 2, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 4]);
266s  assert (D(2,:), [1, 0, 1, 3, 0, 1, 0, 3, 0, 0, 0, 1, 0, 3, 9]);
266s  assert (D(3,:), [1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16]);
266s ***** test
266s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
266s  D = x2fx (x, 'cos');
266s  assert (D(1,:), [0.5403, -0.4161, -0.9900], 1e-4);
266s  assert (D(2,:), [-0.4161, -0.9900, -0.6536], 1e-4);
266s  assert (D(3,:), [-0.9900, -0.6536, 0.2837], 1e-4);
266s ***** error <x2fx: category index exceeds number of columns in X.> ...
266s  x2fx ([1, 2, 3; 2, 3, 4], 'quadratic', [1, 4])
266s ***** error <x2fx: unrecognized function 'cosine'.> ...
266s  D = x2fx ([1, 2, 3; 2, 3, 4; 3, 4, 5], 'cosine')
266s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
266s  x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
266s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
266s  x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
267s 11 tests, 11 passed, 0 known failure, 0 skipped
267s [inst/tiedrank.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/tiedrank.m
267s ***** test
267s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
267s  assert (r, [1, 2.5, 4, 5, 2.5]);
267s  assert (tieadj, 3);
267s ***** test
267s  [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
267s  assert (r, [1; 2.5; 4; 5; 2.5]);
267s  assert (tieadj, 3);
267s ***** test
267s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
267s  assert (r, [1, 2.5, 4, 5, 2.5]);
267s  assert (tieadj, [1; 0; 18]);
267s ***** test
267s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
267s  assert (r, [1, 2.5, 2, 1, 2.5]);
267s  assert (tieadj, 3);
267s ***** test
267s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
267s  assert (r, [1, 2.5, 2, 1, 2.5]);
267s  assert (tieadj, [1; 0; 18]);
267s ***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
267s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], [1, 1])
267s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], "A")
267s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], [true, true])
267s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
267s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], 0, "A")
267s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
267s  tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
267s 12 tests, 12 passed, 0 known failure, 0 skipped
267s [inst/silhouette.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/silhouette.m
267s ***** demo
267s  load fisheriris;
267s  X = meas(:,3:4);
267s  cidcs = kmeans (X, 3, "Replicates", 5);
267s  silhouette (X, cidcs);
267s  y_labels(cidcs([1 51 101])) = unique (species);
267s  set (gca, "yticklabel", y_labels);
267s  title ("Fisher's iris data");
267s ***** error silhouette ();
267s ***** error silhouette ([1 2; 1 1]);
267s ***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
267s ***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
267s 4 tests, 4 passed, 0 known failure, 0 skipped
267s [inst/signtest.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/signtest.m
267s ***** test
267s  [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
267s  assert (pval, 1.091701889420221e-218, 1e-14);
267s  assert (h, 1);
267s  assert (stats.zval, -31.5437631079266, 1e-14);
267s ***** test
267s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
267s  assert (pval, 0.6875000000000006, 1e-14);
267s  assert (h, 0);
267s  assert (stats.zval, NaN);
267s  assert (stats.sign, 4);
267s ***** test
267s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
267s  assert (pval, 0.6830913983096086, 1e-14);
267s  assert (h, 0);
267s  assert (stats.zval, 0.4082482904638631, 1e-14);
267s  assert (stats.sign, 4);
267s ***** error <signtest: X must be a vector.> signtest (ones (2))
267s ***** error <signtest: Y must be either a scalar of a vector.> ...
267s  signtest ([1, 2, 3, 4], ones (2))
267s ***** error <signtest: X and Y vectors have different lengths.> ...
267s  signtest ([1, 2, 3, 4], [1, 2, 3])
267s ***** error <signtest: optional arguments must be in pairs.> ...
267s  signtest ([1, 2, 3, 4], [], 'tail')
267s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
267s  signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
267s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
267s  signtest ([1, 2, 3, 4], [], 'alpha', 0)
267s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
267s  signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
267s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
267s  signtest ([1, 2, 3, 4], [], 'alpha', "a")
267s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
267s  signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
267s ***** error <signtest: 'tail' argument must be a character vector.> ...
267s  signtest ([1, 2, 3, 4], [], 'tail', 0.01)
267s ***** error <signtest: 'tail' argument must be a character vector.> ...
267s  signtest ([1, 2, 3, 4], [], 'tail', {"both"})
267s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
267s  signtest ([1, 2, 3, 4], [], 'tail', "some")
267s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
267s  signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
267s ***** error <signtest: 'method' argument must be a character vector.> ...
267s  signtest ([1, 2, 3, 4], [], 'method', 0.01)
267s ***** error <signtest: 'method' argument must be a character vector.> ...
267s  signtest ([1, 2, 3, 4], [], 'method', {"exact"})
267s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
267s  signtest ([1, 2, 3, 4], [], 'method', "some")
267s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
267s  signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
267s 20 tests, 20 passed, 0 known failure, 0 skipped
267s [inst/fitcdiscr.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitcdiscr.m
267s ***** demo
267s  ## Train a linear discriminant classifier for Gamma = 0.5
267s  ## and plot the decision boundaries.
267s 
267s  load fisheriris
267s  idx = ! strcmp (species, "setosa");
267s  X = meas(idx,3:4);
267s  Y = cast (strcmpi (species(idx), "virginica"), "double");
267s  obj = fitcdiscr (X, Y, "Gamma", 0.5)
267s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
267s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
267s  [x1G, x2G] = meshgrid (x1, x2);
267s  XGrid = [x1G(:), x2G(:)];
267s  pred = predict (obj, XGrid);
267s  gidx = logical (pred);
267s 
267s  figure
267s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
267s  hold on
267s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
267s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
267s  xlabel ("Petal length (cm)");
267s  ylabel ("Petal width (cm)");
267s  title ("Linear Discriminant Analysis Decision Boundary");
267s  legend ({"Versicolor Region", "Virginica Region", ...
267s          "Sampled Versicolor", "Sampled Virginica"}, ...
267s          "location", "northwest")
267s  axis tight
267s  hold off
267s ***** test
267s  load fisheriris
267s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
267s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
267s  assert (label, {'versicolor'})
267s  assert (score, [0, 0.9999, 0.0001], 1e-4)
267s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
267s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
267s  assert (label, {'versicolor'})
267s  assert (score, [0, 0.6368, 0.3632], 1e-4)
267s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
267s  assert (class (Mdl), "ClassificationDiscriminant");
267s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
267s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
267s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
267s  assert (Mdl.ClassNames, unique (species))
267s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
267s           0.046361, 0.115388, 0.027622, 0.016355; ...
267s           0.083757, 0.027622, 0.185188, 0.021333; ...
267s           0.019201, 0.016355, 0.021333, 0.041882];
267s  assert (Mdl.Sigma, sigma, 1e-6)
267s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
267s        5.9360, 2.7700, 4.2600, 1.3260; ...
267s        6.5880, 2.9740, 5.5520, 2.0260];
267s  assert (Mdl.Mu, mu, 1e-14)
267s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
267s ***** error<fitcdiscr: too few arguments.> fitcdiscr ()
267s ***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
267s ***** error<fitcdiscr: name-value arguments must be in pairs.>
267s  fitcdiscr (ones (4,2), ones (4, 1), "K")
267s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
267s  fitcdiscr (ones (4,2), ones (3, 1))
267s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
267s  fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
267s 6 tests, 6 passed, 0 known failure, 0 skipped
267s [inst/einstein.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/einstein.m
267s ***** demo
267s  einstein (0.4, 0.6)
267s ***** demo
267s  einstein (0.2, 0.5)
267s ***** demo
267s  einstein (0.6, 0.1)
267s ***** test
267s  hf = figure ("visible", "off");
267s  unwind_protect
267s    tiles = einstein (0.4, 0.6);
267s    assert (isstruct (tiles), true);
267s  unwind_protect_cleanup
267s    close (hf);
267s  end_unwind_protect
267s ***** error<Invalid call to einstein.  Correct usage is> einstein
267s ***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
267s ***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
267s ***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
267s ***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
267s 6 tests, 6 passed, 0 known failure, 0 skipped
267s [inst/glmval.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/glmval.m
267s ***** demo
267s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
267s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
267s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
267s  b = glmfit (x, [y n], "binomial", "Link", "probit");
267s  yfit = glmval (b, x, "probit", "Size", n);
267s  plot (x, y./n, 'o', x, yfit ./ n, '-')
267s ***** error <glmval: too few input arguments.> glmval ()
267s ***** error <glmval: too few input arguments.> glmval (1)
267s ***** error <glmval: too few input arguments.> glmval (1, 2)
267s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
267s  glmval ("asd", [1; 1; 1], 'probit')
267s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
267s  glmval ([], [1; 1; 1], 'probit')
267s ***** error <glmval: X must be a numeric matrix.> ...
267s  glmval ([0.1; 0.3; 0.4], [], 'probit')
267s ***** error <glmval: X must be a numeric matrix.> ...
267s  glmval ([0.1; 0.3; 0.4], "asd", 'probit')
267s ***** error <glmval: structure with custom link functions must be a scalar.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
267s ***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
267s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
267s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
267s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
267s ***** error <glmval: invalid custom 'Link' function.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
267s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
267s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
267s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
267s ***** error <glmval: invalid custom 'Derivative' function.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
267s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
267s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
267s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
267s ***** error <glmval: invalid custom 'Inverse' function.> ...
267s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
267s ***** error <glmval: cell array with custom link functions must have three elements.> ...
267s  glmval (rand (3,1), rand (5,2), {'log'})
267s ***** error <glmval: cell array with custom link functions must have three elements.> ...
267s  glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
267s ***** error <glmval: cell array with custom link functions must have three elements.> ...
267s  glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
267s ***** error <glmval: bad 'Link' function in custom link function cell array.> ...
267s  glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
267s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
267s ***** error <glmval: invalid custom 'Link' function.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
267s ***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
267s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
267s ***** error <glmval: invalid custom 'Derivative' function.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
267s ***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
267s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
267s ***** error <glmval: invalid custom 'Inverse' function.> ...
267s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
267s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
267s  glmval (rand (3,1), rand (5,2), NaN)
267s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
267s  glmval (rand (3,1), rand (5,2), [1, 2])
267s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
267s  glmval (rand (3,1), rand (5,2), [1i])
267s ***** error <glmval: canonical link function name must be a character vector.> ...
267s  glmval (rand (3,1), rand (5,2), ["log"; "log1"])
267s ***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
267s  glmval (rand (3,1), rand (5,2), 'somelinkfunction')
267s ***** error <glmval: invalid value for custom link function.> ...
267s  glmval (rand (3,1), rand (5,2), true)
267s ***** error <glmval: invalid 'stats' structure.> ...
267s  glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
267s ***** error <glmval: Name-Value arguments must be in pairs.> ...
267s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
267s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
267s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
267s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
267s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
267s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
267s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
267s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
267s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
267s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
267s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
267s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
267s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
267s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
267s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
267s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
267s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
267s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
267s ***** error <glmval: unknown parameter name.> ...
267s  glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
267s ***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
267s  [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
267s 57 tests, 57 passed, 0 known failure, 0 skipped
267s [inst/ecdf.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ecdf.m
267s ***** demo
267s  y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
267s  d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
267s  t = min (y, d);            ## we observe the minimum of these times
267s  censored = (y > d);        ## we also observe whether the subject failed
267s 
267s  ## Calculate and plot the empirical cdf and confidence bounds
267s  [f, x, flo, fup] = ecdf (t, "censoring", censored);
267s  stairs (x, f);
267s  hold on;
267s  stairs (x, flo, "r:"); stairs (x, fup, "r:");
267s 
267s  ## Superimpose a plot of the known true cdf
267s  xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
267s  hold off;
267s ***** demo
267s  R = wblrnd (100, 2, 100, 1);
267s  ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
267s  hold on
267s  x = 1:1:250;
267s  wblsurv = 1 - cdf ("weibull", x, 100, 2);
267s  plot (x, wblsurv, "g-", "LineWidth", 2)
267s  legend ("Empirical survivor function", "Lower confidence bound", ...
267s          "Upper confidence bound", "Weibull survivor function", ...
267s          "Location", "northeast");
267s  hold off
267s ***** error ecdf ();
267s ***** error ecdf (randi (15,2));
267s ***** error ecdf ([3,2,4,3+2i,5]);
267s ***** error kstest ([2,3,4,5,6],"tail");
267s ***** error kstest ([2,3,4,5,6],"tail", "whatever");
267s ***** error kstest ([2,3,4,5,6],"function", "");
267s ***** error kstest ([2,3,4,5,6],"badoption", 0.51);
267s ***** error kstest ([2,3,4,5,6],"tail", 0);
267s ***** error kstest ([2,3,4,5,6],"alpha", 0);
267s ***** error kstest ([2,3,4,5,6],"alpha", NaN);
267s ***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
267s ***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
267s ***** test
267s  hf = figure ("visible", "off");
267s  unwind_protect
267s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
267s    [F, x, Flo, Fup] = ecdf (x);
267s    F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
267s    assert (F, F_out, ones (10,1) * 1e-4);
267s    x_out = [0 0 2 3 4 5 6 7 8 9]';
267s    assert (x, x_out);
267s    Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
267s    assert (Flo, Flo_out, ones (10,1) * 1e-4);
267s    Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
267s    assert (Fup, Fup_out, ones (10,1) * 1e-4);
267s  unwind_protect_cleanup
267s    close (hf);
267s  end_unwind_protect
267s ***** test
267s  hf = figure ("visible", "off");
267s  unwind_protect
267s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
267s    ecdf (x);
267s  unwind_protect_cleanup
267s    close (hf);
267s  end_unwind_protect
267s 14 tests, 14 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/betastat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/betastat.m
267s ***** error<betastat: function called with too few input arguments.> betastat ()
267s ***** error<betastat: function called with too few input arguments.> betastat (1)
267s ***** error<betastat: A and B must be numeric.> betastat ({}, 2)
267s ***** error<betastat: A and B must be numeric.> betastat (1, "")
267s ***** error<betastat: A and B must not be complex.> betastat (i, 2)
267s ***** error<betastat: A and B must not be complex.> betastat (1, i)
267s ***** error<betastat: A and B must be of common size or scalars.> ...
267s  betastat (ones (3), ones (2))
267s ***** error<betastat: A and B must be of common size or scalars.> ...
267s  betastat (ones (2), ones (3))
267s ***** test
267s  a = -2:6;
267s  b = 0.4:0.2:2;
267s  [m, v] = betastat (a, b);
267s  expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
267s  expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
267s  assert (m, expected_m, eps*100);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  a = -2:1:6;
267s  [m, v] = betastat (a, 1.5);
267s  expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
267s  expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
267s  assert (m, expected_m);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  a = [14  Inf   10  NaN  10];
267s  b = [12    9  NaN  Inf  12];
267s  [m, v] = betastat (a, b);
267s  expected_m = [14/26 NaN NaN NaN 10/22];
267s  expected_v = [168/18252 NaN NaN NaN 120/11132];
267s  assert (m, expected_m);
267s  assert (v, expected_v);
267s ***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
267s ***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
267s ***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
267s ***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
267s 15 tests, 15 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/wblstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/wblstat.m
267s ***** error<wblstat: function called with too few input arguments.> wblstat ()
267s ***** error<wblstat: function called with too few input arguments.> wblstat (1)
267s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
267s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
267s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
267s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
267s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
267s  wblstat (ones (3), ones (2))
267s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
267s  wblstat (ones (2), ones (3))
267s ***** test
267s  lambda = 3:8;
267s  k = 1:6;
267s  [m, v] = wblstat (lambda, k);
267s  expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
267s  expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
267s  assert (m, expected_m, 0.001);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  k = 1:6;
267s  [m, v] = wblstat (6, k);
267s  expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
267s  expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
267s  assert (m, expected_m, 0.001);
267s  assert (v, expected_v, 0.001);
267s 10 tests, 10 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/fstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/fstat.m
267s ***** error<fstat: function called with too few input arguments.> fstat ()
267s ***** error<fstat: function called with too few input arguments.> fstat (1)
267s ***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
267s ***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
267s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
267s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
267s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
267s  fstat (ones (3), ones (2))
267s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
267s  fstat (ones (2), ones (3))
267s ***** test
267s  df1 = 1:6;
267s  df2 = 5:10;
267s  [m, v] = fstat (df1, df2);
267s  expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
267s  expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
267s  assert (m, expected_mn, 0.001);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  df1 = 1:6;
267s  [m, v] = fstat (df1, 5);
267s  expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
267s  expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
267s  assert (m, expected_mn, 0.001);
267s  assert (v, expected_v, 0.001);
267s 10 tests, 10 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/lognstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/lognstat.m
267s ***** error<lognstat: function called with too few input arguments.> lognstat ()
267s ***** error<lognstat: function called with too few input arguments.> lognstat (1)
267s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
267s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
267s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
267s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
267s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
267s  lognstat (ones (3), ones (2))
267s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
267s  lognstat (ones (2), ones (3))
267s ***** test
267s  mu = 0:0.2:1;
267s  sigma = 0.2:0.2:1.2;
267s  [m, v] = lognstat (mu, sigma);
267s  expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
267s  expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
267s  assert (m, expected_m, 0.001);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  sigma = 0.2:0.2:1.2;
267s  [m, v] = lognstat (0, sigma);
267s  expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
267s  expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
267s  assert (m, expected_m, 0.001);
267s  assert (v, expected_v, 0.001);
267s 10 tests, 10 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/geostat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/geostat.m
267s ***** error<geostat: function called with too few input arguments.> geostat ()
267s ***** error<geostat: PS must be numeric.> geostat ({})
267s ***** error<geostat: PS must be numeric.> geostat ("")
267s ***** error<geostat: PS must not be complex.> geostat (i)
267s ***** test
267s  ps = 1 ./ (1:6);
267s  [m, v] = geostat (ps);
267s  assert (m, [0, 1, 2, 3, 4, 5], 0.001);
267s  assert (v, [0, 2, 6, 12, 20, 30], 0.001);
267s 5 tests, 5 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/hygestat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/hygestat.m
267s ***** error<hygestat: function called with too few input arguments.> hygestat ()
267s ***** error<hygestat: function called with too few input arguments.> hygestat (1)
267s ***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
267s ***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
267s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
267s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
267s ***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
267s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
267s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
267s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
267s  hygestat (ones (3), ones (2), 3)
267s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
267s  hygestat (ones (2), 2, ones (3))
267s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
267s  hygestat (1, ones (2), ones (3))
267s ***** test
267s  m = 4:9;
267s  k = 0:5;
267s  n = 1:6;
267s  [mn, v] = hygestat (m, k, n);
267s  expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
267s  expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
267s  assert (mn, expected_mn, 0.001);
267s  assert (v, expected_v, 0.001);
267s ***** test
267s  m = 4:9;
267s  k = 0:5;
267s  [mn, v] = hygestat (m, k, 2);
267s  expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
267s  expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
267s  assert (mn, expected_mn, 0.001);
267s  assert (v, expected_v, 0.001);
267s 14 tests, 14 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/evstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/evstat.m
267s ***** error<evstat: function called with too few input arguments.> evstat ()
267s ***** error<evstat: function called with too few input arguments.> evstat (1)
267s ***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
267s ***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
267s ***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
267s ***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
267s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
267s  evstat (ones (3), ones (2))
267s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
267s  evstat (ones (2), ones (3))
267s ***** shared x, y0, y1
267s  x = [-5, 0, 1, 2, 3];
267s  y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
267s  y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
267s ***** assert (evstat (x, x), y0, 1e-4)
267s ***** assert (evstat (x, x+6), y1, 1e-4)
267s ***** assert (evstat (x, x-6), NaN (1,5))
267s 11 tests, 11 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/gevstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/gevstat.m
267s ***** error<gevstat: function called with too few input arguments.> gevstat ()
267s ***** error<gevstat: function called with too few input arguments.> gevstat (1)
267s ***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
267s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
267s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
267s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
267s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
267s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
267s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
267s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
267s  gevstat (ones (3), ones (2), 3)
267s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
267s  gevstat (ones (2), 2, ones (3))
267s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
267s  gevstat (1, ones (2), ones (3))
267s ***** test
267s  k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
267s  sigma = 2;
267s  mu = 1;
267s  [m, v] = gevstat (k, sigma, mu);
267s  expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
267s  expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
267s  assert (m, expected_m, -0.001);
267s  assert (v, expected_v, -0.001);
267s 13 tests, 13 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/tstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/tstat.m
267s ***** error<tstat: function called with too few input arguments.> tstat ()
267s ***** error<tstat: DF must be numeric.> tstat ({})
267s ***** error<tstat: DF must be numeric.> tstat ("")
267s ***** error<tstat: DF must not be complex.> tstat (i)
267s ***** test
267s  df = 3:8;
267s  [m, v] = tstat (df);
267s  expected_m = [0, 0, 0, 0, 0, 0];
267s  expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
267s  assert (m, expected_m);
267s  assert (v, expected_v, 0.001);
267s 5 tests, 5 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/ncfstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/ncfstat.m
267s ***** error<ncfstat: function called with too few input arguments.> ncfstat ()
267s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
267s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
267s  ncfstat (ones (3), ones (2), 3)
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
267s  ncfstat (ones (2), 2, ones (3))
267s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
267s  ncfstat (1, ones (2), ones (3))
267s ***** shared df1, df2, lambda
267s  df1 = [2, 0, -1, 1, 4, 5];
267s  df2 = [2, 4, -1, 5, 6, 7];
267s  lambda = [1, NaN, 3, 0, 2, -1];
267s ***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
267s ***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
267s ***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
267s ***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
267s ***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
267s ***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
267s ***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
267s ***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
267s 20 tests, 20 passed, 0 known failure, 0 skipped
267s [inst/dist_stat/invgstat.m]
267s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/invgstat.m
267s ***** error<invgstat: function called with too few input arguments.> invgstat ()
267s ***** error<invgstat: function called with too few input arguments.> invgstat (1)
267s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
267s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
267s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
267s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
267s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
267s  invgstat (ones (3), ones (2))
267s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
267s  invgstat (ones (2), ones (3))
267s ***** test
267s  [m, v] = invgstat (1, 1);
267s  assert (m, 1);
267s  assert (v, 1);
267s ***** test
267s  [m, v] = invgstat (2, 1);
267s  assert (m, 2);
267s  assert (v, 8);
268s ***** test
268s  [m, v] = invgstat (2, 2);
268s  assert (m, 2);
268s  assert (v, 4);
268s ***** test
268s  [m, v] = invgstat (2, 2.5);
268s  assert (m, 2);
268s  assert (v, 3.2);
268s ***** test
268s  [m, v] = invgstat (1.5, 0.5);
268s  assert (m, 1.5);
268s  assert (v, 6.75);
268s 13 tests, 13 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/poisstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/poisstat.m
268s ***** error<poisstat: function called with too few input arguments.> poisstat ()
268s ***** error<poisstat: SIGMA must be numeric.> poisstat ({})
268s ***** error<poisstat: SIGMA must be numeric.> poisstat ("")
268s ***** error<poisstat: SIGMA must not be complex.> poisstat (i)
268s ***** test
268s  lambda = 1 ./ (1:6);
268s  [m, v] = poisstat (lambda);
268s  assert (m, lambda);
268s  assert (v, lambda);
268s 5 tests, 5 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/hnstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/hnstat.m
268s ***** error<hnstat: function called with too few input arguments.> hnstat ()
268s ***** error<hnstat: function called with too few input arguments.> hnstat (1)
268s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
268s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
268s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
268s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
268s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
268s  hnstat (ones (3), ones (2))
268s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
268s  hnstat (ones (2), ones (3))
268s ***** test
268s  [m, v] = hnstat (0, 1);
268s  assert (m, 0.7979, 1e-4);
268s  assert (v, 0.3634, 1e-4);
268s ***** test
268s  [m, v] = hnstat (2, 1);
268s  assert (m, 2.7979, 1e-4);
268s  assert (v, 0.3634, 1e-4);
268s ***** test
268s  [m, v] = hnstat (2, 2);
268s  assert (m, 3.5958, 1e-4);
268s  assert (v, 1.4535, 1e-4);
268s ***** test
268s  [m, v] = hnstat (2, 2.5);
268s  assert (m, 3.9947, 1e-4);
268s  assert (v, 2.2711, 1e-4);
268s ***** test
268s  [m, v] = hnstat (1.5, 0.5);
268s  assert (m, 1.8989, 1e-4);
268s  assert (v, 0.0908, 1e-4);
268s ***** test
268s  [m, v] = hnstat (-1.5, 0.5);
268s  assert (m, -1.1011, 1e-4);
268s  assert (v, 0.0908, 1e-4);
268s 14 tests, 14 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/nctstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/nctstat.m
268s ***** error<nctstat: function called with too few input arguments.> nctstat ()
268s ***** error<nctstat: function called with too few input arguments.> nctstat (1)
268s ***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
268s ***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
268s ***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
268s ***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
268s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
268s  nctstat (ones (3), ones (2))
268s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
268s  nctstat (ones (2), ones (3))
268s ***** shared df, mu
268s  df = [2, 0, -1, 1, 4];
268s  mu = [1, NaN, 3, -1, 2];
268s ***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
268s ***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
268s ***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
268s ***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
268s ***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
268s ***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
268s ***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
268s ***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
268s 16 tests, 16 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/unidstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/unidstat.m
268s ***** error<unidstat: function called with too few input arguments.> unidstat ()
268s ***** error<unidstat: N must be numeric.> unidstat ({})
268s ***** error<unidstat: N must be numeric.> unidstat ("")
268s ***** error<unidstat: N must not be complex.> unidstat (i)
268s ***** test
268s  N = 1:6;
268s  [m, v] = unidstat (N);
268s  expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
268s  expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s 5 tests, 5 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/raylstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/raylstat.m
268s ***** error<raylstat: function called with too few input arguments.> raylstat ()
268s ***** error<raylstat: SIGMA must be numeric.> raylstat ({})
268s ***** error<raylstat: SIGMA must be numeric.> raylstat ("")
268s ***** error<raylstat: SIGMA must not be complex.> raylstat (i)
268s ***** test
268s  sigma = 1:6;
268s  [m, v] = raylstat (sigma);
268s  expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
268s  expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s 5 tests, 5 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/nbinstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/nbinstat.m
268s ***** error<nbinstat: function called with too few input arguments.> nbinstat ()
268s ***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
268s ***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
268s ***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
268s ***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
268s ***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
268s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
268s  nbinstat (ones (3), ones (2))
268s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
268s  nbinstat (ones (2), ones (3))
268s ***** test
268s  r = 1:4;
268s  ps = 0.2:0.2:0.8;
268s  [m, v] = nbinstat (r, ps);
268s  expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
268s  expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  r = 1:4;
268s  [m, v] = nbinstat (r, 0.5);
268s  expected_m = [1, 2, 3, 4];
268s  expected_v = [2, 4, 6, 8];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/gpstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/gpstat.m
268s ***** error<gpstat: function called with too few input arguments.> gpstat ()
268s ***** error<gpstat: function called with too few input arguments.> gpstat (1)
268s ***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
268s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
268s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
268s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
268s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
268s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
268s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
268s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
268s  gpstat (ones (3), ones (2), 3)
268s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
268s  gpstat (ones (2), 2, ones (3))
268s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
268s  gpstat (1, ones (2), ones (3))
268s ***** shared x, y
268s  x = [-Inf, -1, 0, 1/2, 1, Inf];
268s  y = [0, 0.5, 1, 2, Inf, Inf];
268s ***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
268s ***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
268s ***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
268s ***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
268s ***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
268s ***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
268s ***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
268s 19 tests, 19 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/expstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/expstat.m
268s ***** error<expstat: function called with too few input arguments.> expstat ()
268s ***** error<expstat: MU must be numeric.> expstat ({})
268s ***** error<expstat: MU must be numeric.> expstat ("")
268s ***** error<expstat: MU must not be complex.> expstat (i)
268s ***** test
268s  mu = 1:6;
268s  [m, v] = expstat (mu);
268s  assert (m, [1, 2, 3, 4, 5, 6], 0.001);
268s  assert (v, [1, 4, 9, 16, 25, 36], 0.001);
268s 5 tests, 5 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/logistat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/logistat.m
268s ***** error<logistat: function called with too few input arguments.> logistat ()
268s ***** error<logistat: function called with too few input arguments.> logistat (1)
268s ***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
268s ***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
268s ***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
268s ***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
268s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
268s  logistat (ones (3), ones (2))
268s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
268s  logistat (ones (2), ones (3))
268s ***** test
268s  [m, v] = logistat (0, 1);
268s  assert (m, 0);
268s  assert (v, 3.2899, 0.001);
268s ***** test
268s  [m, v] = logistat (0, 0.8);
268s  assert (m, 0);
268s  assert (v, 2.1055, 0.001);
268s ***** test
268s  [m, v] = logistat (1, 0.6);
268s  assert (m, 1);
268s  assert (v, 1.1844, 0.001);
268s ***** test
268s  [m, v] = logistat (0, 0.4);
268s  assert (m, 0);
268s  assert (v, 0.5264, 0.001);
268s ***** test
268s  [m, v] = logistat (-1, 0.2);
268s  assert (m, -1);
268s  assert (v, 0.1316, 0.001);
268s 13 tests, 13 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/loglstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/loglstat.m
268s ***** error<loglstat: function called with too few input arguments.> loglstat ()
268s ***** error<loglstat: function called with too few input arguments.> loglstat (1)
268s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
268s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
268s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
268s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
268s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
268s  loglstat (ones (3), ones (2))
268s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
268s  loglstat (ones (2), ones (3))
268s ***** test
268s  [m, v] = loglstat (0, 1);
268s  assert (m, Inf, 0.001);
268s  assert (v, Inf, 0.001);
268s ***** test
268s  [m, v] = loglstat (0, 0.8);
268s  assert (m, 4.2758, 0.001);
268s  assert (v, Inf, 0.001);
268s ***** test
268s  [m, v] = loglstat (0, 0.6);
268s  assert (m, 1.9820, 0.001);
268s  assert (v, Inf, 0.001);
268s ***** test
268s  [m, v] = loglstat (0, 0.4);
268s  assert (m, 1.3213, 0.001);
268s  assert (v, 2.5300, 0.001);
268s ***** test
268s  [m, v] = loglstat (0, 0.2);
268s  assert (m, 1.0690, 0.001);
268s  assert (v, 0.1786, 0.001);
268s 13 tests, 13 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/normstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/normstat.m
268s ***** error<normstat: function called with too few input arguments.> normstat ()
268s ***** error<normstat: function called with too few input arguments.> normstat (1)
268s ***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
268s ***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
268s ***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
268s ***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
268s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
268s  normstat (ones (3), ones (2))
268s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
268s  normstat (ones (2), ones (3))
268s ***** test
268s  mu = 1:6;
268s  sigma = 0.2:0.2:1.2;
268s  [m, v] = normstat (mu, sigma);
268s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
268s  assert (m, mu);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  sigma = 0.2:0.2:1.2;
268s  [m, v] = normstat (0, sigma);
268s  expected_mn = [0, 0, 0, 0, 0, 0];
268s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
268s  assert (m, expected_mn, 0.001);
268s  assert (v, expected_v, 0.001);
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/nakastat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/nakastat.m
268s ***** error<nakastat: function called with too few input arguments.> nakastat ()
268s ***** error<nakastat: function called with too few input arguments.> nakastat (1)
268s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
268s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
268s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
268s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
268s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
268s  nakastat (ones (3), ones (2))
268s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
268s  nakastat (ones (2), ones (3))
268s ***** test
268s  [m, v] = nakastat (1, 1);
268s  assert (m, 0.8862269254, 1e-10);
268s  assert (v, 0.2146018366, 1e-10);
268s ***** test
268s  [m, v] = nakastat (1, 2);
268s  assert (m, 1.25331413731, 1e-10);
268s  assert (v, 0.42920367321, 1e-10);
268s ***** test
268s  [m, v] = nakastat (2, 1);
268s  assert (m, 0.93998560299, 1e-10);
268s  assert (v, 0.11642706618, 1e-10);
268s 11 tests, 11 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/gamstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/gamstat.m
268s ***** error<gamstat: function called with too few input arguments.> gamstat ()
268s ***** error<gamstat: function called with too few input arguments.> gamstat (1)
268s ***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
268s ***** error<gamstat: A and B must be numeric.> gamstat (1, "")
268s ***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
268s ***** error<gamstat: A and B must not be complex.> gamstat (1, i)
268s ***** error<gamstat: A and B must be of common size or scalars.> ...
268s  gamstat (ones (3), ones (2))
268s ***** error<gamstat: A and B must be of common size or scalars.> ...
268s  gamstat (ones (2), ones (3))
268s ***** test
268s  a = 1:6;
268s  b = 1:0.2:2;
268s  [m, v] = gamstat (a, b);
268s  expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
268s  expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  a = 1:6;
268s  [m, v] = gamstat (a, 1.5);
268s  expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
268s  expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/ncx2stat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/ncx2stat.m
268s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
268s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
268s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
268s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
268s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
268s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
268s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
268s  ncx2stat (ones (3), ones (2))
268s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
268s  ncx2stat (ones (2), ones (3))
268s ***** shared df, d1
268s  df = [2, 0, -1, 1, 4];
268s  d1 = [1, NaN, 3, -1, 2];
268s ***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
268s ***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
268s ***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
268s ***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
268s ***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
268s ***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
268s ***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
268s ***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
268s 16 tests, 16 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/ricestat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/ricestat.m
268s ***** error<ricestat: function called with too few input arguments.> ricestat ()
268s ***** error<ricestat: function called with too few input arguments.> ricestat (1)
268s ***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
268s ***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
268s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
268s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
268s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
268s  ricestat (ones (3), ones (2))
268s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
268s  ricestat (ones (2), ones (3))
268s ***** shared s, sigma
268s  s = [2, 0, -1, 1, 4];
268s  sigma = [1, NaN, 3, -1, 2];
268s ***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
268s ***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
268s ***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
268s ***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
268s ***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
268s ***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
268s ***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
268s ***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
268s 16 tests, 16 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/unifstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/unifstat.m
268s ***** error<unifstat: function called with too few input arguments.> unifstat ()
268s ***** error<unifstat: function called with too few input arguments.> unifstat (1)
268s ***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
268s ***** error<unifstat: A and B must be numeric.> unifstat (1, "")
268s ***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
268s ***** error<unifstat: A and B must not be complex.> unifstat (1, i)
268s ***** error<unifstat: A and B must be of common size or scalars.> ...
268s  unifstat (ones (3), ones (2))
268s ***** error<unifstat: A and B must be of common size or scalars.> ...
268s  unifstat (ones (2), ones (3))
268s ***** test
268s  a = 1:6;
268s  b = 2:2:12;
268s  [m, v] = unifstat (a, b);
268s  expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
268s  expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  a = 1:6;
268s  [m, v] = unifstat (a, 10);
268s  expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
268s  expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/burrstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/burrstat.m
268s ***** error<burrstat: function called with too few input arguments.> burrstat ()
268s ***** error<burrstat: function called with too few input arguments.> burrstat (1)
268s ***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
268s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
268s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
268s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
268s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
268s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
268s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
268s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
268s  burrstat (ones (3), ones (2), 3)
268s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
268s  burrstat (ones (2), 2, ones (3))
268s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
268s  burrstat (1, ones (2), ones (3))
268s ***** test
268s  [m, v] = burrstat (1, 2, 5);
268s  assert (m, 0.4295, 1e-4);
268s  assert (v, 0.0655, 1e-4);
268s ***** test
268s  [m, v] = burrstat (1, 1, 1);
268s  assert (m, Inf);
268s  assert (v, Inf);
268s ***** test
268s  [m, v] = burrstat (2, 4, 1);
268s  assert (m, 2.2214, 1e-4);
268s  assert (v, 1.3484, 1e-4);
268s 15 tests, 15 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/chi2stat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/chi2stat.m
268s ***** error<chi2stat: function called with too few input arguments.> chi2stat ()
268s ***** error<chi2stat: DF must be numeric.> chi2stat ({})
268s ***** error<chi2stat: DF must be numeric.> chi2stat ("")
268s ***** error<chi2stat: DF must not be complex.> chi2stat (i)
268s ***** test
268s  df = 1:6;
268s  [m, v] = chi2stat (df);
268s  assert (m, df);
268s  assert (v, [2, 4, 6, 8, 10, 12], 0.001);
268s 5 tests, 5 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/tristat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/tristat.m
268s ***** error<tristat: function called with too few input arguments.> tristat ()
268s ***** error<tristat: function called with too few input arguments.> tristat (1)
268s ***** error<tristat: function called with too few input arguments.> tristat (1, 2)
268s ***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
268s ***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
268s ***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
268s ***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
268s ***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
268s ***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
268s ***** test
268s  a = 1:5;
268s  b = 3:7;
268s  c = 5:9;
268s  [m, v] = tristat (a, b, c);
268s  expected_m = [3, 4, 5, 6, 7];
268s  assert (m, expected_m);
268s  assert (v, ones (1, 5) * (2/3));
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/plstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/plstat.m
268s ***** shared x, Fx
268s  x = [0, 1, 3, 4, 7, 10];
268s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
268s ***** assert (plstat (x, Fx), 4.15)
268s ***** test
268s  [m, v] = plstat (x, Fx);
268s  assert (v, 10.3775, 1e-14)
268s ***** error<plstat: function called with too few input arguments.> plstat ()
268s ***** error<plstat: function called with too few input arguments.> plstat (1)
268s ***** error<plstat: X and FX must be vectors of equal size.> ...
268s  plstat ([0, 1, 2], [0, 1])
268s ***** error<plstat: X and FX must be at least two-elements long.> ...
268s  plstat ([0], [1])
268s ***** error<plstat: FX must be bounded in the range> ...
268s  plstat ([0, 1, 2], [0, 1, 1.5])
268s ***** error<plstat: FX must be bounded in the range> ...
268s  plstat ([0, 1, 2], [0, i, 1])
268s ***** error<plstat: X and FX must not be complex.> ...
268s  plstat ([0, i, 2], [0, 0.5, 1])
268s ***** error<plstat: X and FX must not be complex.> ...
268s  plstat ([0, i, 2], [0, 0.5i, 1])
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/bisastat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/bisastat.m
268s ***** error<bisastat: function called with too few input arguments.> bisastat ()
268s ***** error<bisastat: function called with too few input arguments.> bisastat (1)
268s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
268s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
268s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
268s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
268s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
268s  bisastat (ones (3), ones (2))
268s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
268s  bisastat (ones (2), ones (3))
268s ***** test
268s  beta = 1:6;
268s  gamma = 1:0.2:2;
268s  [m, v] = bisastat (beta, gamma);
268s  expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
268s  expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
268s  assert (m, expected_m, 1e-2);
268s  assert (v, expected_v, 1e-3);
268s ***** test
268s  beta = 1:6;
268s  [m, v] = bisastat (beta, 1.5);
268s  expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
268s  expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
268s  assert (m, expected_m, 1e-3);
268s  assert (v, expected_v, 1e-4);
268s 10 tests, 10 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/binostat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/binostat.m
268s ***** error<binostat: function called with too few input arguments.> binostat ()
268s ***** error<binostat: function called with too few input arguments.> binostat (1)
268s ***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
268s ***** error<binostat: N and PS must be numeric.> binostat (1, "")
268s ***** error<binostat: N and PS must not be complex.> binostat (i, 2)
268s ***** error<binostat: N and PS must not be complex.> binostat (1, i)
268s ***** error<binostat: N and PS must be of common size or scalars.> ...
268s  binostat (ones (3), ones (2))
268s ***** error<binostat: N and PS must be of common size or scalars.> ...
268s  binostat (ones (2), ones (3))
268s ***** test
268s  n = 1:6;
268s  ps = 0:0.2:1;
268s  [m, v] = binostat (n, ps);
268s  expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
268s  expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  n = 1:6;
268s  [m, v] = binostat (n, 0.5);
268s  expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
268s  expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
268s  assert (m, expected_m, 0.001);
268s  assert (v, expected_v, 0.001);
268s ***** test
268s  n = [-Inf -3 5 0.5 3 NaN 100, Inf];
268s  [m, v] = binostat (n, 0.5);
268s  assert (isnan (m), [true true false true false true false false])
268s  assert (isnan (v), [true true false true false true false false])
268s  assert (m(end), Inf);
268s  assert (v(end), Inf);
268s ***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
268s ***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
268s ***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
268s ***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
268s 15 tests, 15 passed, 0 known failure, 0 skipped
268s [inst/dist_stat/tlsstat.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_stat/tlsstat.m
268s ***** error<tlsstat: function called with too few input arguments.> tlsstat ()
268s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
268s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
268s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
268s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
268s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
268s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
268s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
268s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
268s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
268s  tlsstat (ones (3), ones (2), 1)
268s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
268s  tlsstat (ones (2), 1, ones (3))
268s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
268s  tlsstat (1, ones (2), ones (3))
268s ***** test
268s  [m, v] = tlsstat (0, 1, 0);
268s  assert (m, NaN);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (0, 1, 1);
268s  assert (m, NaN);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (2, 1, 1);
268s  assert (m, NaN);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (-2, 1, 1);
268s  assert (m, NaN);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (0, 1, 2);
268s  assert (m, 0);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (2, 1, 2);
268s  assert (m, 2);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (-2, 1, 2);
268s  assert (m, -2);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (0, 2, 2);
268s  assert (m, 0);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (2, 2, 2);
268s  assert (m, 2);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (-2, 2, 2);
268s  assert (m, -2);
268s  assert (v, NaN);
268s ***** test
268s  [m, v] = tlsstat (0, 1, 3);
268s  assert (m, 0);
268s  assert (v, 3);
268s ***** test
268s  [m, v] = tlsstat (0, 2, 3);
268s  assert (m, 0);
268s  assert (v, 6);
268s ***** test
268s  [m, v] = tlsstat (2, 1, 3);
268s  assert (m, 2);
268s  assert (v, 3);
268s ***** test
268s  [m, v] = tlsstat (2, 2, 3);
268s  assert (m, 2);
268s  assert (v, 6);
268s ***** test
268s  [m, v] = tlsstat (-2, 1, 3);
268s  assert (m, -2);
268s  assert (v, 3);
268s ***** test
268s  [m, v] = tlsstat (-2, 2, 3);
268s  assert (m, -2);
268s  assert (v, 6);
268s 28 tests, 28 passed, 0 known failure, 0 skipped
268s [inst/logit.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/logit.m
268s ***** test
268s  p = [0.01:0.01:0.99];
268s  assert (logit (p), log (p ./ (1-p)), 25*eps);
268s ***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
268s ***** error logit ()
268s ***** error logit (1, 2)
268s 4 tests, 4 passed, 0 known failure, 0 skipped
268s [inst/rangesearch.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/rangesearch.m
268s ***** demo
268s  ## Generate 1000 random 2D points from each of five distinct multivariate
268s  ## normal distributions that form five separate classes
268s  N = 1000;
268s  d = 10;
268s  randn ("seed", 5);
268s  X1 = mvnrnd (d * [0, 0], eye (2), 1000);
268s  randn ("seed", 6);
268s  X2 = mvnrnd (d * [1, 1], eye (2), 1000);
268s  randn ("seed", 7);
268s  X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
268s  randn ("seed", 8);
268s  X4 = mvnrnd (d * [1, -1], eye (2), 1000);
268s  randn ("seed", 8);
268s  X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
268s  X = [X1; X2; X3; X4; X5];
268s 
268s  ## For each point in X, find the points in X that are within a radius d
268s  ## away from the points in X.
268s  Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");
268s 
268s  ## Select the first point in X (corresponding to the first class) and find
268s  ## its nearest neighbors within the radius d.  Display these points in
268s  ## one color and the remaining points in a different color.
268s  x = X(1,:);
268s  nearestPoints = X (Idx{1},:);
268s  nonNearestIdx = true (size (X, 1), 1);
268s  nonNearestIdx(Idx{1}) = false;
268s 
268s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
268s  hold on
268s  scatter (nearestPoints(:,1),nearestPoints(:,2))
268s  scatter (x(1), x(2), "black", "filled")
268s  hold off
268s 
268s  ## Select the last point in X (corresponding to the fifth class) and find
268s  ## its nearest neighbors within the radius d.  Display these points in
268s  ## one color and the remaining points in a different color.
268s  x = X(end,:);
268s  nearestPoints = X (Idx{1},:);
268s  nonNearestIdx = true (size (X, 1), 1);
268s  nonNearestIdx(Idx{1}) = false;
268s 
268s  figure
268s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
268s  hold on
268s  scatter (nearestPoints(:,1),nearestPoints(:,2))
268s  scatter (x(1), x(2), "black", "filled")
268s  hold off
268s ***** shared x, y, X, Y
268s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
268s  y = [2, 3, 4; 1, 4, 3];
268s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
268s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4);
268s  assert (idx, {[1; 4; 2]; [1; 4]});
268s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
268s  assert (idx, {[1, 4, 2]; [1, 4]});
268s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
268s  assert (idx, {[1; 4; 2]; [1; 4]});
268s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
268s  assert (idx, {[1; 4; 2]; [1; 4]});
268s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
268s  assert (idx, {[1; 2; 4]; [1; 4]});
268s  assert (D, {[1.7321; 3.4641; 3.3166]; [2; 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
268s                          "SortIndices", false);
268s  assert (idx, {[1, 2, 4]; [1, 4]});
268s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
268s ***** test
268s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
268s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
268s  assert (idx, {[1, 4, 2]; [1, 4]});
268s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
268s ***** test
268s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
268s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
268s                          "NSMethod", "exhaustive");
268s  assert (idx, {[1, 4, 2]; [1, 4]});
268s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
268s                          "NSMethod", "exhaustive");
268s  assert (idx, {[1, 4, 2]; [1, 4]});
268s  assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
268s                          "NSMethod", "exhaustive", "SortIndices", false);
268s  assert (idx, {[1, 2, 4]; [1, 4]});
268s  assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (X, Y, 4);
268s  assert (idx, {[1; 2]; [1; 2; 3]});
268s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (X, Y, 2);
268s  assert (idx, {[1]; [1; 2]});
268s  assert (D, {[1.4142]; [1.4142; 1.4142]}, 1e-4);
268s ***** test
268s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
268s  [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
268s  assert (idx, {[1, 2]; [1, 2, 3]});
268s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
268s  assert (idx, {[1; 2]; [1; 2; 3]});
268s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
268s ***** test
268s  [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
268s                          "NSMethod", "exhaustive");
268s  assert (idx, {[1, 2]; [1, 2, 3]});
268s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
268s ***** error<rangesearch: too few input arguments.> rangesearch (1)
268s ***** error<rangesearch: too few input arguments.> rangesearch (ones (4, 5))
268s ***** error<rangesearch: too few input arguments.> ...
268s  rangesearch (ones (4, 5), ones (4))
268s ***** error<rangesearch: number of columns in X and Y must match.> ...
268s  rangesearch (ones (4, 5), ones (4), 1)
268s ***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
268s  rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
268s ***** error<rangesearch: only a single distance parameter can be defined.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
268s ***** error<rangesearch: invalid value of Minkowski Exponent.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "P", -2)
268s ***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
268s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
268s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
268s ***** error<rangesearch: invalid value of bucketsize.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
268s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
268s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
268s 33 tests, 33 passed, 0 known failure, 0 skipped
268s [inst/boxplot.m]
268s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/boxplot.m
268s ***** demo
268s  axis ([0, 3]);
268s  randn ("seed", 1);    # for reproducibility
268s  girls = randn (10, 1) * 5 + 140;
268s  randn ("seed", 2);    # for reproducibility
268s  boys = randn (13, 1) * 8 + 135;
268s  boxplot ({girls, boys});
268s  set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
268s  title ("Grade 3 heights");
268s ***** demo
268s  randn ("seed", 7);    # for reproducibility
268s  A = randn (10, 1) * 5 + 140;
268s  randn ("seed", 8);    # for reproducibility
268s  B = randn (25, 1) * 8 + 135;
268s  randn ("seed", 9);    # for reproducibility
268s  C = randn (20, 1) * 6 + 165;
268s  data = [A; B; C];
268s  groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
268s  labels = {"Team A", "Team B", "Team C"};
268s  pos = [2, 1, 3];
268s  boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
268s           "OutlierTags", "on", "BoxStyle", "filled");
268s  title ("Example of Group splitting with paired vectors");
268s ***** demo
268s  randn ("seed", 1);    # for reproducibility
268s  data = randn (100, 9);
268s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
268s           "colors", "ygcwkmb", "whisker", 1.2);
268s  title ("Example of different colors specified with characters");
268s ***** demo
268s  randn ("seed", 5);    # for reproducibility
268s  data = randn (100, 13);
268s  colors = [0.7 0.7 0.7; ...
268s            0.0 0.4 0.9; ...
268s            0.7 0.4 0.3; ...
268s            0.7 0.1 0.7; ...
268s            0.8 0.7 0.4; ...
268s            0.1 0.8 0.5; ...
268s            0.9 0.9 0.2];
268s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
268s           "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
268s  title ("Example of different colors specified as RGB values");
268s ***** error <numerical array or cell array containing> boxplot ("a")
268s ***** error <data cells must contain> boxplot ({[1 2 3], "a"})
268s ***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
268s ***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
268s ***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
268s ***** error <illegal Notch value> boxplot ([1:10], "notch", i)
268s ***** error <illegal Notch value> boxplot ([1:10], "notch", {})
268s ***** error <must be character> boxplot (1, "symbol", 1)
269s ***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
269s ***** error <illegal Orientation value> boxplot (1, "orientation", {})
269s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
269s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
269s ***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
269s ***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
269s ***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
269s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
269s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
269s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
269s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
269s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
269s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
269s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
269s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
269s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
269s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
269s ***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
269s ***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
269s ***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
269s ***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
269s ***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
269s ***** error <with the formalism> boxplot (rand (3, 3), [1 2])
269s ***** test
269s  hf = figure ("visible", "off");
269s  unwind_protect
269s    [a, b] = boxplot (rand (10, 3));
269s    assert (size (a), [7, 3]);
269s    assert (numel (b.box), 3);
269s    assert (numel (b.whisker), 12);
269s    assert (numel (b.median), 3);
269s  unwind_protect_cleanup
269s    close (hf);
269s  end_unwind_protect
269s ***** test
269s  hf = figure ("visible", "off");
269s  unwind_protect
269s    [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
269s    assert (numel (b.box_fill), 3);
269s  unwind_protect_cleanup
269s    close (hf);
269s  end_unwind_protect
269s ***** test
269s  hf = figure ("visible", "off");
269s  unwind_protect
269s    hold on
269s    [a, b] = boxplot (rand (10, 3));
269s    assert (ishold, true);
269s  unwind_protect_cleanup
269s    close (hf);
269s  end_unwind_protect
269s 34 tests, 34 passed, 0 known failure, 0 skipped
269s [inst/multcompare.m]
269s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/multcompare.m
269s ***** demo
269s 
269s  ## Demonstration using balanced one-way ANOVA from anova1
269s 
269s  x = ones (50, 4) .* [-2, 0, 1, 5];
269s  randn ("seed", 1);    # for reproducibility
269s  x = x + normrnd (0, 2, 50, 4);
269s  groups = {"A", "B", "C", "D"};
269s  [p, tbl, stats] = anova1 (x, groups, "off");
269s  multcompare (stats);
269s ***** demo
269s 
269s  ## Demonstration using unbalanced one-way ANOVA example from anovan
269s 
269s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
269s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
269s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
269s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
269s         25.694 ]';
269s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
269s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
269s 
269s  [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
269s                                   "ControlGroup", 1, "display", "on")
269s 
269s ***** demo
269s 
269s  ## Demonstration using factorial ANCOVA example from anovan
269s 
269s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
269s  97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
269s  81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
269s  84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
269s  100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
269s  91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
269s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
269s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
269s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
269s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
269s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
269s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
269s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
269s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
269s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
269s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
269s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
269s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
269s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
269s  58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
269s  75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
269s 
269s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
269s                             [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
269s                             "sstype", "h", "display", "off", "contrasts", ...
269s                             {"simple","poly",""});
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
269s                                   "display", "on")
269s 
269s ***** demo
269s 
269s  ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
269s  ## squares to account for heteroskedasticity.
269s 
269s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
269s       2, 2, 2, 2, 2, 2, 2, 2, ...
269s       3, 3, 3, 3, 3, 3, 3, 3]';
269s 
269s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
269s       10, 25, 66, 43, 47, 56,  6, 39, ...
269s       11, 39, 26, 35, 25, 14, 24, 17]';
269s 
269s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
269s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
269s  b = polyfit (fitted, abs (STATS.resid), 1);
269s  v = polyval (b, fitted);  # Variance as a function of the fitted values
269s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
269s  [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
269s ***** demo
269s 
269s  ## Demonstration of p-value adjustments to control the false discovery rate
269s  ## Data from Westfall (1997) JASA. 92(437):299-306
269s 
269s  p = [.005708; .023544; .024193; .044895; ...
269s        .048805; .221227; .395867; .693051; .775755];
269s 
269s  padj = multcompare(p,'ctype','fdr')
269s ***** test
269s 
269s  ## Tests using unbalanced one-way ANOVA example from anovan and anova1
269s 
269s  ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"
269s 
269s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
269s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
269s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
269s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
269s         25.694 ]';
269s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
269s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
269s 
269s  [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
269s                                   "display", "off");
269s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
269s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
269s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
269s  assert (C(5,6), 0.546591417210693, 1e-09);
269s  assert (C(6,6), 0.0845897945254446, 1e-09);
269s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
269s  assert (C(8,6), 0.188873478781067, 1e-09);
269s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
269s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
269s  assert (M(1,1), 10, 1e-09);
269s  assert (M(2,1), 18, 1e-09);
269s  assert (M(3,1), 19, 1e-09);
269s  assert (M(4,1), 21.0001428571429, 1e-09);
269s  assert (M(5,1), 29.0001111111111, 1e-09);
269s  assert (M(1,2), 1.0177537954095, 1e-09);
269s  assert (M(2,2), 1.28736803631001, 1e-09);
269s  assert (M(3,2), 1.0177537954095, 1e-09);
269s  assert (M(4,2), 1.0880245732889, 1e-09);
269s  assert (M(5,2), 0.959547480416536, 1e-09);
269s 
269s  ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
269s                                   "display", "off");
269s  assert (C(1,6), 4.08303457454140e-05, 1e-09);
269s  assert (C(2,6), 1.04587348240817e-06, 1e-09);
269s  assert (C(3,6), 1.06397381604573e-07, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
269s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
269s  assert (C(6,6), 1.05737243156806e-01, 1e-09);
269s  assert (C(7,6), 2.36859139493832e-07, 1e-09);
269s  assert (C(8,6), 2.09859420867852e-01, 1e-09);
269s  assert (C(9,6), 1.36324670121399e-07, 1e-09);
269s  assert (C(10,6), 7.40712246958735e-06, 1e-09);
269s 
269s  ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
269s                                   "display", "off");
269s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
269s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
269s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
269s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
269s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
269s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
269s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
269s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
269s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
269s 
269s  ## Compare "holm" adjusted p-values to those obtained using p.adjust in R
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
269s                                   "display", "off");
269s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
269s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
269s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
269s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
269s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
269s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
269s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
269s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
269s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
269s 
269s  ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
269s                                   "display", "off");
269s  assert (C(1,6), 0.00108105386141085, 1e-09);
269s  assert (C(2,6), 2.7779386789517e-05, 1e-09);
269s  assert (C(3,6), 1.3599854038198e-06, 1e-09);
269s  assert (C(4,6), 7.58830197867751e-13, 1e-09);
269s  assert (C(5,6), 0.984039948220281, 1e-09);
269s  assert (C(6,6), 0.539077018557706, 1e-09);
269s  assert (C(7,6), 5.59475764460574e-06, 1e-09);
269s  assert (C(8,6), 0.771173490574105, 1e-09);
269s  assert (C(9,6), 2.52838425729905e-06, 1e-09);
269s  assert (C(10,6), 0.000200719143889168, 1e-09);
269s 
269s  ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R
269s 
269s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
269s                                   "display", "off");
269s  assert (C(1,6), 2.85812420217898e-04, 1e-09);
269s  assert (C(2,6), 5.22936741204085e-06, 1e-09);
269s  assert (C(3,6), 2.12794763209146e-07, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
269s  assert (C(5,6), 1.00000000000000e+00, 1e-09);
269s  assert (C(6,6), 8.45897945254446e-01, 1e-09);
269s  assert (C(7,6), 9.47436557975328e-07, 1e-09);
269s  assert (C(8,6), 1.00000000000000e+00, 1e-09);
269s  assert (C(9,6), 4.08974010364197e-07, 1e-09);
269s  assert (C(10,6), 4.44427348175241e-05, 1e-09);
269s 
269s  ## Test for anova1 ("equal")- comparison of results from Matlab
269s 
269s  [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
269s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
269s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
269s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
269s  assert (C(5,6), 0.546591417210693, 1e-09);
269s  assert (C(6,6), 0.0845897945254446, 1e-09);
269s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
269s  assert (C(8,6), 0.188873478781067, 1e-09);
269s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
269s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
269s  assert (M(1,1), 10, 1e-09);
269s  assert (M(2,1), 18, 1e-09);
269s  assert (M(3,1), 19, 1e-09);
269s  assert (M(4,1), 21.0001428571429, 1e-09);
269s  assert (M(5,1), 29.0001111111111, 1e-09);
269s  assert (M(1,2), 1.0177537954095, 1e-09);
269s  assert (M(2,2), 1.28736803631001, 1e-09);
269s  assert (M(3,2), 1.0177537954095, 1e-09);
269s  assert (M(4,2), 1.0880245732889, 1e-09);
269s  assert (M(5,2), 0.959547480416536, 1e-09);
269s 
269s  ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
269s  [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
269s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 0.001247025266382, 1e-09);
269s  assert (C(2,6), 0.000018037115146, 1e-09);
269s  assert (C(3,6), 0.000002974595187, 1e-09);
269s  assert (C(4,6), 0.000000000786046, 1e-09);
269s  assert (C(5,6), 0.5693192886650109, 1e-09);
269s  assert (C(6,6), 0.110501699029776, 1e-09);
269s  assert (C(7,6), 0.000131226488700, 1e-09);
269s  assert (C(8,6), 0.1912101409715992, 1e-09);
269s  assert (C(9,6), 0.000005385256394, 1e-09);
269s  assert (C(10,6), 0.000074089106171, 1e-09);
269s ***** test
269s 
269s  ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
269s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
269s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
269s  [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
269s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
269s                                   "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 1.49311100811177e-05, 1e-09);
269s  assert (C(2,6), 2.20506904243535e-07, 1e-09);
269s  assert (C(3,6), 0.00449897860490058, 1e-09);
269s  assert (M(1,1), 6.25, 1e-09);
269s  assert (M(2,1), 4.75, 1e-09);
269s  assert (M(3,1), 4, 1e-09);
269s  assert (M(1,2), 0.152145154862547, 1e-09);
269s  assert (M(2,2), 0.152145154862547, 1e-09);
269s  assert (M(3,2), 0.152145154862547, 1e-09);
269s ***** test
269s 
269s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
269s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
269s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
269s  [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
269s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
269s                                   "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 0.000020799832702, 1e-09);
269s  assert (C(2,6), 0.000000035812410, 1e-09);
269s  assert (C(3,6), 0.003038942449215, 1e-09);
269s ***** test
269s 
269s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
269s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
269s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
269s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
269s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
269s  [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
269s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
269s                                   "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 0.261031111511073, 1e-09);
269s  assert (C(2,6), 0.065879755907745, 1e-09);
269s  assert (C(3,6), 0.241874613529270, 1e-09);
269s ***** shared visibility_setting
269s  visibility_setting = get (0, "DefaultFigureVisible");
269s ***** test
269s  set (0, "DefaultFigureVisible", "off");
269s 
269s  ## Test for kruskalwallis - comparison with results from MATLAB
269s  data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
269s          4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
269s  group = [1:3] .* ones (10,3);
269s  [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
269s  C = multcompare (STATS, "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 0.000163089828959986, 1e-09);
269s  assert (C(2,6), 0.630298044801257, 1e-09);
269s  assert (C(3,6), 0.00100567660695682, 1e-09);
269s  C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
269s  assert (C(1,6), 0.000489269486879958, 1e-09);
269s  assert (C(2,6), 1, 1e-09);
269s  assert (C(3,6), 0.00301702982087047, 1e-09);
269s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
269s  assert (C(1,6), 0.000819054880289573, 1e-09);
269s  assert (C(2,6), 0.890628039849261, 1e-09);
269s  assert (C(3,6), 0.00447816059021654, 1e-09);
269s  set (0, "DefaultFigureVisible", visibility_setting);
269s ***** test
269s  set (0, "DefaultFigureVisible", "off");
269s  ## Test for friedman - comparison with results from MATLAB
269s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
269s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
269s  [P, ATAB, STATS] = friedman (popcorn, 3, "off");
269s  C = multcompare(STATS, "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 0.227424558028569, 1e-09);
269s  assert (C(2,6), 0.0327204848315735, 1e-09);
269s  assert (C(3,6), 0.353160353315988, 1e-09);
269s  C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
269s  assert (C(1,6), 0.682273674085708, 1e-09);
269s  assert (C(2,6), 0.0981614544947206, 1e-09);
269s  assert (C(3,6), 1, 1e-09);
269s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
269s  assert (C(1,6), 0.482657360384373, 1e-09);
269s  assert (C(2,6), 0.102266573027672, 1e-09);
269s  assert (C(3,6), 0.649836502233148, 1e-09);
269s  set (0, "DefaultFigureVisible", visibility_setting);
269s ***** test
269s  set (0, "DefaultFigureVisible", "off");
269s  ## Test for fitlm - same comparisons as for first anovan example
269s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
269s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
269s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
269s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
269s         25.694 ]';
269s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
269s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
269s                       "contrasts","simple");
269s  [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
269s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
269s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
269s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
269s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
269s  assert (C(5,6), 0.546591417210693, 1e-09);
269s  assert (C(6,6), 0.0845897945254446, 1e-09);
269s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
269s  assert (C(8,6), 0.188873478781067, 1e-09);
269s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
269s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
269s  assert (M(1,1), 10, 1e-09);
269s  assert (M(2,1), 18, 1e-09);
269s  assert (M(3,1), 19, 1e-09);
269s  assert (M(4,1), 21.0001428571429, 1e-09);
269s  assert (M(5,1), 29.0001111111111, 1e-09);
269s  assert (M(1,2), 1.0177537954095, 1e-09);
269s  assert (M(2,2), 1.28736803631001, 1e-09);
269s  assert (M(3,2), 1.0177537954095, 1e-09);
269s  assert (M(4,2), 1.0880245732889, 1e-09);
269s  assert (M(5,2), 0.959547480416536, 1e-09);
269s  set (0, "DefaultFigureVisible", visibility_setting);
270s ***** test
270s  ## Test p-value adjustments compared to R stats package function p.adjust
270s  ## Data from Westfall (1997) JASA. 92(437):299-306
270s  p = [.005708; .023544; .024193; .044895; ...
270s        .048805; .221227; .395867; .693051; .775755];
270s  padj = multcompare (p);
270s  assert (padj(1), 0.051372, 1e-06);
270s  assert (padj(2), 0.188352, 1e-06);
270s  assert (padj(3), 0.188352, 1e-06);
270s  assert (padj(4), 0.269370, 1e-06);
270s  assert (padj(5), 0.269370, 1e-06);
270s  assert (padj(6), 0.884908, 1e-06);
270s  assert (padj(7), 1.000000, 1e-06);
270s  assert (padj(8), 1.000000, 1e-06);
270s  assert (padj(9), 1.000000, 1e-06);
270s  padj = multcompare(p,'ctype','holm');
270s  assert (padj(1), 0.051372, 1e-06);
270s  assert (padj(2), 0.188352, 1e-06);
270s  assert (padj(3), 0.188352, 1e-06);
270s  assert (padj(4), 0.269370, 1e-06);
270s  assert (padj(5), 0.269370, 1e-06);
270s  assert (padj(6), 0.884908, 1e-06);
270s  assert (padj(7), 1.000000, 1e-06);
270s  assert (padj(8), 1.000000, 1e-06);
270s  assert (padj(9), 1.000000, 1e-06);
270s  padj = multcompare(p,'ctype','hochberg');
270s  assert (padj(1), 0.051372, 1e-06);
270s  assert (padj(2), 0.169351, 1e-06);
270s  assert (padj(3), 0.169351, 1e-06);
270s  assert (padj(4), 0.244025, 1e-06);
270s  assert (padj(5), 0.244025, 1e-06);
270s  assert (padj(6), 0.775755, 1e-06);
270s  assert (padj(7), 0.775755, 1e-06);
270s  assert (padj(8), 0.775755, 1e-06);
270s  assert (padj(9), 0.775755, 1e-06);
270s  padj = multcompare(p,'ctype','fdr');
270s  assert (padj(1), 0.0513720, 1e-07);
270s  assert (padj(2), 0.0725790, 1e-07);
270s  assert (padj(3), 0.0725790, 1e-07);
270s  assert (padj(4), 0.0878490, 1e-07);
270s  assert (padj(5), 0.0878490, 1e-07);
270s  assert (padj(6), 0.3318405, 1e-07);
270s  assert (padj(7), 0.5089719, 1e-07);
270s  assert (padj(8), 0.7757550, 1e-07);
270s  assert (padj(9), 0.7757550, 1e-07);
270s 8 tests, 8 passed, 0 known failure, 0 skipped
270s [inst/vartestn.m]
270s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/vartestn.m
270s ***** demo
270s  ## Test the null hypothesis that the variances are equal across the five
270s  ## columns of data in the students’ exam grades matrix, grades.
270s 
270s  load examgrades
270s  vartestn (grades)
270s ***** demo
270s  ## Test the null hypothesis that the variances in miles per gallon (MPG) are
270s  ## equal across different model years.
270s 
270s  load carsmall
270s  vartestn (MPG, Model_Year)
270s ***** demo
270s  ## Use Levene’s test to test the null hypothesis that the variances in miles
270s  ## per gallon (MPG) are equal across different model years.
270s 
270s  load carsmall
270s  p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
270s ***** demo
270s  ## Test the null hypothesis that the variances are equal across the five
270s  ## columns of data in the students’ exam grades matrix, grades, using the
270s  ## Brown-Forsythe test.  Suppress the display of the summary table of
270s  ## statistics and the box plot.
270s 
270s  load examgrades
270s  [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
270s ***** error<vartestn: too few input arguments.> vartestn ();
270s ***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
270s ***** error<vartestn: if X is a vector then a group vector is required.> ...
270s  vartestn ([1, 2, 3, 4, 5, 6, 7]);
270s ***** error<vartestn: if X is a vector then a group vector is required.> ...
270s  vartestn ([1, 2, 3, 4, 5, 6, 7], []);
270s ***** error<vartestn: if X is a vector then a group vector is required.> ...
270s  vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
270s ***** error<vartestn: if X is a vector then a group vector is required.> ...
270s  vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
270s ***** error<vartestn: invalid value for display.> ...
270s  vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
270s ***** error<vartestn: invalid value for display.> ...
270s  vartestn (ones (50,3), "Display", "some");
270s ***** error<vartestn: invalid value for testtype.> ...
270s  vartestn (ones (50,3), "Display", "off", "testtype", "some");
270s ***** error<vartestn: optional arguments must be in name/value pairs.> ...
270s  vartestn (ones (50,3), [], "som");
270s ***** error<vartestn: invalid name for optional arguments.> ...
270s  vartestn (ones (50,3), [], "some", "some");
270s ***** error<vartestn: columns in X and GROUP length do not match.> ...
270s  vartestn (ones (50,3), [1, 2], "Display", "off");
270s ***** test
270s  load examgrades
270s  [p, stat] = vartestn (grades, "Display", "off");
270s  assert (p, 7.908647337018238e-08, 1e-14);
270s  assert (stat.chisqstat, 38.7332, 1e-4);
270s  assert (stat.df, 4);
270s ***** test
270s  load examgrades
270s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
270s  assert (p, 9.523239714592791e-07, 1e-14);
270s  assert (stat.fstat, 8.5953, 1e-4);
270s  assert (stat.df, [4, 595]);
270s ***** test
270s  load examgrades
270s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
270s  assert (p, 7.219514351897161e-07, 1e-14);
270s  assert (stat.fstat, 8.7503, 1e-4);
270s  assert (stat.df, [4, 595]);
270s ***** test
270s  load examgrades
270s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
270s  assert (p, 1.312093241723211e-06, 1e-14);
270s  assert (stat.fstat, 8.4160, 1e-4);
270s  assert (stat.df, [4, 595]);
270s ***** test
270s  load examgrades
270s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
270s  assert (p, 8.235660885480556e-07, 1e-14);
270s  assert (stat.fstat, 8.6766, 1e-4);
270s  assert (stat.df, [4, 595]);
270s 17 tests, 17 passed, 0 known failure, 0 skipped
270s [inst/sampsizepwr.m]
270s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/sampsizepwr.m
270s ***** demo
270s  ## Compute the mean closest to 100 that can be determined to be
270s  ## significantly different from 100 using a t-test with a sample size
270s  ## of 60 and a power of 0.8.
270s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
270s  disp (mu1);
270s ***** demo
270s  ## Compute the sample sizes required to distinguish mu0 = 100 from
270s  ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
270s  ## smaller sample sizes of 1.5 and a power of 0.6.
270s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
270s ***** demo
270s  ## Compute the sample size N required to distinguish p=.26 from p=.2
270s  ## with a binomial test.  The result is approximate, so make a plot to
270s  ## see if any smaller N values also have the required power of 0.6.
270s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
270s  nn = 1:250;
270s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
270s  Nexact = min (nn(pwr >= 0.6));
270s  plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
270s  grid on
270s ***** demo
270s  ## The company must test 52 bottles to detect the difference between a mean
270s  ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
270s  ## to visualize how the sample size affects the power of the test.
270s 
270s  nout = sampsizepwr('t',[100 5],102,0.80);
270s  nn = 1:100;
270s  pwrout = sampsizepwr('t',[100 5],102,[],nn);
270s 
270s  figure;
270s  plot (nn, pwrout, "b-", nout, 0.8, "ro")
270s  title ("Power versus Sample Size")
270s  xlabel ("Sample Size")
270s  ylabel ("Power")
270s ***** error<sampsizepwr: test type must be a non-empty string.> ...
270s  out = sampsizepwr ([], [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: test type must be a non-empty string.> ...
270s  out = sampsizepwr (3, [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: test type must be a non-empty string.> ...
270s  out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: invalid test type.> ...
270s  out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: parameters must be numeric.> ...
270s  out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("z", 100, [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("t", 100, [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("t2", 60, [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
270s  out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
270s  [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
270s  [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
270s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
270s  [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
270s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
270s  [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
270s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
270s  [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("var", 0, [], 0.8, 60);
270s ***** error<sampsizepwr: negative or zero variance.> ...
270s  out = sampsizepwr ("var", -5, [], 0.8, 60);
270s ***** error<sampsizepwr: out of range probability.> ...
270s  out = sampsizepwr ("p", 0, [], 0.8, 60);
270s ***** error<sampsizepwr: out of range probability.> ...
270s  out = sampsizepwr ("p", 1.2, [], 0.8, 60);
270s ***** error<sampsizepwr: out of range regression coefficient.> ...
270s  out = sampsizepwr ("r", -1.5, [], 0.8, 60);
270s ***** error<sampsizepwr: out of range regression coefficient.> ...
270s  out = sampsizepwr ("r", -1, [], 0.8, 60);
270s ***** error<sampsizepwr: out of range regression coefficient.> ...
270s  out = sampsizepwr ("r", 1.2, [], 0.8, 60);
270s ***** error<sampsizepwr: regression coefficient must not be 0.> ...
270s  out = sampsizepwr ("r", 0, [], 0.8, 60);
270s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
270s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
270s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
270s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
270s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
270s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
270s ***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
270s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
270s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
270s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
270s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
270s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
270s  out = sampsizepwr ("z", [100, 5], [], [], 60);
270s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
270s  out = sampsizepwr ("z", [100, 5], 110, [], []);
270s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
270s  out = sampsizepwr ("z", [100, 5], [], 0.8, []);
270s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
270s  out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
270s ***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
270s  out = sampsizepwr ("z", [100, 5], "mu", [], 60);
270s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
270s  out = sampsizepwr ("var", 5, -1, [], 60);
270s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
270s  out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
270s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
270s  out = sampsizepwr ("r", 0.8, 1.2, [], 60);
270s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
270s  out = sampsizepwr ("r", 0.8, -1.2, [], 60);
270s ***** error<sampsizepwr: invalid value for POWER.> ...
270s  out = sampsizepwr ("z", [100, 5], 110, 1.2);
270s ***** error<sampsizepwr: invalid value for POWER.> ...
270s  out = sampsizepwr ("z", [100, 5], 110, 0);
270s ***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
270s  out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
270s ***** error<sampsizepwr: input arguments size mismatch.> ...
270s  out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
270s ***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
270s  out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
270s ***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
270s  out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
270s ***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
270s  out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
270s ***** warning<sampsizepwr: approximate N.> ...
270s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
270s ***** warning<sampsizepwr: approximate N.> ...
270s  Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
270s ***** test
270s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
270s  assert (mu1, 103.67704316, 1e-8);
271s ***** test
271s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
271s  assert (N1, 9);
271s  assert (N2, 14);
271s ***** test
271s  nn = 1:250;
271s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
271s  pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
271s  assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
271s  pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
271s  assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
271s ***** test
271s  nout = sampsizepwr ("t", [100, 5], 102, 0.80);
271s  assert (nout, 52);
271s ***** test
271s  power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
271s  assert (power, 0.5797373588621888, 1e-14);
271s ***** test
271s  nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
271s  assert (nout, 18);
271s ***** test
271s  p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
271s  assert (p1out, 25.65317979360237, 2e-14);
272s ***** test
272s  pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
272s  assert (pwr, 0.716504004686586, 1e-14);
272s ***** test
272s  n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
272s  assert (n, 11);
272s ***** test
272s  [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
272s  assert ([n1, n2], [8, 16]);
273s 68 tests, 68 passed, 0 known failure, 0 skipped
273s [inst/crosstab.m]
273s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/crosstab.m
273s ***** error crosstab ()
273s ***** error crosstab (1)
273s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
273s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
273s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
273s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
273s ***** test
273s  load carbig
273s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
273s  assert (table(2,3,1), 38);
273s  assert (labels{3,3}, "Japan");
273s ***** test
273s  load carbig
273s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
273s  assert (table(2,3,2), 17);
273s  assert (labels{1,3}, "USA");
273s ***** test
273s  x = [1, 1, 2, 3, 1];
273s  y = [1, 2, 5, 3, 1];
273s  t = crosstab (x, y);
273s  assert (t, [2, 1, 0, 0; 0, 0, 0, 1; 0, 0, 1, 0]);
273s ***** test
273s  x = [1, 1, 2, 3, 1];
273s  y = [1, 2, 3, 5, 1];
273s  t = crosstab (x, y);
273s  assert (t, [2, 1, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1]);
273s ***** test
273s  x1 = [1, 3, 7, 7, 8];
273s  x2 = [4, 2, 1, 1, 1];
273s  x3 = [6, 2, 6, 2, NaN];
273s  T1 = [0, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 0];
273s  T2 = [0, 0, 1; 0, 0, 0; 1, 0, 0; 0, 0, 0];
273s  T = zeros (4, 3, 2);
273s  T(:,:,1) = T1;
273s  T(:,:,2) = T2;
273s  t = crosstab (x1, x2, x3);
273s  assert (t, T);
273s ***** test
273s  x = [1, 2, NaN, 1];
273s  y = [1, 2, 3, NaN];
273s  t = crosstab (x, y);
273s  assert (t, [1, 0, 0; 0, 1, 0]);
273s 12 tests, 12 passed, 0 known failure, 0 skipped
273s [inst/fitcsvm.m]
273s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitcsvm.m
273s ***** demo
273s  ## Use a subset of Fisher's iris data set
273s 
273s  load fisheriris
273s  inds = ! strcmp (species, 'setosa');
273s  X = meas(inds, [3,4]);
273s  Y = species(inds);
273s 
273s  ## Train a linear SVM classifier
273s  SVMModel = fitcsvm (X, Y)
273s 
273s  ## Plot a scatter diagram of the data and circle the support vectors.
273s  sv = SVMModel.SupportVectors;
273s  figure
273s  gscatter (X(:,1), X(:,2), Y)
273s  hold on
273s  plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
273s  legend ('versicolor', 'virginica', 'Support Vector')
273s  hold off
273s ***** test
273s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
273s  y = {"a"; "a"; "b"; "b"};
273s  a = fitcsvm (x, y);
273s  assert (class (a), "ClassificationSVM");
273s  assert ({a.X, a.Y}, {x, y})
273s  assert (a.NumObservations, 4)
273s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
273s  assert (a.ModelParameters.SVMtype, "c_svc")
273s  assert (a.ClassNames, {"a"; "b"})
273s ***** test
273s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
273s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
273s  a = fitcsvm (x, y);
273s  assert (class (a), "ClassificationSVM");
273s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
273s  assert (a.ModelParameters.BoxConstraint, 1)
273s  assert (a.ModelParameters.KernelOffset, 0)
273s  assert (a.ClassNames, [1; -1])
273s ***** test
273s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
273s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
273s  a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
273s  "KernelOffset", 2);
273s  assert (class (a), "ClassificationSVM");
273s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
273s  assert (a.ModelParameters.BoxConstraint, 2)
273s  assert (a.ModelParameters.KernelOffset, 2)
273s  assert (isempty (a.Alpha), true)
273s  assert (isempty (a.Beta), false)
273s ***** test
273s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
273s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
273s  a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
273s  assert (class (a), "ClassificationSVM");
273s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
273s  assert (a.ModelParameters.PolynomialOrder, 3)
273s  assert (isempty (a.Alpha), true)
273s  assert (isempty (a.Beta), false)
273s ***** test
273s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
273s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
273s  a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
273s  assert (class (a), "ClassificationSVM");
273s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
273s  assert (a.ModelParameters.PolynomialOrder, 3)
273s  assert (isempty (a.Alpha), false)
273s  assert (isempty (a.Beta), true)
273s ***** test
273s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
273s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
273s  a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
273s  assert (class (a), "ClassificationPartitionedModel");
273s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
273s  assert (a.ModelParameters.PolynomialOrder, 3)
273s  assert (isempty (a.Trained{1}.Alpha), false)
273s  assert (isempty (a.Trained{1}.Beta), true)
273s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
273s warning: called from
273s     cvpartition at line 764 column 19
273s     crossval at line 1300 column 9
273s     fitcsvm at line 284 column 7
273s     __test__ at line 5 column 2
273s     test at line 685 column 11
273s     /tmp/tmp.YdhB1UcfDH at line 558 column 2
273s 
273s ***** error<fitcsvm: too few arguments.> fitcsvm ()
273s ***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
273s ***** error<fitcsvm: Name-Value arguments must be in pairs.>
273s  fitcsvm (ones (4,2), ones (4, 1), 'KFold')
273s ***** error<fitcsvm: number of rows in X and Y must be equal.>
273s  fitcsvm (ones (4,2), ones (3, 1))
273s ***** error<fitcsvm: number of rows in X and Y must be equal.>
273s  fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
273s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
273s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
273s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
273s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
273s ***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
273s  fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
273s 14 tests, 14 passed, 0 known failure, 0 skipped
273s [inst/multiway.m]
273s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/multiway.m
273s ***** test
273s  numbers = [4, 5, 6, 7, 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
273s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
273s ***** test
273s  numbers = [1, 2, 3, 4, 5, 6];
273s  num_parts = 3;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
273s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
273s ***** test
273s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
273s  num_parts = 3;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
273s  assert (sort (cellfun (@sum, partition)), sort ([38, 38, 37]));
273s ***** test
273s  numbers = [10, 10, 10];
273s  num_parts = 3;
273s  [~, partition] = multiway (numbers, num_parts, "completeKK");
273s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
273s ***** test
273s  numbers = 1:10;
273s  num_parts = 2;
273s  [~, partition] = multiway (numbers, num_parts, "completeKK");
273s  assert (sort (cellfun (@sum, partition)), [27, 28]);
273s ***** test
273s  numbers = [4, 5, 6, 7, 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
273s ***** test
273s  numbers = [1, 2, 3, 4, 5, 6];
273s  num_parts = 3;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
273s ***** test
273s  numbers = [10, 7, 5, 5, 6, 4, 10, 11, 12, 9, 10, 4, 3, 4, 5];
273s  num_parts = 4;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), sort ([27, 27, 27, 24]));
273s ***** test
273s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
273s  num_parts = 3;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), sort ([35, 37, 41]));
273s ***** test
273s  numbers = [10, 10, 10];
273s  num_parts = 3;
273s  [~, partition] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
273s ***** test
273s  numbers = 1:10;
273s  num_parts = 2;
273s  [~, partition] = multiway (numbers, num_parts, "greedy");
273s  assert (sort (cellfun (@sum, partition)), [27, 28]);
273s ***** test
273s  grpidx_ckk = multiway ([3 2 4 3 9 3 64], 3);
273s  grpidx_greedy = multiway ([3 2 4 3 9 3 64], 3, 'greedy');
273s  assert (isequal (grpidx_ckk, grpidx_greedy), false);
273s ***** test
273s  numbers = [4; 5; 6; 7; 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
273s  assert (iscolumn (groupindex), true)
273s  assert (iscolumn (groupsizes), true);
273s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
273s  numbers = [4; 5; 6; 7; 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (iscolumn (groupindex), true)
273s  assert (iscolumn (groupsizes), true);
273s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
273s ***** test
273s  numbers = [4, 5, 6, 7, 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
273s  assert (isrow (groupindex), true)
273s  assert (isrow (groupsizes), true);
273s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
273s ***** test
273s  numbers = [4, 5, 6, 7, 8];
273s  num_parts = 2;
273s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
273s  assert (isrow (groupindex), true)
273s  assert (isrow (groupsizes), true);
273s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
273s ***** error<multiway: too few input arguments.> multiway ()
273s ***** error<multiway: too few input arguments.> multiway ([1, 2])
273s ***** error<multiway: METHOD value must be a character vector.> ...
273s  multiway ([1, 2, 3], 2, 1)
273s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway ([], 2)
273s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway (ones (2, 2), 2)
273s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
273s  multiway ({1, 2, 3}, 2)
273s ***** error<multiway: NUMBERS must be non-negative.> multiway ([1, -2, 3], 2)
273s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
273s  multiway ([1, 2, NaN], 2)
273s ***** error<multiway: NUM_PARTS must be a scalar.> multiway ([1,2,3], [1,2])
273s ***** error<multiway: NUM_PARTS must be a real numeric value.> ...
273s  multiway ([1, 2, 3], "2")
273s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 0)
273s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 1.5)
273s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], -1)
273s ***** error<multiway: NUM_PARTS cannot be greater than number of elements in NUMBERS.> ...
273s  multiway ([1, 2], 3)
273s ***** error <multiway: unsupported method 'greedyalgo'.> ...
273s  multiway ([1,2,3], 2, "greedyalgo")
274s 30 tests, 30 passed, 0 known failure, 0 skipped
274s [inst/wblplot.m]
274s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/wblplot.m
274s ***** demo
274s  x = [16 34 53 75 93 120];
274s  wblplot (x);
274s ***** demo
274s  x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
274s  c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
274s  [h, p] = wblplot (x, c);
274s  p
274s ***** demo
274s  x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
274s  [h, p] = wblplot (x, [], [], 0.05);
274s  p
274s  ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
274s ***** demo
274s  x = [46 64 83 105 123 150 150];
274s  c = [0 0 0 0 0 0 1];
274s  f = [1 1 1 1 1 1 4];
274s  wblplot (x, c, f, 0.05);
274s ***** demo
274s  x = [46 64 83 105 123 150 150];
274s  c = [0 0 0 0 0 0 1];
274s  f = [1 1 1 1 1 1 4];
274s  ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
274s  wblplot (x - 30.92, c, f, 0.05);
274s ***** test
274s  hf = figure ("visible", "off");
274s  unwind_protect
274s    x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
274s    [h, p] = wblplot (x, [], [], 0.05);
274s    assert (numel (h), 4)
274s    assert (p(1), 146.2545, 1E-4)
274s    assert (p(2), 1.1973, 1E-4)
274s    assert (p(3), 0.9999, 5E-5)
274s  unwind_protect_cleanup
274s    close (hf);
274s  end_unwind_protect
274s 1 test, 1 passed, 0 known failure, 0 skipped
274s [inst/cholcov.m]
274s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cholcov.m
274s ***** demo
274s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
274s  T = cholcov (C1)
274s  C2 = T'*T
274s ***** test
274s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
274s  T = cholcov (C1);
274s  assert (C1, T'*T, 1e-15 * ones (size (C1)));
274s 1 test, 1 passed, 0 known failure, 0 skipped
274s [inst/standardizeMissing.m]
274s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/standardizeMissing.m
274s ***** assert (standardizeMissing (1, 1), NaN)
274s ***** assert (standardizeMissing (1, 0), 1)
274s ***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
274s ***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
274s ***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
274s ***** assert (standardizeMissing ('foo', 'a'), 'foo')
274s ***** assert (standardizeMissing ('foo', 'f'), ' oo')
274s ***** assert (standardizeMissing ('foo', 'o'), 'f  ')
274s ***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
274s ***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
274s ***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
274s ***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
274s ***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
274s ***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
274s ***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
274s ***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
274s ***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
274s ***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
274s ***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
274s ***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
274s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
274s ***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
274s ***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
274s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
274s ***** assert (standardizeMissing (double (1), single (1)), double (NaN))
274s ***** assert (standardizeMissing (single (1), single (1)), single (NaN))
274s ***** assert (standardizeMissing (single (1), double (1)), single (NaN))
274s ***** assert (standardizeMissing (single (1), true), single (NaN))
274s ***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
274s ***** assert (standardizeMissing (true, true), true)
274s ***** assert (standardizeMissing (true, 1), true)
274s ***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
274s ***** assert (standardizeMissing (int32 (1), 1), int32 (1))
274s ***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
274s ***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
274s ***** error standardizeMissing ();
274s ***** error standardizeMissing (1);
274s ***** error standardizeMissing (1,2,3);
274s ***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
274s ***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
274s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
274s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
274s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
274s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
274s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
274s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
274s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
274s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
274s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
274s 49 tests, 49 passed, 0 known failure, 0 skipped
274s [inst/fitcgam.m]
274s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitcgam.m
274s ***** demo
274s  ## Train a GAM classifier for binary classification
274s  ## using specific data and plot the decision boundaries.
274s 
274s  ## Define specific data
274s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
274s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
274s  Y = [0; 0; 0; 0; 0; ...
274s      1; 1; 1; 1; 1];
274s 
274s  ## Train the GAM model
274s  obj = fitcgam (X, Y, "Interactions", "all");
274s 
274s  ## Create a grid of values for prediction
274s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
274s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
274s  [x1G, x2G] = meshgrid (x1, x2);
274s  XGrid = [x1G(:), x2G(:)];
274s  pred = predict (obj, XGrid);
274s 
274s  ## Plot decision boundaries and data points
274s  predNumeric = str2double (pred);
274s  gidx = predNumeric > 0.5;
274s 
274s  figure
274s  scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
274s  hold on
274s  scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
274s  plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
274s  xlabel("Feature 1");
274s  ylabel("Feature 2");
274s  title("Generalized Additive Model (GAM) Decision Boundary");
274s  legend({"Class 1 Region", "Class 0 Region", ...
274s        "Class 1 Samples", "Class 0 Samples"}, ...
274s        "location", "northwest")
274s  axis tight
274s  hold off
274s ***** test
274s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
274s  y = [0; 0; 1; 1];
274s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
274s  a = fitcgam (x, y, "PredictorNames", PredictorNames);
274s  assert (class (a), "ClassificationGAM");
274s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
274s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
274s  assert (a.ClassNames, {'0'; '1'})
274s  assert (a.PredictorNames, PredictorNames)
274s  assert (a.BaseModel.Intercept, 0)
276s ***** test
276s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
276s  y = [1; 0; 1; 0; 1];
276s  a = fitcgam (x, y, "interactions", "all");
276s  assert (class (a), "ClassificationGAM");
276s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
276s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
276s  assert (a.ClassNames, {'1'; '0'})
276s  assert (a.PredictorNames, {'x1', 'x2'})
276s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
278s ***** test
278s  load fisheriris
278s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
278s  X = meas(inds, :);
278s  Y = species(inds, :)';
278s  Y = strcmp (Y, 'virginica')';
278s  a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
278s  assert (class (a), "ClassificationGAM");
278s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
278s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
278s  assert (a.ClassNames, {'0'; '1'})
278s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
278s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
278s  assert (a.ModelwInt.Intercept, 0)
283s ***** error<fitcgam: too few arguments.> fitcgam ()
283s ***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
283s ***** error<fitcgam: name-value arguments must be in pairs.>
283s  fitcgam (ones (4,2), ones (4, 1), "K")
283s ***** error<fitcgam: number of rows in X and Y must be equal.>
283s  fitcgam (ones (4,2), ones (3, 1))
283s ***** error<fitcgam: number of rows in X and Y must be equal.>
283s  fitcgam (ones (4,2), ones (3, 1), "K", 2)
283s 8 tests, 8 passed, 0 known failure, 0 skipped
283s [inst/manovacluster.m]
283s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/manovacluster.m
283s ***** demo
283s  load carbig
283s  X = [MPG Acceleration Weight Displacement];
283s  [d, p, stats] = manova1 (X, Origin);
283s  manovacluster (stats)
283s ***** test
283s  hf = figure ("visible", "off");
283s  unwind_protect
283s    load carbig
283s    X = [MPG Acceleration Weight Displacement];
283s    [d, p, stats] = manova1 (X, Origin);
283s    manovacluster (stats);
283s  unwind_protect_cleanup
283s    close (hf);
283s  end_unwind_protect
283s ***** error manovacluster (stats, "some");
283s 2 tests, 2 passed, 0 known failure, 0 skipped
283s [inst/kstest2.m]
283s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/kstest2.m
283s ***** error kstest2 ([1,2,3,4,5,5])
283s ***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
283s ***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
283s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
283s ***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
283s ***** test
283s  load examgrades
283s  [h, p] = kstest2 (grades(:,1), grades(:,2));
283s  assert (h, false);
283s  assert (p, 0.1222791870137312, 1e-14);
283s ***** test
283s  load examgrades
283s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
283s  assert (h, false);
283s  assert (p, 0.1844421391011258, 1e-14);
283s ***** test
283s  load examgrades
283s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
283s  assert (h, false);
283s  assert (p, 0.06115357930171663, 1e-14);
283s ***** test
283s  load examgrades
283s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
283s  assert (h, true);
283s  assert (p, 0.06115357930171663, 1e-14);
283s 14 tests, 14 passed, 0 known failure, 0 skipped
283s [inst/sigma_pts.m]
283s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/sigma_pts.m
283s ***** demo
283s  K      = [1 0.5; 0.5 1]; # covariance matrix
283s  # calculate and build associated ellipse
283s  [R,S,~] = svd (K);
283s  theta   = atan2 (R(2,1), R(1,1));
283s  v       = sqrt (diag (S));
283s  v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
283s  t       = linspace (0, 2*pi, 100).';
283s  xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
283s  ye      = v(1,2) * cos (t) + v(2,2) * sin (t);
283s 
283s  figure(1); clf; hold on
283s  # Plot ellipse and axes
283s  line ([0 0; v(:,1).'],[0 0; v(:,2).'])
283s  plot (xe,ye,'-r');
283s 
283s  col = 'rgb';
283s  l     = [-1.8 -1 1.5];
283s  for li = 1:3
283s   p     = sigma_pts (2, [], K, l(li));
283s   tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
283s                p(1,1), p(1,2), ['o' col(li)]);
283s   h(li) = tmp(1);
283s  endfor
283s  hold off
283s  axis image
283s  legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
283s ***** test
283s  p = sigma_pts (5);
283s  assert (mean (p), zeros(1,5), sqrt(eps));
283s  assert (cov (p), eye(5), sqrt(eps));
283s ***** test
283s  m = randn(1, 5);
283s  p = sigma_pts (5, m);
283s  assert (mean (p), m, sqrt(eps));
283s  assert (cov (p), eye(5), sqrt(eps));
283s ***** test
283s  x = linspace (0,1,5);
283s  K = exp (- (x.' - x).^2/ 0.5);
283s  p = sigma_pts (5, [], K);
283s  assert (mean (p), zeros(1,5), sqrt(eps));
283s  assert (cov (p), K, sqrt(eps));
283s ***** error sigma_pts(2,1);
283s ***** error sigma_pts(2,[],1);
283s ***** error sigma_pts(2,1,1);
283s ***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
283s 7 tests, 7 passed, 0 known failure, 0 skipped
283s [inst/grpstats.m]
283s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/grpstats.m
283s ***** demo
283s  load carsmall;
283s  [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
283s  n = length(m);
283s  errorbar((1:n)',m,p(:,2)-m);
283s  set (gca, "xtick", 1:n, "xticklabel", g);
283s  title ("95% prediction intervals for mean weight by year");
283s ***** demo
283s  load carsmall;
283s  [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
283s                      {"mean", "meanci", "gname"}, 0.05)
283s  [c,r] = size (m);
283s  errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
283s  set (gca, "xtick", 1:c, "xticklabel", g);
283s  title ("95% prediction intervals for mean weight by year");
283s ***** test
283s  load carsmall
283s  means = grpstats (Acceleration, Origin);
283s  assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
283s ***** test
283s  load carsmall
283s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
283s  assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
283s  assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
283s ***** test
283s  load carsmall
283s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
283s  assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
283s ***** test
283s  load carsmall
283s  [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
283s                      {"mean", "meanci", "gname"}, 0.05);
283s  assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
283s                  [1e-14, 2e-14, 1e-14]');
283s ***** test
283s  [mC, g] = grpstats ([], []);
283s  assert (isempty (mC), true);
283s  assert (isempty (g), true);
283s ***** error<grpstats: X must be a vector or 2d matrix.> ...
283s  grpstats (ones (3, 3, 3));
283s ***** error<grpstats: samples in X and GROUPS mismatch.> ...
283s  grpstats ([], {'A'; 'B'; 'A'; 'B'})
283s ***** error<grpstats: missing VALUE for optional 'alpha' parameter.> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha");
283s ***** error<grpstats: invalid NAME for optional 'alpha' parameter.> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "somename", -0.1);
283s ***** error<grpstats: invalid fourth input argument.> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", {2, 3}, -0.1);
283s ***** error<grpstats: 'alpha' must be a real scalar in the range \(0,1\).> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha", -0.1);
283s ***** error<grpstats: inconsistent number of output arguments.> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, {'mean', 'sum'});
283s ***** error<grpstats: inconsistent number of output arguments.> ...
283s  [q, w] = grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'});
283s ***** error<grpstats: wrong whichstats option.> ...
283s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "whatever");
283s 14 tests, 14 passed, 0 known failure, 0 skipped
283s [inst/fillmissing.m]
283s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fillmissing.m
283s ***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
283s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
283s ***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
283s ***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
283s ***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
283s ***** test
283s  x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
283s  y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
283s  assert (fillmissing (x, "constant", 99), y);
283s  y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
283s  assert (fillmissing (x, "constant", [94:99]), y);
283s  assert (fillmissing (x, "constant", [94:99]'), y);
283s  assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
283s  assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
283s  assert (fillmissing (x, "constant", [94:99], 1), y);
283s  y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
283s  assert (fillmissing (x, "constant", [96:99], 2), y);
283s  y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
283s  assert (fillmissing (x, "constant", [94:99], 3), y);
283s  y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
283s  assert (fillmissing (x, "constant", [88:99], 99), y);
283s ***** test
283s  x = reshape ([1:24], 4, 3, 2);
283s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  y = x;
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
283s  assert (fillmissing (x, "constant", [94:99], 1), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
283s  assert (fillmissing (x, "constant", [92:99], 2), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
283s  assert (fillmissing (x, "constant", [88:99], 3), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
283s  assert (fillmissing (x, "constant", [76:99], 99), y);
283s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
283s ***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
283s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
283s ***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
283s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
283s ***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
283s ***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
283s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
283s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
283s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  y = x;
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
283s  y([8]) = 99;
283s  assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
283s  assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
283s  y = x;
283s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
283s  y([6, 18, 20, 21]) = 99;
283s  assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
283s  y(y == 99) = 88;
283s  assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
283s  assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
283s  assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
283s  y([8]) = 94;
283s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
283s  y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
283s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
283s  y = x;
283s  y(isnan (y)) = 88;
283s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
283s  y = x;
283s  y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
283s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
283s  y = x;
283s  y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
283s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
283s  y = x;
283s  y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
283s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
283s  assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
283s ***** test
283s  x = reshape ([1:24],4,3,2);
283s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  y = x;
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
283s  assert (fillmissing (x, "constant", [94:99], 1), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
283s  assert (fillmissing (x, "constant", [92:99], 2), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
283s  assert (fillmissing (x, "constant", [88:99], 3), y);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
283s  assert (fillmissing (x, "constant", [76:99], 99), y);
283s ***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
283s ***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
283s ***** test
283s  x = reshape ([1:24], 4, 3, 2);
283s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  y = x;
283s  y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
283s  assert (fillmissing (x, "next", 1), y);
283s  y = x;
283s  y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
283s  assert (fillmissing (x, "next", 2), y);
283s  y = x;
283s  y([1, 6, 9, 12]) = [13, 18, 21, 24];
283s  assert (fillmissing (x, "next", 3), y);
283s  assert (fillmissing (x, "next", 99), x);
283s  y = x;
283s  y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
283s  assert (fillmissing (x, "previous", 1), y);
283s  y = x;
283s  y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
283s  assert (fillmissing (x, "previous", 2), y);
283s  y = x;
283s  y([14, 16, 22, 23]) = [2, 4, 10, 11];
283s  assert (fillmissing (x, "previous", 3), y);
283s  assert (fillmissing (x, "previous", 99), x);
283s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
283s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
283s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
283s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
283s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
283s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
283s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
283s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
283s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([5, 6, 8, 18]) = [4, 4, 0, 17];
283s  assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
283s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
283s  y = x;
283s  y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
283s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
283s  y = x;
283s  y([16, 19, 21]) = [4, 7, 9];
283s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
283s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
283s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
283s  y = x;
283s  y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
283s  assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
283s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
283s  y = x;
283s  y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
283s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
283s  y = x;
283s  y([2, 5]) = [14, 17];
283s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
283s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
283s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
283s ***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
283s ***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
283s ***** test
283s  x = reshape ([1:24], 4, 3, 2);
283s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  y = x;
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
283s  assert (fillmissing (x, "nearest", 1), y);
283s  y = x;
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
283s  assert (fillmissing (x, "nearest", 2), y);
283s  y = x;
283s  y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
283s  assert (fillmissing (x, "nearest", 3), y);
283s  assert (fillmissing (x, "nearest", 99), x);
283s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
283s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
283s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
283s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
283s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
283s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
283s  assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
283s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
283s  y = x;
283s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
283s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
283s  y = x;
283s  y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
283s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
283s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
283s ***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
283s ***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
283s ***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
283s ***** test
283s  x = reshape ([1:24], 4, 3, 2);
283s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
283s  y = reshape ([1:24], 4, 3, 2);
283s  y([1, 9, 14, 19, 22, 23]) = NaN;
283s  assert (fillmissing (x, "linear", 2), y);
283s  y = reshape ([1:24], 4, 3, 2);
283s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
283s  assert (fillmissing (x, "linear", 3), y);
283s  assert (fillmissing (x, "linear", 99), x);
283s ***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
283s ***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
283s ***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
283s ***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
283s ***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
283s  y(8) = 8;
283s  assert (fillmissing (x, "linear", "endvalues", 0), y);
283s  assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
283s  y = x;
283s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
283s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
283s  assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
283s  y = x;
283s  y(isnan(y)) = 0;
283s  assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
283s  assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
283s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
283s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
283s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
283s  assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
283s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
283s  assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
283s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
283s  assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
283s ***** test <60965>
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
283s  assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
283s !!!!! known bug: https://octave.org/testfailure/?60965
283s interp1: invalid METHOD 'makima'
283s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
283s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
283s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
283s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
283s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
283s ***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
283s ***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2 3, 4, 4.5]), [1, 1, 3, 5, 5])
283s ***** test
283s  x = reshape ([1:24], 3, 4, 2);
283s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
283s  y = x;
283s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
283s  assert (fillmissing (x, "movmean", 3), y);
283s  assert (fillmissing (x, "movmean", [1, 1]), y);
283s  assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
283s  assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
283s  y = x;
283s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
283s  assert (fillmissing (x, "movmean", 3, 2), y);
283s  assert (fillmissing (x, "movmean", [1, 1], 2), y);
283s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
283s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
283s  y([1, 18]) = NaN;
283s  y(6) = 9;
283s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
283s  y = x;
283s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
283s  y(8) = 8;
283s  assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
283s  y = x;
283s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
283s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
283s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
283s ***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
283s ***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
283s ***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
283s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
283s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
283s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
283s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
283s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
283s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 3, 5, 5])
284s ***** test
284s  x = reshape ([1:24], 3, 4, 2);
284s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
284s  y = x;
284s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
284s  assert (fillmissing (x, "movmedian", 3), y);
284s  assert (fillmissing (x, "movmedian", [1, 1]), y);
284s  assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
284s  assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
284s  y = x;
284s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
284s  assert (fillmissing (x, "movmedian", 3, 2), y);
284s  assert (fillmissing (x, "movmedian", [1, 1], 2), y);
284s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
284s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
284s  y([1,18]) = NaN;
284s  y(6) = 9;
284s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
284s  y = x;
284s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
284s  y(8) = 8;
284s  assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
284s  y = x;
284s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
284s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
284s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
284s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
284s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
284s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
284s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
284s ***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
284s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
284s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
284s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
284s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
284s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
284s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
284s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
284s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
284s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
284s ***** function A = testfcn (x, y, z)
284s   if (isempty (y))
284s     A = z;
284s   elseif (numel (y) == 1)
284s     A = repelem (x(1), numel(z));
284s   else
284s     A = interp1 (y, x, z, "linear", "extrap");
284s   endif
284s ***** endfunction
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
284s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
284s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
284s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 1), [1, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 2), [1, NaN, 3, NaN, 5]') ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]'
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
284s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 2.5, 3, 3.5]), [1, 2.6, 3.4, 4.2, 5], 10*eps)
284s ***** assert (fillmissing ([NaN, NaN, 3, NaN, 5], @testfcn, 99, 1), [NaN, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
284s ***** test
284s ***** function A = testfcn (x, y, z)
284s   if (isempty (y))
284s     A = z;
284s   elseif (numel (y) == 1)
284s     A = repelem (x(1), numel(z));
284s   else
284s     A = interp1 (y, x, z, "linear", "extrap");
284s   endif
284s ***** endfunction
284s  x = reshape ([1:24], 3, 4, 2);
284s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
284s  y = x;
284s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
284s  assert (fillmissing (x, @testfcn, 3), y);
284s  assert (fillmissing (x, @testfcn, [1, 1]), y);
284s  assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
284s  assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
284s  y= x;
284s  y(isnan (x)) = 99;
284s  y(8) = 8;
284s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
284s  y = x;
284s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
284s  assert (fillmissing (x, @testfcn, 3, 2), y);
284s  assert (fillmissing (x, @testfcn, [1, 1], 2), y);
284s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
284s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
284s  y(1) = NaN;
284s  y([6, 18, 21]) = [9, 24, 24];
284s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
284s  y = x;
284s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
284s  y(8) = 8;
284s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
284s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
284s  y(8) = 99;
284s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
284s  y([6, 18, 20, 21]) = 99;
284s  assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
284s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
284s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
284s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
284s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
284s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
284s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
284s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
284s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
284s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
284s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
284s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
284s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
284s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
284s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5; 1, NaN, 3, NaN, 5], "constant", 0, 2, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5; 1, NaN, 3, 0, 5])
284s ***** test
284s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
284s  assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
284s  y = x;
284s  y([4, 7, 12]) = 0;
284s  assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
284s  assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
284s  y = x;
284s  y([5, 7, 12]) = 0;
284s  assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
284s  y = x;
284s  y([4, 5, 7]) = 0;
284s  assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
284s ***** test
284s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
284s  [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
284s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
284s  [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
284s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
284s  [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
284s  assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
284s  [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
284s  assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
284s ***** test
284s  x = [NaN, 2, 3];
284s  [~, idx] = fillmissing (x, "previous");
284s  assert (idx, logical ([0, 0, 0]));
284s  [~, idx] = fillmissing (x, "movmean", 1);
284s  assert (idx, logical ([0, 0, 0]));
284s  x = [1:3; 4:6; 7:9];
284s  x([2, 4, 7, 9]) = NaN;
284s  [~, idx] = fillmissing (x, "linear");
284s  assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
284s  [~, idx] = fillmissing (x, "movmean", 2);
284s  assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
284s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
284s  assert (A, [1, 2, 3, 3, NaN]);
284s  assert (idx, logical ([0, 0, 0, 1, 0]));
284s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
284s  assert (A, [1, 2, 3, 3, NaN]);
284s  assert (idx, logical ([0, 0, 0, 1, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
284s  assert (A, [1, 2, 3, 3, NaN]);
284s  assert (idx, logical ([0, 0, 0, 1, 0]));
284s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
284s  assert (A, [1, 2, 1, 4, 1]);
284s  assert (idx, logical ([0, 1, 0, 1, 0]));
284s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
284s  assert (A, [1, NaN, 1, NaN, 1]);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
284s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
284s ***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
284s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
284s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
284s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
284s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
284s ***** test
284s  [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s  [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
284s  assert (idx, logical ([0, 1, 1, 1, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
284s  assert (A, [1, 2, 2, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
284s  assert (A, [1, 1, 1, 1, 1]);
284s  assert (idx, logical ([0, 1, 0, 1, 1]));
284s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
284s  assert (A, [1, NaN, 1, NaN, NaN]);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s ***** test
284s  [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
284s  assert (A, [1, 3, 5]);
284s  assert (idx, logical ([0, 1, 0]));
284s ***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
284s ***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
284s ***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
284s ***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
284s ***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
284s ***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
284s ***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
284s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
284s ***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
284s ***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
284s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
284s ***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
284s ***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
284s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
284s ***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
284s ***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
284s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
284s ***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
284s ***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
284s ***** test
284s  [A, idx] = fillmissing (" a b c", "constant", " ");
284s  assert (A, " a b c");
284s  assert (idx, logical ([0, 0, 0, 0, 0, 0]));
284s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
284s  assert (A, {"foo", "", "bar", ""});
284s  assert (idx, logical ([0, 0, 0, 0]));
284s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
284s  assert (A, {"foo", "", "bar", ""});
284s  assert (idx, logical ([0, 0, 0, 0]));
284s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
284s  assert (A, "afaoaoa");
284s  assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
284s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
284s  assert (A, " f o o ");
284s  assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
284s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
284s  assert (A, {"a", "foo", "a"});
284s  assert (idx, logical ([1, 0, 1]));
284s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
284s  assert (A, {"", "foo", ""});
284s  assert (idx, logical ([0, 0, 0]));
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
284s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
284s ***** test
284s  x = logical ([1, 0, 1, 0, 1]);
284s  [~, idx] = fillmissing (x, "constant", true);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s  [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([0, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 0]));
284s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
284s  assert (idx, logical ([0, 0, 0, 0, 0]))
284s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]))
284s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([0, 0, 1, 0, 1]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([11, 2, 13, 4, 15]))
284s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 13, 4, 15]))
284s ***** test
284s  x = int32 ([1, 2, 3, 4, 5]);
284s  [~, idx] = fillmissing (x, "constant", 0);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s  [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
284s  assert (idx, logical ([0, 0, 1, 0, 0]));
284s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([0, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 0]));
284s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
284s  assert (idx, logical ([0, 0, 0, 0, 0]));
284s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([1, 0, 1, 0, 1]));
284s  [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
284s  assert (idx, logical ([0, 0, 1, 0, 1]));
284s ***** test
284s  [A, idx] = fillmissing ([struct, struct], "constant", 1);
284s  assert (A, [struct, struct])
284s  assert (idx, [false, false])
284s ***** error <Invalid call> fillmissing ()
284s ***** error <Invalid call> fillmissing (1)
284s ***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
284s ***** error <second input must be a> fillmissing (1, 2)
284s ***** error <unknown fill method 'foo'> fillmissing (1, "foo")
284s ***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
284s ***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
284s ***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
284s ***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
284s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
284s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
284s ***** error <'constant' method must be followed by> fillmissing (1, "constant")
284s ***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
284s ***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
284s ***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
284s ***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
284s ***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
284s ***** error <moving window method must be followed by> fillmissing (1, "movmean")
284s ***** error <moving window method must be followed by> fillmissing (1, "movmedian")
284s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
284s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
284s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
284s ***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
284s ***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
284s ***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
284s ***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
284s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
284s ***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
284s ***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
284s ***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
284s ***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
284s ***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
284s ***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
284s ***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
284s ***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
284s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
284s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
284s ***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
284s ***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
284s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
284s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
284s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
284s ***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
284s ***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
284s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
284s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
284s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
284s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
284s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
284s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
284s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
284s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
284s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
284s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
284s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
284s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
284s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
284s ***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
284s ***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
284s ***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
284s ***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
284s ***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
284s 380 tests, 379 passed, 0 known failure, 1 skipped
284s [inst/correlation_test.m]
284s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/correlation_test.m
284s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
284s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
284s ***** error<correlation_test: X must contain finite real numbers.> ...
284s  correlation_test ([1 2 NaN]', [2 3 4]');
284s ***** error<correlation_test: X must contain finite real numbers.> ...
284s  correlation_test ([1 2 Inf]', [2 3 4]');
284s ***** error<correlation_test: X must contain finite real numbers.> ...
284s  correlation_test ([1 2 3+i]', [2 3 4]');
284s ***** error<correlation_test: Y must contain finite real numbers.> ...
284s  correlation_test ([1 2 3]', [2 3 NaN]');
284s ***** error<correlation_test: Y must contain finite real numbers.> ...
284s  correlation_test ([1 2 3]', [2 3 Inf]');
284s ***** error<correlation_test: Y must contain finite real numbers.> ...
284s  correlation_test ([1 2 3]', [3 4 3+i]');
284s ***** error<correlation_test: X and Y must be vectors of equal length.> ...
284s  correlation_test ([1 2 3]', [3 4 4 5]');
284s ***** error<correlation_test: invalid value for alpha.> ...
284s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
284s ***** error<correlation_test: invalid value for alpha.> ...
284s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
284s ***** error<correlation_test: invalid value for alpha.> ...
284s  correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
284s ***** error<correlation_test: invalid value for alpha.> ...
284s  correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
284s ***** error<correlation_test: invalid Name argument.> ...
284s  correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
284s ***** error<correlation_test: invalid value for tail.>  ...
284s  correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
284s ***** error<correlation_test: invalid value for tail.>  ...
284s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
284s ***** error<correlation_test: invalid value for method.>  ...
284s  correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
284s ***** error<correlation_test: invalid value for method.>  ...
284s  correlation_test ([1 2 3]', [2 3 4]', "method", "some");
284s ***** test
284s  x = [6 7 7 9 10 12 13 14 15 17];
284s  y = [19 22 27 25 30 28 30 29 25 32];
284s  [h, pval, stats] = correlation_test (x, y);
284s  assert (stats.corrcoef, corr (x', y'), 1e-14);
284s  assert (pval, 0.0223, 1e-4);
284s ***** test
284s  x = [6 7 7 9 10 12 13 14 15 17]';
284s  y = [19 22 27 25 30 28 30 29 25 32]';
284s  [h, pval, stats] = correlation_test (x, y);
284s  assert (stats.corrcoef, corr (x, y), 1e-14);
284s  assert (pval, 0.0223, 1e-4);
284s 20 tests, 20 passed, 0 known failure, 0 skipped
284s [inst/geomean.m]
284s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/geomean.m
284s ***** test
284s  x = [0:10];
284s  y = [x;x+5;x+10];
284s  assert (geomean (x), 0);
284s  m = [0 9.462942809849169 14.65658770861967];
284s  assert (geomean (y, 2), m', 4e-14);
284s  assert (geomean (y, "all"), 0);
284s  y(2,4) = NaN;
284s  m(2) = 9.623207231679554;
284s  assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
284s  assert (geomean (y', "omitnan"), m, 4e-14);
284s  z = y + 20;
284s  assert (geomean (z, "all"), NaN);
284s  assert (geomean (z, "all", "includenan"), NaN);
284s  assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
284s  m = [24.79790781765634 NaN 34.85638839503932];
284s  assert (geomean (z'), m, 4e-14);
284s  assert (geomean (z', "includenan"), m, 4e-14);
284s  m(2) = 30.02181156156319;
284s  assert (geomean (z', "omitnan"), m, 4e-14);
284s  assert (geomean (z, 2, "omitnan"), m', 4e-14);
284s ***** test
284s  x = repmat ([1:20;6:25], [5 2 6 3]);
284s  assert (size (geomean (x, [3 2])), [10 1 1 3]);
284s  assert (size (geomean (x, [1 2])), [1 1 6 3]);
284s  assert (size (geomean (x, [1 2 4])), [1 1 6]);
284s  assert (size (geomean (x, [1 4 3])), [1 40]);
284s  assert (size (geomean (x, [1 2 3 4])), [1 1]);
284s ***** test
284s  x = repmat ([1:20;6:25], [5 2 6 3]);
284s  m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
284s  assert (geomean (x, [3 2]), m, 4e-13);
284s  x(2,5,6,3) = NaN;
284s  m(2,3) = NaN;
284s  assert (geomean (x, [3 2]), m, 4e-13);
284s  m(2,3) = 14.3292729579901;
284s  assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
284s ***** error <geomean: X must contain real nonnegative values.> geomean ("char")
284s ***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
284s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
284s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
284s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
284s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
284s ***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
284s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
284s 8 tests, 8 passed, 0 known failure, 0 skipped
284s [inst/fitcknn.m]
284s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitcknn.m
284s ***** demo
284s  ## Train a k-nearest neighbor classifier for k = 10
284s  ## and plot the decision boundaries.
284s 
284s  load fisheriris
284s  idx = ! strcmp (species, "setosa");
284s  X = meas(idx,3:4);
284s  Y = cast (strcmpi (species(idx), "virginica"), "double");
284s  obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
284s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
284s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
284s  [x1G, x2G] = meshgrid (x1, x2);
284s  XGrid = [x1G(:), x2G(:)];
284s  pred = predict (obj, XGrid);
284s  gidx = logical (pred);
284s 
284s  figure
284s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
284s  hold on
284s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
284s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
284s  xlabel ("Petal length (cm)");
284s  ylabel ("Petal width (cm)");
284s  title ("5-Nearest Neighbor Classifier Decision Boundary");
284s  legend ({"Versicolor Region", "Virginica Region", ...
284s          "Sampled Versicolor", "Sampled Virginica"}, ...
284s          "location", "northwest")
284s  axis tight
284s  hold off
284s ***** test
284s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
284s  y = ["a"; "a"; "b"; "b"];
284s  a = fitcknn (x, y);
284s  assert (class (a), "ClassificationKNN");
284s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
284s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
284s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y, "NSMethod", "exhaustive");
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  k = 10;
285s  a = fitcknn (x, y, "NumNeighbors" ,k);
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = ones (4, 11);
285s  y = ["a"; "a"; "b"; "b"];
285s  k = 10;
285s  a = fitcknn (x, y, "NumNeighbors" ,k);
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  k = 10;
285s  a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  k = 10;
285s  a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  weights = ones (4,1);
285s  a = fitcknn (x, y, "Standardize", 1);
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.Standardize}, {true})
285s  assert ({a.Sigma}, {std(x, [], 1)})
285s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  weights = ones (4,1);
285s  a = fitcknn (x, y, "Standardize", false);
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.Standardize}, {false})
285s  assert ({a.Sigma}, {[]})
285s  assert ({a.Mu}, {[]})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  s = ones (1, 3);
285s  a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.DistParameter}, {s})
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
285s  assert (class (a), "ClassificationKNN");
285s  assert (a.DistParameter, 5)
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
285s                     "NSMethod", "exhaustive");
285s  assert (class (a), "ClassificationKNN");
285s  assert (a.DistParameter, 5)
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
285s  assert (class (a), "ClassificationKNN");
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
285s  assert ({a.BucketSize}, {20})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y, "IncludeTies", true);
285s  assert (class (a), "ClassificationKNN");
285s  assert (a.IncludeTies, true);
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y);
285s  assert (class (a), "ClassificationKNN");
285s  assert (a.IncludeTies, false);
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  a = fitcknn (x, y);
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Prior, [0.5; 0.5])
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  prior = [0.5; 0.5];
285s  a = fitcknn (x, y, "Prior", "empirical");
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Prior, prior)
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "a"; "b"];
285s  prior = [0.75; 0.25];
285s  a = fitcknn (x, y, "Prior", "empirical");
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Prior, prior)
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "a"; "b"];
285s  prior = [0.5; 0.5];
285s  a = fitcknn (x, y, "Prior", "uniform");
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Prior, prior)
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  cost = eye (2);
285s  a = fitcknn (x, y, "Cost", cost);
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Cost, [1, 0; 0, 1])
285s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  cost = eye (2);
285s  a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
285s  assert (class (a), "ClassificationKNN")
285s  assert (a.Cost, [1, 0; 0, 1])
285s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
285s  assert ({a.BucketSize}, {50})
285s ***** test
285s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
285s  y = ["a"; "a"; "b"; "b"];
285s  rand ("seed", 23);
285s  a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
285s  assert (class (a), "ClassificationPartitionedModel");
285s  assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
285s  assert (a.ModelParameters.NSMethod, "exhaustive")
285s  assert (a.ModelParameters.Distance, "euclidean")
285s  assert ({a.Trained{1}.BucketSize}, {50})
285s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
285s warning: called from
285s     cvpartition at line 764 column 19
285s     crossval at line 1701 column 9
285s     fitcknn at line 354 column 7
285s     __test__ at line 6 column 2
285s     test at line 685 column 11
285s     /tmp/tmp.YdhB1UcfDH at line 662 column 2
285s 
285s ***** error<fitcknn: too few arguments.> fitcknn ()
285s ***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
285s ***** error<fitcknn: Name-Value arguments must be in pairs.>
285s  fitcknn (ones (4,2), ones (4, 1), "K")
285s ***** error<fitcknn: number of rows in X and Y must be equal.>
285s  fitcknn (ones (4,2), ones (3, 1))
285s ***** error<fitcknn: number of rows in X and Y must be equal.>
285s  fitcknn (ones (4,2), ones (3, 1), "K", 2)
285s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
285s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
285s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
285s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
285s ***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
285s  fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
285s 29 tests, 29 passed, 0 known failure, 0 skipped
285s [inst/hist3.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hist3.m
285s ***** demo
285s  X = [
285s     1    1
285s     1    1
285s     1   10
285s     1   10
285s     5    5
285s     5    5
285s     5    5
285s     5    5
285s     5    5
285s     7    3
285s     7    3
285s     7    3
285s    10   10
285s    10   10];
285s  hist3 (X)
285s ***** test
285s  N_exp = [ 0  0  0  5 20
285s            0  0 10 15  0
285s            0 15 10  0  0
285s           20  5  0  0  0];
285s 
285s  n = 100;
285s  x = [1:n]';
285s  y = [n:-1:1]';
285s  D = [x y];
285s  N = hist3 (D, [4 5]);
285s  assert (N, N_exp);
285s ***** test
285s  N_exp = [0  0  0  0  1
285s           0  0  0  0  1
285s           0  0  0  0  1
285s           1  1  1  1 93];
285s 
285s  n = 100;
285s  x = [1:n]';
285s  y = [n:-1:1]';
285s  D = [x y];
285s  C{1} = [1 1.7 3 4];
285s  C{2} = [1:5];
285s  N = hist3 (D, C);
285s  assert (N, N_exp);
285s ***** test
285s  D = [1 1; 3 1; 3 3; 3 1];
285s  [c, nn] = hist3 (D, {0:4, 0:4});
285s  exp_c = zeros (5);
285s  exp_c([7 9 19]) = [1 2 1];
285s  assert (c, exp_c);
285s  assert (nn, {0:4, 0:4});
285s ***** test
285s  for i = 10
285s    assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
285s  endfor
285s ***** test
285s  edge_1 = linspace (0, 10, 10);
285s  edge_2 = linspace (0, 50, 10);
285s  [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
285s  exp_c = zeros (10, 10);
285s  exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
285s  assert (c, exp_c);
285s 
285s  assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
285s  assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
285s ***** shared X
285s  X = [
285s   5  2
285s   5  3
285s   1  4
285s   5  3
285s   4  4
285s   1  2
285s   2  3
285s   3  3
285s   5  4
285s   5  3];
285s ***** test
285s  N = zeros (10);
285s  N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
285s  C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
285s  assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
285s ***** test
285s  N = zeros (5, 7);
285s  N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
285s  C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
285s  assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
285s  assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
285s ***** test
285s  N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
285s  C = {(2:5), (2.5:1:4.5)};
285s  assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
285s ***** test
285s  N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
285s  C = {(1.2:3.2), (0:5)};
285s  assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
285s  assert (nthargout ([1 2], @hist3, X, C), {N C})
285s ***** test
285s  [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
285s                                          [-1 .05 .07 .2 .3 .5 .89 1.2]});
285s  C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
285s           [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
285s  assert (C, C_exp, eps*10^2)
285s ***** test
285s  Xv = repmat ([1:10]', [1 2]);
285s 
285s  ## Test Centers
285s  assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))
285s 
285s  N_exp = eye (6);
285s  N_exp([1 end]) = 3;
285s  assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)
285s 
285s  N_exp = zeros (8, 6);
285s  N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
285s  assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)
285s 
285s  ## Test Edges
285s  assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
285s  assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
285s  assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])
285s 
285s  N_exp = zeros (14);
285s  N_exp(3:12, 3:12) = eye (10);
285s  assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)
285s 
285s  ## Test for Nbins
285s  assert (hist3 (Xv), eye (10))
285s  assert (hist3 (Xv, [10 10]), eye (10))
285s  assert (hist3 (Xv, "nbins", [10 10]), eye (10))
285s  assert (hist3 (Xv, [5 5]), eye (5) * 2)
285s 
285s  N_exp = zeros (7, 5);
285s  N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
285s  assert (hist3 (Xv, [7 5]), N_exp)
285s ***** test # bug #51059
285s  D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
285s  [c, nn] = hist3 (D, {0:4, 0:4});
285s  exp_c = zeros (5);
285s  exp_c([7 9 19]) = [1 2 1];
285s  assert (c, exp_c)
285s  assert (nn, {0:4, 0:4})
285s ***** test
285s  [c, nn] = hist3 ([1 8]);
285s  exp_c = zeros (10, 10);
285s  exp_c(6, 6) = 1;
285s  exp_nn = {-4:5, 3:12};
285s  assert (c, exp_c)
285s  assert (nn, exp_nn, eps)
285s 
285s  [c, nn] = hist3 ([1 8], [10 11]);
285s  exp_c = zeros (10, 11);
285s  exp_c(6, 6) = 1;
285s  exp_nn = {-4:5, 3:13};
285s  assert (c, exp_c)
285s  assert (nn, exp_nn, eps)
285s ***** test
285s  [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
285s  exp_c = zeros (10, 10);
285s  exp_c(2, 1) = 1;
285s  exp_c(8, 10) = 1;
285s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
285s  assert (c, exp_c)
285s  assert (nn, exp_nn, eps*100)
285s ***** test
285s  [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
285s  exp_c = zeros (10, 10);
285s  exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
285s  assert (c, exp_c)
285s  assert (nn, exp_nn, eps*100)
285s ***** test
285s  [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
285s  exp_c = zeros (10, 10);
285s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
285s  assert (c, exp_c)
285s  assert (nn, exp_nn, eps*100)
285s 16 tests, 16 passed, 0 known failure, 0 skipped
285s [inst/logistic_regression.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/logistic_regression.m
285s ***** test
285s  # Output compared to following MATLAB commands
285s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
285s  # P = mnrval(B,X)
285s  X = [1.489381332449196, 1.1534152241851305; ...
285s       1.8110085304863965, 0.9449666896938425; ...
285s       -0.04453299665130296, 0.34278203449678646; ...
285s       -0.36616019468850347, 1.130254275908322; ...
285s        0.15339143291005095, -0.7921044310668951; ...
285s       -1.6031878794469698, -1.8343471035233376; ...
285s       -0.14349521143198166, -0.6762996896828459; ...
285s       -0.4403818557740143, -0.7921044310668951; ...
285s       -0.7372685001160434, -0.027793137932169563; ...
285s       -0.11875465773681024, 0.5512305689880763];
285s  Y = [1,1,1,1,1,0,0,0,0,0]';
285s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
285s ***** test
285s  # Output compared to following MATLAB commands
285s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
285s  load carbig
285s  X = [Acceleration Displacement Horsepower Weight];
285s  miles = [1,1,1,1,1,1,1,1,1,1,NaN,NaN,NaN,NaN,NaN,1,1,NaN,1,1,2,2,1,2,2,2, ...
285s           2,2,2,2,2,1,1,1,1,2,2,2,2,NaN,2,1,1,2,1,1,1,1,1,1,1,1,1,2,2,1,2, ...
285s           2,3,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,2,2, ...
285s           2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,1,1,2,2,2,1,2,2, ...
285s           2,1,1,3,2,2,2,1,2,2,1,2,2,2,1,3,2,3,2,1,1,1,1,1,1,1,1,3,2,2,3,3, ...
285s           2,2,2,2,2,3,2,1,1,1,1,1,1,1,1,1,1,1,2,2,1,3,2,2,2,2,2,2,1,3,2,2, ...
285s           2,2,2,3,2,2,2,2,2,1,1,1,1,2,2,2,2,3,2,3,3,2,1,1,1,3,3,2,2,2,1,2, ...
285s           2,1,1,1,1,1,3,3,3,2,3,1,1,1,1,1,2,2,1,1,1,1,1,3,2,2,2,3,3,3,3,2, ...
285s           2,2,4,3,3,4,3,2,2,2,2,2,2,2,2,2,2,2,1,1,2,1,1,1,3,2,2,3,2,2,2,2, ...
285s           2,1,2,1,3,3,2,2,2,2,2,1,1,1,1,1,1,2,1,3,3,3,2,2,2,2,2,3,3,3,3,2, ...
285s           2,2,3,4,3,3,3,2,2,2,2,3,3,3,3,3,4,2,4,4,4,3,3,4,4,3,3,3,2,3,2,3, ...
285s           2,2,2,2,3,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,NaN,3,2,2,2,2,2,1,2, ...
285s           2,3,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,2,4,3,2,3]';
285s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
285s  assert (DEV, 433.197174495549, 1e-05);
285s  assert (INTERCEPT(1), -16.6895155618903, 1e-05);
285s  assert (INTERCEPT(2), -11.7207818178493, 1e-05);
285s  assert (INTERCEPT(3), -8.0605768506075, 1e-05);
285s  assert (SLOPE(1), 0.104762463756714, 1e-05);
285s  assert (SLOPE(2), 0.0103357623191891, 1e-05);
285s  assert (SLOPE(3), 0.0645199313242276, 1e-05);
285s  assert (SLOPE(4), 0.00166377028388103, 1e-05);
285s 2 tests, 2 passed, 0 known failure, 0 skipped
285s [inst/optimalleaforder.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/optimalleaforder.m
285s ***** demo
285s  randn ("seed", 5)  # for reproducibility
285s  X = randn (10, 2);
285s  D = pdist (X);
285s  tree = linkage(D, 'average');
285s  optimalleaforder (tree, D, 'Transformation', 'linear')
285s ***** error optimalleaforder ()
285s ***** error optimalleaforder (1)
285s ***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
285s ***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
285s ***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
285s ***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
285s ***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
285s 7 tests, 7 passed, 0 known failure, 0 skipped
285s [inst/regression_ttest.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/regression_ttest.m
285s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
285s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
285s ***** error<regression_ttest: Y must contain finite real numbers.> ...
285s  regression_ttest ([1 2 NaN]', [2 3 4]');
285s ***** error<regression_ttest: Y must contain finite real numbers.> ...
285s  regression_ttest ([1 2 Inf]', [2 3 4]');
285s ***** error<regression_ttest: Y must contain finite real numbers.> ...
285s  regression_ttest ([1 2 3+i]', [2 3 4]');
285s ***** error<regression_ttest: X must contain finite real numbers.> ...
285s  regression_ttest ([1 2 3]', [2 3 NaN]');
285s ***** error<regression_ttest: X must contain finite real numbers.> ...
285s  regression_ttest ([1 2 3]', [2 3 Inf]');
285s ***** error<regression_ttest: X must contain finite real numbers.> ...
285s  regression_ttest ([1 2 3]', [3 4 3+i]');
285s ***** error<regression_ttest: Y and X must be vectors of equal length.> ...
285s  regression_ttest ([1 2 3]', [3 4 4 5]');
285s ***** error<regression_ttest: invalid value for alpha.> ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
285s ***** error<regression_ttest: invalid value for alpha.> ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
285s ***** error<regression_ttest: invalid value for alpha.> ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
285s ***** error<regression_ttest: invalid value for alpha.> ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
285s ***** error<regression_ttest: invalid Name argument.> ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
285s ***** error<regression_ttest: invalid value for tail.>  ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
285s ***** error<regression_ttest: invalid value for tail.>  ...
285s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
285s 16 tests, 16 passed, 0 known failure, 0 skipped
285s [inst/dist_obj/WeibullDistribution.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/WeibullDistribution.m
285s ***** demo
285s  ## Generate a data set of 5000 random samples from a Weibull distribution with
285s  ## parameters lambda = 1 and k = 2.  Fit a Weibull distribution to this data and plot
285s  ## a PDF of the fitted distribution superimposed on a histogram of a data.
285s 
285s  pd_fixed = makedist ("Weibull", "lambda", 1, "k", 2)
285s  rand ("seed", 2);
285s  data = random (pd_fixed, 5000, 1);
285s  pd_fitted = fitdist (data, "Weibull")
285s  plot (pd_fitted)
285s  msg = "Fitted Weibull distribution with lambda = %0.2f and k = %0.2f";
285s  title (sprintf (msg, pd_fitted.lambda, pd_fitted.k))
285s ***** shared pd, t
285s  pd = WeibullDistribution;
285s  t = truncate (pd, 2, 4);
285s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
285s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
285s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
285s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
285s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
285s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
285s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
285s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
285s ***** assert (iqr (pd), 1.0986, 1e-4);
285s ***** assert (iqr (t), 0.8020, 1e-4);
285s ***** assert (mean (pd), 1, 1e-14);
285s ***** assert (mean (t), 2.6870, 1e-4);
285s ***** assert (median (pd), 0.6931, 1e-4);
285s ***** assert (median (t), 2.5662, 1e-4);
285s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
285s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
285s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
285s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
285s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
285s ***** assert (any (random (t, 1000, 1) < 2), false);
285s ***** assert (any (random (t, 1000, 1) > 4), false);
285s ***** assert (std (pd), 1, 1e-14);
285s ***** assert (std (t), 0.5253, 1e-4);
285s ***** assert (var (pd), 1, 1e-14);
285s ***** assert (var (t), 0.2759, 1e-4);
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution(0, 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution(-1, 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution(Inf, 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution(i, 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution("a", 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution([1, 2], 1)
285s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
285s  WeibullDistribution(NaN, 1)
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, 0)
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, -1)
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, Inf)
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, i)
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, "a")
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, [1, 2])
285s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
285s  WeibullDistribution(1, NaN)
285s ***** error <cdf: invalid argument for upper tail.> ...
285s  cdf (WeibullDistribution, 2, "uper")
285s ***** error <cdf: invalid argument for upper tail.> ...
285s  cdf (WeibullDistribution, 2, 3)
285s ***** shared x
285s  x = wblrnd (1, 1, [1, 100]);
285s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha")
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 0)
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 1)
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", "")
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", {0.05})
285s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
285s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
285s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
285s           "parameter", {"lambda", "k", "param"})
285s ***** error <paramci: unknown distribution parameter.> ...
285s  paramci (WeibullDistribution.fit (x), "parameter", "param")
285s ***** error <paramci: unknown distribution parameter.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
285s ***** error <paramci: invalid NAME for optional argument.> ...
285s  paramci (WeibullDistribution.fit (x), "NAME", "value")
285s ***** error <paramci: invalid NAME for optional argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
285s ***** error <paramci: invalid NAME for optional argument.> ...
285s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
285s           "NAME", "value")
285s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
285s  plot (WeibullDistribution, "Parent")
285s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
285s  plot (WeibullDistribution, "PlotType", 12)
285s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
285s  plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
285s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
285s  plot (WeibullDistribution, "PlotType", "pdfcdf")
285s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
285s  plot (WeibullDistribution, "Discrete", "pdfcdf")
285s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
285s  plot (WeibullDistribution, "Discrete", [1, 0])
285s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
285s  plot (WeibullDistribution, "Discrete", {true})
285s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
285s  plot (WeibullDistribution, "Parent", 12)
285s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
285s  plot (WeibullDistribution, "Parent", "hax")
285s ***** error <plot: invalid NAME for optional argument.> ...
285s  plot (WeibullDistribution, "invalidNAME", "pdf")
285s ***** error <plot: no fitted DATA to plot a probability plot.> ...
285s  plot (WeibullDistribution, "PlotType", "probability")
285s ***** error <proflik: no fitted data available.> ...
285s  proflik (WeibullDistribution, 2)
285s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
285s  proflik (WeibullDistribution.fit (x), 3)
285s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
285s  proflik (WeibullDistribution.fit (x), [1, 2])
285s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
285s  proflik (WeibullDistribution.fit (x), {1})
285s ***** error <proflik: SETPARAM must be a numeric vector.> ...
285s  proflik (WeibullDistribution.fit (x), 1, ones (2))
285s ***** error <proflik: missing VALUE for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display")
285s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display", 1)
285s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display", {1})
285s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
285s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
285s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
285s ***** error <proflik: invalid NAME for optional arguments.> ...
285s  proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
285s ***** error <proflik: invalid optional argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
285s ***** error <proflik: invalid optional argument.> ...
285s  proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
285s ***** error <truncate: missing input argument.> ...
285s  truncate (WeibullDistribution)
285s ***** error <truncate: missing input argument.> ...
285s  truncate (WeibullDistribution, 2)
285s ***** error <truncate: invalid lower upper limits.> ...
285s  truncate (WeibullDistribution, 4, 2)
285s ***** shared pd
285s  pd = WeibullDistribution(1, 1);
285s  pd(2) = WeibullDistribution(1, 3);
285s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
285s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
285s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
285s ***** error <mean: requires a scalar probability distribution.> mean (pd)
285s ***** error <median: requires a scalar probability distribution.> median (pd)
285s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
285s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
285s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
285s ***** error <plot: requires a scalar probability distribution.> plot (pd)
285s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
285s ***** error <random: requires a scalar probability distribution.> random (pd)
285s ***** error <std: requires a scalar probability distribution.> std (pd)
285s ***** error <truncate: requires a scalar probability distribution.> ...
285s  truncate (pd, 2, 4)
285s ***** error <var: requires a scalar probability distribution.> var (pd)
285s 97 tests, 97 passed, 0 known failure, 0 skipped
285s [inst/dist_obj/ExponentialDistribution.m]
285s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/ExponentialDistribution.m
285s ***** shared pd, t
285s  pd = ExponentialDistribution (1);
285s  t = truncate (pd, 2, 4);
285s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
285s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
285s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
285s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
285s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
285s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
285s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
285s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
285s ***** assert (iqr (pd), 1.0986, 1e-4);
285s ***** assert (iqr (t), 0.8020, 1e-4);
285s ***** assert (mean (pd), 1);
285s ***** assert (mean (t), 2.6870, 1e-4);
285s ***** assert (median (pd), 0.6931, 1e-4);
285s ***** assert (median (t), 2.5662, 1e-4);
285s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
285s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
285s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
285s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
285s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
285s ***** assert (any (random (t, 1000, 1) < 2), false);
285s ***** assert (any (random (t, 1000, 1) > 4), false);
286s ***** assert (std (pd), 1);
286s ***** assert (std (t), 0.5253, 1e-4);
286s ***** assert (var (pd), 1);
286s ***** assert (var (t), 0.2759, 1e-4);
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution(0)
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution(-1)
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution(Inf)
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution(i)
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution("a")
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution([1, 2])
286s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
286s  ExponentialDistribution(NaN)
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (ExponentialDistribution, 2, "uper")
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (ExponentialDistribution, 2, 3)
286s ***** shared x
286s  x = exprnd (1, [100, 1]);
286s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha")
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 0)
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 1)
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", "")
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
286s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
286s           "alpha", {0.05})
286s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
286s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
286s           "parameter", {"mu", "param"})
286s ***** error <paramci: unknown distribution parameter.> ...
286s  paramci (ExponentialDistribution.fit (x), "parameter", "param")
286s ***** error <paramci: unknown distribution parameter.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
286s ***** error <paramci: invalid NAME for optional argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "NAME", "value")
286s ***** error <paramci: invalid NAME for optional argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
286s ***** error <paramci: invalid NAME for optional argument.> ...
286s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
286s           "parameter", "mu", "NAME", "value")
286s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
286s  plot (ExponentialDistribution, "Parent")
286s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
286s  plot (ExponentialDistribution, "PlotType", 12)
286s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
286s  plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
286s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
286s  plot (ExponentialDistribution, "PlotType", "pdfcdf")
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (ExponentialDistribution, "Discrete", "pdfcdf")
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (ExponentialDistribution, "Discrete", [1, 0])
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (ExponentialDistribution, "Discrete", {true})
286s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
286s  plot (ExponentialDistribution, "Parent", 12)
286s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
286s  plot (ExponentialDistribution, "Parent", "hax")
286s ***** error <plot: invalid NAME for optional argument.> ...
286s  plot (ExponentialDistribution, "invalidNAME", "pdf")
286s ***** error <plot: no fitted DATA to plot a probability plot.> ...
286s  plot (ExponentialDistribution, "PlotType", "probability")
286s ***** error <proflik: no fitted data available.> ...
286s  proflik (ExponentialDistribution, 2)
286s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
286s  proflik (ExponentialDistribution.fit (x), 3)
286s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
286s  proflik (ExponentialDistribution.fit (x), [1, 2])
286s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
286s  proflik (ExponentialDistribution.fit (x), {1})
286s ***** error <proflik: SETPARAM must be a numeric vector.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, ones (2))
286s ***** error <proflik: missing VALUE for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display")
286s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
286s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
286s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
286s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
286s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
286s ***** error <proflik: invalid NAME for optional arguments.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
286s ***** error <proflik: invalid optional argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
286s ***** error <proflik: invalid optional argument.> ...
286s  proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
286s ***** error <truncate: missing input argument.> ...
286s  truncate (ExponentialDistribution)
286s ***** error <truncate: missing input argument.> ...
286s  truncate (ExponentialDistribution, 2)
286s ***** error <truncate: invalid lower upper limits.> ...
286s  truncate (ExponentialDistribution, 4, 2)
286s ***** shared pd
286s  pd = ExponentialDistribution(1);
286s  pd(2) = ExponentialDistribution(3);
286s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
286s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
286s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
286s ***** error <mean: requires a scalar probability distribution.> mean (pd)
286s ***** error <median: requires a scalar probability distribution.> median (pd)
286s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
286s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
286s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
286s ***** error <plot: requires a scalar probability distribution.> plot (pd)
286s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
286s ***** error <random: requires a scalar probability distribution.> random (pd)
286s ***** error <std: requires a scalar probability distribution.> std (pd)
286s ***** error <truncate: requires a scalar probability distribution.> ...
286s  truncate (pd, 2, 4)
286s ***** error <var: requires a scalar probability distribution.> var (pd)
286s 90 tests, 90 passed, 0 known failure, 0 skipped
286s [inst/dist_obj/LoguniformDistribution.m]
286s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/LoguniformDistribution.m
286s ***** demo
286s  ## Generate a data set of 5000 random samples from a Log-uniform distribution with
286s  ## parameters Lower = 1 and Upper = 10.  Plot a PDF of the distribution superimposed
286s  ## on a histogram of the data.
286s 
286s  pd_fixed = makedist ("Loguniform", "Lower", 1, "Upper", 10);
286s  rand ("seed", 2);
286s  data = random (pd_fixed, 5000, 1);
286s  plot (pd_fixed)
286s  hold on
286s  hist (data, 50)
286s  hold off
286s  msg = "Log-uniform distribution with Lower = %0.2f and Upper = %0.2f";
286s  title (sprintf (msg, pd_fixed.Lower, pd_fixed.Upper))
286s ***** shared pd, t
286s  pd = LoguniformDistribution (1, 4);
286s  t = truncate (pd, 2, 4);
286s ***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
286s ***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
286s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
286s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
286s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
286s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
286s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
286s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
286s ***** assert (iqr (pd), 1.4142, 1e-4);
286s ***** assert (iqr (t), 0.9852, 1e-4);
286s ***** assert (mean (pd), 2.1640, 1e-4);
286s ***** assert (mean (t), 2.8854, 1e-4);
286s ***** assert (median (pd), 2);
286s ***** assert (median (t), 2.8284, 1e-4);
286s ***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
286s ***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
286s ***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
286s ***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
286s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
286s ***** assert (any (random (pd, 1000, 1) < 1), false);
286s ***** assert (any (random (pd, 1000, 1) > 4), false);
286s ***** assert (any (random (t, 1000, 1) < 2), false);
286s ***** assert (any (random (t, 1000, 1) > 4), false);
286s ***** assert (std (pd), 0.8527, 1e-4);
286s ***** assert (std (t), 0.5751, 1e-4);
286s ***** assert (var (pd), 0.7270, 1e-4);
286s ***** assert (var (t), 0.3307, 1e-4);
286s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
286s  LoguniformDistribution (i, 1)
286s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
286s  LoguniformDistribution (Inf, 1)
286s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
286s  LoguniformDistribution ([1, 2], 1)
286s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
286s  LoguniformDistribution ("a", 1)
286s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
286s  LoguniformDistribution (NaN, 1)
286s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
286s  LoguniformDistribution (1, i)
286s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
286s  LoguniformDistribution (1, Inf)
286s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
286s  LoguniformDistribution (1, [1, 2])
286s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
286s  LoguniformDistribution (1, "a")
286s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
286s  LoguniformDistribution (1, NaN)
286s ***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
286s  LoguniformDistribution (2, 1)
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (LoguniformDistribution, 2, "uper")
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (LoguniformDistribution, 2, 3)
286s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
286s  plot (LoguniformDistribution, "Parent")
286s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
286s  plot (LoguniformDistribution, "PlotType", 12)
286s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
286s  plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
286s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
286s  plot (LoguniformDistribution, "PlotType", "pdfcdf")
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (LoguniformDistribution, "Discrete", "pdfcdf")
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (LoguniformDistribution, "Discrete", [1, 0])
286s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
286s  plot (LoguniformDistribution, "Discrete", {true})
286s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
286s  plot (LoguniformDistribution, "Parent", 12)
286s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
286s  plot (LoguniformDistribution, "Parent", "hax")
286s ***** error <plot: invalid NAME for optional argument.> ...
286s  plot (LoguniformDistribution, "invalidNAME", "pdf")
286s ***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
286s  plot (LoguniformDistribution, "PlotType", "probability")
286s ***** error <truncate: missing input argument.> ...
286s  truncate (LoguniformDistribution)
286s ***** error <truncate: missing input argument.> ...
286s  truncate (LoguniformDistribution, 2)
286s ***** error <truncate: invalid lower upper limits.> ...
286s  truncate (LoguniformDistribution, 4, 2)
286s ***** shared pd
286s  pd = LoguniformDistribution(1, 4);
286s  pd(2) = LoguniformDistribution(2, 5);
286s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
286s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
286s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
286s ***** error <mean: requires a scalar probability distribution.> mean (pd)
286s ***** error <median: requires a scalar probability distribution.> median (pd)
286s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
286s ***** error <plot: requires a scalar probability distribution.> plot (pd)
286s ***** error <random: requires a scalar probability distribution.> random (pd)
286s ***** error <std: requires a scalar probability distribution.> std (pd)
286s ***** error <truncate: requires a scalar probability distribution.> ...
286s  truncate (pd, 2, 4)
286s ***** error <var: requires a scalar probability distribution.> var (pd)
286s 65 tests, 65 passed, 0 known failure, 0 skipped
286s [inst/dist_obj/NormalDistribution.m]
286s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/NormalDistribution.m
286s ***** demo
286s  ## Generate a data set of 5000 random samples from a Normal distribution with
286s  ## parameters mu = 0 and sigma = 1.  Fit a Normal distribution to this data and plot
286s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
286s 
286s  pd_fixed = makedist ("Normal", "mu", 0, "sigma", 1)
286s  randn ("seed", 2);
286s  data = random (pd_fixed, 5000, 1);
286s  pd_fitted = fitdist (data, "Normal")
286s  plot (pd_fitted)
286s  msg = "Fitted Normal distribution with mu = %0.2f and sigma = %0.2f";
286s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
286s ***** shared pd, t
286s  pd = NormalDistribution;
286s  t = truncate (pd, -2, 2);
286s ***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
286s ***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
286s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
286s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
286s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
286s ***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
286s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
286s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
286s ***** assert (iqr (pd), 1.3490, 1e-4);
286s ***** assert (iqr (t), 1.2782, 1e-4);
286s ***** assert (mean (pd), 0);
286s ***** assert (mean (t), 0, 3e-16);
286s ***** assert (median (pd), 0);
286s ***** assert (median (t), 0, 3e-16);
286s ***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
286s ***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
286s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
286s ***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
286s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
286s ***** assert (any (random (t, 1000, 1) < -2), false);
286s ***** assert (any (random (t, 1000, 1) > 2), false);
286s ***** assert (std (pd), 1);
286s ***** assert (std (t), 0.8796, 1e-4);
286s ***** assert (var (pd), 1);
286s ***** assert (var (t), 0.7737, 1e-4);
286s ***** error <NormalDistribution: MU must be a real scalar.> ...
286s  NormalDistribution(Inf, 1)
286s ***** error <NormalDistribution: MU must be a real scalar.> ...
286s  NormalDistribution(i, 1)
286s ***** error <NormalDistribution: MU must be a real scalar.> ...
286s  NormalDistribution("a", 1)
286s ***** error <NormalDistribution: MU must be a real scalar.> ...
286s  NormalDistribution([1, 2], 1)
286s ***** error <NormalDistribution: MU must be a real scalar.> ...
286s  NormalDistribution(NaN, 1)
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, 0)
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, -1)
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, Inf)
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, i)
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, "a")
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, [1, 2])
286s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
286s  NormalDistribution(1, NaN)
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (NormalDistribution, 2, "uper")
286s ***** error <cdf: invalid argument for upper tail.> ...
286s  cdf (NormalDistribution, 2, 3)
286s ***** shared x
286s  x = normrnd (1, 1, [1, 100]);
287s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
287s  paramci (NormalDistribution.fit (x), "alpha")
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 0)
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 1)
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", "")
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", {0.05})
287s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
287s  paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
287s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
287s  paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
287s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
287s           "parameter", {"mu", "sigma", "param"})
287s ***** error <paramci: unknown distribution parameter.> ...
287s  paramci (NormalDistribution.fit (x), "parameter", "param")
287s ***** error <paramci: unknown distribution parameter.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
287s ***** error <paramci: invalid NAME for optional argument.> ...
287s  paramci (NormalDistribution.fit (x), "NAME", "value")
287s ***** error <paramci: invalid NAME for optional argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
287s ***** error <paramci: invalid NAME for optional argument.> ...
287s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
287s           "NAME", "value")
288s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
288s  plot (NormalDistribution, "Parent")
288s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
288s  plot (NormalDistribution, "PlotType", 12)
288s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
288s  plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
288s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
288s  plot (NormalDistribution, "PlotType", "pdfcdf")
288s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
288s  plot (NormalDistribution, "Discrete", "pdfcdf")
288s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
288s  plot (NormalDistribution, "Discrete", [1, 0])
288s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
288s  plot (NormalDistribution, "Discrete", {true})
288s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
288s  plot (NormalDistribution, "Parent", 12)
288s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
288s  plot (NormalDistribution, "Parent", "hax")
288s ***** error <plot: invalid NAME for optional argument.> ...
288s  plot (NormalDistribution, "invalidNAME", "pdf")
288s ***** error <plot: no fitted DATA to plot a probability plot.> ...
288s  plot (NormalDistribution, "PlotType", "probability")
288s ***** error <proflik: no fitted data available.> ...
288s  proflik (NormalDistribution, 2)
288s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
288s  proflik (NormalDistribution.fit (x), 3)
288s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
288s  proflik (NormalDistribution.fit (x), [1, 2])
288s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
288s  proflik (NormalDistribution.fit (x), {1})
288s ***** error <proflik: SETPARAM must be a numeric vector.> ...
288s  proflik (NormalDistribution.fit (x), 1, ones (2))
288s ***** error <proflik: missing VALUE for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display")
288s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display", 1)
288s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display", {1})
288s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
288s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
288s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
288s ***** error <proflik: invalid NAME for optional arguments.> ...
288s  proflik (NormalDistribution.fit (x), 1, "NAME", "on")
288s ***** error <proflik: invalid optional argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
288s ***** error <proflik: invalid optional argument.> ...
288s  proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
289s ***** error <truncate: missing input argument.> ...
289s  truncate (NormalDistribution)
289s ***** error <truncate: missing input argument.> ...
289s  truncate (NormalDistribution, 2)
289s ***** error <truncate: invalid lower upper limits.> ...
289s  truncate (NormalDistribution, 4, 2)
289s ***** shared pd
289s  pd = NormalDistribution(1, 1);
289s  pd(2) = NormalDistribution(1, 3);
289s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
289s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
289s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
289s ***** error <mean: requires a scalar probability distribution.> mean (pd)
289s ***** error <median: requires a scalar probability distribution.> median (pd)
289s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
289s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
289s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
289s ***** error <plot: requires a scalar probability distribution.> plot (pd)
289s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
289s ***** error <random: requires a scalar probability distribution.> random (pd)
289s ***** error <std: requires a scalar probability distribution.> std (pd)
289s ***** error <truncate: requires a scalar probability distribution.> ...
289s  truncate (pd, 2, 4)
289s ***** error <var: requires a scalar probability distribution.> var (pd)
289s 95 tests, 95 passed, 0 known failure, 0 skipped
289s [inst/dist_obj/PiecewiseLinearDistribution.m]
289s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/PiecewiseLinearDistribution.m
289s ***** demo
289s  ## Generate a data set of 5000 random samples from a Beta distribution with
289s  ## parameters a = 2 and b = 5 scaled to [0,10].
289s  ## Compute empirical CDF, subsample, create PiecewiseLinearDistribution,
289s  ## and plot the PDF superimposed on a histogram of the data.
289s 
289s  randg ("seed", 2);
289s  data = betarnd (2, 5, 5000, 1) * 10;
289s  [f, x] = ecdf (data);
289s  f = f(1:5:end);
289s  x = x(1:5:end);
289s  pd = PiecewiseLinearDistribution (x, f);
289s  [counts, centers] = hist (data, 50);
289s  bin_width = centers(2) - centers(1);
289s  bar (centers, counts / (sum (counts) * bin_width), 1);
289s  hold on
289s  vals = min (data):0.1:max (data);
289s  y = pdf (pd, vals);
289s  plot (vals, y, "-r", "LineWidth", 2)
289s  hold off
289s  title ("Piecewise Linear approximation to scaled Beta(2,5) data")
289s  legend ("Histogram", "Piecewise PDF")
289s ***** shared pd, t
289s  load patients
289s  [f, x] = ecdf (Weight);
289s  f = f(1:5:end);
289s  x = x(1:5:end);
289s  pd = PiecewiseLinearDistribution (x, f);
289s  t = truncate (pd, 130, 180);
289s ***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
289s ***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
289s ***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
289s ***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
289s ***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
289s ***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
289s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
289s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
289s ***** assert (iqr (pd), 50.0833, 1e-4);
289s ***** assert (iqr (t), 36.8077, 1e-4);
289s ***** assert (mean (pd), 153.61, 1e-10);
289s ***** assert (mean (t), 152.311, 1e-3);
293s ***** assert (median (pd), 142, 1e-10);
293s ***** assert (median (t), 141.9462, 1e-4);
293s ***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0004], 6e-3);
293s ***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
293s ***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
293s ***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
293s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
293s ***** assert (any (random (t, 1000, 1) < 130), false);
293s ***** assert (any (random (t, 1000, 1) > 180), false);
293s ***** assert (std (pd), 26.5196, 1e-4);
293s ***** assert (std (t), 18.2941, 1e-4);
301s ***** assert (var (pd), 703.2879, 1e-4);
301s ***** assert (var (t), 334.6757, 1e-4);
310s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, i], [0, 1])
310s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, Inf], [0, 1])
310s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
310s  PiecewiseLinearDistribution (["a", "c"], [0, 1])
310s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
310s  PiecewiseLinearDistribution ([NaN, 1], [0, 1])
310s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, 1], [0, i])
310s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, 1], [0, Inf])
310s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, 1], ["a", "c"])
310s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
310s  PiecewiseLinearDistribution ([0, 1], [NaN, 1])
310s ***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
310s  PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
310s ***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
310s  PiecewiseLinearDistribution ([0], [1])
310s ***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
310s  PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
310s ***** error <cdf: invalid argument for upper tail.> ...
310s  cdf (PiecewiseLinearDistribution, 2, "uper")
310s ***** error <cdf: invalid argument for upper tail.> ...
310s  cdf (PiecewiseLinearDistribution, 2, 3)
310s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
310s  plot (PiecewiseLinearDistribution, "Parent")
310s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
310s  plot (PiecewiseLinearDistribution, "PlotType", 12)
310s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
310s  plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
310s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
310s  plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
310s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
310s  plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
310s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
310s  plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
310s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
310s  plot (PiecewiseLinearDistribution, "Discrete", {true})
310s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
310s  plot (PiecewiseLinearDistribution, "Parent", 12)
310s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
310s  plot (PiecewiseLinearDistribution, "Parent", "hax")
310s ***** error <plot: invalid NAME for optional argument.> ...
310s  plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
310s ***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
310s  plot (PiecewiseLinearDistribution, "PlotType", "probability")
310s ***** error <truncate: missing input argument.> ...
310s  truncate (PiecewiseLinearDistribution)
310s ***** error <truncate: missing input argument.> ...
310s  truncate (PiecewiseLinearDistribution, 2)
310s ***** error <truncate: invalid lower upper limits.> ...
310s  truncate (PiecewiseLinearDistribution, 4, 2)
310s ***** shared pd
310s  pd = PiecewiseLinearDistribution ();
310s  pd(2) = PiecewiseLinearDistribution ();
310s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
310s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
310s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
310s ***** error <mean: requires a scalar probability distribution.> mean (pd)
310s ***** error <median: requires a scalar probability distribution.> median (pd)
310s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
310s ***** error <plot: requires a scalar probability distribution.> plot (pd)
310s ***** error <random: requires a scalar probability distribution.> random (pd)
310s ***** error <std: requires a scalar probability distribution.> std (pd)
310s ***** error <truncate: requires a scalar probability distribution.> ...
310s  truncate (pd, 2, 4)
310s ***** error <var: requires a scalar probability distribution.> var (pd)
310s 63 tests, 63 passed, 0 known failure, 0 skipped
310s [inst/dist_obj/RicianDistribution.m]
310s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/RicianDistribution.m
310s ***** demo
310s  ## Generate a data set of 5000 random samples from a Rician distribution with
310s  ## parameters s = 2 and sigma = 1. Fit a Rician distribution to this data and
310s  ## plot a PDF of the fitted distribution superimposed on a histogram of the data.
310s 
310s  pd_fixed = makedist ("Rician", "s", 2, "sigma", 1)
310s  rand ("seed", 2);
310s  data = random (pd_fixed, 5000, 1);
310s  pd_fitted = fitdist (data, "Rician")
310s  plot (pd_fitted)
310s  msg = "Fitted Rician distribution with s = %0.2f and sigma = %0.2f";
310s  title (sprintf (msg, pd_fitted.s, pd_fitted.sigma))
310s ***** shared pd, t
310s  pd = RicianDistribution;
310s  t = truncate (pd, 2, 4);
310s ***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
310s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
310s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
310s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
310s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
310s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
310s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
310s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
310s ***** assert (iqr (pd), 1.0890, 1e-4);
310s ***** assert (iqr (t), 0.5928, 1e-4);
310s ***** assert (mean (pd), 1.5486, 1e-4);
310s ***** assert (mean (t), 2.5380, 1e-4);
310s ***** assert (median (pd), 1.4755, 1e-4);
310s ***** assert (median (t), 2.4341, 1e-4);
310s ***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
310s ***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
310s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
310s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
310s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
310s ***** assert (any (random (t, 1000, 1) < 2), false);
310s ***** assert (any (random (t, 1000, 1) > 4), false);
310s ***** assert (std (pd), 0.7758, 1e-4);
310s ***** assert (std (t), 0.4294, 1e-4);
310s ***** assert (var (pd), 0.6019, 1e-4);
310s ***** assert (var (t), 0.1844, 1e-4);
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution(-eps, 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution(-1, 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution(Inf, 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution(i, 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution("a", 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution([1, 2], 1)
310s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
310s  RicianDistribution(NaN, 1)
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, 0)
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, -1)
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, Inf)
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, i)
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, "a")
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, [1, 2])
310s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
310s  RicianDistribution(1, NaN)
310s ***** error <cdf: invalid argument for upper tail.> ...
310s  cdf (RicianDistribution, 2, "uper")
310s ***** error <cdf: invalid argument for upper tail.> ...
310s  cdf (RicianDistribution, 2, 3)
310s ***** shared x
310s  x = gevrnd (1, 1, 1, [1, 100]);
310s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
310s  paramci (RicianDistribution.fit (x), "alpha")
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 0)
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 1)
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", "")
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", {0.05})
311s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
311s  paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
311s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
311s  paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
311s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
311s           "parameter", {"s", "sigma", "param"})
311s ***** error <paramci: unknown distribution parameter.> ...
311s  paramci (RicianDistribution.fit (x), "parameter", "param")
311s ***** error <paramci: unknown distribution parameter.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
311s ***** error <paramci: invalid NAME for optional argument.> ...
311s  paramci (RicianDistribution.fit (x), "NAME", "value")
311s ***** error <paramci: invalid NAME for optional argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
311s ***** error <paramci: invalid NAME for optional argument.> ...
311s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
311s           "NAME", "value")
311s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
311s  plot (RicianDistribution, "Parent")
311s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
311s  plot (RicianDistribution, "PlotType", 12)
311s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
311s  plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
311s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
311s  plot (RicianDistribution, "PlotType", "pdfcdf")
311s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
311s  plot (RicianDistribution, "Discrete", "pdfcdf")
311s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
311s  plot (RicianDistribution, "Discrete", [1, 0])
311s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
311s  plot (RicianDistribution, "Discrete", {true})
311s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
311s  plot (RicianDistribution, "Parent", 12)
311s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
311s  plot (RicianDistribution, "Parent", "hax")
311s ***** error <plot: invalid NAME for optional argument.> ...
311s  plot (RicianDistribution, "invalidNAME", "pdf")
311s ***** error <plot: no fitted DATA to plot a probability plot.> ...
311s  plot (RicianDistribution, "PlotType", "probability")
311s ***** error <proflik: no fitted data available.> ...
311s  proflik (RicianDistribution, 2)
311s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
311s  proflik (RicianDistribution.fit (x), 3)
311s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
311s  proflik (RicianDistribution.fit (x), [1, 2])
312s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
312s  proflik (RicianDistribution.fit (x), {1})
312s ***** error <proflik: SETPARAM must be a numeric vector.> ...
312s  proflik (RicianDistribution.fit (x), 1, ones (2))
312s ***** error <proflik: missing VALUE for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display")
312s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display", 1)
312s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display", {1})
312s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
312s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
312s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
312s ***** error <proflik: invalid NAME for optional arguments.> ...
312s  proflik (RicianDistribution.fit (x), 1, "NAME", "on")
312s ***** error <proflik: invalid optional argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
312s ***** error <proflik: invalid optional argument.> ...
312s  proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
312s ***** error <truncate: missing input argument.> ...
312s  truncate (RicianDistribution)
312s ***** error <truncate: missing input argument.> ...
312s  truncate (RicianDistribution, 2)
312s ***** error <truncate: invalid lower upper limits.> ...
312s  truncate (RicianDistribution, 4, 2)
312s ***** shared pd
312s  pd = RicianDistribution(1, 1);
312s  pd(2) = RicianDistribution(1, 3);
312s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
312s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
312s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
312s ***** error <mean: requires a scalar probability distribution.> mean (pd)
312s ***** error <median: requires a scalar probability distribution.> median (pd)
312s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
312s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
312s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
312s ***** error <plot: requires a scalar probability distribution.> plot (pd)
312s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
312s ***** error <random: requires a scalar probability distribution.> random (pd)
312s ***** error <std: requires a scalar probability distribution.> std (pd)
312s ***** error <truncate: requires a scalar probability distribution.> ...
312s  truncate (pd, 2, 4)
312s ***** error <var: requires a scalar probability distribution.> var (pd)
312s 97 tests, 97 passed, 0 known failure, 0 skipped
312s [inst/dist_obj/BirnbaumSaundersDistribution.m]
312s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/BirnbaumSaundersDistribution.m
312s ***** demo
312s  ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
312s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
312s  ## distribution to this data and plot a PDF of the fitted distribution
312s  ## superimposed on a histogram of the data.
312s 
312s  pd_fixed = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
312s  randg ("seed", 21);
312s  data = random (pd_fixed, 5000, 1);
312s  pd_fitted = fitdist (data, "BirnbaumSaunders")
312s  plot (pd_fitted)
312s  msg = "Fitted Birnbaum-Saunders distribution with beta = %0.2f and gamma = %0.2f";
312s  title (sprintf (msg, pd_fitted.beta, pd_fitted.gamma))
312s ***** shared pd, t
312s  pd = BirnbaumSaundersDistribution;
312s  t = truncate (pd, 2, 4);
312s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
312s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
312s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
312s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
312s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
312s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
312s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
312s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
312s ***** assert (iqr (pd), 1.4236, 1e-4);
312s ***** assert (iqr (t), 0.8968, 1e-4);
312s ***** assert (mean (pd), 1.5, eps);
312s ***** assert (mean (t), 2.7723, 1e-4);
312s ***** assert (median (pd), 1, 1e-4);
312s ***** assert (median (t), 2.6711, 1e-4);
312s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
312s ***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
312s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
312s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
312s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
312s ***** assert (any (random (t, 1000, 1) < 2), false);
312s ***** assert (any (random (t, 1000, 1) > 4), false);
312s ***** assert (std (pd), 1.5, eps);
312s ***** assert (std (t), 0.5528, 1e-4);
312s ***** assert (var (pd), 2.25, eps);
312s ***** assert (var (t), 0.3056, 1e-4);
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(0, 1)
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(Inf, 1)
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(i, 1)
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution("beta", 1)
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution([1, 2], 1)
312s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(NaN, 1)
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, 0)
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, -1)
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, Inf)
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, i)
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, "beta")
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, [1, 2])
312s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
312s  BirnbaumSaundersDistribution(1, NaN)
312s ***** error <cdf: invalid argument for upper tail.> ...
312s  cdf (BirnbaumSaundersDistribution, 2, "uper")
312s ***** error <cdf: invalid argument for upper tail.> ...
312s  cdf (BirnbaumSaundersDistribution, 2, 3)
312s ***** shared x
312s  rand ("seed", 5);
312s  x = bisarnd (1, 1, [100, 1]);
312s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
312s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
313s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
313s           "beta", "alpha", {0.05})
313s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), ...
313s           "parameter", {"beta", "gamma", "param"})
313s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
313s           "parameter", {"beta", "gamma", "param"})
313s ***** error <paramci: unknown distribution parameter.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
313s ***** error <paramci: unknown distribution parameter.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
313s           "parameter", "param")
313s ***** error <paramci: invalid NAME for optional argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
313s ***** error <paramci: invalid NAME for optional argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
313s           "NAME", "value")
313s ***** error <paramci: invalid NAME for optional argument.> ...
313s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
313s           "parameter", "beta", "NAME", "value")
313s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
313s  plot (BirnbaumSaundersDistribution, "Parent")
313s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "PlotType", 12)
313s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
313s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
313s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
313s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
313s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "Discrete", {true})
313s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "Parent", 12)
313s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
313s  plot (BirnbaumSaundersDistribution, "Parent", "hax")
313s ***** error <plot: invalid NAME for optional argument.> ...
313s  plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
313s ***** error <plot: no fitted DATA to plot a probability plot.> ...
313s  plot (BirnbaumSaundersDistribution, "PlotType", "probability")
313s ***** error <proflik: no fitted data available.> ...
313s  proflik (BirnbaumSaundersDistribution, 2)
313s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 3)
313s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
313s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), {1})
313s ***** error <proflik: SETPARAM must be a numeric vector.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
313s ***** error <proflik: missing VALUE for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
313s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
313s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
313s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
313s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
313s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
313s ***** error <proflik: invalid NAME for optional arguments.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
313s ***** error <proflik: invalid optional argument.> ...
313s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
314s ***** error <proflik: invalid optional argument.> ...
314s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
314s ***** error <truncate: missing input argument.> ...
314s  truncate (BirnbaumSaundersDistribution)
314s ***** error <truncate: missing input argument.> ...
314s  truncate (BirnbaumSaundersDistribution, 2)
314s ***** error <truncate: invalid lower upper limits.> ...
314s  truncate (BirnbaumSaundersDistribution, 4, 2)
314s ***** shared pd
314s  pd = BirnbaumSaundersDistribution(1, 1);
314s  pd(2) = BirnbaumSaundersDistribution(1, 3);
314s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
314s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
314s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
314s ***** error <mean: requires a scalar probability distribution.> mean (pd)
314s ***** error <median: requires a scalar probability distribution.> median (pd)
314s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
314s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
314s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
314s ***** error <plot: requires a scalar probability distribution.> plot (pd)
314s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
314s ***** error <random: requires a scalar probability distribution.> random (pd)
314s ***** error <std: requires a scalar probability distribution.> std (pd)
314s ***** error <truncate: requires a scalar probability distribution.> ...
314s  truncate (pd, 2, 4)
314s ***** error <var: requires a scalar probability distribution.> var (pd)
314s 96 tests, 96 passed, 0 known failure, 0 skipped
314s [inst/dist_obj/GammaDistribution.m]
314s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/GammaDistribution.m
314s ***** shared pd, t
314s  pd = GammaDistribution (1, 1);
314s  t = truncate (pd, 2, 4);
314s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
314s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
314s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
314s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
314s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
314s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
314s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
314s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
314s ***** assert (iqr (pd), 1.0986, 1e-4);
314s ***** assert (iqr (t), 0.8020, 1e-4);
314s ***** assert (mean (pd), 1);
314s ***** assert (mean (t), 2.6870, 1e-4);
314s ***** assert (median (pd), 0.6931, 1e-4);
314s ***** assert (median (t), 2.5662, 1e-4);
314s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
314s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
314s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
314s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
314s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
314s ***** assert (any (random (t, 1000, 1) < 2), false);
314s ***** assert (any (random (t, 1000, 1) > 4), false);
314s ***** assert (std (pd), 1);
314s ***** assert (std (t), 0.5253, 1e-4);
314s ***** assert (var (pd), 1);
314s ***** assert (var (t), 0.2759, 1e-4);
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution(0, 1)
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution(Inf, 1)
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution(i, 1)
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution("a", 1)
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution([1, 2], 1)
314s ***** error <GammaDistribution: A must be a positive real scalar.> ...
314s  GammaDistribution(NaN, 1)
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, 0)
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, -1)
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, Inf)
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, i)
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, "a")
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, [1, 2])
314s ***** error <GammaDistribution: B must be a positive real scalar.> ...
314s  GammaDistribution(1, NaN)
314s ***** error <cdf: invalid argument for upper tail.> ...
314s  cdf (GammaDistribution, 2, "uper")
314s ***** error <cdf: invalid argument for upper tail.> ...
314s  cdf (GammaDistribution, 2, 3)
314s ***** shared x
314s  x = gamrnd (1, 1, [100, 1]);
314s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
314s  paramci (GammaDistribution.fit (x), "alpha")
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 0)
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 1)
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", "")
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", {0.05})
314s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
314s  paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
314s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
314s  paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
314s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
314s           "parameter", {"a", "b", "param"})
314s ***** error <paramci: unknown distribution parameter.> ...
314s  paramci (GammaDistribution.fit (x), "parameter", "param")
314s ***** error <paramci: unknown distribution parameter.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
314s ***** error <paramci: invalid NAME for optional argument.> ...
314s  paramci (GammaDistribution.fit (x), "NAME", "value")
314s ***** error <paramci: invalid NAME for optional argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
314s ***** error <paramci: invalid NAME for optional argument.> ...
314s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
314s           "NAME", "value")
314s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
314s  plot (GammaDistribution, "Parent")
314s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
314s  plot (GammaDistribution, "PlotType", 12)
314s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
314s  plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
314s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
314s  plot (GammaDistribution, "PlotType", "pdfcdf")
314s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
314s  plot (GammaDistribution, "Discrete", "pdfcdf")
314s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
314s  plot (GammaDistribution, "Discrete", [1, 0])
314s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
314s  plot (GammaDistribution, "Discrete", {true})
314s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
314s  plot (GammaDistribution, "Parent", 12)
314s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
314s  plot (GammaDistribution, "Parent", "hax")
314s ***** error <plot: invalid NAME for optional argument.> ...
314s  plot (GammaDistribution, "invalidNAME", "pdf")
314s ***** error <plot: no fitted DATA to plot a probability plot.> ...
314s  plot (GammaDistribution, "PlotType", "probability")
314s ***** error <proflik: no fitted data available.> ...
314s  proflik (GammaDistribution, 2)
314s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
314s  proflik (GammaDistribution.fit (x), 3)
314s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
314s  proflik (GammaDistribution.fit (x), [1, 2])
314s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
314s  proflik (GammaDistribution.fit (x), {1})
314s ***** error <proflik: SETPARAM must be a numeric vector.> ...
314s  proflik (GammaDistribution.fit (x), 1, ones (2))
314s ***** error <proflik: missing VALUE for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display")
314s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display", 1)
314s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display", {1})
314s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
314s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
314s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
314s ***** error <proflik: invalid NAME for optional arguments.> ...
314s  proflik (GammaDistribution.fit (x), 1, "NAME", "on")
314s ***** error <proflik: invalid optional argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
314s ***** error <proflik: invalid optional argument.> ...
314s  proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
314s ***** error <truncate: missing input argument.> ...
314s  truncate (GammaDistribution)
314s ***** error <truncate: missing input argument.> ...
314s  truncate (GammaDistribution, 2)
314s ***** error <truncate: invalid lower upper limits.> ...
314s  truncate (GammaDistribution, 4, 2)
314s ***** shared pd
314s  pd = GammaDistribution(1, 1);
314s  pd(2) = GammaDistribution(1, 3);
314s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
314s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
314s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
314s ***** error <mean: requires a scalar probability distribution.> mean (pd)
314s ***** error <median: requires a scalar probability distribution.> median (pd)
314s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
314s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
314s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
314s ***** error <plot: requires a scalar probability distribution.> plot (pd)
314s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
314s ***** error <random: requires a scalar probability distribution.> random (pd)
314s ***** error <std: requires a scalar probability distribution.> std (pd)
314s ***** error <truncate: requires a scalar probability distribution.> ...
314s  truncate (pd, 2, 4)
314s ***** error <var: requires a scalar probability distribution.> var (pd)
314s 96 tests, 96 passed, 0 known failure, 0 skipped
314s [inst/dist_obj/MultinomialDistribution.m]
314s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/MultinomialDistribution.m
314s ***** demo
314s  ## Generate a data set of 5000 random samples from a Multinomial distribution
314s  ## with parameters Probabilities = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1].  Create
314s  ## the distribution and plot the PDF superimposed on a histogram of the data.
314s 
314s  probs = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1];
314s  pd = makedist ("Multinomial", "Probabilities", probs);
314s  rand ("seed", 2);
314s  data = random (pd, 5000, 1);
314s  hist (data, length (probs));
314s  hold on
314s  x = 1:length (probs);
314s  y = pdf (pd, x) * 5000;
314s  stem (x, y, "r", "LineWidth", 2);
314s  hold off
314s  msg = "Multinomial distribution with Probabilities = [%s]";
314s  probs_str = num2str (probs, "%0.1f ");
314s  title (sprintf (msg, probs_str))
314s ***** shared pd, t
314s  pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
314s  t = truncate (pd, 2, 4);
314s ***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
314s ***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
314s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
314s ***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
314s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
314s ***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
314s ***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
314s ***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
314s ***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
314s ***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
314s ***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
314s ***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
314s ***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
314s ***** assert (iqr (pd), 2);
314s ***** assert (iqr (t), 2);
314s ***** assert (mean (pd), 3.3, 1e-14);
314s ***** assert (mean (t), 3, eps);
314s ***** assert (median (pd), 3);
314s ***** assert (median (t), 3);
314s ***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
314s ***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
314s  [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
314s ***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
314s  [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
314s ***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
314s  [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
314s ***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
314s ***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
314s ***** assert (std (pd), 1.4177, 1e-4);
314s ***** assert (std (t), 0.7559, 1e-4);
314s ***** assert (var (pd), 2.0100, 1e-4);
314s ***** assert (var (t), 0.5714, 1e-4);
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution(0)
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution(-1)
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution(Inf)
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution(i)
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution("a")
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution([1, 2])
314s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
314s  MultinomialDistribution(NaN)
314s ***** error <cdf: invalid argument for upper tail.> ...
314s  cdf (MultinomialDistribution, 2, "uper")
314s ***** error <cdf: invalid argument for upper tail.> ...
314s  cdf (MultinomialDistribution, 2, 3)
314s ***** error <cdf: X must be real.> ...
314s  cdf (MultinomialDistribution, i)
314s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
314s  plot (MultinomialDistribution, "Parent")
314s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
314s  plot (MultinomialDistribution, "PlotType", 12)
314s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
314s  plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
315s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
315s  plot (MultinomialDistribution, "PlotType", "pdfcdf")
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (MultinomialDistribution, "Discrete", "pdfcdf")
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (MultinomialDistribution, "Discrete", [1, 0])
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (MultinomialDistribution, "Discrete", {true})
315s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
315s  plot (MultinomialDistribution, "Parent", 12)
315s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
315s  plot (MultinomialDistribution, "Parent", "hax")
315s ***** error <plot: invalid NAME for optional argument.> ...
315s  plot (MultinomialDistribution, "invalidNAME", "pdf")
315s ***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
315s  plot (MultinomialDistribution, "PlotType", "probability")
315s ***** error <truncate: is_nan input argument.> ...
315s  truncate (MultinomialDistribution)
315s ***** error <truncate: is_nan input argument.> ...
315s  truncate (MultinomialDistribution, 2)
315s ***** error <truncate: invalid lower upper limits.> ...
315s  truncate (MultinomialDistribution, 4, 2)
315s ***** shared pd
315s  pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
315s  pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
315s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
315s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
315s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
315s ***** error <mean: requires a scalar probability distribution.> mean (pd)
315s ***** error <median: requires a scalar probability distribution.> median (pd)
315s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
315s ***** error <plot: requires a scalar probability distribution.> plot (pd)
315s ***** error <random: requires a scalar probability distribution.> random (pd)
315s ***** error <std: requires a scalar probability distribution.> std (pd)
315s ***** error <truncate: requires a scalar probability distribution.> ...
315s  truncate (pd, 2, 4)
315s ***** error <var: requires a scalar probability distribution.> var (pd)
315s 64 tests, 64 passed, 0 known failure, 0 skipped
315s [inst/dist_obj/BinomialDistribution.m]
315s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/BinomialDistribution.m
315s ***** shared pd, t, t_inf
315s  pd = BinomialDistribution (5, 0.5);
315s  t = truncate (pd, 2, 4);
315s  t_inf = truncate (pd, 2, Inf);
315s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
315s ***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
315s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
315s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
315s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
315s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
315s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
315s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
315s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
315s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
315s ***** assert (iqr (pd), 1);
315s ***** assert (iqr (t), 1);
315s ***** assert (mean (pd), 2.5, 1e-10);
315s ***** assert (mean (t), 2.8, 1e-10);
315s ***** assert (mean (t_inf), 2.8846, 1e-4);
315s ***** assert (median (pd), 2.5);
315s ***** assert (median (t), 3);
315s ***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
315s ***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
315s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
315s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
315s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
315s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
315s ***** assert (any (random (t, 1000, 1) < 2), false);
315s ***** assert (any (random (t, 1000, 1) > 4), false);
315s ***** assert (std (pd), 1.1180, 1e-4);
315s ***** assert (std (t), 0.7483, 1e-4);
315s ***** assert (std (t_inf), 0.8470, 1e-4);
315s ***** assert (var (pd), 1.2500, 1e-4);
315s ***** assert (var (t), 0.5600, 1e-4);
315s ***** assert (var (t_inf), 0.7175, 1e-4);
315s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
315s  BinomialDistribution(Inf, 0.5)
315s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
315s  BinomialDistribution(i, 0.5)
315s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
315s  BinomialDistribution("a", 0.5)
315s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
315s  BinomialDistribution([1, 2], 0.5)
315s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
315s  BinomialDistribution(NaN, 0.5)
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, 1.01)
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, -0.01)
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, Inf)
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, i)
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, "a")
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, [1, 2])
315s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
315s  BinomialDistribution(1, NaN)
315s ***** error <cdf: invalid argument for upper tail.> ...
315s  cdf (BinomialDistribution, 2, "uper")
315s ***** error <cdf: invalid argument for upper tail.> ...
315s  cdf (BinomialDistribution, 2, 3)
315s ***** shared x
315s  rand ("seed", 2);
315s  x = binornd (5, 0.5, [1, 100]);
315s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha")
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", "")
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
315s           "alpha", {0.05})
315s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), ...
315s           "parameter", {"N", "p", "param"})
315s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
315s           "parameter", {"N", "p", "param"})
315s ***** error <paramci: unknown distribution parameter.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
315s ***** error <paramci: unknown distribution parameter.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
315s ***** error <paramci: unknown distribution parameter.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
315s           "parameter", "param")
315s ***** error <paramci: invalid NAME for optional argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
315s ***** error <paramci: invalid NAME for optional argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
315s           "NAME", "value")
315s ***** error <paramci: invalid NAME for optional argument.> ...
315s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
315s           "parameter", "p", "NAME", "value")
315s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
315s  plot (BinomialDistribution, "Parent")
315s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
315s  plot (BinomialDistribution, "PlotType", 12)
315s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
315s  plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
315s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
315s  plot (BinomialDistribution, "PlotType", "pdfcdf")
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (BinomialDistribution, "Discrete", "pdfcdf")
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (BinomialDistribution, "Discrete", [1, 0])
315s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
315s  plot (BinomialDistribution, "Discrete", {true})
315s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
315s  plot (BinomialDistribution, "Parent", 12)
315s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
315s  plot (BinomialDistribution, "Parent", "hax")
315s ***** error <plot: invalid NAME for optional argument.> ...
315s  plot (BinomialDistribution, "invalidNAME", "pdf")
315s ***** error <plot: no fitted DATA to plot a probability plot.> ...
315s  plot (BinomialDistribution, "PlotType", "probability")
315s ***** error <proflik: no fitted data available.> ...
315s  proflik (BinomialDistribution, 2)
315s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 3)
315s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
315s  proflik (BinomialDistribution.fit (x, 6), [1, 2])
315s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
315s  proflik (BinomialDistribution.fit (x, 6), {1})
315s ***** error <proflik: SETPARAM must be a numeric vector.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
315s ***** error <proflik: missing VALUE for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display")
315s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
315s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
315s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
315s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
315s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
315s ***** error <proflik: invalid NAME for optional arguments.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
315s ***** error <proflik: invalid optional argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
315s ***** error <proflik: invalid optional argument.> ...
315s  proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
315s ***** error <truncate: missing input argument.> ...
315s  truncate (BinomialDistribution)
315s ***** error <truncate: missing input argument.> ...
315s  truncate (BinomialDistribution, 2)
315s ***** error <truncate: invalid lower upper limits.> ...
315s  truncate (BinomialDistribution, 4, 2)
315s ***** shared pd
315s  pd = BinomialDistribution(1, 0.5);
315s  pd(2) = BinomialDistribution(1, 0.6);
315s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
315s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
315s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
315s ***** error <mean: requires a scalar probability distribution.> mean (pd)
315s ***** error <median: requires a scalar probability distribution.> median (pd)
315s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
315s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
315s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
315s ***** error <plot: requires a scalar probability distribution.> plot (pd)
315s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
315s ***** error <random: requires a scalar probability distribution.> random (pd)
315s ***** error <std: requires a scalar probability distribution.> std (pd)
315s ***** error <truncate: requires a scalar probability distribution.> ...
315s  truncate (pd, 2, 4)
315s ***** error <var: requires a scalar probability distribution.> var (pd)
315s 102 tests, 102 passed, 0 known failure, 0 skipped
315s [inst/dist_obj/RayleighDistribution.m]
315s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/RayleighDistribution.m
315s ***** demo
315s  ## Generate a data set of 5000 random samples from a Rayleigh distribution with
315s  ## parameter sigma = 2.  Fit a Rayleigh distribution to this data and plot
315s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
315s 
315s  pd_fixed = makedist ("Rayleigh", "sigma", 2)
315s  rand ("seed", 2);
315s  data = random (pd_fixed, 5000, 1);
315s  pd_fitted = fitdist (data, "Rayleigh")
315s  plot (pd_fitted)
315s  msg = "Fitted Rayleigh distribution with sigma = %0.2f";
315s  title (sprintf (msg, pd_fitted.sigma))
315s ***** shared pd, t
315s  pd = RayleighDistribution;
315s  t = truncate (pd, 2, 4);
315s ***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
315s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
315s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
315s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
315s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
315s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
315s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
315s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
315s ***** assert (iqr (pd), 0.9066, 1e-4);
315s ***** assert (iqr (t), 0.4609, 1e-4);
315s ***** assert (mean (pd), 1.2533, 1e-4);
315s ***** assert (mean (t), 2.4169, 1e-4);
315s ***** assert (median (pd), 1.1774, 1e-4);
315s ***** assert (median (t), 2.3198, 1e-4);
315s ***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
315s ***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
315s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
315s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
315s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
315s ***** assert (any (random (t, 1000, 1) < 2), false);
315s ***** assert (any (random (t, 1000, 1) > 4), false);
315s ***** assert (std (pd), 0.6551, 1e-4);
315s ***** assert (std (t), 0.3591, 1e-4);
315s ***** assert (var (pd), 0.4292, 1e-4);
315s ***** assert (var (t), 0.1290, 1e-4);
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution(0)
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution(-1)
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution(Inf)
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution(i)
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution("a")
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution([1, 2])
315s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
315s  RayleighDistribution(NaN)
315s ***** error <cdf: invalid argument for upper tail.> ...
315s  cdf (RayleighDistribution, 2, "uper")
315s ***** error <cdf: invalid argument for upper tail.> ...
315s  cdf (RayleighDistribution, 2, 3)
315s ***** shared x
315s  x = raylrnd (1, [1, 100]);
315s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha")
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha", 0)
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha", 1)
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha", "")
315s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
315s  paramci (RayleighDistribution.fit (x), "alpha", {0.05})
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
316s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
316s  paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
316s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
316s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
316s           "parameter", {"sigma", "param"})
316s ***** error <paramci: unknown distribution parameter.> ...
316s  paramci (RayleighDistribution.fit (x), "parameter", "param")
316s ***** error <paramci: unknown distribution parameter.> ...
316s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (RayleighDistribution.fit (x), "NAME", "value")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
316s           "parameter", "sigma", "NAME", "value")
316s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
316s  plot (RayleighDistribution, "Parent")
316s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
316s  plot (RayleighDistribution, "PlotType", 12)
316s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
316s  plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
316s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
316s  plot (RayleighDistribution, "PlotType", "pdfcdf")
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (RayleighDistribution, "Discrete", "pdfcdf")
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (RayleighDistribution, "Discrete", [1, 0])
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (RayleighDistribution, "Discrete", {true})
316s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
316s  plot (RayleighDistribution, "Parent", 12)
316s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
316s  plot (RayleighDistribution, "Parent", "hax")
316s ***** error <plot: invalid NAME for optional argument.> ...
316s  plot (RayleighDistribution, "invalidNAME", "pdf")
316s ***** error <plot: no fitted DATA to plot a probability plot.> ...
316s  plot (RayleighDistribution, "PlotType", "probability")
316s ***** error <proflik: no fitted data available.> ...
316s  proflik (RayleighDistribution, 2)
316s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
316s  proflik (RayleighDistribution.fit (x), 3)
316s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
316s  proflik (RayleighDistribution.fit (x), [1, 2])
316s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
316s  proflik (RayleighDistribution.fit (x), {1})
316s ***** error <proflik: SETPARAM must be a numeric vector.> ...
316s  proflik (RayleighDistribution.fit (x), 1, ones (2))
316s ***** error <proflik: missing VALUE for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display")
316s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display", 1)
316s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display", {1})
316s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
316s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
316s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
316s ***** error <proflik: invalid NAME for optional arguments.> ...
316s  proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
316s ***** error <proflik: invalid optional argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
316s ***** error <proflik: invalid optional argument.> ...
316s  proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
316s ***** error <truncate: missing input argument.> ...
316s  truncate (RayleighDistribution)
316s ***** error <truncate: missing input argument.> ...
316s  truncate (RayleighDistribution, 2)
316s ***** error <truncate: invalid lower upper limits.> ...
316s  truncate (RayleighDistribution, 4, 2)
316s ***** shared pd
316s  pd = RayleighDistribution(1);
316s  pd(2) = RayleighDistribution(3);
316s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
316s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
316s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
316s ***** error <mean: requires a scalar probability distribution.> mean (pd)
316s ***** error <median: requires a scalar probability distribution.> median (pd)
316s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
316s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
316s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
316s ***** error <plot: requires a scalar probability distribution.> plot (pd)
316s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
316s ***** error <random: requires a scalar probability distribution.> random (pd)
316s ***** error <std: requires a scalar probability distribution.> std (pd)
316s ***** error <truncate: requires a scalar probability distribution.> ...
316s  truncate (pd, 2, 4)
316s ***** error <var: requires a scalar probability distribution.> var (pd)
316s 90 tests, 90 passed, 0 known failure, 0 skipped
316s [inst/dist_obj/GeneralizedParetoDistribution.m]
316s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/GeneralizedParetoDistribution.m
316s ***** shared pd, t
316s  pd = GeneralizedParetoDistribution (1, 1, 1);
316s  t = truncate (pd, 2, 4);
316s ***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
316s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
316s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
316s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
316s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
316s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
316s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
316s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
316s ***** assert (iqr (pd), 2.6667, 1e-4);
316s ***** assert (iqr (t), 0.9143, 1e-4);
316s ***** assert (mean (pd), Inf);
316s ***** assert (mean (t), 2.7726, 1e-4);
316s ***** assert (median (pd), 2);
316s ***** assert (median (t), 2.6667, 1e-4);
316s ***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
316s ***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
316s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
316s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
316s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
316s ***** assert (any (random (t, 1000, 1) < 2), false);
316s ***** assert (any (random (t, 1000, 1) > 4), false);
316s ***** assert (std (pd), Inf);
316s ***** assert (std (t), 0.5592, 1e-4);
316s ***** assert (var (pd), Inf);
316s ***** assert (var (t), 0.3128, 1e-4);
316s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
316s  GeneralizedParetoDistribution(Inf, 1, 1)
316s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
316s  GeneralizedParetoDistribution(i, 1, 1)
316s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
316s  GeneralizedParetoDistribution("a", 1, 1)
316s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
316s  GeneralizedParetoDistribution([1, 2], 1, 1)
316s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
316s  GeneralizedParetoDistribution(NaN, 1, 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, 0, 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, -1, 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, Inf, 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, i, 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, "a", 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, [1, 2], 1)
316s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
316s  GeneralizedParetoDistribution(1, NaN, 1)
316s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
316s  GeneralizedParetoDistribution(1, 1, Inf)
316s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
316s  GeneralizedParetoDistribution(1, 1, i)
316s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
316s  GeneralizedParetoDistribution(1, 1, "a")
316s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
316s  GeneralizedParetoDistribution(1, 1, [1, 2])
316s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
316s  GeneralizedParetoDistribution(1, 1, NaN)
316s ***** error <cdf: invalid argument for upper tail.> ...
316s  cdf (GeneralizedParetoDistribution, 2, "uper")
316s ***** error <cdf: invalid argument for upper tail.> ...
316s  cdf (GeneralizedParetoDistribution, 2, 3)
316s ***** shared x
316s  x = gprnd (1, 1, 1, [1, 100]);
316s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
316s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
316s           "parameter", "sigma", "alpha", {0.05})
316s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
316s           "parameter", {"k", "sigma", "param"})
316s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
316s           "parameter", {"k", "sigma", "param"})
316s ***** error <paramci: unknown distribution parameter.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
316s ***** error <paramci: unknown distribution parameter.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
316s           "parameter", "param")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
316s           "NAME", "value")
316s ***** error <paramci: invalid NAME for optional argument.> ...
316s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
316s           "parameter", "sigma", "NAME", "value")
316s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
316s  plot (GeneralizedParetoDistribution, "Parent")
316s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
316s  plot (GeneralizedParetoDistribution, "PlotType", 12)
316s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
316s  plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
316s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
316s  plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
316s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
316s  plot (GeneralizedParetoDistribution, "Discrete", {true})
316s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
316s  plot (GeneralizedParetoDistribution, "Parent", 12)
316s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
316s  plot (GeneralizedParetoDistribution, "Parent", "hax")
316s ***** error <plot: invalid NAME for optional argument.> ...
316s  plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
316s ***** error <plot: no fitted DATA to plot a probability plot.> ...
316s  plot (GeneralizedParetoDistribution, "PlotType", "probability")
316s ***** error <proflik: no fitted data available.> ...
316s  proflik (GeneralizedParetoDistribution, 2)
316s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
316s  proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
316s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
316s  proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
317s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
317s ***** error <proflik: SETPARAM must be a numeric vector.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
317s ***** error <proflik: missing VALUE for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
317s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
317s           "Display", ["on"; "on"])
317s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
317s ***** error <proflik: invalid NAME for optional arguments.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
317s ***** error <proflik: invalid optional argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
317s ***** error <proflik: invalid optional argument.> ...
317s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
317s           "Display", "on")
317s ***** error <truncate: missing input argument.> ...
317s  truncate (GeneralizedParetoDistribution)
317s ***** error <truncate: missing input argument.> ...
317s  truncate (GeneralizedParetoDistribution, 2)
317s ***** error <truncate: invalid lower upper limits.> ...
317s  truncate (GeneralizedParetoDistribution, 4, 2)
317s ***** shared pd
317s  pd = GeneralizedParetoDistribution(1, 1, 1);
317s  pd(2) = GeneralizedParetoDistribution(1, 3, 1);
317s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
317s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
317s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
317s ***** error <mean: requires a scalar probability distribution.> mean (pd)
317s ***** error <median: requires a scalar probability distribution.> median (pd)
317s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
317s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
317s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
317s ***** error <plot: requires a scalar probability distribution.> plot (pd)
317s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
317s ***** error <random: requires a scalar probability distribution.> random (pd)
317s ***** error <std: requires a scalar probability distribution.> std (pd)
317s ***** error <truncate: requires a scalar probability distribution.> ...
317s  truncate (pd, 2, 4)
317s ***** error <var: requires a scalar probability distribution.> var (pd)
317s 100 tests, 100 passed, 0 known failure, 0 skipped
317s [inst/dist_obj/InverseGaussianDistribution.m]
317s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/InverseGaussianDistribution.m
317s ***** shared pd, t
317s  pd = InverseGaussianDistribution (1, 1);
317s  t = truncate (pd, 2, 4);
317s ***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
317s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
317s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
317s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
317s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
317s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
317s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
317s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
317s ***** assert (iqr (pd), 0.8643, 1e-4);
317s ***** assert (iqr (t), 0.8222, 1e-4);
317s ***** assert (mean (pd), 1);
317s ***** assert (mean (t), 2.6953, 1e-4);
317s ***** assert (median (pd), 0.6758, 1e-4);
317s ***** assert (median (t), 2.5716, 1e-4);
317s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
317s ***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
317s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
317s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
317s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
317s ***** assert (any (random (t, 1000, 1) < 2), false);
317s ***** assert (any (random (t, 1000, 1) > 4), false);
317s ***** assert (std (pd), 1);
317s ***** assert (std (t), 0.5332, 1e-4);
317s ***** assert (var (pd), 1);
317s ***** assert (var (t), 0.2843, 1e-4);
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution(0, 1)
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution(Inf, 1)
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution(i, 1)
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution("a", 1)
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution([1, 2], 1)
317s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
317s  InverseGaussianDistribution(NaN, 1)
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, 0)
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, -1)
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, Inf)
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, i)
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, "a")
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, [1, 2])
317s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
317s  InverseGaussianDistribution(1, NaN)
317s ***** error <cdf: invalid argument for upper tail.> ...
317s  cdf (InverseGaussianDistribution, 2, "uper")
317s ***** error <cdf: invalid argument for upper tail.> ...
317s  cdf (InverseGaussianDistribution, 2, 3)
317s ***** shared x
317s  x = invgrnd (1, 1, [1, 100]);
317s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha")
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", "")
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
317s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
317s           "alpha", {0.05})
317s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), ...
317s           "parameter", {"mu", "lambda", "param"})
317s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
317s           "parameter", {"mu", "lambda", "param"})
317s ***** error <paramci: unknown distribution parameter.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
317s ***** error <paramci: unknown distribution parameter.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
317s           "parameter", "param")
317s ***** error <paramci: invalid NAME for optional argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
317s ***** error <paramci: invalid NAME for optional argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
317s ***** error <paramci: invalid NAME for optional argument.> ...
317s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
317s           "parameter", "mu", "NAME", "value")
317s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
317s  plot (InverseGaussianDistribution, "Parent")
317s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
317s  plot (InverseGaussianDistribution, "PlotType", 12)
317s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
317s  plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
317s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
317s  plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
317s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
317s  plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
317s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
317s  plot (InverseGaussianDistribution, "Discrete", [1, 0])
317s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
317s  plot (InverseGaussianDistribution, "Discrete", {true})
317s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
317s  plot (InverseGaussianDistribution, "Parent", 12)
317s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
317s  plot (InverseGaussianDistribution, "Parent", "hax")
317s ***** error <plot: invalid NAME for optional argument.> ...
317s  plot (InverseGaussianDistribution, "invalidNAME", "pdf")
317s ***** error <plot: no fitted DATA to plot a probability plot.> ...
317s  plot (InverseGaussianDistribution, "PlotType", "probability")
317s ***** error <proflik: no fitted data available.> ...
317s  proflik (InverseGaussianDistribution, 2)
317s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 3)
317s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
317s  proflik (InverseGaussianDistribution.fit (x), [1, 2])
317s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
317s  proflik (InverseGaussianDistribution.fit (x), {1})
317s ***** error <proflik: SETPARAM must be a numeric vector.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
317s ***** error <proflik: missing VALUE for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display")
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
317s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
317s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
317s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
317s ***** error <proflik: invalid NAME for optional arguments.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
317s ***** error <proflik: invalid optional argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
317s ***** error <proflik: invalid optional argument.> ...
317s  proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
317s ***** error <truncate: missing input argument.> ...
317s  truncate (InverseGaussianDistribution)
317s ***** error <truncate: missing input argument.> ...
317s  truncate (InverseGaussianDistribution, 2)
317s ***** error <truncate: invalid lower upper limits.> ...
317s  truncate (InverseGaussianDistribution, 4, 2)
317s ***** shared pd
317s  pd = InverseGaussianDistribution(1, 1);
317s  pd(2) = InverseGaussianDistribution(1, 3);
317s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
317s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
317s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
317s ***** error <mean: requires a scalar probability distribution.> mean (pd)
317s ***** error <median: requires a scalar probability distribution.> median (pd)
317s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
317s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
317s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
317s ***** error <plot: requires a scalar probability distribution.> plot (pd)
317s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
317s ***** error <random: requires a scalar probability distribution.> random (pd)
317s ***** error <std: requires a scalar probability distribution.> std (pd)
317s ***** error <truncate: requires a scalar probability distribution.> ...
317s  truncate (pd, 2, 4)
317s ***** error <var: requires a scalar probability distribution.> var (pd)
317s 96 tests, 96 passed, 0 known failure, 0 skipped
317s [inst/dist_obj/tLocationScaleDistribution.m]
317s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/tLocationScaleDistribution.m
317s ***** demo
317s  ## Generate a data set of 5000 random samples from a t Location-Scale distribution
317s  ## with parameters mu = 0, sigma = 1, and nu = 5. Fit a t Location-Scale
317s  ## distribution to this data and plot a PDF of the fitted distribution
317s  ## superimposed on a histogram of the data.
317s 
317s  pd_fixed = makedist ("tLocationScale", "mu", 0, "sigma", 1, "nu", 5);
317s  rand ("seed", 2);
317s  data = random (pd_fixed, 5000, 1);
317s  pd_fitted = fitdist (data, "tLocationScale");
317s  plot (pd_fitted);
317s  msg = "Fitted t Location-Scale distribution with mu = %0.2f, sigma = %0.2f, nu = %0.2f";
317s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma, pd_fitted.nu));
317s ***** shared pd, t
317s  pd = tLocationScaleDistribution;
317s  t = truncate (pd, 2, 4);
317s ***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
317s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
317s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
317s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
317s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
317s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
318s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
318s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
318s ***** assert (iqr (pd), 1.4534, 1e-4);
318s ***** assert (iqr (t), 0.7139, 1e-4);
318s ***** assert (mean (pd), 0, eps);
318s ***** assert (mean (t), 2.6099, 1e-4);
318s ***** assert (median (pd), 0, eps);
318s ***** assert (median (t), 2.4758, 1e-4);
318s ***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
318s ***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
318s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
318s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
318s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
318s ***** assert (any (random (t, 1000, 1) < 2), false);
318s ***** assert (any (random (t, 1000, 1) > 4), false);
318s ***** assert (std (pd), 1.2910, 1e-4);
318s ***** assert (std (t), 0.4989, 1e-4);
318s ***** assert (var (pd), 1.6667, 1e-4);
318s ***** assert (var (t), 0.2489, 1e-4);
318s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
318s  tLocationScaleDistribution(i, 1, 1)
318s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
318s  tLocationScaleDistribution(Inf, 1, 1)
318s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
318s  tLocationScaleDistribution([1, 2], 1, 1)
318s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
318s  tLocationScaleDistribution("a", 1, 1)
318s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
318s  tLocationScaleDistribution(NaN, 1, 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 0, 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, -1, 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, Inf, 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, i, 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, "a", 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, [1, 2], 1)
318s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, NaN, 1)
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, 0)
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, -1)
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, Inf)
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, i)
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, "a")
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, [1, 2])
318s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
318s  tLocationScaleDistribution(0, 1, NaN)
318s ***** error <cdf: invalid argument for upper tail.> ...
318s  cdf (tLocationScaleDistribution, 2, "uper")
318s ***** error <cdf: invalid argument for upper tail.> ...
318s  cdf (tLocationScaleDistribution, 2, 3)
318s ***** shared x
318s  x = tlsrnd (0, 1, 1, [1, 100]);
318s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha")
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha", "")
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
318s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
318s           "alpha", {0.05})
318s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
318s  paramci (tLocationScaleDistribution.fit (x), ...
318s           "parameter", {"mu", "sigma", "nu", "param"})
319s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
319s           "parameter", {"mu", "sigma", "nu", "param"})
319s ***** error <paramci: unknown distribution parameter.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
319s ***** error <paramci: unknown distribution parameter.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
319s           "parameter", "param")
319s ***** error <paramci: invalid NAME for optional argument.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
319s ***** error <paramci: invalid NAME for optional argument.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
319s ***** error <paramci: invalid NAME for optional argument.> ...
319s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
319s           "parameter", "mu", "NAME", "value")
319s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
319s  plot (tLocationScaleDistribution, "Parent")
319s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
319s  plot (tLocationScaleDistribution, "PlotType", 12)
319s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
319s  plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
319s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
319s  plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
319s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
319s  plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
319s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
319s  plot (tLocationScaleDistribution, "Discrete", [1, 0])
319s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
319s  plot (tLocationScaleDistribution, "Discrete", {true})
319s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
319s  plot (tLocationScaleDistribution, "Parent", 12)
319s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
319s  plot (tLocationScaleDistribution, "Parent", "hax")
319s ***** error <plot: invalid NAME for optional argument.> ...
319s  plot (tLocationScaleDistribution, "invalidNAME", "pdf")
319s ***** error <plot: no fitted DATA to plot a probability plot.> ...
319s  plot (tLocationScaleDistribution, "PlotType", "probability")
319s ***** error <proflik: no fitted data available.> ...
319s  proflik (tLocationScaleDistribution, 2)
319s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
319s  proflik (tLocationScaleDistribution.fit (x), 4)
319s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
319s  proflik (tLocationScaleDistribution.fit (x), [1, 2])
319s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
319s  proflik (tLocationScaleDistribution.fit (x), {1})
319s ***** error <proflik: SETPARAM must be a numeric vector.> ...
319s  proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
319s ***** error <proflik: missing VALUE for 'Display' argument.> ...
319s  proflik (tLocationScaleDistribution.fit (x), 1, "Display")
320s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
320s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
320s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
320s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
320s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
320s ***** error <proflik: invalid NAME for optional arguments.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
320s ***** error <proflik: invalid optional argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
320s ***** error <proflik: invalid optional argument.> ...
320s  proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
320s ***** error <truncate: missing input argument.> ...
320s  truncate (tLocationScaleDistribution)
320s ***** error <truncate: missing input argument.> ...
320s  truncate (tLocationScaleDistribution, 2)
320s ***** error <truncate: invalid lower upper limits.> ...
320s  truncate (tLocationScaleDistribution, 4, 2)
320s ***** shared pd
320s  pd = tLocationScaleDistribution (0, 1, 1);
320s  pd(2) = tLocationScaleDistribution (0, 1, 3);
320s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
320s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
320s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
320s ***** error <mean: requires a scalar probability distribution.> mean (pd)
320s ***** error <median: requires a scalar probability distribution.> median (pd)
320s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
320s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
320s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
320s ***** error <plot: requires a scalar probability distribution.> plot (pd)
320s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
320s ***** error <random: requires a scalar probability distribution.> random (pd)
320s ***** error <std: requires a scalar probability distribution.> std (pd)
320s ***** error <truncate: requires a scalar probability distribution.> ...
320s  truncate (pd, 2, 4)
320s ***** error <var: requires a scalar probability distribution.> var (pd)
320s 102 tests, 102 passed, 0 known failure, 0 skipped
320s [inst/dist_obj/BurrDistribution.m]
320s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/BurrDistribution.m
320s ***** demo
320s  ## Generate a data set of 5000 random samples from a Burr type XII
320s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
320s  ## XII distribution to this data and plot a PDF of the fitted distribution
320s  ## superimposed on a histogram of the data
320s 
320s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
320s  rand ("seed", 21);
320s  data = random (pd, 5000, 1);
320s  pd = fitdist (data, "Burr")
320s  plot (pd)
320s  msg = strcat (["Fitted Burr type XII distribution with"], ...
320s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
320s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
320s ***** shared pd, t
320s  pd = BurrDistribution;
320s  t = truncate (pd, 2, 4);
320s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
320s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
320s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
320s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
320s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
320s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
320s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
320s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
320s ***** assert (iqr (pd), 2.6667, 1e-4);
320s ***** assert (iqr (t), 0.9524, 1e-4);
320s ***** assert (mean (pd), Inf);
320s ***** assert (mean (t), 2.8312, 1e-4);
320s ***** assert (median (pd), 1, 1e-4);
320s ***** assert (median (t), 2.75, 1e-4);
321s ***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
321s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
321s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
321s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
321s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
321s ***** assert (any (random (t, 1000, 1) < 2), false);
321s ***** assert (any (random (t, 1000, 1) > 4), false);
321s ***** assert (std (pd), Inf);
321s ***** assert (std (t), 0.5674, 1e-4);
321s ***** assert (var (pd), Inf);
321s ***** assert (var (t), 0.3220, 1e-4);
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution(0, 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution(-1, 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution(Inf, 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution(i, 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution("a", 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution([1, 2], 1, 1)
321s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
321s  BurrDistribution(NaN, 1, 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, 0, 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, -1, 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, Inf, 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, i, 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, "a", 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, [1, 2], 1)
321s ***** error <BurrDistribution: C must be a positive real scalar.> ...
321s  BurrDistribution(1, NaN, 1)
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, 0)
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, -1)
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, Inf)
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, i)
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, "a")
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, [1, 2])
321s ***** error <BurrDistribution: K must be a positive real scalar.> ...
321s  BurrDistribution(1, 1, NaN)
321s ***** error <cdf: invalid argument for upper tail.> ...
321s  cdf (BurrDistribution, 2, "uper")
321s ***** error <cdf: invalid argument for upper tail.> ...
321s  cdf (BurrDistribution, 2, 3)
321s ***** shared x
321s  rand ("seed", 4);
321s  x = burrrnd (1, 1, 1, [1, 100]);
321s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
321s  paramci (BurrDistribution.fit (x), "alpha")
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", 0)
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", 1)
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", "")
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", {0.05})
321s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
321s  paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
321s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
321s  paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
321s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
321s           "parameter", {"alpha", "c", "k", "param"})
321s ***** error <paramci: unknown distribution parameter.> ...
321s  paramci (BurrDistribution.fit (x), "parameter", "param")
321s ***** error <paramci: unknown distribution parameter.> ...
321s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
322s ***** error <paramci: invalid NAME for optional argument.> ...
322s  paramci (BurrDistribution.fit (x), "NAME", "value")
322s ***** error <paramci: invalid NAME for optional argument.> ...
322s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
322s ***** error <paramci: invalid NAME for optional argument.> ...
322s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
322s           "NAME", "value")
322s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
322s  plot (BurrDistribution, "Parent")
322s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
322s  plot (BurrDistribution, "PlotType", 12)
322s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
322s  plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
322s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
322s  plot (BurrDistribution, "PlotType", "pdfcdf")
322s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
322s  plot (BurrDistribution, "Discrete", "pdfcdf")
322s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
322s  plot (BurrDistribution, "Discrete", [1, 0])
322s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
322s  plot (BurrDistribution, "Discrete", {true})
322s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
322s  plot (BurrDistribution, "Parent", 12)
322s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
322s  plot (BurrDistribution, "Parent", "hax")
322s ***** error <plot: invalid NAME for optional argument.> ...
322s  plot (BurrDistribution, "invalidNAME", "pdf")
322s ***** error <plot: no fitted DATA to plot a probability plot.> ...
322s  plot (BurrDistribution, "PlotType", "probability")
322s ***** error <proflik: no fitted data available.> ...
322s  proflik (BurrDistribution, 2)
322s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
322s  proflik (BurrDistribution.fit (x), 4)
322s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
322s  proflik (BurrDistribution.fit (x), [1, 2])
322s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
322s  proflik (BurrDistribution.fit (x), {1})
322s ***** error <proflik: SETPARAM must be a numeric vector.> ...
322s  proflik (BurrDistribution.fit (x), 1, ones (2))
322s ***** error <proflik: missing VALUE for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display")
322s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display", 1)
322s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display", {1})
322s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
322s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
322s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
322s  proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
323s ***** error <proflik: invalid NAME for optional arguments.> ...
323s  proflik (BurrDistribution.fit (x), 1, "NAME", "on")
323s ***** error <proflik: invalid optional argument.> ...
323s  proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
323s ***** error <proflik: invalid optional argument.> ...
323s  proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
323s ***** error <truncate: missing input argument.> ...
323s  truncate (BurrDistribution)
323s ***** error <truncate: missing input argument.> ...
323s  truncate (BurrDistribution, 2)
323s ***** error <truncate: invalid lower upper limits.> ...
323s  truncate (BurrDistribution, 4, 2)
323s ***** shared pd
323s  pd = BurrDistribution(1, 1, 1);
323s  pd(2) = BurrDistribution(1, 3, 1);
323s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
323s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
323s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
323s ***** error <mean: requires a scalar probability distribution.> mean (pd)
323s ***** error <median: requires a scalar probability distribution.> median (pd)
323s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
323s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
323s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
323s ***** error <plot: requires a scalar probability distribution.> plot (pd)
323s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
323s ***** error <random: requires a scalar probability distribution.> random (pd)
323s ***** error <std: requires a scalar probability distribution.> std (pd)
323s ***** error <truncate: requires a scalar probability distribution.> ...
323s  truncate (pd, 2, 4)
323s ***** error <var: requires a scalar probability distribution.> var (pd)
323s 104 tests, 104 passed, 0 known failure, 0 skipped
323s [inst/dist_obj/HalfNormalDistribution.m]
323s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/HalfNormalDistribution.m
323s ***** shared pd, t
323s  pd = HalfNormalDistribution (0, 1);
323s  t = truncate (pd, 2, 4);
323s ***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
323s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
323s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
323s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
323s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
323s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
323s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
323s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
323s ***** assert (iqr (pd), 0.8317, 1e-4);
323s ***** assert (iqr (t), 0.4111, 1e-4);
323s ***** assert (mean (pd), 0.7979, 1e-4);
323s ***** assert (mean (t), 2.3706, 1e-4);
323s ***** assert (median (pd), 0.6745, 1e-4);
323s ***** assert (median (t), 2.2771, 1e-4);
323s ***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
323s ***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
323s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
323s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
323s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
323s ***** assert (any (random (t, 1000, 1) < 2), false);
323s ***** assert (any (random (t, 1000, 1) > 4), false);
323s ***** assert (std (pd), 0.6028, 1e-4);
323s ***** assert (std (t), 0.3310, 1e-4);
323s ***** assert (var (pd), 0.3634, 1e-4);
323s ***** assert (var (t), 0.1096, 1e-4);
323s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
323s  HalfNormalDistribution(Inf, 1)
323s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
323s  HalfNormalDistribution(i, 1)
323s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
323s  HalfNormalDistribution("a", 1)
323s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
323s  HalfNormalDistribution([1, 2], 1)
323s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
323s  HalfNormalDistribution(NaN, 1)
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, 0)
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, -1)
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, Inf)
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, i)
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, "a")
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, [1, 2])
323s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
323s  HalfNormalDistribution(1, NaN)
323s ***** error <cdf: invalid argument for upper tail.> ...
323s  cdf (HalfNormalDistribution, 2, "uper")
323s ***** error <cdf: invalid argument for upper tail.> ...
323s  cdf (HalfNormalDistribution, 2, 3)
323s ***** shared x
323s  x = hnrnd (1, 1, [1, 100]);
323s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha")
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
323s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
323s           "alpha", {0.05})
323s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), ...
323s           "parameter", {"mu", "sigma", "param"})
323s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
323s           "parameter", {"mu", "sigma", "param"})
323s ***** error <paramci: unknown distribution parameter.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
323s ***** error <paramci: unknown distribution parameter.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
323s           "parameter", "param")
323s ***** error <paramci: invalid NAME for optional argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
323s ***** error <paramci: invalid NAME for optional argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
323s           "NAME", "value")
323s ***** error <paramci: invalid NAME for optional argument.> ...
323s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
323s           "parameter", "sigma", "NAME", "value")
324s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
324s  plot (HalfNormalDistribution, "Parent")
324s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
324s  plot (HalfNormalDistribution, "PlotType", 12)
324s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
324s  plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
324s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
324s  plot (HalfNormalDistribution, "PlotType", "pdfcdf")
324s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
324s  plot (HalfNormalDistribution, "Discrete", "pdfcdf")
324s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
324s  plot (HalfNormalDistribution, "Discrete", [1, 0])
324s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
324s  plot (HalfNormalDistribution, "Discrete", {true})
324s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
324s  plot (HalfNormalDistribution, "Parent", 12)
324s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
324s  plot (HalfNormalDistribution, "Parent", "hax")
324s ***** error <plot: invalid NAME for optional argument.> ...
324s  plot (HalfNormalDistribution, "invalidNAME", "pdf")
324s ***** error <plot: no fitted DATA to plot a probability plot.> ...
324s  plot (HalfNormalDistribution, "PlotType", "probability")
324s ***** error <proflik: no fitted data available.> ...
324s  proflik (HalfNormalDistribution, 2)
324s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 3)
324s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
324s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), {1})
324s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 1)
324s ***** error <proflik: SETPARAM must be a numeric vector.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
324s ***** error <proflik: missing VALUE for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
324s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
324s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
324s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
324s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
324s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
324s ***** error <proflik: invalid NAME for optional arguments.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
324s ***** error <proflik: invalid optional argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
324s ***** error <proflik: invalid optional argument.> ...
324s  proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
324s           "Display", "on")
324s ***** error <truncate: missing input argument.> ...
324s  truncate (HalfNormalDistribution)
324s ***** error <truncate: missing input argument.> ...
324s  truncate (HalfNormalDistribution, 2)
324s ***** error <truncate: invalid lower upper limits.> ...
324s  truncate (HalfNormalDistribution, 4, 2)
324s ***** shared pd
324s  pd = HalfNormalDistribution(1, 1);
324s  pd(2) = HalfNormalDistribution(1, 3);
324s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
324s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
324s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
324s ***** error <mean: requires a scalar probability distribution.> mean (pd)
324s ***** error <median: requires a scalar probability distribution.> median (pd)
324s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
324s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
324s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
324s ***** error <plot: requires a scalar probability distribution.> plot (pd)
324s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
324s ***** error <random: requires a scalar probability distribution.> random (pd)
324s ***** error <std: requires a scalar probability distribution.> std (pd)
324s ***** error <truncate: requires a scalar probability distribution.> ...
324s  truncate (pd, 2, 4)
324s ***** error <var: requires a scalar probability distribution.> var (pd)
324s 96 tests, 96 passed, 0 known failure, 0 skipped
324s [inst/dist_obj/GeneralizedExtremeValueDistribution.m]
324s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/GeneralizedExtremeValueDistribution.m
324s ***** shared pd, t
324s  pd = GeneralizedExtremeValueDistribution;
324s  t = truncate (pd, 2, 4);
324s ***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
324s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
324s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
324s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
324s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
324s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
324s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
324s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
324s ***** assert (iqr (pd), 1.5725, 1e-4);
324s ***** assert (iqr (t), 0.8164, 1e-4);
324s ***** assert (mean (pd), 0.5772, 1e-4);
324s ***** assert (mean (t), 2.7043, 1e-4);
324s ***** assert (median (pd), 0.3665, 1e-4);
324s ***** assert (median (t), 2.5887, 1e-4);
324s ***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
324s ***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
324s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
324s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
324s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
324s ***** assert (any (random (t, 1000, 1) < 2), false);
324s ***** assert (any (random (t, 1000, 1) > 4), false);
324s ***** assert (std (pd), 1.2825, 1e-4);
324s ***** assert (std (t), 0.5289, 1e-4);
324s ***** assert (var (pd), 1.6449, 1e-4);
324s ***** assert (var (t), 0.2798, 1e-4);
324s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(Inf, 1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(i, 1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution("a", 1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution([1, 2], 1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(NaN, 1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 0, 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, -1, 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, Inf, 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, i, 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, "a", 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, [1, 2], 1)
324s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, NaN, 1)
324s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 1, Inf)
324s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 1, i)
324s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 1, "a")
324s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 1, [1, 2])
324s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
324s  GeneralizedExtremeValueDistribution(1, 1, NaN)
324s ***** error <cdf: invalid argument for upper tail.> ...
324s  cdf (GeneralizedExtremeValueDistribution, 2, "uper")
324s ***** error <cdf: invalid argument for upper tail.> ...
324s  cdf (GeneralizedExtremeValueDistribution, 2, 3)
324s ***** shared x
324s  x = gevrnd (1, 1, 1, [1, 100]);
324s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
324s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
324s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
324s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
325s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
325s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
325s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
325s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
325s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
325s           "parameter", "sigma", "alpha", {0.05})
325s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
325s           "parameter", {"k", "sigma", "mu", "param"})
325s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
325s           "parameter", {"k", "sigma", "mu", "param"})
325s ***** error <paramci: unknown distribution parameter.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
325s ***** error <paramci: unknown distribution parameter.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
325s           "parameter", "param")
325s ***** error <paramci: invalid NAME for optional argument.> ...
325s  paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
326s ***** error <paramci: invalid NAME for optional argument.> ...
326s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
326s           "NAME", "value")
326s ***** error <paramci: invalid NAME for optional argument.> ...
326s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
326s           "parameter", "sigma", "NAME", "value")
326s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Parent")
326s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
326s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
326s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
326s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
326s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
326s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
326s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Parent", 12)
326s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
326s ***** error <plot: invalid NAME for optional argument.> ...
326s  plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
326s ***** error <plot: no fitted DATA to plot a probability plot.> ...
326s  plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
326s ***** error <proflik: no fitted data available.> ...
326s  proflik (GeneralizedExtremeValueDistribution, 2)
326s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
326s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
326s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
326s ***** error <proflik: SETPARAM must be a numeric vector.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
326s ***** error <proflik: missing VALUE for 'Display' argument.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
326s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
326s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
326s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
326s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
327s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
327s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
327s           "Display", ["on"; "on"])
327s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
327s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
327s ***** error <proflik: invalid NAME for optional arguments.> ...
327s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
327s ***** error <proflik: invalid optional argument.> ...
327s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
327s ***** error <proflik: invalid optional argument.> ...
327s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
327s           "Display", "on")
327s ***** error <truncate: missing input argument.> ...
327s  truncate (GeneralizedExtremeValueDistribution)
327s ***** error <truncate: missing input argument.> ...
327s  truncate (GeneralizedExtremeValueDistribution, 2)
327s ***** error <truncate: invalid lower upper limits.> ...
327s  truncate (GeneralizedExtremeValueDistribution, 4, 2)
327s ***** shared pd
327s  pd = GeneralizedExtremeValueDistribution(1, 1, 1);
327s  pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
327s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
327s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
327s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
327s ***** error <mean: requires a scalar probability distribution.> mean (pd)
327s ***** error <median: requires a scalar probability distribution.> median (pd)
327s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
327s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
327s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
327s ***** error <plot: requires a scalar probability distribution.> plot (pd)
327s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
327s ***** error <random: requires a scalar probability distribution.> random (pd)
327s ***** error <std: requires a scalar probability distribution.> std (pd)
327s ***** error <truncate: requires a scalar probability distribution.> ...
327s  truncate (pd, 2, 4)
327s ***** error <var: requires a scalar probability distribution.> var (pd)
327s 100 tests, 100 passed, 0 known failure, 0 skipped
327s [inst/dist_obj/LognormalDistribution.m]
327s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/LognormalDistribution.m
327s ***** demo
327s  ## Generate a data set of 5000 random samples from a Lognormal distribution with
327s  ## parameters mu = 0 and sigma = 1.  Fit a Lognormal distribution to this data and plot
327s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
327s 
327s  pd_fixed = makedist ("Lognormal", "mu", 0, "sigma", 1)
327s  randn ("seed", 2);
327s  data = random (pd_fixed, 5000, 1);
327s  pd_fitted = fitdist (data, "Lognormal")
327s  plot (pd_fitted)
327s  msg = "Fitted Lognormal distribution with mu = %0.2f and sigma = %0.2f";
327s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
327s ***** shared pd, t
327s  pd = LognormalDistribution;
327s  t = truncate (pd, 2, 4);
327s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
327s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
327s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
327s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
327s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
327s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
327s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
327s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
327s ***** assert (iqr (pd), 1.4536, 1e-4);
327s ***** assert (iqr (t), 0.8989, 1e-4);
327s ***** assert (mean (pd), 1.6487, 1e-4);
327s ***** assert (mean (t), 2.7692, 1e-4);
327s ***** assert (median (pd), 1, 1e-4);
327s ***** assert (median (t), 2.6653, 1e-4);
327s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
327s ***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
327s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
327s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
327s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
327s ***** assert (any (random (t, 1000, 1) < 2), false);
327s ***** assert (any (random (t, 1000, 1) > 4), false);
327s ***** assert (std (pd), 2.1612, 1e-4);
327s ***** assert (std (t), 0.5540, 1e-4);
327s ***** assert (var (pd), 4.6708, 1e-4);
327s ***** assert (var (t), 0.3069, 1e-4);
327s ***** error <LognormalDistribution: MU must be a real scalar.> ...
327s  LognormalDistribution(Inf, 1)
327s ***** error <LognormalDistribution: MU must be a real scalar.> ...
327s  LognormalDistribution(i, 1)
327s ***** error <LognormalDistribution: MU must be a real scalar.> ...
327s  LognormalDistribution("a", 1)
327s ***** error <LognormalDistribution: MU must be a real scalar.> ...
327s  LognormalDistribution([1, 2], 1)
327s ***** error <LognormalDistribution: MU must be a real scalar.> ...
327s  LognormalDistribution(NaN, 1)
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, 0)
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, -1)
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, Inf)
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, i)
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, "a")
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, [1, 2])
327s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
327s  LognormalDistribution(1, NaN)
327s ***** error <cdf: invalid argument for upper tail.> ...
327s  cdf (LognormalDistribution, 2, "uper")
327s ***** error <cdf: invalid argument for upper tail.> ...
327s  cdf (LognormalDistribution, 2, 3)
327s ***** shared x
327s  randn ("seed", 1);
327s  x = lognrnd (1, 1, [1, 100]);
327s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
327s  paramci (LognormalDistribution.fit (x), "alpha")
327s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
327s  paramci (LognormalDistribution.fit (x), "alpha", 0)
327s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
327s  paramci (LognormalDistribution.fit (x), "alpha", 1)
327s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
327s  paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
328s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", "")
328s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", {0.05})
328s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
328s  paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
328s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
328s  paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
328s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
328s           "parameter", {"mu", "sigma", "param"})
328s ***** error <paramci: unknown distribution parameter.> ...
328s  paramci (LognormalDistribution.fit (x), "parameter", "param")
328s ***** error <paramci: unknown distribution parameter.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
328s ***** error <paramci: invalid NAME for optional argument.> ...
328s  paramci (LognormalDistribution.fit (x), "NAME", "value")
328s ***** error <paramci: invalid NAME for optional argument.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
328s ***** error <paramci: invalid NAME for optional argument.> ...
328s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
328s           "NAME", "value")
328s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
328s  plot (LognormalDistribution, "Parent")
328s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
328s  plot (LognormalDistribution, "PlotType", 12)
328s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
328s  plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
328s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
328s  plot (LognormalDistribution, "PlotType", "pdfcdf")
328s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
328s  plot (LognormalDistribution, "Discrete", "pdfcdf")
328s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
328s  plot (LognormalDistribution, "Discrete", [1, 0])
328s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
328s  plot (LognormalDistribution, "Discrete", {true})
328s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
328s  plot (LognormalDistribution, "Parent", 12)
328s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
328s  plot (LognormalDistribution, "Parent", "hax")
328s ***** error <plot: invalid NAME for optional argument.> ...
328s  plot (LognormalDistribution, "invalidNAME", "pdf")
328s ***** error <plot: no fitted DATA to plot a probability plot.> ...
328s  plot (LognormalDistribution, "PlotType", "probability")
328s ***** error <proflik: no fitted data available.> ...
328s  proflik (LognormalDistribution, 2)
328s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
328s  proflik (LognormalDistribution.fit (x), 3)
328s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
328s  proflik (LognormalDistribution.fit (x), [1, 2])
328s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
328s  proflik (LognormalDistribution.fit (x), {1})
329s ***** error <proflik: SETPARAM must be a numeric vector.> ...
329s  proflik (LognormalDistribution.fit (x), 1, ones (2))
329s ***** error <proflik: missing VALUE for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display")
329s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display", 1)
329s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display", {1})
329s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
329s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
329s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
329s ***** error <proflik: invalid NAME for optional arguments.> ...
329s  proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
329s ***** error <proflik: invalid optional argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
329s ***** error <proflik: invalid optional argument.> ...
329s  proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
329s ***** error <truncate: missing input argument.> ...
329s  truncate (LognormalDistribution)
329s ***** error <truncate: missing input argument.> ...
329s  truncate (LognormalDistribution, 2)
329s ***** error <truncate: invalid lower upper limits.> ...
329s  truncate (LognormalDistribution, 4, 2)
329s ***** shared pd
329s  pd = LognormalDistribution(1, 1);
329s  pd(2) = LognormalDistribution(1, 3);
329s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
329s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
329s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
329s ***** error <mean: requires a scalar probability distribution.> mean (pd)
329s ***** error <median: requires a scalar probability distribution.> median (pd)
329s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
329s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
329s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
329s ***** error <plot: requires a scalar probability distribution.> plot (pd)
329s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
329s ***** error <random: requires a scalar probability distribution.> random (pd)
329s ***** error <std: requires a scalar probability distribution.> std (pd)
329s ***** error <truncate: requires a scalar probability distribution.> ...
329s  truncate (pd, 2, 4)
329s ***** error <var: requires a scalar probability distribution.> var (pd)
329s 95 tests, 95 passed, 0 known failure, 0 skipped
329s [inst/dist_obj/BetaDistribution.m]
329s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/BetaDistribution.m
329s ***** demo
329s  ## Generate a data set of 5000 random samples from a Beta distribution with
329s  ## parameters a = 2 and b = 5.  Fit a Beta distribution to this data and plot
329s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
329s 
329s  pd_fixed = makedist ("Beta", "a", 2, "b", 5)
329s  randg ("seed", 2);
329s  data = random (pd_fixed, 5000, 1);
329s  pd_fitted = fitdist (data, "Beta")
329s  plot (pd_fitted)
329s  msg = "Fitted Beta distribution with a = %0.2f and b = %0.2f";
329s  title (sprintf (msg, pd_fitted.a, pd_fitted.b))
329s ***** shared pd, t
329s  pd = BetaDistribution;
329s  t = truncate (pd, 0.2, 0.8);
329s ***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
329s ***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
329s ***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
329s ***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
329s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
329s ***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
329s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
329s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
329s ***** assert (iqr (pd), 0.5, 1e-4);
329s ***** assert (iqr (t), 0.3, 1e-4);
329s ***** assert (mean (pd), 0.5);
329s ***** assert (mean (t), 0.5, 1e-6);
329s ***** assert (median (pd), 0.5);
329s ***** assert (median (t), 0.5, 1e-6);
329s ***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
329s ***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
329s ***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
329s ***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
329s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
329s ***** assert (any (random (t, 1000, 1) < 0.2), false);
329s ***** assert (any (random (t, 1000, 1) > 0.8), false);
329s ***** assert (std (pd), 0.2887, 1e-4);
329s ***** assert (std (t), 0.1732, 1e-4);
330s ***** assert (var (pd), 0.0833, 1e-4);
330s ***** assert (var (t), 0.0300, 1e-4);
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution(0, 1)
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution(Inf, 1)
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution(i, 1)
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution("a", 1)
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution([1, 2], 1)
330s ***** error <BetaDistribution: A must be a positive real scalar.> ...
330s  BetaDistribution(NaN, 1)
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, 0)
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, -1)
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, Inf)
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, i)
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, "a")
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, [1, 2])
330s ***** error <BetaDistribution: B must be a positive real scalar.> ...
330s  BetaDistribution(1, NaN)
330s ***** error <cdf: invalid argument for upper tail.> ...
330s  cdf (BetaDistribution, 2, "uper")
330s ***** error <cdf: invalid argument for upper tail.> ...
330s  cdf (BetaDistribution, 2, 3)
330s ***** shared x
330s  randg ("seed", 1);
330s  x = betarnd (1, 1, [100, 1]);
330s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
330s  paramci (BetaDistribution.fit (x), "alpha")
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 0)
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 1)
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", "")
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", {0.05})
330s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
330s  paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
330s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
330s  paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
330s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
330s           "parameter", {"a", "b", "param"})
330s ***** error <paramci: unknown distribution parameter.> ...
330s  paramci (BetaDistribution.fit (x), "parameter", "param")
330s ***** error <paramci: unknown distribution parameter.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
330s ***** error <paramci: invalid NAME for optional argument.> ...
330s  paramci (BetaDistribution.fit (x), "NAME", "value")
330s ***** error <paramci: invalid NAME for optional argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
330s ***** error <paramci: invalid NAME for optional argument.> ...
330s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
330s           "NAME", "value")
330s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
330s  plot (BetaDistribution, "Parent")
330s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
330s  plot (BetaDistribution, "PlotType", 12)
330s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
330s  plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
330s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
330s  plot (BetaDistribution, "PlotType", "pdfcdf")
330s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
330s  plot (BetaDistribution, "Discrete", "pdfcdf")
330s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
330s  plot (BetaDistribution, "Discrete", [1, 0])
330s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
330s  plot (BetaDistribution, "Discrete", {true})
330s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
330s  plot (BetaDistribution, "Parent", 12)
330s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
330s  plot (BetaDistribution, "Parent", "hax")
330s ***** error <plot: invalid NAME for optional argument.> ...
330s  plot (BetaDistribution, "invalidNAME", "pdf")
330s ***** error <plot: no fitted DATA to plot a probability plot.> ...
330s  plot (BetaDistribution, "PlotType", "probability")
330s ***** error <proflik: no fitted data available.> ...
330s  proflik (BetaDistribution, 2)
330s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
330s  proflik (BetaDistribution.fit (x), 3)
330s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
330s  proflik (BetaDistribution.fit (x), [1, 2])
330s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
330s  proflik (BetaDistribution.fit (x), {1})
330s ***** error <proflik: SETPARAM must be a numeric vector.> ...
330s  proflik (BetaDistribution.fit (x), 1, ones (2))
330s ***** error <proflik: missing VALUE for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display")
330s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display", 1)
330s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display", {1})
330s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
330s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
330s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
330s ***** error <proflik: invalid NAME for optional arguments.> ...
330s  proflik (BetaDistribution.fit (x), 1, "NAME", "on")
330s ***** error <proflik: invalid optional argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
330s ***** error <proflik: invalid optional argument.> ...
330s  proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
330s ***** error <truncate: missing input argument.> ...
330s  truncate (BetaDistribution)
330s ***** error <truncate: missing input argument.> ...
330s  truncate (BetaDistribution, 2)
330s ***** error <truncate: invalid lower upper limits.> ...
330s  truncate (BetaDistribution, 4, 2)
330s ***** shared pd
330s  pd = BetaDistribution(1, 1);
330s  pd(2) = BetaDistribution(1, 3);
330s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
330s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
330s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
330s ***** error <mean: requires a scalar probability distribution.> mean (pd)
330s ***** error <median: requires a scalar probability distribution.> median (pd)
330s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
330s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
330s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
330s ***** error <plot: requires a scalar probability distribution.> plot (pd)
330s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
330s ***** error <random: requires a scalar probability distribution.> random (pd)
330s ***** error <std: requires a scalar probability distribution.> std (pd)
330s ***** error <truncate: requires a scalar probability distribution.> ...
330s  truncate (pd, 2, 4)
330s ***** error <var: requires a scalar probability distribution.> var (pd)
330s 96 tests, 96 passed, 0 known failure, 0 skipped
330s [inst/dist_obj/NakagamiDistribution.m]
330s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/NakagamiDistribution.m
330s ***** demo
330s  ## Generate a data set of 5000 random samples from a Nakagami distribution with
330s  ## parameters mu = 1 and omega = 1.  Fit a Nakagami distribution to this data and plot
330s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
330s 
330s  pd_fixed = makedist ("Nakagami", "mu", 1, "omega", 1)
330s  rand ("seed", 2);
330s  data = random (pd_fixed, 5000, 1);
330s  pd_fitted = fitdist (data, "Nakagami")
330s  plot (pd_fitted)
330s  msg = "Fitted Nakagami distribution with mu = %0.2f and omega = %0.2f";
330s  title (sprintf (msg, pd_fitted.mu, pd_fitted.omega))
330s ***** shared pd, t
330s  pd = NakagamiDistribution;
330s  t = truncate (pd, 2, 4);
330s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
330s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
330s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
330s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
330s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
331s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
331s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
331s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
331s ***** assert (iqr (pd), 0.6411, 1e-4);
331s ***** assert (iqr (t), 0.2502, 1e-4);
331s ***** assert (mean (pd), 0.8862, 1e-4);
331s ***** assert (mean (t), 2.2263, 1e-4);
331s ***** assert (median (pd), 0.8326, 1e-4);
331s ***** assert (median (t), 2.1664, 1e-4);
331s ***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
331s ***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
331s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
331s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
331s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
331s ***** assert (any (random (t, 1000, 1) < 2), false);
331s ***** assert (any (random (t, 1000, 1) > 4), false);
331s ***** assert (std (pd), 0.4633, 1e-4);
331s ***** assert (std (t), 0.2083, 1e-4);
331s ***** assert (var (pd), 0.2146, 1e-4);
331s ***** assert (var (t), 0.0434, 1e-4);
331s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
331s  NakagamiDistribution(Inf, 1)
331s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
331s  NakagamiDistribution(i, 1)
331s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
331s  NakagamiDistribution("a", 1)
331s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
331s  NakagamiDistribution([1, 2], 1)
331s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
331s  NakagamiDistribution(NaN, 1)
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, 0)
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, -1)
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, Inf)
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, i)
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, "a")
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, [1, 2])
331s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
331s  NakagamiDistribution(1, NaN)
331s ***** error <cdf: invalid argument for upper tail.> ...
331s  cdf (NakagamiDistribution, 2, "uper")
331s ***** error <cdf: invalid argument for upper tail.> ...
331s  cdf (NakagamiDistribution, 2, 3)
331s ***** shared x
331s  x = nakarnd (1, 0.5, [1, 100]);
331s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha")
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 0)
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 1)
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", "")
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
331s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
331s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
331s           "parameter", {"mu", "omega", "param"})
331s ***** error <paramci: unknown distribution parameter.> ...
331s  paramci (NakagamiDistribution.fit (x), "parameter", "param")
331s ***** error <paramci: unknown distribution parameter.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
331s ***** error <paramci: invalid NAME for optional argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "NAME", "value")
331s ***** error <paramci: invalid NAME for optional argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
331s ***** error <paramci: invalid NAME for optional argument.> ...
331s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
331s           "NAME", "value")
331s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
331s  plot (NakagamiDistribution, "Parent")
331s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
331s  plot (NakagamiDistribution, "PlotType", 12)
331s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
331s  plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
331s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
331s  plot (NakagamiDistribution, "PlotType", "pdfcdf")
331s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
331s  plot (NakagamiDistribution, "Discrete", "pdfcdf")
331s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
331s  plot (NakagamiDistribution, "Discrete", [1, 0])
331s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
331s  plot (NakagamiDistribution, "Discrete", {true})
331s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
331s  plot (NakagamiDistribution, "Parent", 12)
331s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
331s  plot (NakagamiDistribution, "Parent", "hax")
331s ***** error <plot: invalid NAME for optional argument.> ...
331s  plot (NakagamiDistribution, "invalidNAME", "pdf")
331s ***** error <plot: no fitted DATA to plot a probability plot.> ...
331s  plot (NakagamiDistribution, "PlotType", "probability")
331s ***** error <proflik: no fitted data available.> ...
331s  proflik (NakagamiDistribution, 2)
331s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
331s  proflik (NakagamiDistribution.fit (x), 3)
331s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
331s  proflik (NakagamiDistribution.fit (x), [1, 2])
331s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
331s  proflik (NakagamiDistribution.fit (x), {1})
331s ***** error <proflik: SETPARAM must be a numeric vector.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, ones (2))
331s ***** error <proflik: missing VALUE for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display")
331s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
331s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
331s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
331s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
331s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
331s ***** error <proflik: invalid NAME for optional arguments.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
331s ***** error <proflik: invalid optional argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
331s ***** error <proflik: invalid optional argument.> ...
331s  proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
331s ***** error <truncate: missing input argument.> ...
331s  truncate (NakagamiDistribution)
331s ***** error <truncate: missing input argument.> ...
331s  truncate (NakagamiDistribution, 2)
331s ***** error <truncate: invalid lower upper limits.> ...
331s  truncate (NakagamiDistribution, 4, 2)
331s ***** shared pd
331s  pd = NakagamiDistribution(1, 0.5);
331s  pd(2) = NakagamiDistribution(1, 0.6);
331s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
331s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
331s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
331s ***** error <mean: requires a scalar probability distribution.> mean (pd)
331s ***** error <median: requires a scalar probability distribution.> median (pd)
331s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
331s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
331s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
331s ***** error <plot: requires a scalar probability distribution.> plot (pd)
331s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
331s ***** error <random: requires a scalar probability distribution.> random (pd)
331s ***** error <std: requires a scalar probability distribution.> std (pd)
331s ***** error <truncate: requires a scalar probability distribution.> ...
331s  truncate (pd, 2, 4)
331s ***** error <var: requires a scalar probability distribution.> var (pd)
331s 95 tests, 95 passed, 0 known failure, 0 skipped
331s [inst/dist_obj/LoglogisticDistribution.m]
331s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/LoglogisticDistribution.m
331s ***** demo
331s  ## Generate a data set of 5000 random samples from a Log-logistic
331s  ## distribution with parameters mu = 0 and sigma = 1.  Fit a Log-logistic
331s  ## distribution to this data and plot a PDF of the fitted distribution
331s  ## superimposed on a histogram of the data.
331s 
331s  pd_fixed = makedist ("Loglogistic", "mu", 0, "sigma", 1)
331s  rand ("seed", 2);
331s  data = random (pd_fixed, 5000, 1);
331s  pd_fitted = fitdist (data, "Loglogistic")
331s  plot (pd_fitted)
331s  msg = "Fitted Log-logistic distribution with mu = %0.2f and sigma = %0.2f";
331s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
331s ***** shared pd, t
331s  pd = LoglogisticDistribution;
331s  t = truncate (pd, 2, 4);
331s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
331s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
331s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
331s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
331s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
331s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
331s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
331s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
331s ***** assert (iqr (pd), 2.6667, 1e-4);
331s ***** assert (iqr (t), 0.9524, 1e-4);
331s ***** assert (mean (pd), Inf);
331s ***** assert (mean (t), 2.8312, 1e-4);
331s ***** assert (median (pd), 1, 1e-4);
331s ***** assert (median (t), 2.75, 1e-4);
331s ***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
331s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
331s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
331s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
331s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
331s ***** assert (any (random (t, 1000, 1) < 2), false);
331s ***** assert (any (random (t, 1000, 1) > 4), false);
331s ***** assert (std (pd), Inf);
331s ***** assert (std (t), 0.5674, 1e-4);
331s ***** assert (var (pd), Inf);
331s ***** assert (var (t), 0.3220, 1e-4);
331s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
331s  LoglogisticDistribution(Inf, 1)
331s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
331s  LoglogisticDistribution(i, 1)
331s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
331s  LoglogisticDistribution("a", 1)
331s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
331s  LoglogisticDistribution([1, 2], 1)
331s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
331s  LoglogisticDistribution(NaN, 1)
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, 0)
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, -1)
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, Inf)
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, i)
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, "a")
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, [1, 2])
331s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
331s  LoglogisticDistribution(1, NaN)
331s ***** error <cdf: invalid argument for upper tail.> ...
331s  cdf (LoglogisticDistribution, 2, "uper")
331s ***** error <cdf: invalid argument for upper tail.> ...
331s  cdf (LoglogisticDistribution, 2, 3)
331s ***** shared x
331s  x = loglrnd (1, 1, [1, 100]);
331s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
331s  paramci (LoglogisticDistribution.fit (x), "alpha")
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (LoglogisticDistribution.fit (x), "alpha", 0)
331s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
331s  paramci (LoglogisticDistribution.fit (x), "alpha", 1)
332s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
332s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", "")
332s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
332s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
332s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
332s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
332s           "parameter", {"mu", "sigma", "param"})
332s ***** error <paramci: unknown distribution parameter.> ...
332s  paramci (LoglogisticDistribution.fit (x), "parameter", "param")
332s ***** error <paramci: unknown distribution parameter.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
332s ***** error <paramci: invalid NAME for optional argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "NAME", "value")
332s ***** error <paramci: invalid NAME for optional argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
332s ***** error <paramci: invalid NAME for optional argument.> ...
332s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
332s           "parameter", "mu", "NAME", "value")
332s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
332s  plot (LoglogisticDistribution, "Parent")
332s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
332s  plot (LoglogisticDistribution, "PlotType", 12)
332s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
332s  plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
332s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
332s  plot (LoglogisticDistribution, "PlotType", "pdfcdf")
332s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
332s  plot (LoglogisticDistribution, "Discrete", "pdfcdf")
332s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
332s  plot (LoglogisticDistribution, "Discrete", [1, 0])
332s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
332s  plot (LoglogisticDistribution, "Discrete", {true})
332s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
332s  plot (LoglogisticDistribution, "Parent", 12)
332s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
332s  plot (LoglogisticDistribution, "Parent", "hax")
332s ***** error <plot: invalid NAME for optional argument.> ...
332s  plot (LoglogisticDistribution, "invalidNAME", "pdf")
332s ***** error <plot: no fitted DATA to plot a probability plot.> ...
332s  plot (LoglogisticDistribution, "PlotType", "probability")
332s ***** error <proflik: no fitted data available.> ...
332s  proflik (LoglogisticDistribution, 2)
332s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
332s  proflik (LoglogisticDistribution.fit (x), 3)
332s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
332s  proflik (LoglogisticDistribution.fit (x), [1, 2])
332s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
332s  proflik (LoglogisticDistribution.fit (x), {1})
332s ***** error <proflik: SETPARAM must be a numeric vector.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, ones (2))
332s ***** error <proflik: missing VALUE for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display")
332s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
332s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
332s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
332s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
332s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
332s ***** error <proflik: invalid NAME for optional arguments.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
332s ***** error <proflik: invalid optional argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
332s ***** error <proflik: invalid optional argument.> ...
332s  proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
332s ***** error <truncate: missing input argument.> ...
332s  truncate (LoglogisticDistribution)
332s ***** error <truncate: missing input argument.> ...
332s  truncate (LoglogisticDistribution, 2)
332s ***** error <truncate: invalid lower upper limits.> ...
332s  truncate (LoglogisticDistribution, 4, 2)
332s ***** shared pd
332s  pd = LoglogisticDistribution(1, 1);
332s  pd(2) = LoglogisticDistribution(1, 3);
332s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
332s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
333s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
333s ***** error <mean: requires a scalar probability distribution.> mean (pd)
333s ***** error <median: requires a scalar probability distribution.> median (pd)
333s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
333s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
333s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
333s ***** error <plot: requires a scalar probability distribution.> plot (pd)
333s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
333s ***** error <random: requires a scalar probability distribution.> random (pd)
333s ***** error <std: requires a scalar probability distribution.> std (pd)
333s ***** error <truncate: requires a scalar probability distribution.> ...
333s  truncate (pd, 2, 4)
333s ***** error <var: requires a scalar probability distribution.> var (pd)
333s 95 tests, 95 passed, 0 known failure, 0 skipped
333s [inst/dist_obj/PoissonDistribution.m]
333s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/PoissonDistribution.m
333s ***** demo
333s  ## Generate a data set of 5000 random samples from a Poisson distribution with
333s  ## parameter lambda = 5.  Fit a Poisson distribution to this data and plot
333s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
333s 
333s  pd_fixed = makedist ("Poisson", "lambda", 5)
333s  rand ("seed", 2);
333s  data = random (pd_fixed, 5000, 1);
333s  pd_fitted = fitdist (data, "Poisson")
333s  plot (pd_fitted)
333s  msg = "Fitted Poisson distribution with lambda = %0.2f";
333s  title (sprintf (msg, pd_fitted.lambda))
333s ***** shared pd, t, t_inf
333s  pd = PoissonDistribution;
333s  t = truncate (pd, 2, 4);
333s  t_inf = truncate (pd, 2, Inf);
333s ***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
333s ***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
333s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
333s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
333s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
333s ***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
333s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
333s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
333s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
333s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
333s ***** assert (iqr (pd), 2);
333s ***** assert (iqr (t), 1);
333s ***** assert (mean (pd), 1);
333s ***** assert (mean (t), 2.3529, 1e-4);
333s ***** assert (mean (t_inf), 2.3922, 1e-4);
333s ***** assert (median (pd), 1);
333s ***** assert (median (t), 2);
333s ***** assert (median (t_inf), 2);
333s ***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
333s ***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
333s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
333s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
333s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
333s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
333s ***** assert (any (random (t, 1000, 1) < 2), false);
333s ***** assert (any (random (t, 1000, 1) > 4), false);
333s ***** assert (std (pd), 1);
333s ***** assert (std (t), 0.5882, 1e-4);
333s ***** assert (std (t_inf), 0.6738, 1e-4);
333s ***** assert (var (pd), 1);
333s ***** assert (var (t), 0.3460, 1e-4);
333s ***** assert (var (t_inf), 0.4540, 1e-4);
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution(0)
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution(-1)
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution(Inf)
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution(i)
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution("a")
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution([1, 2])
333s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
333s  PoissonDistribution(NaN)
333s ***** error <cdf: invalid argument for upper tail.> ...
333s  cdf (PoissonDistribution, 2, "uper")
333s ***** error <cdf: invalid argument for upper tail.> ...
333s  cdf (PoissonDistribution, 2, 3)
333s ***** shared x
333s  x = poissrnd (1, [1, 100]);
333s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha")
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 0)
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 1)
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", "")
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", {0.05})
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
333s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
333s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
333s           "parameter", {"lambda", "param"})
333s ***** error <paramci: unknown distribution parameter.> ...
333s  paramci (PoissonDistribution.fit (x), "parameter", "param")
333s ***** error <paramci: unknown distribution parameter.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
333s ***** error <paramci: invalid NAME for optional argument.> ...
333s  paramci (PoissonDistribution.fit (x), "NAME", "value")
333s ***** error <paramci: invalid NAME for optional argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
333s ***** error <paramci: invalid NAME for optional argument.> ...
333s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
333s           "parameter", "lambda", "NAME", "value")
333s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
333s  plot (PoissonDistribution, "Parent")
333s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
333s  plot (PoissonDistribution, "PlotType", 12)
333s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
333s  plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
333s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
333s  plot (PoissonDistribution, "PlotType", "pdfcdf")
333s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
333s  plot (PoissonDistribution, "Discrete", "pdfcdf")
333s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
333s  plot (PoissonDistribution, "Discrete", [1, 0])
333s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
333s  plot (PoissonDistribution, "Discrete", {true})
333s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
333s  plot (PoissonDistribution, "Parent", 12)
333s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
333s  plot (PoissonDistribution, "Parent", "hax")
333s ***** error <plot: invalid NAME for optional argument.> ...
333s  plot (PoissonDistribution, "invalidNAME", "pdf")
333s ***** error <plot: no fitted DATA to plot a probability plot.> ...
333s  plot (PoissonDistribution, "PlotType", "probability")
333s ***** error <proflik: no fitted data available.> ...
333s  proflik (PoissonDistribution, 2)
333s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
333s  proflik (PoissonDistribution.fit (x), 3)
333s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
333s  proflik (PoissonDistribution.fit (x), [1, 2])
333s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
333s  proflik (PoissonDistribution.fit (x), {1})
333s ***** error <proflik: SETPARAM must be a numeric vector.> ...
333s  proflik (PoissonDistribution.fit (x), 1, ones (2))
333s ***** error <proflik: missing VALUE for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display")
333s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display", 1)
333s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display", {1})
333s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
333s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
333s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
333s ***** error <proflik: invalid NAME for optional arguments.> ...
333s  proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
333s ***** error <proflik: invalid optional argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
333s ***** error <proflik: invalid optional argument.> ...
333s  proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
333s ***** error <truncate: missing input argument.> ...
333s  truncate (PoissonDistribution)
333s ***** error <truncate: missing input argument.> ...
333s  truncate (PoissonDistribution, 2)
333s ***** error <truncate: invalid lower upper limits.> ...
333s  truncate (PoissonDistribution, 4, 2)
333s ***** shared pd
333s  pd = PoissonDistribution(1);
333s  pd(2) = PoissonDistribution(3);
333s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
333s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
333s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
333s ***** error <mean: requires a scalar probability distribution.> mean (pd)
333s ***** error <median: requires a scalar probability distribution.> median (pd)
333s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
333s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
333s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
333s ***** error <plot: requires a scalar probability distribution.> plot (pd)
333s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
333s ***** error <random: requires a scalar probability distribution.> random (pd)
333s ***** error <std: requires a scalar probability distribution.> std (pd)
333s ***** error <truncate: requires a scalar probability distribution.> ...
333s  truncate (pd, 2, 4)
333s ***** error <var: requires a scalar probability distribution.> var (pd)
333s 97 tests, 97 passed, 0 known failure, 0 skipped
333s [inst/dist_obj/LogisticDistribution.m]
333s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/LogisticDistribution.m
333s ***** shared pd, t
333s  pd = LogisticDistribution (0, 1);
333s  t = truncate (pd, 2, 4);
333s ***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
333s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
333s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
333s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
333s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
333s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
333s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
333s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
333s ***** assert (iqr (pd), 2.1972, 1e-4);
333s ***** assert (iqr (t), 0.8286, 1e-4);
333s ***** assert (mean (pd), 0, 1e-4);
333s ***** assert (mean (t), 2.7193, 1e-4);
333s ***** assert (median (pd), 0);
333s ***** assert (median (t), 2.6085, 1e-4);
333s ***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
333s ***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
333s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
333s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
333s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
333s ***** assert (any (random (t, 1000, 1) < 2), false);
333s ***** assert (any (random (t, 1000, 1) > 4), false);
333s ***** assert (std (pd), 1.8138, 1e-4);
333s ***** assert (std (t), 0.5320, 1e-4);
333s ***** assert (var (pd), 3.2899, 1e-4);
333s ***** assert (var (t), 0.2830, 1e-4);
333s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
333s  LogisticDistribution(Inf, 1)
333s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
333s  LogisticDistribution(i, 1)
333s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
333s  LogisticDistribution("a", 1)
333s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
333s  LogisticDistribution([1, 2], 1)
333s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
333s  LogisticDistribution(NaN, 1)
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, 0)
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, -1)
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, Inf)
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, i)
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, "a")
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, [1, 2])
333s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
333s  LogisticDistribution(1, NaN)
333s ***** error <cdf: invalid argument for upper tail.> ...
333s  cdf (LogisticDistribution, 2, "uper")
333s ***** error <cdf: invalid argument for upper tail.> ...
333s  cdf (LogisticDistribution, 2, 3)
333s ***** shared x
333s  x = logirnd (1, 1, [1, 100]);
333s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha")
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", 0)
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", 1)
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", "")
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", {0.05})
333s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
333s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
333s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
333s           "parameter", {"mu", "sigma", "param"})
333s ***** error <paramci: unknown distribution parameter.> ...
333s  paramci (LogisticDistribution.fit (x), "parameter", "param")
333s ***** error <paramci: unknown distribution parameter.> ...
333s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
334s ***** error <paramci: invalid NAME for optional argument.> ...
334s  paramci (LogisticDistribution.fit (x), "NAME", "value")
334s ***** error <paramci: invalid NAME for optional argument.> ...
334s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
334s ***** error <paramci: invalid NAME for optional argument.> ...
334s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
334s           "NAME", "value")
334s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
334s  plot (LogisticDistribution, "Parent")
334s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
334s  plot (LogisticDistribution, "PlotType", 12)
334s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
334s  plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
334s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
334s  plot (LogisticDistribution, "PlotType", "pdfcdf")
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (LogisticDistribution, "Discrete", "pdfcdf")
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (LogisticDistribution, "Discrete", [1, 0])
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (LogisticDistribution, "Discrete", {true})
334s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
334s  plot (LogisticDistribution, "Parent", 12)
334s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
334s  plot (LogisticDistribution, "Parent", "hax")
334s ***** error <plot: invalid NAME for optional argument.> ...
334s  plot (LogisticDistribution, "invalidNAME", "pdf")
334s ***** error <plot: no fitted DATA to plot a probability plot.> ...
334s  plot (LogisticDistribution, "PlotType", "probability")
334s ***** error <proflik: no fitted data available.> ...
334s  proflik (LogisticDistribution, 2)
334s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
334s  proflik (LogisticDistribution.fit (x), 3)
334s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
334s  proflik (LogisticDistribution.fit (x), [1, 2])
334s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
334s  proflik (LogisticDistribution.fit (x), {1})
334s ***** error <proflik: SETPARAM must be a numeric vector.> ...
334s  proflik (LogisticDistribution.fit (x), 1, ones (2))
334s ***** error <proflik: missing VALUE for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display")
334s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display", 1)
334s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display", {1})
334s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
334s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
334s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
334s ***** error <proflik: invalid NAME for optional arguments.> ...
334s  proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
334s ***** error <proflik: invalid optional argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
334s ***** error <proflik: invalid optional argument.> ...
334s  proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
334s ***** error <truncate: missing input argument.> ...
334s  truncate (LogisticDistribution)
334s ***** error <truncate: missing input argument.> ...
334s  truncate (LogisticDistribution, 2)
334s ***** error <truncate: invalid lower upper limits.> ...
334s  truncate (LogisticDistribution, 4, 2)
334s ***** shared pd
334s  pd = LogisticDistribution(1, 1);
334s  pd(2) = LogisticDistribution(1, 3);
334s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
334s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
334s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
334s ***** error <mean: requires a scalar probability distribution.> mean (pd)
334s ***** error <median: requires a scalar probability distribution.> median (pd)
334s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
334s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
334s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
334s ***** error <plot: requires a scalar probability distribution.> plot (pd)
334s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
334s ***** error <random: requires a scalar probability distribution.> random (pd)
334s ***** error <std: requires a scalar probability distribution.> std (pd)
334s ***** error <truncate: requires a scalar probability distribution.> ...
334s  truncate (pd, 2, 4)
334s ***** error <var: requires a scalar probability distribution.> var (pd)
334s 95 tests, 95 passed, 0 known failure, 0 skipped
334s [inst/dist_obj/TriangularDistribution.m]
334s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/TriangularDistribution.m
334s ***** demo
334s  ## Generate a data set of 5000 random samples from a Triangular distribution
334s  ## with parameters A = 0, B = 1, C = 2. Fit a Triangular distribution to
334s  ## this data and plot a PDF of the fitted distribution superimposed on a
334s  ## histogram of the data.
334s 
334s  pd_fixed = makedist ("Triangular", "A", 0, "B", 1, "C", 2);
334s  rand ("seed", 2);
334s  data = random (pd_fixed, 5000, 1);
334s  A = min (data);
334s  C = mean (data);
334s  B = max (data);
334s 
334s  [counts, centers] = hist (data, 50);
334s  bin_width = centers(2) - centers(1);
334s  normalized_counts = counts / (sum (counts) * bin_width);
334s  bar (centers, normalized_counts, 1);
334s  hold on;
334s 
334s  x = linspace (A, B, 100);
334s  y = (2 * (x - A) / (C - A) .* (x <= C)) + (2 * (B - x) / (B - C) .* (x > C));
334s 
334s  plot (x, y, 'r-', 'LineWidth', 2);
334s 
334s  msg = sprintf ("Fitted Triangular distribution with A = %0.2f, C = %0.2f, B = %0.2f", A, C, B);
334s  title (msg);
334s 
334s  hold off;
334s ***** shared pd, t
334s  pd = TriangularDistribution (0, 3, 5);
334s  t = truncate (pd, 2, 4);
334s ***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
334s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
334s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
334s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
334s ***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
334s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
334s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
334s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
334s ***** assert (iqr (pd), 1.4824, 1e-4);
334s ***** assert (iqr (t), 0.8678, 1e-4);
334s ***** assert (mean (pd), 2.6667, 1e-4);
334s ***** assert (mean (t), 2.9649, 1e-4);
334s ***** assert (median (pd), 2.7386, 1e-4);
334s ***** assert (median (t), 2.9580, 1e-4);
334s ***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
334s ***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
334s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
334s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
334s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
334s ***** assert (any (random (t, 1000, 1) < 2), false);
334s ***** assert (any (random (t, 1000, 1) > 4), false);
334s ***** assert (std (pd), 1.0274, 1e-4);
334s ***** assert (std (t), 0.5369, 1e-4);
334s ***** assert (var (pd), 1.0556, 1e-4);
334s ***** assert (var (t), 0.2882, 1e-4);
334s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
334s  TriangularDistribution (i, 1, 2)
334s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
334s  TriangularDistribution (Inf, 1, 2)
334s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
334s  TriangularDistribution ([1, 2], 1, 2)
334s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
334s  TriangularDistribution ("a", 1, 2)
334s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
334s  TriangularDistribution (NaN, 1, 2)
334s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
334s  TriangularDistribution (1, i, 2)
334s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
334s  TriangularDistribution (1, Inf, 2)
334s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
334s  TriangularDistribution (1, [1, 2], 2)
334s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
334s  TriangularDistribution (1, "a", 2)
334s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
334s  TriangularDistribution (1, NaN, 2)
334s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
334s  TriangularDistribution (1, 2, i)
334s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
334s  TriangularDistribution (1, 2, Inf)
334s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
334s  TriangularDistribution (1, 2, [1, 2])
334s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
334s  TriangularDistribution (1, 2, "a")
334s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
334s  TriangularDistribution (1, 2, NaN)
334s ***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
334s  TriangularDistribution (1, 1, 1)
334s ***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
334s  TriangularDistribution (1, 0.5, 2)
334s ***** error <cdf: invalid argument for upper tail.> ...
334s  cdf (TriangularDistribution, 2, "uper")
334s ***** error <cdf: invalid argument for upper tail.> ...
334s  cdf (TriangularDistribution, 2, 3)
334s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
334s  plot (TriangularDistribution, "Parent")
334s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
334s  plot (TriangularDistribution, "PlotType", 12)
334s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
334s  plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
334s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
334s  plot (TriangularDistribution, "PlotType", "pdfcdf")
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (TriangularDistribution, "Discrete", "pdfcdf")
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (TriangularDistribution, "Discrete", [1, 0])
334s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
334s  plot (TriangularDistribution, "Discrete", {true})
334s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
334s  plot (TriangularDistribution, "Parent", 12)
334s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
334s  plot (TriangularDistribution, "Parent", "hax")
334s ***** error <plot: invalid NAME for optional argument.> ...
334s  plot (TriangularDistribution, "invalidNAME", "pdf")
334s ***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
334s  plot (TriangularDistribution, "PlotType", "probability")
334s ***** error <truncate: missing input argument.> ...
334s  truncate (TriangularDistribution)
334s ***** error <truncate: missing input argument.> ...
334s  truncate (TriangularDistribution, 2)
334s ***** error <truncate: invalid lower upper limits.> ...
334s  truncate (TriangularDistribution, 4, 2)
334s ***** shared pd
334s  pd = TriangularDistribution (0, 1, 2);
334s  pd(2) = TriangularDistribution (0, 1, 2);
334s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
334s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
334s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
334s ***** error <mean: requires a scalar probability distribution.> mean (pd)
334s ***** error <median: requires a scalar probability distribution.> median (pd)
334s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
334s ***** error <plot: requires a scalar probability distribution.> plot (pd)
334s ***** error <random: requires a scalar probability distribution.> random (pd)
334s ***** error <std: requires a scalar probability distribution.> std (pd)
334s ***** error <truncate: requires a scalar probability distribution.> ...
334s  truncate (pd, 2, 4)
334s ***** error <var: requires a scalar probability distribution.> var (pd)
334s 69 tests, 69 passed, 0 known failure, 0 skipped
334s [inst/dist_obj/ExtremeValueDistribution.m]
334s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/ExtremeValueDistribution.m
334s ***** shared pd, t
334s  pd = ExtremeValueDistribution (0, 1);
334s  t = truncate (pd, 2, 4);
334s ***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
334s ***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
334s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
334s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
334s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
335s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
335s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
335s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
335s ***** assert (iqr (pd), 1.5725, 1e-4);
335s ***** assert (iqr (t), 0.1338, 1e-4);
335s ***** assert (mean (pd), -0.5772, 1e-4);
335s ***** assert (mean (t), 2.1206, 1e-4);
335s ***** assert (median (pd), -0.3665, 1e-4);
335s ***** assert (median (t), 2.0897, 1e-4);
335s ***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
335s ***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
335s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
335s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
335s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
335s ***** assert (any (random (t, 1000, 1) < 2), false);
335s ***** assert (any (random (t, 1000, 1) > 4), false);
335s ***** assert (std (pd), 1.2825, 1e-4);
335s ***** assert (std (t), 0.1091, 1e-4);
335s ***** assert (var (pd), 1.6449, 1e-4);
335s ***** assert (var (t), 0.0119, 1e-4);
335s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
335s  ExtremeValueDistribution(Inf, 1)
335s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
335s  ExtremeValueDistribution(i, 1)
335s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
335s  ExtremeValueDistribution("a", 1)
335s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
335s  ExtremeValueDistribution([1, 2], 1)
335s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
335s  ExtremeValueDistribution(NaN, 1)
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, 0)
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, -1)
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, Inf)
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, i)
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, "a")
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, [1, 2])
335s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
335s  ExtremeValueDistribution(1, NaN)
335s ***** error <cdf: invalid argument for upper tail.> ...
335s  cdf (ExtremeValueDistribution, 2, "uper")
335s ***** error <cdf: invalid argument for upper tail.> ...
335s  cdf (ExtremeValueDistribution, 2, 3)
335s ***** shared x
335s  rand ("seed", 1);
335s  x = evrnd (1, 1, [1000, 1]);
335s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha")
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", "")
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
335s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), ...
335s           "parameter", "mu", "alpha", {0.05})
335s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), ...
335s           "parameter", {"mu", "sigma", "param"})
335s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
335s           "parameter", {"mu", "sigma", "param"})
335s ***** error <paramci: unknown distribution parameter.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
335s ***** error <paramci: unknown distribution parameter.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
335s           "parameter", "param")
335s ***** error <paramci: invalid NAME for optional argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
335s ***** error <paramci: invalid NAME for optional argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
335s ***** error <paramci: invalid NAME for optional argument.> ...
335s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
335s           "parameter", "mu", "NAME", "value")
335s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
335s  plot (ExtremeValueDistribution, "Parent")
335s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
335s  plot (ExtremeValueDistribution, "PlotType", 12)
335s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
335s  plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
335s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
335s  plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
335s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
335s  plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
335s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
335s  plot (ExtremeValueDistribution, "Discrete", [1, 0])
335s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
335s  plot (ExtremeValueDistribution, "Discrete", {true})
335s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
335s  plot (ExtremeValueDistribution, "Parent", 12)
335s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
335s  plot (ExtremeValueDistribution, "Parent", "hax")
335s ***** error <plot: invalid NAME for optional argument.> ...
335s  plot (ExtremeValueDistribution, "invalidNAME", "pdf")
335s ***** error <plot: no fitted DATA to plot a probability plot.> ...
335s  plot (ExtremeValueDistribution, "PlotType", "probability")
335s ***** error <proflik: no fitted data available.> ...
335s  proflik (ExtremeValueDistribution, 2)
335s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 3)
335s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
335s  proflik (ExtremeValueDistribution.fit (x), [1, 2])
335s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
335s  proflik (ExtremeValueDistribution.fit (x), {1})
335s ***** error <proflik: SETPARAM must be a numeric vector.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
335s ***** error <proflik: missing VALUE for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display")
335s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
335s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
335s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
335s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
335s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
335s ***** error <proflik: invalid NAME for optional arguments.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
335s ***** error <proflik: invalid optional argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
335s ***** error <proflik: invalid optional argument.> ...
335s  proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
335s ***** error <truncate: missing input argument.> ...
335s  truncate (ExtremeValueDistribution)
335s ***** error <truncate: missing input argument.> ...
335s  truncate (ExtremeValueDistribution, 2)
335s ***** error <truncate: invalid lower upper limits.> ...
335s  truncate (ExtremeValueDistribution, 4, 2)
335s ***** shared pd
335s  pd = ExtremeValueDistribution(1, 1);
335s  pd(2) = ExtremeValueDistribution(1, 3);
335s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
335s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
335s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
335s ***** error <mean: requires a scalar probability distribution.> mean (pd)
335s ***** error <median: requires a scalar probability distribution.> median (pd)
335s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
335s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
335s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
335s ***** error <plot: requires a scalar probability distribution.> plot (pd)
335s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
335s ***** error <random: requires a scalar probability distribution.> random (pd)
335s ***** error <std: requires a scalar probability distribution.> std (pd)
335s ***** error <truncate: requires a scalar probability distribution.> ...
335s  truncate (pd, 2, 4)
335s ***** error <var: requires a scalar probability distribution.> var (pd)
335s 95 tests, 95 passed, 0 known failure, 0 skipped
335s [inst/dist_obj/UniformDistribution.m]
335s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/UniformDistribution.m
335s ***** demo
335s  ## Generate a data set of 5000 random samples from a Uniform distribution with
335s  ## parameters Lower = 0 and Upper = 10. Create a Uniform distribution with these
335s  ## parameters and plot its PDF superimposed on a histogram of the data.
335s 
335s  pd = makedist ("Uniform", "Lower", 0, "Upper", 10);
335s  rand ("seed", 21);
335s  data = random (pd, 5000, 1);
335s  
335s  x = linspace (pd.Lower - 1, pd.Upper + 1, 500);
335s  y = pdf (pd, x);
335s  plot (x, y, 'r-', 'LineWidth', 2);
335s  hold on;
335s  
335s  [counts, centers] = hist (data, 50);
335s  bin_width = centers(2) - centers(1);
335s  normalized_counts = counts / (sum (counts) * bin_width);
335s  bar (centers, normalized_counts, 1);
335s  
335s  msg = "Uniform distribution with Lower = %0.2f and Upper = %0.2f";
335s  title (sprintf (msg, pd.Lower, pd.Upper));
335s  legend ("PDF", "Histogram", "location", "northeast");
335s  
335s  hold off;
335s ***** shared pd, t
335s  pd = UniformDistribution (0, 5);
335s  t = truncate (pd, 2, 4);
335s ***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
335s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
335s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
335s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
335s ***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
335s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
335s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
335s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
335s ***** assert (iqr (pd), 2.5, 1e-14);
335s ***** assert (iqr (t), 1, 1e-14);
335s ***** assert (mean (pd), 2.5, 1e-14);
335s ***** assert (mean (t), 3, 1e-14);
335s ***** assert (median (pd), 2.5, 1e-14);
335s ***** assert (median (t), 3, 1e-14);
335s ***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
335s ***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
335s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
335s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
335s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
335s ***** assert (any (random (t, 1000, 1) < 2), false);
335s ***** assert (any (random (t, 1000, 1) > 4), false);
335s ***** assert (std (pd), 1.4434, 1e-4);
335s ***** assert (std (t), 0.5774, 1e-4);
335s ***** assert (var (pd), 2.0833, 1e-4);
335s ***** assert (var (t), 0.3333, 1e-4);
335s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
335s  UniformDistribution (i, 1)
335s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
335s  UniformDistribution (Inf, 1)
335s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
335s  UniformDistribution ([1, 2], 1)
335s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
335s  UniformDistribution ("a", 1)
335s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
335s  UniformDistribution (NaN, 1)
335s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
335s  UniformDistribution (1, i)
335s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
335s  UniformDistribution (1, Inf)
335s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
335s  UniformDistribution (1, [1, 2])
335s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
335s  UniformDistribution (1, "a")
336s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
336s  UniformDistribution (1, NaN)
336s ***** error <UniformDistribution: LOWER must be less than UPPER.> ...
336s  UniformDistribution (2, 1)
336s ***** error <cdf: invalid argument for upper tail.> ...
336s  cdf (UniformDistribution, 2, "uper")
336s ***** error <cdf: invalid argument for upper tail.> ...
336s  cdf (UniformDistribution, 2, 3)
336s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
336s  plot (UniformDistribution, "Parent")
336s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
336s  plot (UniformDistribution, "PlotType", 12)
336s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
336s  plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
336s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
336s  plot (UniformDistribution, "PlotType", "pdfcdf")
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (UniformDistribution, "Discrete", "pdfcdf")
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (UniformDistribution, "Discrete", [1, 0])
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (UniformDistribution, "Discrete", {true})
336s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
336s  plot (UniformDistribution, "Parent", 12)
336s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
336s  plot (UniformDistribution, "Parent", "hax")
336s ***** error <plot: invalid NAME for optional argument.> ...
336s  plot (UniformDistribution, "invalidNAME", "pdf")
336s ***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
336s  plot (UniformDistribution, "PlotType", "probability")
336s ***** error <truncate: missing input argument.> ...
336s  truncate (UniformDistribution)
336s ***** error <truncate: missing input argument.> ...
336s  truncate (UniformDistribution, 2)
336s ***** error <truncate: invalid lower upper limits.> ...
336s  truncate (UniformDistribution, 4, 2)
336s ***** shared pd
336s  pd = UniformDistribution (0, 1);
336s  pd(2) = UniformDistribution (0, 2);
336s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
336s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
336s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
336s ***** error <mean: requires a scalar probability distribution.> mean (pd)
336s ***** error <median: requires a scalar probability distribution.> median (pd)
336s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
336s ***** error <plot: requires a scalar probability distribution.> plot (pd)
336s ***** error <random: requires a scalar probability distribution.> random (pd)
336s ***** error <std: requires a scalar probability distribution.> std (pd)
336s ***** error <truncate: requires a scalar probability distribution.> ...
336s  truncate (pd, 2, 4)
336s ***** error <var: requires a scalar probability distribution.> var (pd)
336s 63 tests, 63 passed, 0 known failure, 0 skipped
336s [inst/dist_obj/NegativeBinomialDistribution.m]
336s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_obj/NegativeBinomialDistribution.m
336s ***** demo
336s  ## Generate a data set of 5000 random samples from a Negative Binomial
336s  ## distribution with parameters R = 5 and P = 0.5.  Fit a Negative Binomial
336s  ## distribution to this data and plot a PDF of the fitted distribution
336s  ## superimposed on a histogram of the data.
336s 
336s  pd_fixed = makedist ("NegativeBinomial", "R", 5, "P", 0.5)
336s  rand ("seed", 2);
336s  data = random (pd_fixed, 5000, 1);
336s  pd_fitted = fitdist (data, "NegativeBinomial")
336s  plot (pd_fitted)
336s  msg = "Fitted Negative Binomial distribution with R = %0.2f and P = %0.2f";
336s  title (sprintf (msg, pd_fitted.R, pd_fitted.P))
336s ***** shared pd, t, t_inf
336s  pd = NegativeBinomialDistribution (5, 0.5);
336s  t = truncate (pd, 2, 4);
336s  t_inf = truncate (pd, 2, Inf);
336s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
336s ***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
336s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
336s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
336s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
336s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
336s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
336s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
336s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
336s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
336s ***** assert (iqr (pd), 4);
336s ***** assert (iqr (t), 2);
336s ***** assert (mean (pd), 5);
336s ***** assert (mean (t), 3.0500, 1e-4);
336s ***** assert (mean (t_inf), 5.5263, 1e-4);
336s ***** assert (median (pd), 4);
336s ***** assert (median (t), 3);
336s ***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
336s ***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
336s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
336s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
336s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
336s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
336s ***** assert (any (random (t, 1000, 1) < 2), false);
336s ***** assert (any (random (t, 1000, 1) > 4), false);
336s ***** assert (std (pd), 3.1623, 1e-4);
336s ***** assert (std (t), 0.8047, 1e-4);
336s ***** assert (std (t_inf), 2.9445, 1e-4);
336s ***** assert (var (pd), 10);
336s ***** assert (var (t), 0.6475, 1e-4);
336s ***** assert (var (t_inf), 8.6704, 1e-4);
336s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
336s  NegativeBinomialDistribution(Inf, 1)
336s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
336s  NegativeBinomialDistribution(i, 1)
336s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
336s  NegativeBinomialDistribution("a", 1)
336s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
336s  NegativeBinomialDistribution([1, 2], 1)
336s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
336s  NegativeBinomialDistribution(NaN, 1)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, 0)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, -1)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, Inf)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, i)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, "a")
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, [1, 2])
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, NaN)
336s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
336s  NegativeBinomialDistribution(1, 1.2)
336s ***** error <cdf: invalid argument for upper tail.> ...
336s  cdf (NegativeBinomialDistribution, 2, "uper")
336s ***** error <cdf: invalid argument for upper tail.> ...
336s  cdf (NegativeBinomialDistribution, 2, 3)
336s ***** shared x
336s  x = nbinrnd (1, 0.5, [1, 100]);
336s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha")
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
336s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
336s           "alpha", {0.05})
336s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), ...
336s           "parameter", {"R", "P", "param"})
336s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
336s           "parameter", {"R", "P", "param"})
336s ***** error <paramci: unknown distribution parameter.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
336s ***** error <paramci: unknown distribution parameter.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
336s           "parameter", "param")
336s ***** error <paramci: invalid NAME for optional argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
336s ***** error <paramci: invalid NAME for optional argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
336s           "NAME", "value")
336s ***** error <paramci: invalid NAME for optional argument.> ...
336s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
336s           "parameter", "R", "NAME", "value")
336s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
336s  plot (NegativeBinomialDistribution, "Parent")
336s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
336s  plot (NegativeBinomialDistribution, "PlotType", 12)
336s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
336s  plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
336s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
336s  plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (NegativeBinomialDistribution, "Discrete", [1, 0])
336s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
336s  plot (NegativeBinomialDistribution, "Discrete", {true})
336s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
336s  plot (NegativeBinomialDistribution, "Parent", 12)
336s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
336s  plot (NegativeBinomialDistribution, "Parent", "hax")
336s ***** error <plot: invalid NAME for optional argument.> ...
336s  plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
336s ***** error <plot: no fitted DATA to plot a probability plot.> ...
336s  plot (NegativeBinomialDistribution, "PlotType", "probability")
336s ***** error <proflik: no fitted data available.> ...
336s  proflik (NegativeBinomialDistribution, 2)
336s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 3)
336s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), [1, 2])
336s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), {1})
336s ***** error <proflik: SETPARAM must be a numeric vector.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
336s ***** error <proflik: missing VALUE for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
336s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
336s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
336s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
336s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
336s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
336s ***** error <proflik: invalid NAME for optional arguments.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
336s ***** error <proflik: invalid optional argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
336s ***** error <proflik: invalid optional argument.> ...
336s  proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
336s ***** error <truncate: missing input argument.> ...
336s  truncate (NegativeBinomialDistribution)
336s ***** error <truncate: missing input argument.> ...
336s  truncate (NegativeBinomialDistribution, 2)
336s ***** error <truncate: invalid lower upper limits.> ...
336s  truncate (NegativeBinomialDistribution, 4, 2)
336s ***** shared pd
336s  pd = NegativeBinomialDistribution(1, 0.5);
336s  pd(2) = NegativeBinomialDistribution(1, 0.6);
336s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
336s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
336s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
336s ***** error <mean: requires a scalar probability distribution.> mean (pd)
336s ***** error <median: requires a scalar probability distribution.> median (pd)
336s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
336s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
336s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
336s ***** error <plot: requires a scalar probability distribution.> plot (pd)
336s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
336s ***** error <random: requires a scalar probability distribution.> random (pd)
336s ***** error <std: requires a scalar probability distribution.> std (pd)
336s ***** error <truncate: requires a scalar probability distribution.> ...
336s  truncate (pd, 2, 4)
336s ***** error <var: requires a scalar probability distribution.> var (pd)
336s 102 tests, 102 passed, 0 known failure, 0 skipped
336s [inst/vartest2.m]
336s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/vartest2.m
336s ***** error<vartest2: too few input arguments.> vartest2 ();
336s ***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
336s ***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
336s  vartest2 (rand (20,1), 5);
336s ***** error<vartest2: invalid value for alpha.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
336s ***** error<vartest2: invalid value for alpha.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
336s ***** error<vartest2: invalid value for alpha.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
336s ***** error<vartest2: invalid value for alpha.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
336s ***** error<vartest2: invalid value for tail.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
336s ***** error<vartest2: invalid value for tail.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
336s ***** error<vartest2: invalid value for operating dimension.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
336s ***** error<vartest2: invalid value for operating dimension.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
336s ***** error<vartest2: invalid name for optional arguments.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
336s ***** error<vartest2: optional arguments must be in name/value pairs.> ...
336s  vartest2 (rand (20,1), rand (25,1)*2, "some");
336s ***** test
336s  load carsmall
336s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
336s  assert (h, 0);
336s  assert (pval, 0.6288022362718455, 1e-13);
336s  assert (ci, [0.4139; 1.7193], 1e-4);
336s  assert (stat.fstat, 0.8384, 1e-4);
336s  assert (stat.df1, 30);
336s  assert (stat.df2, 33);
336s ***** test
336s  load carsmall
336s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
336s                                  "tail", "left");
336s  assert (h, 0);
336s  assert (pval, 0.314401118135922, 1e-13);
336s  assert (ci, [0; 1.5287], 1e-4);
336s  assert (stat.fstat, 0.8384, 1e-4);
336s  assert (stat.df1, 30);
336s  assert (stat.df2, 33);
336s ***** test
336s  load carsmall
336s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
336s                                  "tail", "right");
336s  assert (h, 0);
336s  assert (pval, 0.685598881864077, 1e-13);
336s  assert (ci, [0.4643; Inf], 1e-4);
336s  assert (stat.fstat, 0.8384, 1e-4);
336s  assert (stat.df1, 30);
336s  assert (stat.df2, 33);
336s 16 tests, 16 passed, 0 known failure, 0 skipped
336s [inst/pdist.m]
336s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/pdist.m
336s ***** shared xy, t, eucl, x
336s  xy = [0 1; 0 2; 7 6; 5 6];
336s  t = 1e-3;
336s  eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
336s  x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
336s ***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
336s ***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
336s ***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
336s ***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
336s ***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
336s ***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
336s ***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
336s ***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
336s ***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
336s ***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
336s ***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
336s ***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
336s ***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
336s ***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
336s ***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
336s ***** assert (pdist (x, "euclidean"), ...
336s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
336s ***** assert (pdist (x, eucl), ...
336s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
336s ***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
336s ***** assert (pdist (x, "seuclidean"), ...
336s         [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
336s ***** warning<pdist: matrix is close to singular> ...
336s  pdist (x, "mahalanobis");
336s ***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
336s ***** assert (pdist (x, "minkowski"), ...
336s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
336s ***** assert (pdist (x, "minkowski", 3), ...
336s         [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
336s ***** assert (pdist (x, "cosine"), ...
336s         [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
336s ***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
336s ***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
336s ***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
336s ***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
336s ***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
336s 29 tests, 29 passed, 0 known failure, 0 skipped
336s [inst/anova1.m]
336s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/anova1.m
336s ***** demo
336s  x = meshgrid (1:6);
336s  randn ("seed", 15);    # for reproducibility
336s  x = x + normrnd (0, 1, 6, 6);
336s  anova1 (x, [], 'off');
336s ***** demo
336s  x = meshgrid (1:6);
336s  randn ("seed", 15);    # for reproducibility
336s  x = x + normrnd (0, 1, 6, 6);
336s  [p, atab] = anova1(x);
336s ***** demo
336s  x = ones (50, 4) .* [-2, 0, 1, 5];
336s  randn ("seed", 13);    # for reproducibility
336s  x = x + normrnd (0, 2, 50, 4);
336s  groups = {"A", "B", "C", "D"};
336s  anova1 (x, groups);
336s ***** demo
336s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
336s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
336s  anova1 (y(:), g(:), "on", "unequal");
336s ***** test
336s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
336s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
336s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
336s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
336s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
336s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
336s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
336s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
336s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
336s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
336s  group = [1:10] .* ones (10,10);
336s  group = group(:);
336s  [p, tbl] = anova1 (data, group, "off");
336s  assert (p, 0.022661, 1e-6);
336s  assert (tbl{2,5}, 2.2969, 1e-4);
336s  assert (tbl{2,3}, 9, 0);
336s  assert (tbl{4,2}, 0.003903, 1e-6);
336s  data = reshape (data, 10, 10);
336s  [p, tbl, stats] = anova1 (data, [], "off");
336s  assert (p, 0.022661, 1e-6);
336s  assert (tbl{2,5}, 2.2969, 1e-4);
336s  assert (tbl{2,3}, 9, 0);
336s  assert (tbl{4,2}, 0.003903, 1e-6);
336s  means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
336s  N = 10 * ones (1, 10);
336s  assert (stats.means, means, 1e-6);
336s  assert (length (stats.gnames), 10, 0);
336s  assert (stats.n, N, 0);
336s ***** test
336s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
336s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
336s  [p, tbl] = anova1 (y(:), g(:), "off", "equal");
336s  assert (p, 0.00004163, 1e-6);
336s  assert (tbl{2,5}, 22.573418, 1e-6);
336s  assert (tbl{2,3}, 2, 0);
336s  assert (tbl{3,3}, 14, 0);
336s  [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
336s  assert (p, 0.00208877, 1e-8);
336s  assert (tbl{2,5}, 15.523192, 1e-6);
336s  assert (tbl{2,3}, 2, 0);
336s  assert (tbl{2,4}, 7.5786897, 1e-6);
336s 2 tests, 2 passed, 0 known failure, 0 skipped
336s [inst/pcares.m]
336s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/pcares.m
336s ***** demo
336s  x = [ 7    26     6    60;
336s        1    29    15    52;
336s       11    56     8    20;
336s       11    31     8    47;
336s        7    52     6    33;
336s       11    55     9    22;
336s        3    71    17     6;
336s        1    31    22    44;
336s        2    54    18    22;
336s       21    47     4    26;
336s        1    40    23    34;
336s       11    66     9    12;
336s       10    68     8    12];
336s 
336s  ## As we increase the number of principal components, the norm
336s  ## of the residuals matrix will decrease
336s  r1 = pcares (x,1);
336s  n1 = norm (r1)
336s  r2 = pcares (x,2);
336s  n2 = norm (r2)
336s  r3 = pcares (x,3);
336s  n3 = norm (r3)
336s  r4 = pcares (x,4);
336s  n4 = norm (r4)
336s ***** test
336s  load hald
336s  r1 = pcares (ingredients,1);
336s  r2 = pcares (ingredients,2);
336s  r3 = pcares (ingredients,3);
336s  assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
336s  assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
336s  assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
336s ***** error<pcares: too few input arguments.> pcares (ones (20, 3))
337s ***** error<pcares: NDIM must be less than or equal to the column of X.> ...
337s  pcares (ones (30, 2), 3)
337s 3 tests, 3 passed, 0 known failure, 0 skipped
337s [inst/nanmean.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/nanmean.m
337s ***** demo
337s  ## Find the column means for a matrix with missing values.,
337s 
337s  x = magic (3);
337s  x([1, 4, 7:9]) = NaN
337s  y = nanmean (x)
337s ***** demo
337s  ## Find the row means for a matrix with missing values.,
337s 
337s  x = magic (3);
337s  x([1, 4, 7:9]) = NaN
337s  y = nanmean (x, 2)
337s ***** demo
337s  ## Find the mean of all the values in a multidimensional array
337s  ## with missing values.
337s 
337s  x = reshape (1:30, [2, 5, 3]);
337s  x([10:12, 25]) = NaN
337s  y = nanmean (x, "all")
337s ***** demo
337s  ## Find the mean of a multidimensional array with missing values over
337s  ## multiple dimensions.
337s 
337s  x = reshape (1:30, [2, 5, 3]);
337s  x([10:12, 25]) = NaN
337s  y = nanmean (x, [2, 3])
337s ***** assert (nanmean ([]), NaN)
337s ***** assert (nanmean (NaN), NaN)
337s ***** assert (nanmean (NaN(3)), [NaN, NaN, NaN])
337s ***** assert (nanmean ([3 2 NaN 7]), 4)
337s ***** assert (nanmean ([2 4 NaN Inf]), Inf)
337s ***** assert (nanmean ([1 NaN 3; NaN 4 6; 7 8 NaN]), [4 6 4.5])
337s ***** assert (nanmean ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [2; 5.5; 7.5])
337s ***** assert (nanmean (uint8 ([2 4 1 7])), 3.5)
337s ***** test
337s  x = magic(3);
337s  x([1 6:9]) = NaN;
337s  assert (nanmean (x), [3.5, 3, NaN])
337s  assert (nanmean (x, 2), [1; 4; 4])
337s ***** test
337s  x = reshape(1:24, [2, 4, 3]);
337s  x([5:6, 20]) = NaN;
337s  assert (nanmean (x, "all"), 269/21)
337s ***** test
337s  x = reshape(1:24,[2, 4, 3]);
337s  x([5:6, 20]) = NaN;
337s  assert (squeeze (nanmean (x, [1, 2])), [25/6; 100/8; 144/7])
337s  assert (nanmean (x, [2, 3]), [139/11; 13])
337s 11 tests, 11 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/tricdf.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tricdf.m
337s ***** demo
337s  ## Plot various CDFs from the triangular distribution
337s  x = 0.001:0.001:10;
337s  p1 = tricdf (x, 3, 4, 6);
337s  p2 = tricdf (x, 1, 2, 5);
337s  p3 = tricdf (x, 2, 3, 9);
337s  p4 = tricdf (x, 2, 5, 9);
337s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
337s  grid on
337s  xlim ([0, 10])
337s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
337s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
337s          "location", "southeast")
337s  title ("Triangular CDF")
337s  xlabel ("values in x")
337s  ylabel ("probability")
337s ***** shared x, y
337s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
337s  y = [0, 0, 0.02, 0.5, 0.98, 1 1];
337s ***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
337s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
337s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
337s ***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
337s ***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
337s ***** assert (tricdf (x, 1, 1.5, 2), y, eps)
337s ***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
337s  [y(1:2), NaN, y(4:7)], eps)
337s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
337s  [y(1:2), NaN, y(4:7)], eps)
337s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
337s  [y(1:2), NaN, y(4:7)], eps)
337s ***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
337s ***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
337s  single ([y, NaN]), eps("single"))
337s ***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
337s  single ([y, NaN]), eps("single"))
337s ***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
337s  single ([y, NaN]), eps("single"))
337s ***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
337s  single ([y, NaN]), eps("single"))
337s ***** error<tricdf: function called with too few input arguments.> tricdf ()
337s ***** error<tricdf: function called with too few input arguments.> tricdf (1)
337s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
337s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
337s ***** error<tricdf: function called with too many inputs> ...
337s  tricdf (1, 2, 3, 4, 5, 6)
337s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
337s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
337s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
337s  tricdf (ones (3), ones (2), ones(2), ones(2))
337s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
337s  tricdf (ones (2), ones (3), ones(2), ones(2))
337s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
337s  tricdf (ones (2), ones (2), ones(3), ones(2))
337s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
337s  tricdf (ones (2), ones (2), ones(2), ones(3))
337s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
337s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
337s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
337s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
337s 29 tests, 29 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/nctpdf.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nctpdf.m
337s ***** demo
337s  ## Plot various PDFs from the noncentral T distribution
337s  x = -5:0.01:10;
337s  y1 = nctpdf (x, 1, 0);
337s  y2 = nctpdf (x, 4, 0);
337s  y3 = nctpdf (x, 1, 2);
337s  y4 = nctpdf (x, 4, 2);
337s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
337s  grid on
337s  xlim ([-5, 10])
337s  ylim ([0, 0.4])
337s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
337s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
337s  title ("Noncentral T PDF")
337s  xlabel ("values in x")
337s  ylabel ("density")
337s ***** demo
337s  ## Compare the noncentral T PDF with MU = 1 to the T PDF
337s  ## with the same number of degrees of freedom (10).
337s 
337s  x = -5:0.1:5;
337s  y1 = nctpdf (x, 10, 1);
337s  y2 = tpdf (x, 10);
337s  plot (x, y1, "-", x, y2, "-");
337s  grid on
337s  xlim ([-5, 5])
337s  ylim ([0, 0.4])
337s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
337s  title ("Noncentral T vs T PDFs")
337s  xlabel ("values in x")
337s  ylabel ("density")
337s ***** shared x1, df, mu
337s  x1 = [-Inf, 2, NaN, 4, Inf];
337s  df = [2, 0, -1, 1, 4];
337s  mu = [1, NaN, 3, -1, 2];
337s ***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
337s ***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
337s ***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
337s ***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
337s ***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
337s ***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
337s                              0.0146500727180389, 0.3082302682110299], 1e-14);
337s ***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
337s                              0.00401787561306999, 0.0972086534042828], 1e-14);
337s ***** error<nctpdf: function called with too few input arguments.> nctpdf ()
337s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
337s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
337s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
337s  nctpdf (ones (3), ones (2), ones (2))
337s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
337s  nctpdf (ones (2), ones (3), ones (2))
337s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
337s  nctpdf (ones (2), ones (2), ones (3))
337s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
337s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
337s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
337s 16 tests, 16 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/exprnd.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/exprnd.m
337s ***** assert (size (exprnd (2)), [1, 1])
337s ***** assert (size (exprnd (ones (2,1))), [2, 1])
337s ***** assert (size (exprnd (ones (2,2))), [2, 2])
337s ***** assert (size (exprnd (1, 3)), [3, 3])
337s ***** assert (size (exprnd (1, [4 1])), [4, 1])
337s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
337s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
337s ***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
337s ***** assert (size (exprnd (1, 0, 1)), [0, 1])
337s ***** assert (size (exprnd (1, 1, 0)), [1, 0])
337s ***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
337s ***** assert (class (exprnd (2)), "double")
337s ***** assert (class (exprnd (single (2))), "single")
337s ***** assert (class (exprnd (single ([2 2]))), "single")
337s ***** error<exprnd: function called with too few input arguments.> exprnd ()
337s ***** error<exprnd: MU must not be complex.> exprnd (i)
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (1, -1)
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (1, 1.2)
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (1, ones (2))
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (1, [2 -1 2])
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (1, [2 0 2.5])
337s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  exprnd (ones (2), ones (2))
337s ***** error<exprnd: dimensions must be non-negative integers.> ...
337s  exprnd (1, 2, -1, 5)
337s ***** error<exprnd: dimensions must be non-negative integers.> ...
337s  exprnd (1, 2, 1.5, 5)
337s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
337s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
337s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
337s 27 tests, 27 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/nctrnd.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nctrnd.m
337s ***** assert (size (nctrnd (1, 1)), [1 1])
337s ***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
337s ***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
337s ***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
337s ***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
337s ***** assert (size (nctrnd (1, 1, 3)), [3, 3])
337s ***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
337s ***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
337s ***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
337s ***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
337s ***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
337s ***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
337s ***** assert (class (nctrnd (1, 1)), "double")
337s ***** assert (class (nctrnd (1, single (1))), "single")
337s ***** assert (class (nctrnd (1, single ([1, 1]))), "single")
337s ***** assert (class (nctrnd (single (1), 1)), "single")
337s ***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
337s ***** error<nctrnd: function called with too few input arguments.> nctrnd ()
337s ***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
337s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
337s  nctrnd (ones (3), ones (2))
337s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
337s  nctrnd (ones (2), ones (3))
337s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
337s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
337s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  nctrnd (1, 2, -1)
337s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  nctrnd (1, 2, 1.2)
337s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  nctrnd (1, 2, ones (2))
337s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  nctrnd (1, 2, [2 -1 2])
337s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
337s  nctrnd (1, 2, [2 0 2.5])
337s ***** error<nctrnd: dimensions must be non-negative integers.> ...
337s  nctrnd (1, 2, 2, -1, 5)
337s ***** error<nctrnd: dimensions must be non-negative integers.> ...
337s  nctrnd (1, 2, 2, 1.5, 5)
337s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
337s  nctrnd (2, ones (2), 3)
337s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
337s  nctrnd (2, ones (2), [3, 2])
337s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
337s  nctrnd (2, ones (2), 3, 2)
337s 33 tests, 33 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/invginv.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/invginv.m
337s ***** demo
337s  ## Plot various iCDFs from the inverse Gaussian distribution
337s  p = 0.001:0.001:0.999;
337s  x1 = invginv (p, 1, 0.2);
337s  x2 = invginv (p, 1, 1);
337s  x3 = invginv (p, 1, 3);
337s  x4 = invginv (p, 3, 0.2);
337s  x5 = invginv (p, 3, 1);
337s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
337s  grid on
337s  ylim ([0, 3])
337s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
337s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
337s  title ("Inverse Gaussian iCDF")
337s  xlabel ("probability")
337s  ylabel ("x")
337s ***** shared p, x
337s  p = [0, 0.3829, 0.6827, 1];
337s  x = [0, 0.5207, 1.0376, Inf];
337s ***** assert (invginv (p, 1, 1), x, 1e-4);
337s ***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
337s ***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
337s ***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
337s ***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
337s ***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
337s ***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
337s ***** error<invginv: function called with too few input arguments.> invginv (1)
337s ***** error<invginv: function called with too few input arguments.> invginv (1, 2)
337s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
337s  invginv (1, ones (2), ones (3))
337s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
337s  invginv (ones (2), 1, ones (3))
337s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
337s  invginv (ones (2), ones (3), 1)
337s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
337s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
337s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
337s 15 tests, 15 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/unifinv.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unifinv.m
337s ***** demo
337s  ## Plot various iCDFs from the continuous uniform distribution
337s  p = 0.001:0.001:0.999;
337s  x1 = unifinv (p, 2, 5);
337s  x2 = unifinv (p, 3, 9);
337s  plot (p, x1, "-b", p, x2, "-g")
337s  grid on
337s  xlim ([0, 1])
337s  ylim ([0, 10])
337s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
337s  title ("Continuous uniform iCDF")
337s  xlabel ("probability")
337s  ylabel ("values in x")
337s ***** shared p
337s  p = [-1 0 0.5 1 2];
337s ***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
337s ***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
337s ***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
337s ***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
337s ***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
337s ***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
337s ***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
337s ***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
337s ***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
337s ***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
337s ***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
337s ***** error<unifinv: function called with too few input arguments.> unifinv ()
337s ***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
337s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
337s  unifinv (ones (3), ones (2), ones (2))
337s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
337s  unifinv (ones (2), ones (3), ones (2))
337s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
337s  unifinv (ones (2), ones (2), ones (3))
337s ***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
337s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
337s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
337s 19 tests, 19 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/ricepdf.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ricepdf.m
337s ***** demo
337s  ## Plot various PDFs from the Rician distribution
337s  x = 0:0.01:8;
337s  y1 = ricepdf (x, 0, 1);
337s  y2 = ricepdf (x, 0.5, 1);
337s  y3 = ricepdf (x, 1, 1);
337s  y4 = ricepdf (x, 2, 1);
337s  y5 = ricepdf (x, 4, 1);
337s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
337s  grid on
337s  ylim ([0, 0.65])
337s  xlim ([0, 8])
337s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
337s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
337s  title ("Rician PDF")
337s  xlabel ("values in x")
337s  ylabel ("density")
337s ***** shared x, y
337s  x = [-1 0 0.5 1 2];
337s  y = [0 0 0.1073 0.1978 0.2846];
337s ***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
337s ***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
337s ***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
337s ***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
337s ***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
337s ***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
337s ***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
337s ***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
337s ***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
337s ***** error<ricepdf: function called with too few input arguments.> ricepdf ()
337s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
337s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
337s ***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
337s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
337s  ricepdf (ones (3), ones (2), ones (2))
337s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
337s  ricepdf (ones (2), ones (3), ones (2))
337s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
337s  ricepdf (ones (2), ones (2), ones (3))
337s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
337s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
337s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
337s 19 tests, 19 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/hncdf.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hncdf.m
337s ***** demo
337s  ## Plot various CDFs from the half-normal distribution
337s  x = 0:0.001:10;
337s  p1 = hncdf (x, 0, 1);
337s  p2 = hncdf (x, 0, 2);
337s  p3 = hncdf (x, 0, 3);
337s  p4 = hncdf (x, 0, 5);
337s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
337s  grid on
337s  xlim ([0, 10])
337s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
337s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
337s  title ("Half-normal CDF")
337s  xlabel ("values in x")
337s  ylabel ("probability")
337s ***** demo
337s  ## Plot half-normal against normal cumulative distribution function
337s  x = -5:0.001:5;
337s  p1 = hncdf (x, 0, 1);
337s  p2 = normcdf (x);
337s  plot (x, p1, "-b", x, p2, "-g")
337s  grid on
337s  xlim ([-5, 5])
337s  legend ({"half-normal with μ = 0, σ = 1", ...
337s           "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
337s  title ("Half-normal against standard normal CDF")
337s  xlabel ("values in x")
337s  ylabel ("probability")
337s ***** shared x, p1, p1u, y2, y2u, y3, y3u
337s  x = [-Inf, -1, 0, 1/2, 1, Inf];
337s  p1 = [0, 0, 0, 0.3829, 0.6827, 1];
337s  p1u = [1, 1, 1, 0.6171, 0.3173, 0];
337s ***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
337s ***** assert (hncdf (x, 0, 1), p1, 1e-4)
337s ***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
337s ***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
337s ***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
337s ***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
337s ***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
337s ***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
337s ***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
337s ***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
337s ***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
337s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
337s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
337s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
337s ***** error<hncdf: function called with too few input arguments.> hncdf ()
337s ***** error<hncdf: function called with too few input arguments.> hncdf (1)
337s ***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
337s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
337s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
337s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
337s  hncdf (ones (3), ones (2), ones(2))
337s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
337s  hncdf (ones (2), ones (3), ones(2))
337s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
337s  hncdf (ones (2), ones (2), ones(3))
337s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
337s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
337s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
337s 25 tests, 25 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/tlscdf.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tlscdf.m
337s ***** demo
337s  ## Plot various CDFs from the location-scale Student's T distribution
337s  x = -8:0.01:8;
337s  p1 = tlscdf (x, 0, 1, 1);
337s  p2 = tlscdf (x, 0, 2, 2);
337s  p3 = tlscdf (x, 3, 2, 5);
337s  p4 = tlscdf (x, -1, 3, Inf);
337s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
337s  grid on
337s  xlim ([-8, 8])
337s  ylim ([0, 1])
337s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
337s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
337s          "location", "northwest")
337s  title ("Location-scale Student's T CDF")
337s  xlabel ("values in x")
337s  ylabel ("probability")
337s ***** shared x,y
337s  x = [-Inf 0 1 Inf];
337s  y = [0 1/2 3/4 1];
337s ***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
337s ***** assert (tlscdf (x, 0, 1, 1), y, eps)
337s ***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
337s ***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
337s ***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
337s ***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
337s ***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
337s ***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
337s ***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
337s ***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
337s ***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
337s ***** error<tlscdf: function called with too few input arguments.> tlscdf ()
337s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
337s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
337s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
337s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
337s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), ones (2), 1, 1)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), 1, ones (2), 1)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), 1, 1, ones (2))
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), ones (2), 1, 1, "upper")
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), 1, ones (2), 1, "upper")
337s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
337s  tlscdf (ones (3), 1, 1, ones (2), "upper")
337s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
337s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
337s 27 tests, 27 passed, 0 known failure, 0 skipped
337s [inst/dist_fun/frnd.m]
337s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/frnd.m
337s ***** assert (size (frnd (1, 1)), [1 1])
337s ***** assert (size (frnd (1, ones (2,1))), [2, 1])
337s ***** assert (size (frnd (1, ones (2,2))), [2, 2])
337s ***** assert (size (frnd (ones (2,1), 1)), [2, 1])
337s ***** assert (size (frnd (ones (2,2), 1)), [2, 2])
337s ***** assert (size (frnd (1, 1, 3)), [3, 3])
337s ***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
337s ***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
337s ***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
337s ***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
337s ***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
338s ***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
338s ***** assert (class (frnd (1, 1)), "double")
338s ***** assert (class (frnd (1, single (1))), "single")
338s ***** assert (class (frnd (1, single ([1, 1]))), "single")
338s ***** assert (class (frnd (single (1), 1)), "single")
338s ***** assert (class (frnd (single ([1, 1]), 1)), "single")
338s ***** error<frnd: function called with too few input arguments.> frnd ()
338s ***** error<frnd: function called with too few input arguments.> frnd (1)
338s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
338s  frnd (ones (3), ones (2))
338s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
338s  frnd (ones (2), ones (3))
338s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
338s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
338s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  frnd (1, 2, -1)
338s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  frnd (1, 2, 1.2)
338s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  frnd (1, 2, ones (2))
338s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  frnd (1, 2, [2 -1 2])
338s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  frnd (1, 2, [2 0 2.5])
338s ***** error<frnd: dimensions must be non-negative integers.> ...
338s  frnd (1, 2, 2, -1, 5)
338s ***** error<frnd: dimensions must be non-negative integers.> ...
338s  frnd (1, 2, 2, 1.5, 5)
338s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
338s  frnd (2, ones (2), 3)
338s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
338s  frnd (2, ones (2), [3, 2])
338s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
338s  frnd (2, ones (2), 3, 2)
338s 33 tests, 33 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/wblrnd.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wblrnd.m
338s ***** assert (size (wblrnd (1, 1)), [1 1])
338s ***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
338s ***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
338s ***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
338s ***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
338s ***** assert (size (wblrnd (1, 1, 3)), [3, 3])
338s ***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
338s ***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
338s ***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
338s ***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
338s ***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
338s ***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
338s ***** assert (class (wblrnd (1, 1)), "double")
338s ***** assert (class (wblrnd (1, single (1))), "single")
338s ***** assert (class (wblrnd (1, single ([1, 1]))), "single")
338s ***** assert (class (wblrnd (single (1), 1)), "single")
338s ***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
338s ***** error<wblrnd: function called with too few input arguments.> wblrnd ()
338s ***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
338s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
338s  wblrnd (ones (3), ones (2))
338s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
338s  wblrnd (ones (2), ones (3))
338s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
338s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
338s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  wblrnd (1, 2, -1)
338s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  wblrnd (1, 2, 1.2)
338s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  wblrnd (1, 2, ones (2))
338s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  wblrnd (1, 2, [2 -1 2])
338s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
338s  wblrnd (1, 2, [2 0 2.5])
338s ***** error<wblrnd: dimensions must be non-negative integers.> ...
338s  wblrnd (1, 2, 2, -1, 5)
338s ***** error<wblrnd: dimensions must be non-negative integers.> ...
338s  wblrnd (1, 2, 2, 1.5, 5)
338s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
338s  wblrnd (2, ones (2), 3)
338s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
338s  wblrnd (2, ones (2), [3, 2])
338s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
338s  wblrnd (2, ones (2), 3, 2)
338s 33 tests, 33 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/tpdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tpdf.m
338s ***** demo
338s  ## Plot various PDFs from the Student's T distribution
338s  x = -5:0.01:5;
338s  y1 = tpdf (x, 1);
338s  y2 = tpdf (x, 2);
338s  y3 = tpdf (x, 5);
338s  y4 = tpdf (x, Inf);
338s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
338s  grid on
338s  xlim ([-5, 5])
338s  ylim ([0, 0.41])
338s  legend ({"df = 1", "df = 2", ...
338s           "df = 5", 'df = \infty'}, "location", "northeast")
338s  title ("Student's T PDF")
338s  xlabel ("values in x")
338s  ylabel ("density")
338s ***** test
338s  x = rand (10,1);
338s  y = 1./(pi * (1 + x.^2));
338s  assert (tpdf (x, 1), y, 5*eps);
338s ***** shared x, y
338s  x = [-Inf 0 0.5 1 Inf];
338s  y = 1./(pi * (1 + x.^2));
338s ***** assert (tpdf (x, ones (1,5)), y, eps)
338s ***** assert (tpdf (x, 1), y, eps)
338s ***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
338s ***** assert (tpdf (x, Inf), normpdf (x))
338s ***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
338s ***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
338s ***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
338s ***** error<tpdf: function called with too few input arguments.> tpdf ()
338s ***** error<tpdf: function called with too few input arguments.> tpdf (1)
338s ***** error<tpdf: X and DF must be of common size or scalars.> ...
338s  tpdf (ones (3), ones (2))
338s ***** error<tpdf: X and DF must be of common size or scalars.> ...
338s  tpdf (ones (2), ones (3))
338s ***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
338s ***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
338s 14 tests, 14 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/exppdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/exppdf.m
338s ***** demo
338s  ## Plot various PDFs from the exponential distribution
338s  x = 0:0.01:5;
338s  y1 = exppdf (x, 2/3);
338s  y2 = exppdf (x, 1.0);
338s  y3 = exppdf (x, 2.0);
338s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
338s  grid on
338s  ylim ([0, 1.5])
338s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
338s  title ("Exponential PDF")
338s  xlabel ("values in x")
338s  ylabel ("density")
338s ***** shared x,y
338s  x = [-1 0 0.5 1 Inf];
338s  y = gampdf (x, 1, 2);
338s ***** assert (exppdf (x, 2*ones (1,5)), y)
338s ***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
338s ***** assert (exppdf ([x, NaN], 2), [y, NaN])
338s ***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
338s ***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
338s ***** error<exppdf: function called with too few input arguments.> exppdf ()
338s ***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
338s ***** error<exppdf: X and MU must be of common size or scalars.> ...
338s  exppdf (ones (3), ones (2))
338s ***** error<exppdf: X and MU must be of common size or scalars.> ...
338s  exppdf (ones (2), ones (3))
338s ***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
338s ***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
338s 11 tests, 11 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/burrpdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/burrpdf.m
338s ***** demo
338s  ## Plot various PDFs from the Burr type XII distribution
338s  x = 0.001:0.001:3;
338s  y1 = burrpdf (x, 1, 1, 1);
338s  y2 = burrpdf (x, 1, 1, 2);
338s  y3 = burrpdf (x, 1, 1, 3);
338s  y4 = burrpdf (x, 1, 2, 1);
338s  y5 = burrpdf (x, 1, 3, 1);
338s  y6 = burrpdf (x, 1, 0.5, 2);
338s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
338s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
338s  grid on
338s  ylim ([0, 2])
338s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
338s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
338s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
338s          "location", "northeast")
338s  title ("Burr type XII PDF")
338s  xlabel ("values in x")
338s  ylabel ("density")
338s ***** shared x, y
338s  x = [-1, 0, 1, 2, Inf];
338s  y = [0, 1, 1/4, 1/9, 0];
338s ***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
338s ***** assert (burrpdf (x, 1, 1, 1), y)
338s ***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
338s ***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
338s ***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
338s ***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
338s ***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
338s ***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
338s ***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
338s ***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
338s ***** error<burrpdf: function called with too few input arguments.> burrpdf ()
338s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
338s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
338s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
338s ***** error<burrpdf: function called with too many inputs> ...
338s  burrpdf (1, 2, 3, 4, 5)
338s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
338s  burrpdf (ones (3), ones (2), ones(2), ones(2))
338s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
338s  burrpdf (ones (2), ones (3), ones(2), ones(2))
338s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
338s  burrpdf (ones (2), ones (2), ones(3), ones(2))
338s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
338s  burrpdf (ones (2), ones (2), ones(2), ones(3))
338s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
338s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
338s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
338s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
338s 23 tests, 23 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/loglpdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/loglpdf.m
338s ***** demo
338s  ## Plot various PDFs from the log-logistic distribution
338s  x = 0.001:0.001:2;
338s  y1 = loglpdf (x, log (1), 1/0.5);
338s  y2 = loglpdf (x, log (1), 1);
338s  y3 = loglpdf (x, log (1), 1/2);
338s  y4 = loglpdf (x, log (1), 1/4);
338s  y5 = loglpdf (x, log (1), 1/8);
338s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
338s  grid on
338s  ylim ([0,3])
338s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
338s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
338s  title ("Log-logistic PDF")
338s  xlabel ("values in x")
338s  ylabel ("density")
338s  text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
338s ***** shared out1, out2
338s  out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
338s  out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
338s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
338s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
338s ***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
338s ***** assert (class (loglpdf (single (1), 2, 3)), "single")
338s ***** assert (class (loglpdf (1, single (2), 3)), "single")
338s ***** assert (class (loglpdf (1, 2, single (3))), "single")
338s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
338s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
338s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
338s  loglpdf (1, ones (2), ones (3))
338s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
338s  loglpdf (ones (2), 1, ones (3))
338s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
338s  loglpdf (ones (2), ones (3), 1)
338s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
338s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
338s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
338s 14 tests, 14 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/poisscdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/poisscdf.m
338s ***** demo
338s  ## Plot various CDFs from the Poisson distribution
338s  x = 0:20;
338s  p1 = poisscdf (x, 1);
338s  p2 = poisscdf (x, 4);
338s  p3 = poisscdf (x, 10);
338s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
338s  grid on
338s  ylim ([0, 1])
338s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
338s  title ("Poisson CDF")
338s  xlabel ("values in x (number of occurences)")
338s  ylabel ("probability")
338s ***** shared x, y
338s  x = [-1 0 1 2 Inf];
338s  y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
338s ***** assert (poisscdf (x, ones (1,5)), y)
338s ***** assert (poisscdf (x, 1), y)
338s ***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
338s ***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
338s ***** assert (poisscdf ([x, NaN], 1), [y, NaN])
338s ***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
338s ***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
338s ***** error<poisscdf: function called with too few input arguments.> poisscdf ()
338s ***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
338s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
338s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
338s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
338s  poisscdf (ones (3), ones (2))
338s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
338s  poisscdf (ones (2), ones (3))
338s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
338s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
338s 15 tests, 15 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/mnpdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mnpdf.m
338s ***** test
338s  x = [1, 4, 2];
338s  pk = [0.2, 0.5, 0.3];
338s  y = mnpdf (x, pk);
338s  assert (y, 0.11812, 0.001);
338s ***** test
338s  x = [1, 4, 2; 1, 0, 9];
338s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
338s  y = mnpdf (x, pk);
338s  assert (y, [0.11812; 0.13422], 0.001);
338s 2 tests, 2 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/cauchycdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/cauchycdf.m
338s ***** demo
338s  ## Plot various CDFs from the Cauchy distribution
338s  x = -5:0.01:5;
338s  p1 = cauchycdf (x, 0, 0.5);
338s  p2 = cauchycdf (x, 0, 1);
338s  p3 = cauchycdf (x, 0, 2);
338s  p4 = cauchycdf (x, -2, 1);
338s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
338s  grid on
338s  xlim ([-5, 5])
338s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
338s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
338s  title ("Cauchy CDF")
338s  xlabel ("values in x")
338s  ylabel ("probability")
338s ***** shared x, y
338s  x = [-1 0 0.5 1 2];
338s  y = 1/pi * atan ((x-1) / 2) + 1/2;
338s ***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
338s ***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
338s ***** assert (cauchycdf (x, ones (1,5), 2), y)
338s ***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
338s ***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
338s ***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
338s ***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
338s ***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
338s ***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
338s ***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
338s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
338s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
338s ***** error<cauchycdf: function called with too few input arguments.> ...
338s  cauchycdf (1, 2)
338s ***** error<cauchycdf: function called with too many inputs> ...
338s  cauchycdf (1, 2, 3, 4, 5)
338s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
338s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
338s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
338s  cauchycdf (ones (3), ones (2), ones (2))
338s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
338s  cauchycdf (ones (2), ones (3), ones (2))
338s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
338s  cauchycdf (ones (2), ones (2), ones (3))
338s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
338s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
338s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
338s 22 tests, 22 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/unidpdf.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unidpdf.m
338s ***** demo
338s  ## Plot various PDFs from the discrete uniform distribution
338s  x = 0:10;
338s  y1 = unidpdf (x, 5);
338s  y2 = unidpdf (x, 9);
338s  plot (x, y1, "*b", x, y2, "*g")
338s  grid on
338s  xlim ([0, 10])
338s  ylim ([0, 0.25])
338s  legend ({"N = 5", "N = 9"}, "location", "northeast")
338s  title ("Discrete uniform PDF")
338s  xlabel ("values in x")
338s  ylabel ("density")
338s ***** shared x, y
338s  x = [-1 0 1 2 10 11];
338s  y = [0 0 0.1 0.1 0.1 0];
338s ***** assert (unidpdf (x, 10*ones (1,6)), y)
338s ***** assert (unidpdf (x, 10), y)
338s ***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
338s ***** assert (unidpdf ([x, NaN], 10), [y, NaN])
338s ***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
338s ***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
338s ***** error<unidpdf: function called with too few input arguments.> unidpdf ()
338s ***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
338s ***** error<unidpdf: X and N must be of common size or scalars.> ...
338s  unidpdf (ones (3), ones (2))
338s ***** error<unidpdf: X and N must be of common size or scalars.> ...
338s  unidpdf (ones (2), ones (3))
338s ***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
338s ***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
338s 12 tests, 12 passed, 0 known failure, 0 skipped
338s [inst/dist_fun/nctinv.m]
338s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nctinv.m
338s ***** demo
338s  ## Plot various iCDFs from the noncentral T distribution
338s  p = 0.001:0.001:0.999;
338s  x1 = nctinv (p, 1, 0);
338s  x2 = nctinv (p, 4, 0);
338s  x3 = nctinv (p, 1, 2);
338s  x4 = nctinv (p, 4, 2);
338s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
338s  grid on
338s  ylim ([-5, 5])
338s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
338s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
338s  title ("Noncentral T iCDF")
338s  xlabel ("probability")
338s  ylabel ("values in x")
338s ***** demo
338s  ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
338s  ## with the same number of degrees of freedom (10).
338s 
338s  p = 0.001:0.001:0.999;
338s  x1 = nctinv (p, 10, 1);
338s  x2 = tinv (p, 10);
338s  plot (p, x1, "-", p, x2, "-");
338s  grid on
338s  ylim ([-5, 5])
338s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
338s  title ("Noncentral T vs T quantile functions")
338s  xlabel ("probability")
338s  ylabel ("values in x")
338s ***** test
338s  x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
338s  assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
339s ***** test
339s  x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
339s  assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
339s ***** test
339s  x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
339s  assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
340s ***** test
340s  x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
340s  assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
340s ***** test
340s  x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
340s  assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
341s ***** test
341s  x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
341s  assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
342s ***** test
342s  assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
344s ***** error<nctinv: function called with too few input arguments.> nctinv ()
344s ***** error<nctinv: function called with too few input arguments.> nctinv (1)
344s ***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
344s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
344s  nctinv (ones (3), ones (2), ones (2))
344s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
344s  nctinv (ones (2), ones (3), ones (2))
344s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
344s  nctinv (ones (2), ones (2), ones (3))
344s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
344s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
344s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
344s 16 tests, 16 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/gevinv.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gevinv.m
344s ***** demo
344s  ## Plot various iCDFs from the generalized extreme value distribution
344s  p = 0.001:0.001:0.999;
344s  x1 = gevinv (p, 1, 1, 1);
344s  x2 = gevinv (p, 0.5, 1, 1);
344s  x3 = gevinv (p, 1, 1, 5);
344s  x4 = gevinv (p, 1, 2, 5);
344s  x5 = gevinv (p, 1, 5, 5);
344s  x6 = gevinv (p, 1, 0.5, 5);
344s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
344s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
344s  grid on
344s  ylim ([-1, 10])
344s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
344s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
344s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
344s          "location", "northwest")
344s  title ("Generalized extreme value iCDF")
344s  xlabel ("probability")
344s  ylabel ("values in x")
344s ***** test
344s  p = 0.1:0.1:0.9;
344s  k = 0;
344s  sigma = 1;
344s  mu = 0;
344s  x = gevinv (p, k, sigma, mu);
344s  c = gevcdf(x, k, sigma, mu);
344s  assert (c, p, 0.001);
344s ***** test
344s  p = 0.1:0.1:0.9;
344s  k = 1;
344s  sigma = 1;
344s  mu = 0;
344s  x = gevinv (p, k, sigma, mu);
344s  c = gevcdf(x, k, sigma, mu);
344s  assert (c, p, 0.001);
344s ***** test
344s  p = 0.1:0.1:0.9;
344s  k = 0.3;
344s  sigma = 1;
344s  mu = 0;
344s  x = gevinv (p, k, sigma, mu);
344s  c = gevcdf(x, k, sigma, mu);
344s  assert (c, p, 0.001);
344s ***** error<gevinv: function called with too few input arguments.> gevinv ()
344s ***** error<gevinv: function called with too few input arguments.> gevinv (1)
344s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
344s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
344s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevinv (ones (3), ones (2), ones(2), ones(2))
344s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevinv (ones (2), ones (3), ones(2), ones(2))
344s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevinv (ones (2), ones (2), ones(3), ones(2))
344s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevinv (ones (2), ones (2), ones(2), ones(3))
344s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
344s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
344s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
344s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
344s 15 tests, 15 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/gevpdf.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gevpdf.m
344s ***** demo
344s  ## Plot various PDFs from the generalized extreme value distribution
344s  x = -1:0.001:10;
344s  y1 = gevpdf (x, 1, 1, 1);
344s  y2 = gevpdf (x, 0.5, 1, 1);
344s  y3 = gevpdf (x, 1, 1, 5);
344s  y4 = gevpdf (x, 1, 2, 5);
344s  y5 = gevpdf (x, 1, 5, 5);
344s  y6 = gevpdf (x, 1, 0.5, 5);
344s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
344s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
344s  grid on
344s  xlim ([-1, 10])
344s  ylim ([0, 1.1])
344s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
344s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
344s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
344s          "location", "northeast")
344s  title ("Generalized extreme value PDF")
344s  xlabel ("values in x")
344s  ylabel ("density")
344s ***** test
344s  x = 0:0.5:2.5;
344s  sigma = 1:6;
344s  k = 1;
344s  mu = 0;
344s  y = gevpdf (x, k, sigma, mu);
344s  expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
344s  assert (y, expected_y, 0.001);
344s ***** test
344s  x = -0.5:0.5:2.5;
344s  sigma = 0.5;
344s  k = 1;
344s  mu = 0;
344s  y = gevpdf (x, k, sigma, mu);
344s  expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
344s  assert (y, expected_y, 0.001);
344s ***** test # check for continuity for k near 0
344s  x = 1;
344s  sigma = 0.5;
344s  k = -0.03:0.01:0.03;
344s  mu = 0;
344s  y = gevpdf (x, k, sigma, mu);
344s  expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
344s  assert (y, expected_y, 0.001);
344s ***** error<gevpdf: function called with too few input arguments.> gevpdf ()
344s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
344s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
344s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
344s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevpdf (ones (3), ones (2), ones(2), ones(2))
344s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevpdf (ones (2), ones (3), ones(2), ones(2))
344s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevpdf (ones (2), ones (2), ones(3), ones(2))
344s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevpdf (ones (2), ones (2), ones(2), ones(3))
344s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
344s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
344s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
344s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
344s 15 tests, 15 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/laplacecdf.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/laplacecdf.m
344s ***** demo
344s  ## Plot various CDFs from the Laplace distribution
344s  x = -10:0.01:10;
344s  p1 = laplacecdf (x, 0, 1);
344s  p2 = laplacecdf (x, 0, 2);
344s  p3 = laplacecdf (x, 0, 4);
344s  p4 = laplacecdf (x, -5, 4);
344s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
344s  grid on
344s  xlim ([-10, 10])
344s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
344s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
344s  title ("Laplace CDF")
344s  xlabel ("values in x")
344s  ylabel ("probability")
344s ***** shared x, y
344s  x = [-Inf, -log(2), 0, log(2), Inf];
344s  y = [0, 1/4, 1/2, 3/4, 1];
344s ***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
344s ***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
344s ***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
344s ***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
344s ***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
344s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
344s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
344s ***** error<laplacecdf: function called with too few input arguments.> ...
344s  laplacecdf (1, 2)
344s ***** error<laplacecdf: function called with too many inputs> ...
344s  laplacecdf (1, 2, 3, 4, 5)
344s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
344s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
344s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
344s  laplacecdf (ones (3), ones (2), ones (2))
344s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
344s  laplacecdf (ones (2), ones (3), ones (2))
344s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
344s  laplacecdf (ones (2), ones (2), ones (3))
344s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
344s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
344s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
344s 17 tests, 17 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/gevcdf.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gevcdf.m
344s ***** demo
344s  ## Plot various CDFs from the generalized extreme value distribution
344s  x = -1:0.001:10;
344s  p1 = gevcdf (x, 1, 1, 1);
344s  p2 = gevcdf (x, 0.5, 1, 1);
344s  p3 = gevcdf (x, 1, 1, 5);
344s  p4 = gevcdf (x, 1, 2, 5);
344s  p5 = gevcdf (x, 1, 5, 5);
344s  p6 = gevcdf (x, 1, 0.5, 5);
344s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
344s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
344s  grid on
344s  xlim ([-1, 10])
344s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
344s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
344s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
344s          "location", "southeast")
344s  title ("Generalized extreme value CDF")
344s  xlabel ("values in x")
344s  ylabel ("probability")
344s ***** test
344s  x = 0:0.5:2.5;
344s  sigma = 1:6;
344s  k = 1;
344s  mu = 0;
344s  p = gevcdf (x, k, sigma, mu);
344s  expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
344s  assert (p, expected_p, 0.001);
344s ***** test
344s  x = -0.5:0.5:2.5;
344s  sigma = 0.5;
344s  k = 1;
344s  mu = 0;
344s  p = gevcdf (x, k, sigma, mu);
344s  expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
344s  assert (p, expected_p, 0.001);
344s ***** test # check for continuity for k near 0
344s  x = 1;
344s  sigma = 0.5;
344s  k = -0.03:0.01:0.03;
344s  mu = 0;
344s  p = gevcdf (x, k, sigma, mu);
344s  expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
344s  assert (p, expected_p, 0.001);
344s ***** error<gevcdf: function called with too few input arguments.> gevcdf ()
344s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
344s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
344s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
344s ***** error<gevcdf: function called with too many inputs> ...
344s  gevcdf (1, 2, 3, 4, 5, 6)
344s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
344s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
344s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevcdf (ones (3), ones (2), ones(2), ones(2))
344s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevcdf (ones (2), ones (3), ones(2), ones(2))
344s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevcdf (ones (2), ones (2), ones(3), ones(2))
344s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
344s  gevcdf (ones (2), ones (2), ones(2), ones(3))
344s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
344s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
344s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
344s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
344s 18 tests, 18 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/wienrnd.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wienrnd.m
344s ***** error wienrnd (0)
344s ***** error wienrnd (1, 3, -50)
344s ***** error wienrnd (5, 0)
344s ***** error wienrnd (0.4, 3, 5)
344s ***** error wienrnd ([1 4], 3, 5)
344s 5 tests, 5 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/lognrnd.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/lognrnd.m
344s ***** assert (size (lognrnd (1, 1)), [1 1])
344s ***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
344s ***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
344s ***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
344s ***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
344s ***** assert (size (lognrnd (1, 1, 3)), [3, 3])
344s ***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
344s ***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
344s ***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
344s ***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
344s ***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
344s ***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
344s ***** assert (class (lognrnd (1, 1)), "double")
344s ***** assert (class (lognrnd (1, single (1))), "single")
344s ***** assert (class (lognrnd (1, single ([1, 1]))), "single")
344s ***** assert (class (lognrnd (single (1), 1)), "single")
344s ***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
344s ***** error<lognrnd: function called with too few input arguments.> lognrnd ()
344s ***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
344s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
344s  lognrnd (ones (3), ones (2))
344s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
344s  lognrnd (ones (2), ones (3))
344s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
344s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
344s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  lognrnd (1, 2, -1)
344s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  lognrnd (1, 2, 1.2)
344s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  lognrnd (1, 2, ones (2))
344s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  lognrnd (1, 2, [2 -1 2])
344s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  lognrnd (1, 2, [2 0 2.5])
344s ***** error<lognrnd: dimensions must be non-negative integers.> ...
344s  lognrnd (1, 2, 2, -1, 5)
344s ***** error<lognrnd: dimensions must be non-negative integers.> ...
344s  lognrnd (1, 2, 2, 1.5, 5)
344s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
344s  lognrnd (2, ones (2), 3)
344s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
344s  lognrnd (2, ones (2), [3, 2])
344s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
344s  lognrnd (2, ones (2), 3, 2)
344s 33 tests, 33 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/ncfrnd.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncfrnd.m
344s ***** assert (size (ncfrnd (1, 1, 1)), [1 1])
344s ***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
344s ***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
344s ***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
344s ***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
344s ***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
344s ***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
344s ***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
344s ***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
344s ***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
344s ***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
344s ***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
344s ***** assert (class (ncfrnd (1, 1, 1)), "double")
344s ***** assert (class (ncfrnd (1, single (1), 1)), "single")
344s ***** assert (class (ncfrnd (1, 1, single (1))), "single")
344s ***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
344s ***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
344s ***** assert (class (ncfrnd (single (1), 1, 1)), "single")
344s ***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
344s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
344s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
344s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
344s  ncfrnd (ones (3), ones (2), ones (2))
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
344s  ncfrnd (ones (2), ones (3), ones (2))
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
344s  ncfrnd (ones (2), ones (2), ones (3))
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
344s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  ncfrnd (1, 2, 3, -1)
344s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  ncfrnd (1, 2, 3, 1.2)
344s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  ncfrnd (1, 2, 3, ones (2))
344s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  ncfrnd (1, 2, 3, [2 -1 2])
344s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
344s  ncfrnd (1, 2, 3, [2 0 2.5])
344s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
344s  ncfrnd (1, 2, 3, 2, -1, 5)
344s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
344s  ncfrnd (1, 2, 3, 2, 1.5, 5)
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
344s  ncfrnd (2, ones (2), 2, 3)
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
344s  ncfrnd (2, ones (2), 2, [3, 2])
344s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
344s  ncfrnd (2, ones (2), 2, 3, 2)
344s 38 tests, 38 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/triinv.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/triinv.m
344s ***** demo
344s  ## Plot various iCDFs from the triangular distribution
344s  p = 0.001:0.001:0.999;
344s  x1 = triinv (p, 3, 6, 4);
344s  x2 = triinv (p, 1, 5, 2);
344s  x3 = triinv (p, 2, 9, 3);
344s  x4 = triinv (p, 2, 9, 5);
344s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
344s  grid on
344s  ylim ([0, 10])
344s  legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
344s           "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
344s          "location", "northwest")
344s  title ("Triangular CDF")
344s  xlabel ("probability")
344s  ylabel ("values in x")
344s ***** shared p, y
344s  p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
344s  y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
344s ***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
344s ***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
344s ***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
344s ***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
344s ***** assert (triinv (p, 1, 1.5, 2), y, eps)
344s ***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
344s ***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
344s ***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
344s ***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
344s ***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
344s ***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
344s ***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
344s ***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
344s ***** error<triinv: function called with too few input arguments.> triinv ()
344s ***** error<triinv: function called with too few input arguments.> triinv (1)
344s ***** error<triinv: function called with too few input arguments.> triinv (1, 2)
344s ***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
344s ***** error<triinv: function called with too many inputs> ...
344s  triinv (1, 2, 3, 4, 5)
344s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
344s  triinv (ones (3), ones (2), ones(2), ones(2))
344s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
344s  triinv (ones (2), ones (3), ones(2), ones(2))
344s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
344s  triinv (ones (2), ones (2), ones(3), ones(2))
344s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
344s  triinv (ones (2), ones (2), ones(2), ones(3))
344s ***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
344s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
344s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
344s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
344s 26 tests, 26 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/gaminv.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gaminv.m
344s ***** demo
344s  ## Plot various iCDFs from the Gamma distribution
344s  p = 0.001:0.001:0.999;
344s  x1 = gaminv (p, 1, 2);
344s  x2 = gaminv (p, 2, 2);
344s  x3 = gaminv (p, 3, 2);
344s  x4 = gaminv (p, 5, 1);
344s  x5 = gaminv (p, 9, 0.5);
344s  x6 = gaminv (p, 7.5, 1);
344s  x7 = gaminv (p, 0.5, 1);
344s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
344s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
344s  ylim ([0, 20])
344s  grid on
344s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
344s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
344s           "α = 0.5, β = 1"}, "location", "northwest")
344s  title ("Gamma iCDF")
344s  xlabel ("probability")
344s  ylabel ("x")
344s ***** shared p
344s  p = [-1 0 0.63212055882855778 1 2];
344s ***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
344s ***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
344s ***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
344s ***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
344s ***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
344s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
344s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
344s ***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
344s ***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
344s ***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
344s ***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
344s ***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
344s ***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
344s ***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
344s  eps ("single"))
344s ***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
344s  eps ("single"))
344s ***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
344s  eps ("single"))
344s ***** error<gaminv: function called with too few input arguments.> gaminv ()
344s ***** error<gaminv: function called with too few input arguments.> gaminv (1)
344s ***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
344s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
344s  gaminv (ones (3), ones (2), ones (2))
344s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
344s  gaminv (ones (2), ones (3), ones (2))
344s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
344s  gaminv (ones (2), ones (2), ones (3))
344s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
344s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
344s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
344s 25 tests, 25 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/ncx2pdf.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncx2pdf.m
344s ***** demo
344s  ## Plot various PDFs from the noncentral chi-squared distribution
344s  x = 0:0.1:10;
344s  y1 = ncx2pdf (x, 2, 1);
344s  y2 = ncx2pdf (x, 2, 2);
344s  y3 = ncx2pdf (x, 2, 3);
344s  y4 = ncx2pdf (x, 4, 1);
344s  y5 = ncx2pdf (x, 4, 2);
344s  y6 = ncx2pdf (x, 4, 3);
344s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
344s        x, y4, "-m", x, y5, "-c", x, y6, "-y")
344s  grid on
344s  xlim ([0, 10])
344s  ylim ([0, 0.32])
344s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
344s           "df = 2, λ = 3", "df = 4, λ = 1", ...
344s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
344s  title ("Noncentral chi-squared PDF")
344s  xlabel ("values in x")
344s  ylabel ("density")
344s ***** demo
344s  ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
344s  ## chi-squared PDF with the same number of degrees of freedom (4).
344s 
344s  x = 0:0.1:10;
344s  y1 = ncx2pdf (x, 4, 2);
344s  y2 = chi2pdf (x, 4);
344s  plot (x, y1, "-", x, y2, "-");
344s  grid on
344s  xlim ([0, 10])
344s  ylim ([0, 0.32])
344s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
344s  title ("Noncentral chi-squared vs chi-squared PDFs")
344s  xlabel ("values in x")
344s  ylabel ("density")
344s ***** shared x1, df, d1
344s  x1 = [-Inf, 2, NaN, 4, Inf];
344s  df = [2, 0, -1, 1, 4];
344s  d1 = [1, NaN, 3, -1, 2];
344s ***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
344s ***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
344s                               0.06160064323277038, 0], 1e-14);
344s ***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
344s                               0.09631299762429098, 0], 1e-14);
344s ***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
344s                               0.08430464047296625, 0], 1e-14);
344s ***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
344s ***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
344s                               NaN, 0.1076346446244688], 1e-14);
344s ***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
344s                               NaN, 0.1192317192431485], 1e-14);
344s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
344s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
344s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
344s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
344s  ncx2pdf (ones (3), ones (2), ones (2))
344s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
344s  ncx2pdf (ones (2), ones (3), ones (2))
344s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
344s  ncx2pdf (ones (2), ones (2), ones (3))
344s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
344s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
344s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
344s 16 tests, 16 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/plinv.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/plinv.m
344s ***** demo
344s  ## Plot various iCDFs from the Piecewise linear distribution
344s  p = 0.001:0.001:0.999;
344s  x1 = [0, 1, 3, 4, 7, 10];
344s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
344s  x2 = [0, 2, 5, 6, 7, 8];
344s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
344s  data1 = plinv (p, x1, Fx1);
344s  data2 = plinv (p, x2, Fx2);
344s  plot (p, data1, "-b", p, data2, "-g")
344s  grid on
344s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
344s  title ("Piecewise linear iCDF")
344s  xlabel ("probability")
344s  ylabel ("values in data")
344s ***** test
344s  p = 0:0.2:1;
344s  data = plinv (p, [0, 1], [0, 1]);
344s  assert (data, p);
344s ***** test
344s  p = 0:0.2:1;
344s  data = plinv (p, [0, 2], [0, 1]);
344s  assert (data, 2 * p);
344s ***** test
344s  p = 0:0.2:1;
344s  data_out = 1:6;
344s  data = plinv (p, [0, 1], [0, 0.5]);
344s  assert (data, [0, 0.4, 0.8, NA, NA, NA]);
344s ***** test
344s  p = 0:0.2:1;
344s  data_out = 1:6;
344s  data = plinv (p, [0, 0.5], [0, 1]);
344s  assert (data, [0:0.1:0.5]);
344s ***** error<plinv: function called with too few input arguments.> plinv ()
344s ***** error<plinv: function called with too few input arguments.> plinv (1)
344s ***** error<plinv: function called with too few input arguments.> plinv (1, 2)
344s ***** error<plinv: X and FX must be vectors of equal size.> ...
344s  plinv (1, [0, 1, 2], [0, 1])
344s ***** error<plinv: X and FX must be at least two-elements long.> ...
344s  plinv (1, [0], [1])
344s ***** error<plinv: FX must be bounded in the range> ...
344s  plinv (1, [0, 1, 2], [0, 1, 1.5])
344s ***** error<plinv: FX must be bounded in the range> ...
344s  plinv (1, [0, 1, 2], [0, i, 1])
344s ***** error<plinv: P, X, and FX must not be complex.> ...
344s  plinv (i, [0, 1, 2], [0, 0.5, 1])
344s ***** error<plinv: P, X, and FX must not be complex.> ...
344s  plinv (1, [0, i, 2], [0, 0.5, 1])
344s ***** error<plinv: P, X, and FX must not be complex.> ...
344s  plinv (1, [0, 1, 2], [0, 0.5i, 1])
344s 14 tests, 14 passed, 0 known failure, 0 skipped
344s [inst/dist_fun/evinv.m]
344s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/evinv.m
344s ***** demo
344s  ## Plot various iCDFs from the extreme value distribution
344s  p = 0.001:0.001:0.999;
344s  x1 = evinv (p, 0.5, 2);
344s  x2 = evinv (p, 1.0, 2);
344s  x3 = evinv (p, 1.5, 3);
344s  x4 = evinv (p, 3.0, 4);
344s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
344s  grid on
344s  ylim ([-10, 10])
344s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
344s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
344s  title ("Extreme value iCDF")
344s  xlabel ("probability")
344s  ylabel ("values in x")
344s ***** shared p, x
344s  p = [0, 0.05, 0.5 0.95];
344s  x = [-Inf, -2.9702, -0.3665, 1.0972];
344s ***** assert (evinv (p), x, 1e-4)
344s ***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
344s ***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
344s ***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
344s ***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
344s ***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
345s ***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
345s ***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
345s ***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
345s ***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
345s ***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
345s ***** error<evinv: invalid number of input arguments.> evinv ()
345s ***** error evinv (1,2,3,4,5,6)
345s ***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
345s  evinv (ones (3), ones (2), ones (2))
345s ***** error<evinv: invalid size of covariance matrix.> ...
345s  [p, plo, pup] = evinv (2, 3, 4, [1, 2])
345s ***** error<evinv: covariance matrix is required for confidence bounds.> ...
345s  [p, plo, pup] = evinv (1, 2, 3)
345s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
345s  evinv (1, 2, 3, [1, 0; 0, 1], 0)
345s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
345s  evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
345s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
345s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
345s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
345s ***** error<evinv: bad covariance matrix.> ...
345s  [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
345s 22 tests, 22 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/unidcdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unidcdf.m
345s ***** demo
345s  ## Plot various CDFs from the discrete uniform distribution
345s  x = 0:10;
345s  p1 = unidcdf (x, 5);
345s  p2 = unidcdf (x, 9);
345s  plot (x, p1, "*b", x, p2, "*g")
345s  grid on
345s  xlim ([0, 10])
345s  ylim ([0, 1])
345s  legend ({"N = 5", "N = 9"}, "location", "southeast")
345s  title ("Discrete uniform CDF")
345s  xlabel ("values in x")
345s  ylabel ("probability")
345s ***** shared x, y
345s  x = [0 1 2.5 10 11];
345s  y = [0, 0.1 0.2 1.0 1.0];
345s ***** assert (unidcdf (x, 10*ones (1,5)), y)
345s ***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
345s ***** assert (unidcdf (x, 10), y)
345s ***** assert (unidcdf (x, 10, "upper"), 1 - y)
345s ***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
345s ***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
345s ***** assert (unidcdf ([x, NaN], 10), [y, NaN])
345s ***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
345s ***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
345s ***** error<unidcdf: function called with too few input arguments.> unidcdf ()
345s ***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
345s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
345s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
345s ***** error<unidcdf: X and N must be of common size or scalars.> ...
345s  unidcdf (ones (3), ones (2))
345s ***** error<unidcdf: X and N must be of common size or scalars.> ...
345s  unidcdf (ones (2), ones (3))
345s ***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
345s ***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
345s 17 tests, 17 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/vmrnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/vmrnd.m
345s ***** assert (size (vmrnd (1, 1)), [1 1])
345s ***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
345s ***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
345s ***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
345s ***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
345s ***** assert (size (vmrnd (1, 1, 3)), [3, 3])
345s ***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
345s ***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
345s ***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
345s ***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
345s ***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
345s ***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
345s ***** assert (class (vmrnd (1, 1)), "double")
345s ***** assert (class (vmrnd (1, single (1))), "single")
345s ***** assert (class (vmrnd (1, single ([1, 1]))), "single")
345s ***** assert (class (vmrnd (single (1), 1)), "single")
345s ***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
345s ***** error<vmrnd: function called with too few input arguments.> vmrnd ()
345s ***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
345s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
345s  vmrnd (ones (3), ones (2))
345s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
345s  vmrnd (ones (2), ones (3))
345s ***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
345s ***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
345s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  vmrnd (1, 2, -1)
345s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  vmrnd (1, 2, 1.2)
345s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  vmrnd (1, 2, ones (2))
345s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  vmrnd (1, 2, [2 -1 2])
345s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  vmrnd (1, 2, [2 0 2.5])
345s ***** error<vmrnd: dimensions must be non-negative integers.> ...
345s  vmrnd (1, 2, 2, -1, 5)
345s ***** error<vmrnd: dimensions must be non-negative integers.> ...
345s  vmrnd (1, 2, 2, 1.5, 5)
345s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
345s  vmrnd (2, ones (2), 3)
345s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
345s  vmrnd (2, ones (2), [3, 2])
345s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
345s  vmrnd (2, ones (2), 3, 2)
345s 33 tests, 33 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/laplaceinv.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/laplaceinv.m
345s ***** demo
345s  ## Plot various iCDFs from the Laplace distribution
345s  p = 0.001:0.001:0.999;
345s  x1 = cauchyinv (p, 0, 1);
345s  x2 = cauchyinv (p, 0, 2);
345s  x3 = cauchyinv (p, 0, 4);
345s  x4 = cauchyinv (p, -5, 4);
345s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
345s  grid on
345s  ylim ([-10, 10])
345s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
345s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
345s  title ("Laplace iCDF")
345s  xlabel ("probability")
345s  ylabel ("values in x")
345s ***** shared p, x
345s  p = [-1 0 0.5 1 2];
345s  x = [NaN, -Inf, 0, Inf, NaN];
345s ***** assert (laplaceinv (p, 0, 1), x)
345s ***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
345s ***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
345s ***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
345s ***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
345s ***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
345s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
345s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
345s ***** error<laplaceinv: function called with too few input arguments.> ...
345s  laplaceinv (1, 2)
345s ***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
345s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
345s  laplaceinv (1, ones (2), ones (3))
345s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
345s  laplaceinv (ones (2), 1, ones (3))
345s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
345s  laplaceinv (ones (2), ones (3), 1)
345s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
345s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
345s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
345s 16 tests, 16 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/plcdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/plcdf.m
345s ***** demo
345s  ## Plot various CDFs from the Piecewise linear distribution
345s  data = 0:0.01:10;
345s  x1 = [0, 1, 3, 4, 7, 10];
345s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
345s  x2 = [0, 2, 5, 6, 7, 8];
345s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
345s  p1 = plcdf (data, x1, Fx1);
345s  p2 = plcdf (data, x2, Fx2);
345s  plot (data, p1, "-b", data, p2, "g")
345s  grid on
345s  ylim ([0, 1])
345s  xlim ([0, 10])
345s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
345s  title ("Piecewise linear CDF")
345s  xlabel ("values in data")
345s  ylabel ("probability")
345s ***** test
345s  data = 0:0.2:1;
345s  p = plcdf (data, [0, 1], [0, 1]);
345s  assert (p, data);
345s ***** test
345s  data = 0:0.2:1;
345s  p = plcdf (data, [0, 2], [0, 1]);
345s  assert (p, 0.5 * data);
345s ***** test
345s  data = 0:0.2:1;
345s  p = plcdf (data, [0, 1], [0, 0.5]);
345s  assert (p, 0.5 * data);
345s ***** test
345s  data = 0:0.2:1;
345s  p = plcdf (data, [0, 0.5], [0, 1]);
345s  assert (p, [0, 0.4, 0.8, 1, 1, 1]);
345s ***** test
345s  data = 0:0.2:1;
345s  p = plcdf (data, [0, 1], [0, 1], "upper");
345s  assert (p, 1 - data);
345s ***** error<plcdf: function called with too few input arguments.> plcdf ()
345s ***** error<plcdf: function called with too few input arguments.> plcdf (1)
345s ***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
345s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
345s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
345s ***** error<plcdf: X and FX must be vectors of equal size.> ...
345s  plcdf (1, [0, 1, 2], [0, 1])
345s ***** error<plcdf: X and FX must be at least two-elements long.> ...
345s  plcdf (1, [0], [1])
345s ***** error<plcdf: FX must be bounded in the range> ...
345s  plcdf (1, [0, 1, 2], [0, 1, 1.5])
345s ***** error<plcdf: FX must be bounded in the range> ...
345s  plcdf (1, [0, 1, 2], [0, i, 1])
345s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
345s  plcdf (i, [0, 1, 2], [0, 0.5, 1])
345s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
345s  plcdf (1, [0, i, 2], [0, 0.5, 1])
345s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
345s  plcdf (1, [0, 1, 2], [0, 0.5i, 1])
345s 17 tests, 17 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/tlsinv.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tlsinv.m
345s ***** demo
345s  ## Plot various iCDFs from the location-scale Student's T distribution
345s  p = 0.001:0.001:0.999;
345s  x1 = tlsinv (p, 0, 1, 1);
345s  x2 = tlsinv (p, 0, 2, 2);
345s  x3 = tlsinv (p, 3, 2, 5);
345s  x4 = tlsinv (p, -1, 3, Inf);
345s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
345s  grid on
345s  xlim ([0, 1])
345s  ylim ([-8, 8])
345s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
345s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
345s          "location", "southeast")
345s  title ("Location-scale Student's T iCDF")
345s  xlabel ("probability")
345s  ylabel ("values in x")
345s ***** shared p
345s  p = [-1 0 0.5 1 2];
345s ***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
345s ***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
345s ***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
345s ***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
345s ***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
345s ***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
345s ***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
345s ***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
345s ***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
345s ***** error<tlsinv: function called with too few input arguments.> tlsinv ()
345s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
345s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
345s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlsinv (ones (3), ones (2), 1, 1)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlsinv (ones (2), 1, ones (3), 1)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlsinv (ones (2), 1, 1, ones (3))
345s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
345s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
345s 20 tests, 20 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/fpdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/fpdf.m
345s ***** demo
345s  ## Plot various PDFs from the F distribution
345s  x = 0.01:0.01:4;
345s  y1 = fpdf (x, 1, 1);
345s  y2 = fpdf (x, 2, 1);
345s  y3 = fpdf (x, 5, 2);
345s  y4 = fpdf (x, 10, 1);
345s  y5 = fpdf (x, 100, 100);
345s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
345s  grid on
345s  ylim ([0, 2.5])
345s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
345s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
345s           "df1 = 100, df2 = 100"}, "location", "northeast")
345s  title ("F PDF")
345s  xlabel ("values in x")
345s  ylabel ("density")
345s ***** shared x, y
345s  x = [-1, 0, 0.5, 1, 2];
345s  y = [0, 0, 4/9, 1/4, 1/9];
345s ***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
345s ***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
345s ***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
345s ***** assert (fpdf (x, [0, NaN, Inf, 2, 2], 2), [NaN, NaN, 0.5413, y(4:5)], 1e-4)
345s ***** assert (fpdf (x, 2, [0, NaN, Inf, 2, 2]), [NaN, NaN, 0.6065, y(4:5)], 1e-4)
345s ***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
345s ***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
345s  rand ("seed", 1234);    # for reproducibility
345s  xr = rand (10,1);
345s  xr = xr(x > 0.1 & x < 0.9);
345s  yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
345s  assert (fpdf (xr, 1, 2), yr, 5*eps);
345s ***** test
345s  yy = fpdf (2, 4, Inf);
345s  assert (yy, 0.1465, 1e-4)
345s ***** test
345s  yy = fpdf (2, 4, 1000000000000000);
345s  assert (yy, 0.1465, 1e-4)
345s ***** test
345s  yy = fpdf (2, Inf, 4);
345s  assert (yy, 0.1839, 1e-4)
345s ***** test
345s  yy = fpdf (2, 10000000000000000, 4);
345s  assert (yy, 0.1839, 1e-4)
345s ***** test
345s  yy = fpdf (2, Inf, Inf);
345s  assert (yy, 0)
345s ***** test
345s  yy = fpdf (NaN, Inf, Inf);
345s  assert (yy, NaN)
345s ***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
345s ***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
345s ***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
345s ***** error<fpdf: function called with too few input arguments.> fpdf ()
345s ***** error<fpdf: function called with too few input arguments.> fpdf (1)
345s ***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
345s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
345s  fpdf (ones (3), ones (2), ones (2))
345s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
345s  fpdf (ones (2), ones (3), ones (2))
345s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
345s  fpdf (ones (2), ones (2), ones (3))
345s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
345s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
345s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
345s 25 tests, 25 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/trnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/trnd.m
345s ***** assert (size (trnd (2)), [1, 1])
345s ***** assert (size (trnd (ones (2,1))), [2, 1])
345s ***** assert (size (trnd (ones (2,2))), [2, 2])
345s ***** assert (size (trnd (1, 3)), [3, 3])
345s ***** assert (size (trnd (1, [4 1])), [4, 1])
345s ***** assert (size (trnd (1, 4, 1)), [4, 1])
345s ***** assert (size (trnd (1, 4, 1)), [4, 1])
345s ***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
345s ***** assert (size (trnd (1, 0, 1)), [0, 1])
345s ***** assert (size (trnd (1, 1, 0)), [1, 0])
345s ***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
345s ***** assert (trnd (0, 1, 1), NaN)
345s ***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
345s ***** assert (class (trnd (2)), "double")
345s ***** assert (class (trnd (single (2))), "single")
345s ***** assert (class (trnd (single ([2 2]))), "single")
345s ***** error<trnd: function called with too few input arguments.> trnd ()
345s ***** error<trnd: DF must not be complex.> trnd (i)
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (1, -1)
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (1, 1.2)
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (1, ones (2))
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (1, [2 -1 2])
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (1, [2 0 2.5])
345s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  trnd (ones (2), ones (2))
345s ***** error<trnd: dimensions must be non-negative integers.> ...
345s  trnd (1, 2, -1, 5)
345s ***** error<trnd: dimensions must be non-negative integers.> ...
345s  trnd (1, 2, 1.5, 5)
345s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
345s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
345s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
345s 29 tests, 29 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/tlspdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tlspdf.m
345s ***** demo
345s  ## Plot various PDFs from the Student's T distribution
345s  x = -8:0.01:8;
345s  y1 = tlspdf (x, 0, 1, 1);
345s  y2 = tlspdf (x, 0, 2, 2);
345s  y3 = tlspdf (x, 3, 2, 5);
345s  y4 = tlspdf (x, -1, 3, Inf);
345s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
345s  grid on
345s  xlim ([-8, 8])
345s  ylim ([0, 0.41])
345s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
345s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
345s          "location", "northwest")
345s  title ("Location-scale Student's T PDF")
345s  xlabel ("values in x")
345s  ylabel ("density")
345s ***** test
345s  x = rand (10,1);
345s  y = 1./(pi * (1 + x.^2));
345s  assert (tlspdf (x, 0, 1, 1), y, 5*eps);
345s  assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
345s  assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
345s ***** shared x, y
345s  x = [-Inf 0 0.5 1 Inf];
345s  y = 1./(pi * (1 + x.^2));
345s ***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
345s ***** assert (tlspdf (x, 0, 1, 1), y, eps)
345s ***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
345s ***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
345s ***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
345s ***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
345s ***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
345s ***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
345s ***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
345s ***** error<tlspdf: function called with too few input arguments.> tlspdf ()
345s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
345s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
345s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlspdf (ones (3), ones (2), 1, 1)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlspdf (ones (2), 1, ones (3), 1)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
345s  tlspdf (ones (2), 1, 1, ones (3))
345s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
345s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
345s 21 tests, 21 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/gevrnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gevrnd.m
345s ***** assert(size (gevrnd (1,2,1)), [1, 1]);
345s ***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
345s ***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
345s ***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
345s ***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
345s ***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
345s ***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
345s ***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
345s ***** assert (class (gevrnd (1,1,1)), "double")
345s ***** assert (class (gevrnd (single (1),1,1)), "single")
345s ***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
345s ***** assert (class (gevrnd (1,single (1),1)), "single")
345s ***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
345s ***** assert (class (gevrnd (1,1,single (1))), "single")
345s ***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
345s ***** error<gevrnd: function called with too few input arguments.> gevrnd ()
345s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
345s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
345s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
345s  gevrnd (ones (3), ones (2), ones (2))
345s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
345s  gevrnd (ones (2), ones (3), ones (2))
345s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
345s  gevrnd (ones (2), ones (2), ones (3))
345s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
345s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
345s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
345s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  gevrnd (1, 2, 3, -1)
345s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  gevrnd (1, 2, 3, 1.2)
345s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  gevrnd (1, 2, 3, ones (2))
345s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  gevrnd (1, 2, 3, [2 -1 2])
345s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  gevrnd (1, 2, 3, [2 0 2.5])
345s ***** error<gevrnd: dimensions must be non-negative integers.> ...
345s  gevrnd (1, 2, 3, 2, -1, 5)
345s ***** error<gevrnd: dimensions must be non-negative integers.> ...
345s  gevrnd (1, 2, 3, 2, 1.5, 5)
345s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
345s  gevrnd (2, ones (2), 2, 3)
345s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
345s  gevrnd (2, ones (2), 2, [3, 2])
345s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
345s  gevrnd (2, ones (2), 2, 3, 2)
345s 34 tests, 34 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/burrrnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/burrrnd.m
345s ***** assert (size (burrrnd (1, 1, 1)), [1 1])
345s ***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
345s ***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
345s ***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
345s ***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
345s ***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
345s ***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
345s ***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
345s ***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
345s ***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
345s ***** assert (class (burrrnd (1,1,1)), "double")
345s ***** assert (class (burrrnd (single (1),1,1)), "single")
345s ***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
345s ***** assert (class (burrrnd (1,single (1),1)), "single")
345s ***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
345s ***** assert (class (burrrnd (1,1,single (1))), "single")
345s ***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
345s ***** error<burrrnd: function called with too few input arguments.> burrrnd ()
345s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
345s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
345s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
345s  burrrnd (ones (3), ones (2), ones (2))
345s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
345s  burrrnd (ones (2), ones (3), ones (2))
345s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
345s  burrrnd (ones (2), ones (2), ones (3))
345s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
345s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
345s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
345s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  burrrnd (1, 2, 3, -1)
345s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  burrrnd (1, 2, 3, 1.2)
345s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  burrrnd (1, 2, 3, ones (2))
345s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  burrrnd (1, 2, 3, [2 -1 2])
345s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  burrrnd (1, 2, 3, [2 0 2.5])
345s ***** error<burrrnd: dimensions must be non-negative integers.> ...
345s  burrrnd (1, 2, 3, 2, -1, 5)
345s ***** error<burrrnd: dimensions must be non-negative integers.> ...
345s  burrrnd (1, 2, 3, 2, 1.5, 5)
345s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
345s  burrrnd (2, ones (2), 2, 3)
345s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
345s  burrrnd (2, ones (2), 2, [3, 2])
345s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
345s  burrrnd (2, ones (2), 2, 3, 2)
345s 36 tests, 36 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/plrnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/plrnd.m
345s ***** shared x, Fx
345s  x = [0, 1, 3, 4, 7, 10];
345s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
345s ***** assert (size (plrnd (x, Fx)), [1, 1])
345s ***** assert (size (plrnd (x, Fx, 3)), [3, 3])
345s ***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
345s ***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
345s ***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
345s ***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
345s ***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
345s ***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
345s ***** assert (class (plrnd (x, Fx)), "double")
345s ***** assert (class (plrnd (x, single (Fx))), "single")
345s ***** assert (class (plrnd (single (x), Fx)), "single")
345s ***** error<plrnd: function called with too few input arguments.> plrnd ()
345s ***** error<plrnd: function called with too few input arguments.> plrnd (1)
345s ***** error<plrnd: X and FX must be vectors of equal size.> ...
345s  plrnd ([0, 1, 2], [0, 1])
345s ***** error<plrnd: X and FX must be at least two-elements long.> ...
345s  plrnd ([0], [1])
345s ***** error<plrnd: FX must be bounded in the range> ...
345s  plrnd ([0, 1, 2], [0, 1, 1.5])
345s ***** error<plrnd: FX must be bounded in the range> ...
345s  plrnd ([0, 1, 2], [0, i, 1])
345s ***** error<plrnd: X and FX must not be complex.> ...
345s  plrnd ([0, i, 2], [0, 0.5, 1])
345s ***** error<plrnd: X and FX must not be complex.> ...
345s  plrnd ([0, i, 2], [0, 0.5i, 1])
345s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  plrnd (x, Fx, -1)
345s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  plrnd (x, Fx, 1.2)
345s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  plrnd (x, Fx, ones (2))
345s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  plrnd (x, Fx, [2 -1 2])
345s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
345s  plrnd (x, Fx, [2 0 2.5])
345s ***** error<plrnd: dimensions must be non-negative integers.> ...
345s  plrnd (x, Fx, 2, -1, 5)
345s ***** error<plrnd: dimensions must be non-negative integers.> ...
345s  plrnd (x, Fx, 2, 1.5, 5)
345s 26 tests, 26 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/mvncdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvncdf.m
345s ***** demo
345s  mu = [1, -1];
345s  Sigma = [0.9, 0.4; 0.4, 0.3];
345s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
345s  X = [X1(:), X2(:)];
345s  p = mvncdf (X, mu, Sigma);
345s  Z = reshape (p, 25, 25);
345s  surf (X1, X2, Z);
345s  title ("Bivariate Normal Distribution");
345s  ylabel "X1"
345s  xlabel "X2"
345s ***** demo
345s  mu = [0, 0];
345s  Sigma = [0.25, 0.3; 0.3, 1];
345s  p = mvncdf ([0 0], [1 1], mu, Sigma);
345s  x1 = -3:.2:3;
345s  x2 = -3:.2:3;
345s  [X1, X2] = meshgrid (x1, x2);
345s  X = [X1(:), X2(:)];
345s  p = mvnpdf (X, mu, Sigma);
345s  p = reshape (p, length (x2), length (x1));
345s  contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
345s  xlabel ("x");
345s  ylabel ("p");
345s  title ("Probability over Rectangular Region");
345s  line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
345s ***** test
345s  fD = (-2:2)';
345s  X = repmat (fD, 1, 4);
345s  p = mvncdf (X);
345s  assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
345s ***** test
345s  mu = [1, -1];
345s  Sigma = [0.9, 0.4; 0.4, 0.3];
345s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
345s  X = [X1(:), X2(:)];
345s  p = mvncdf (X, mu, Sigma);
345s  p_out = [0.00011878988774500, 0.00034404112322371, ...
345s           0.00087682502191813, 0.00195221905058185, ...
345s           0.00378235566873474, 0.00638175749734415, ...
345s           0.00943764224329656, 0.01239164888125426, ...
345s           0.01472750274376648, 0.01623228313374828]';
345s  assert (p([1:10]), p_out, 1e-16);
345s ***** test
345s  mu = [1, -1];
345s  Sigma = [0.9, 0.4; 0.4, 0.3];
345s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
345s  X = [X1(:), X2(:)];
345s  p = mvncdf (X, mu, Sigma);
345s  p_out = [0.8180695783608276, 0.8854485749482751, ...
345s           0.9308108777385832, 0.9579855743025508, ...
345s           0.9722897881414742, 0.9788150170059926, ...
345s           0.9813597788804785, 0.9821977956568989, ...
345s           0.9824283794464095, 0.9824809345614861]';
345s  assert (p([616:625]), p_out, 3e-16);
345s ***** test
345s  mu = [0, 0];
345s  Sigma = [0.25, 0.3; 0.3, 1];
345s  [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
345s  assert (p, 0.2097424404755626, 1e-16);
345s  assert (err, 1e-08);
345s ***** test
345s  x = [1 2];
345s  mu = [0.5 1.5];
345s  sigma = [1.0, 0.5; 0.5, 1.0];
345s  p = mvncdf (x, mu, sigma);
345s  assert (p, 0.546244443857090, 1e-15);
345s ***** test
345s  x = [1 2];
345s  mu = [0.5 1.5];
345s  sigma = [1.0, 0.5; 0.5, 1.0];
345s  a = [-inf 0];
345s  p = mvncdf (a, x, mu, sigma);
345s  assert (p, 0.482672935215631, 1e-15);
345s ***** error p = mvncdf (randn (25,26), [], eye (26));
345s ***** error p = mvncdf (randn (25,8), [], eye (9));
345s ***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
345s ***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
345s ***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
345s ***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
345s 12 tests, 12 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/iwishrnd.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/iwishrnd.m
345s ***** assert(size (iwishrnd (1,2,1)), [1, 1]);
345s ***** assert(size (iwishrnd ([],2,1)), [1, 1]);
345s ***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
345s ***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
345s ***** error iwishrnd ()
345s ***** error iwishrnd (1)
345s ***** error iwishrnd ([-3 1; 1 3],1)
345s ***** error iwishrnd ([1; 1],1)
345s 8 tests, 8 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/logncdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logncdf.m
345s ***** demo
345s  ## Plot various CDFs from the log-normal distribution
345s  x = 0:0.01:3;
345s  p1 = logncdf (x, 0, 1);
345s  p2 = logncdf (x, 0, 0.5);
345s  p3 = logncdf (x, 0, 0.25);
345s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
345s  grid on
345s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
345s          "location", "southeast")
345s  title ("Log-normal CDF")
345s  xlabel ("values in x")
345s  ylabel ("probability")
345s ***** shared x, y
345s  x = [-1, 0, 1, e, Inf];
345s  y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
345s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
345s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
345s ***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
345s ***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
345s ***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
345s ***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
345s ***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
345s ***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
345s ***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
345s ***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
345s ***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
345s ***** error<logncdf: invalid number of input arguments.> logncdf ()
345s ***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
345s ***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
345s ***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
345s  logncdf (ones (3), ones (2), ones (2))
345s ***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
345s ***** error<logncdf: covariance matrix is required for confidence bounds.> ...
345s  [p, plo, pup] = logncdf (1, 2, 3)
345s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
345s  logncdf (1, 2, 3, [1, 0; 0, 1], 0)
345s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
345s  logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
345s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
345s  logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
345s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
345s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
345s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
345s ***** error<logncdf: bad covariance matrix.> ...
345s  [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
345s 24 tests, 24 passed, 0 known failure, 0 skipped
345s [inst/dist_fun/invgcdf.m]
345s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/invgcdf.m
345s ***** demo
345s  ## Plot various CDFs from the inverse Gaussian distribution
345s  x = 0:0.001:3;
345s  p1 = invgcdf (x, 1, 0.2);
345s  p2 = invgcdf (x, 1, 1);
345s  p3 = invgcdf (x, 1, 3);
345s  p4 = invgcdf (x, 3, 0.2);
345s  p5 = invgcdf (x, 3, 1);
345s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
345s  grid on
345s  xlim ([0, 3])
345s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
345s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
345s  title ("Inverse Gaussian CDF")
345s  xlabel ("values in x")
345s  ylabel ("probability")
345s ***** shared x, p1, p1u, y2, y2u, y3, y3u
345s  x = [-Inf, -1, 0, 1/2, 1, Inf];
345s  p1 = [0, 0, 0, 0.3650, 0.6681, 1];
345s  p1u = [1, 1, 1, 0.6350, 0.3319, 0];
345s ***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
345s ***** assert (invgcdf (x, 1, 1), p1, 1e-4)
345s ***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
345s ***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
345s ***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
345s ***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
345s ***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
345s ***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
345s ***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
345s ***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
345s ***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
346s ***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
346s ***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
346s ***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
346s ***** error<invgcdf: function called with too few input arguments.> invgcdf ()
346s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
346s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
346s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
346s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
346s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
346s  invgcdf (ones (3), ones (2), ones(2))
346s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
346s  invgcdf (ones (2), ones (3), ones(2))
346s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
346s  invgcdf (ones (2), ones (2), ones(3))
346s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
346s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
346s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
346s 25 tests, 25 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/burrinv.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/burrinv.m
346s ***** demo
346s  ## Plot various iCDFs from the Burr type XII distribution
346s  p = 0.001:0.001:0.999;
346s  x1 = burrinv (p, 1, 1, 1);
346s  x2 = burrinv (p, 1, 1, 2);
346s  x3 = burrinv (p, 1, 1, 3);
346s  x4 = burrinv (p, 1, 2, 1);
346s  x5 = burrinv (p, 1, 3, 1);
346s  x6 = burrinv (p, 1, 0.5, 2);
346s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
346s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
346s  grid on
346s  ylim ([0, 5])
346s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
346s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
346s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
346s          "location", "northwest")
346s  title ("Burr type XII iCDF")
346s  xlabel ("probability")
346s  ylabel ("values in x")
346s ***** shared p, y
346s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
346s  y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
346s ***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
346s ***** assert (burrinv (p, 1, 1, 1), y, eps)
346s ***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
346s ***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
346s ***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
346s ***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
346s ***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
346s ***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
346s ***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
346s ***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
346s ***** error<burrinv: function called with too few input arguments.> burrinv ()
346s ***** error<burrinv: function called with too few input arguments.> burrinv (1)
346s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
346s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
346s ***** error<burrinv: function called with too many inputs> ...
346s  burrinv (1, 2, 3, 4, 5)
346s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
346s  burrinv (ones (3), ones (2), ones(2), ones(2))
346s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
346s  burrinv (ones (2), ones (3), ones(2), ones(2))
346s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
346s  burrinv (ones (2), ones (2), ones(3), ones(2))
346s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
346s  burrinv (ones (2), ones (2), ones(2), ones(3))
346s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
346s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
346s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
346s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
346s 23 tests, 23 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/hnrnd.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hnrnd.m
346s ***** assert (size (hnrnd (1, 1, 1)), [1, 1])
346s ***** assert (size (hnrnd (1, 1, 2)), [2, 2])
346s ***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
346s ***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
346s ***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
346s ***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
346s ***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
346s ***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
346s ***** assert (size (hnrnd (1, 1, 3)), [3, 3])
346s ***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
346s ***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
346s ***** test
346s  r =  hnrnd (1, [1, 0, -1]);
346s  assert (r([2:3]), [NaN, NaN])
346s ***** assert (class (hnrnd (1, 0)), "double")
346s ***** assert (class (hnrnd (1, single (0))), "single")
346s ***** assert (class (hnrnd (1, single ([0 0]))), "single")
346s ***** assert (class (hnrnd (1, single (1))), "single")
346s ***** assert (class (hnrnd (1, single ([1 1]))), "single")
346s ***** assert (class (hnrnd (single (1), 1)), "single")
346s ***** assert (class (hnrnd (single ([1 1]), 1)), "single")
346s ***** error<hnrnd: function called with too few input arguments.> hnrnd ()
346s ***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
346s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
346s  hnrnd (ones (3), ones (2))
346s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
346s  hnrnd (ones (2), ones (3))
346s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
346s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
346s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  hnrnd (1, 2, -1)
346s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  hnrnd (1, 2, 1.2)
346s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  hnrnd (1, 2, ones (2))
346s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  hnrnd (1, 2, [2 -1 2])
346s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  hnrnd (1, 2, [2 0 2.5])
346s ***** error<hnrnd: dimensions must be non-negative integers.> ...
346s  hnrnd (1, 2, 2, -1, 5)
346s ***** error<hnrnd: dimensions must be non-negative integers.> ...
346s  hnrnd (1, 2, 2, 1.5, 5)
346s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
346s  hnrnd (2, ones (2), 3)
346s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
346s  hnrnd (2, ones (2), [3, 2])
346s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
346s  hnrnd (2, ones (2), 3, 2)
346s 35 tests, 35 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/nbinrnd.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nbinrnd.m
346s ***** assert (size (nbinrnd (1, 0.5)), [1 1])
346s ***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
346s ***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
346s ***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
346s ***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
346s ***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
346s ***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
346s ***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
346s ***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
346s ***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
346s ***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
346s ***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
346s ***** assert (class (nbinrnd (1, 0.5)), "double")
346s ***** assert (class (nbinrnd (1, single (0.5))), "single")
346s ***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
346s ***** assert (class (nbinrnd (single (1), 0.5)), "single")
346s ***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
346s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
346s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
346s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
346s  nbinrnd (ones (3), ones (2))
346s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
346s  nbinrnd (ones (2), ones (3))
346s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
346s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
346s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  nbinrnd (1, 2, -1)
346s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  nbinrnd (1, 2, 1.2)
346s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  nbinrnd (1, 2, ones (2))
346s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  nbinrnd (1, 2, [2 -1 2])
346s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  nbinrnd (1, 2, [2 0 2.5])
346s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
346s  nbinrnd (1, 2, 2, -1, 5)
346s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
346s  nbinrnd (1, 2, 2, 1.5, 5)
346s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
346s  nbinrnd (2, ones (2), 3)
346s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
346s  nbinrnd (2, ones (2), [3, 2])
346s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
346s  nbinrnd (2, ones (2), 3, 2)
346s 33 tests, 33 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/tlsrnd.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tlsrnd.m
346s ***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
346s ***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
346s ***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
346s ***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
346s ***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
346s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
346s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
346s ***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
346s ***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
346s ***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
346s ***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
346s ***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
346s ***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
346s ***** assert (class (tlsrnd (1, 2, 3)), "double")
346s ***** assert (class (tlsrnd (single (1), 2, 3)), "single")
346s ***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
346s ***** assert (class (tlsrnd (1, single (2), 3)), "single")
346s ***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
346s ***** assert (class (tlsrnd (1, 2, single (3))), "single")
346s ***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
346s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
346s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
346s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
346s  tlsrnd (ones (3), ones (2), 1)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
346s  tlsrnd (ones (2), 1, ones (3))
346s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
346s  tlsrnd (1, ones (2), ones (3))
346s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
346s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
346s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (1, 2, 3, -1)
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (1, 2, 3, 1.2)
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (1, 2, 3, ones (2))
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (1, 2, 3, [2 -1 2])
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (1, 2, 3, [2 0 2.5])
346s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  tlsrnd (ones (2), 2, 3, ones (2))
346s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
346s  tlsrnd (1, 2, 3, 2, -1, 5)
346s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
346s  tlsrnd (1, 2, 3, 2, 1.5, 5)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
346s  tlsrnd (ones (2,2), 2, 3, 3)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
346s  tlsrnd (1, ones (2,2), 3, 3)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
346s  tlsrnd (1, 2, ones (2,2), 3)
346s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
346s  tlsrnd (1, 2, ones (2,2), [3, 3])
346s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
346s  tlsrnd (1, 2, ones (2,2), 2, 3)
346s 42 tests, 42 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/logipdf.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logipdf.m
346s ***** demo
346s  ## Plot various PDFs from the logistic distribution
346s  x = -5:0.01:20;
346s  y1 = logipdf (x, 5, 2);
346s  y2 = logipdf (x, 9, 3);
346s  y3 = logipdf (x, 9, 4);
346s  y4 = logipdf (x, 6, 2);
346s  y5 = logipdf (x, 2, 1);
346s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
346s  grid on
346s  ylim ([0, 0.3])
346s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
346s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
346s  title ("Logistic PDF")
346s  xlabel ("values in x")
346s  ylabel ("density")
346s ***** shared x, y
346s  x = [-Inf -log(4) 0 log(4) Inf];
346s  y = [0, 0.16, 1/4, 0.16, 0];
346s ***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
346s ***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
346s ***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
346s ***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
346s ***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
346s ***** error<logipdf: function called with too few input arguments.> logipdf ()
346s ***** error<logipdf: function called with too few input arguments.> logipdf (1)
346s ***** error<logipdf: function called with too few input arguments.> ...
346s  logipdf (1, 2)
346s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
346s  logipdf (1, ones (2), ones (3))
346s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
346s  logipdf (ones (2), 1, ones (3))
346s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
346s  logipdf (ones (2), ones (3), 1)
346s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
346s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
346s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
346s 14 tests, 14 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/logiinv.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logiinv.m
346s ***** demo
346s  ## Plot various iCDFs from the logistic distribution
346s  p = 0.001:0.001:0.999;
346s  x1 = logiinv (p, 5, 2);
346s  x2 = logiinv (p, 9, 3);
346s  x3 = logiinv (p, 9, 4);
346s  x4 = logiinv (p, 6, 2);
346s  x5 = logiinv (p, 2, 1);
346s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
346s  grid on
346s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
346s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
346s  title ("Logistic iCDF")
346s  xlabel ("probability")
346s  ylabel ("x")
346s ***** test
346s  p = [0.01:0.01:0.99];
346s  assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
346s ***** shared p
346s  p = [-1 0 0.5 1 2];
346s ***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
346s ***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
346s ***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
346s ***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
346s ***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
346s ***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
346s ***** error<logiinv: function called with too few input arguments.> logiinv ()
346s ***** error<logiinv: function called with too few input arguments.> logiinv (1)
346s ***** error<logiinv: function called with too few input arguments.> ...
346s  logiinv (1, 2)
346s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
346s  logiinv (1, ones (2), ones (3))
346s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
346s  logiinv (ones (2), 1, ones (3))
346s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
346s  logiinv (ones (2), ones (3), 1)
346s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
346s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
346s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
346s 16 tests, 16 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/ricernd.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ricernd.m
346s ***** assert (size (ricernd (2, 1/2)), [1, 1])
346s ***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
346s ***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
346s ***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
346s ***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
346s ***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
346s ***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
346s ***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
346s ***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
346s ***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
346s ***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
346s ***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
346s ***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
346s ***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
346s ***** assert (class (ricernd (1, 1)), "double")
346s ***** assert (class (ricernd (1, single (0))), "single")
346s ***** assert (class (ricernd (1, single ([0, 0]))), "single")
346s ***** assert (class (ricernd (1, single (1), 2)), "single")
346s ***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
346s ***** assert (class (ricernd (single (1), 1, 2)), "single")
346s ***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
346s ***** error<ricernd: function called with too few input arguments.> ricernd ()
346s ***** error<ricernd: function called with too few input arguments.> ricernd (1)
346s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
346s  ricernd (ones (3), ones (2))
346s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
346s  ricernd (ones (2), ones (3))
346s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
346s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
346s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  ricernd (1, 1/2, -1)
346s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  ricernd (1, 1/2, 1.2)
346s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  ricernd (1, 1/2, ones (2))
346s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  ricernd (1, 1/2, [2 -1 2])
346s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
346s  ricernd (1, 1/2, [2 0 2.5])
346s ***** error<ricernd: dimensions must be non-negative integers.> ...
346s  ricernd (1, 1/2, 2, -1, 5)
346s ***** error<ricernd: dimensions must be non-negative integers.> ...
346s  ricernd (1, 1/2, 2, 1.5, 5)
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2, 1/2 * ones (2), 3)
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2, 1/2 * ones (2), [3, 2])
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2, 1/2 * ones (2), 3, 2)
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2 * ones (2), 1/2, 3)
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2 * ones (2), 1/2, [3, 2])
346s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
346s  ricernd (2 * ones (2), 1/2, 3, 2)
346s 40 tests, 40 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/riceinv.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/riceinv.m
346s ***** demo
346s  ## Plot various iCDFs from the Rician distribution
346s  p = 0.001:0.001:0.999;
346s  x1 = riceinv (p, 0, 1);
346s  x2 = riceinv (p, 0.5, 1);
346s  x3 = riceinv (p, 1, 1);
346s  x4 = riceinv (p, 2, 1);
346s  x5 = riceinv (p, 4, 1);
346s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
346s  grid on
346s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
346s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
346s  title ("Rician iCDF")
346s  xlabel ("probability")
346s  ylabel ("values in x")
346s ***** shared p
346s  p = [-1 0 0.75 1 2];
346s ***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
346s ***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
346s ***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
346s ***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
346s ***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
346s ***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
346s ***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
346s ***** assert (riceinv (single ([p, NaN]), 1, 2), ...
346s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
346s ***** assert (riceinv ([p, NaN], single (1), 2), ...
346s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
346s ***** assert (riceinv ([p, NaN], 1, single (2)), ...
346s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
346s ***** error<riceinv: function called with too few input arguments.> riceinv ()
346s ***** error<riceinv: function called with too few input arguments.> riceinv (1)
346s ***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
346s ***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
346s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
346s  riceinv (ones (3), ones (2), ones (2))
346s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
346s  riceinv (ones (2), ones (3), ones (2))
346s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
346s  riceinv (ones (2), ones (2), ones (3))
346s ***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
346s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
346s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
346s 20 tests, 20 passed, 0 known failure, 0 skipped
346s [inst/dist_fun/tinv.m]
346s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tinv.m
346s ***** demo
346s  ## Plot various iCDFs from the Student's T distribution
346s  p = 0.001:0.001:0.999;
346s  x1 = tinv (p, 1);
346s  x2 = tinv (p, 2);
346s  x3 = tinv (p, 5);
346s  x4 = tinv (p, Inf);
346s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
346s  grid on
346s  xlim ([0, 1])
346s  ylim ([-5, 5])
346s  legend ({"df = 1", "df = 2", ...
346s           "df = 5", 'df = \infty'}, "location", "northwest")
346s  title ("Student's T iCDF")
346s  xlabel ("probability")
346s  ylabel ("values in x")
346s ***** shared p
346s  p = [-1 0 0.5 1 2];
346s ***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
346s ***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
346s ***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
346s ***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
346s ***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
346s ***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
346s ***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
346s ***** error<tinv: function called with too few input arguments.> tinv ()
346s ***** error<tinv: function called with too few input arguments.> tinv (1)
346s ***** error<tinv: P and DF must be of common size or scalars.> ...
346s  tinv (ones (3), ones (2))
346s ***** error<tinv: P and DF must be of common size or scalars.> ...
346s  tinv (ones (2), ones (3))
346s ***** error<tinv: P and DF must not be complex.> tinv (i, 2)
346s ***** error<tinv: P and DF must not be complex.> tinv (2, i)
347s 13 tests, 13 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/nbincdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nbincdf.m
347s ***** demo
347s  ## Plot various CDFs from the negative binomial distribution
347s  x = 0:50;
347s  p1 = nbincdf (x, 2, 0.15);
347s  p2 = nbincdf (x, 5, 0.2);
347s  p3 = nbincdf (x, 4, 0.4);
347s  p4 = nbincdf (x, 10, 0.3);
347s  plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
347s  grid on
347s  xlim ([0, 40])
347s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
347s           "r = 10, ps = 0.3"}, "location", "southeast")
347s  title ("Negative binomial CDF")
347s  xlabel ("values in x (number of failures)")
347s  ylabel ("probability")
347s ***** shared x, y
347s  x = [-1 0 1 2 Inf];
347s  y = [0 1/2 3/4 7/8 1];
347s ***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
347s ***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
347s ***** assert (nbincdf (x, ones (1,5), 0.5), y)
347s ***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
347s ***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
347s  [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
347s ***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
347s ***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
347s ***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
347s ***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
347s ***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
347s ***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
347s ***** error<nbincdf: function called with too few input arguments.> nbincdf ()
347s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
347s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
347s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
347s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
347s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
347s  nbincdf (ones (3), ones (2), ones (2))
347s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
347s  nbincdf (ones (2), ones (3), ones (2))
347s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
347s  nbincdf (ones (2), ones (2), ones (3))
347s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
347s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
347s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
347s 22 tests, 22 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/bisapdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bisapdf.m
347s ***** demo
347s  ## Plot various PDFs from the Birnbaum-Saunders distribution
347s  x = 0.01:0.01:4;
347s  y1 = bisapdf (x, 1, 0.5);
347s  y2 = bisapdf (x, 1, 1);
347s  y3 = bisapdf (x, 1, 2);
347s  y4 = bisapdf (x, 1, 5);
347s  y5 = bisapdf (x, 1, 10);
347s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
347s  grid on
347s  ylim ([0, 1.5])
347s  legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
347s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
347s  title ("Birnbaum-Saunders PDF")
347s  xlabel ("values in x")
347s  ylabel ("density")
347s ***** demo
347s  ## Plot various PDFs from the Birnbaum-Saunders distribution
347s  x = 0.01:0.01:6;
347s  y1 = bisapdf (x, 1, 0.3);
347s  y2 = bisapdf (x, 2, 0.3);
347s  y3 = bisapdf (x, 1, 0.5);
347s  y4 = bisapdf (x, 3, 0.5);
347s  y5 = bisapdf (x, 5, 0.5);
347s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
347s  grid on
347s  ylim ([0, 1.5])
347s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
347s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
347s  title ("Birnbaum-Saunders CDF")
347s  xlabel ("values in x")
347s  ylabel ("density")
347s ***** shared x, y
347s  x = [-1, 0, 1, 2, Inf];
347s  y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
347s ***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
347s ***** assert (bisapdf (x, 1, 1), y, eps)
347s ***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
347s ***** assert (bisapdf (x, ones (1,5), 1), y, eps)
347s ***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
347s ***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
347s ***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
347s ***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
347s ***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
347s ***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
347s ***** error<bisapdf: function called with too few input arguments.> bisapdf ()
347s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
347s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
347s ***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
347s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
347s  bisapdf (ones (3), ones (2), ones(2))
347s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
347s  bisapdf (ones (2), ones (3), ones(2))
347s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
347s  bisapdf (ones (2), ones (2), ones(3))
347s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
347s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
347s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
347s 20 tests, 20 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/burrcdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/burrcdf.m
347s ***** demo
347s  ## Plot various CDFs from the Burr type XII distribution
347s  x = 0.001:0.001:5;
347s  p1 = burrcdf (x, 1, 1, 1);
347s  p2 = burrcdf (x, 1, 1, 2);
347s  p3 = burrcdf (x, 1, 1, 3);
347s  p4 = burrcdf (x, 1, 2, 1);
347s  p5 = burrcdf (x, 1, 3, 1);
347s  p6 = burrcdf (x, 1, 0.5, 2);
347s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
347s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
347s  grid on
347s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
347s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
347s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
347s          "location", "southeast")
347s  title ("Burr type XII CDF")
347s  xlabel ("values in x")
347s  ylabel ("probability")
347s ***** shared x, y
347s  x = [-1, 0, 1, 2, Inf];
347s  y = [0, 0, 1/2, 2/3, 1];
347s ***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
347s ***** assert (burrcdf (x, 1, 1, 1), y, eps)
347s ***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
347s ***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
347s ***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
347s ***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
347s ***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
347s ***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
347s ***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
347s ***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
347s ***** error<burrcdf: function called with too few input arguments.> burrcdf ()
347s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
347s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
347s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
347s ***** error<burrcdf: function called with too many inputs> ...
347s  burrcdf (1, 2, 3, 4, 5, 6)
347s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
347s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
347s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
347s  burrcdf (ones (3), ones (2), ones(2), ones(2))
347s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
347s  burrcdf (ones (2), ones (3), ones(2), ones(2))
347s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
347s  burrcdf (ones (2), ones (2), ones(3), ones(2))
347s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
347s  burrcdf (ones (2), ones (2), ones(2), ones(3))
347s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
347s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
347s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
347s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
347s 25 tests, 25 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/nakacdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nakacdf.m
347s ***** demo
347s  ## Plot various CDFs from the Nakagami distribution
347s  x = 0:0.01:3;
347s  p1 = nakacdf (x, 0.5, 1);
347s  p2 = nakacdf (x, 1, 1);
347s  p3 = nakacdf (x, 1, 2);
347s  p4 = nakacdf (x, 1, 3);
347s  p5 = nakacdf (x, 2, 1);
347s  p6 = nakacdf (x, 2, 2);
347s  p7 = nakacdf (x, 5, 1);
347s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
347s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
347s  grid on
347s  xlim ([0, 3])
347s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
347s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
347s           "μ = 5, ω = 1"}, "location", "southeast")
347s  title ("Nakagami CDF")
347s  xlabel ("values in x")
347s  ylabel ("probability")
347s ***** shared x, y
347s  x = [-1, 0, 1, 2, Inf];
347s  y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
347s ***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
347s ***** assert (nakacdf (x, 1, 1), y, eps)
347s ***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
347s ***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
347s ***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
347s ***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
347s ***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
347s ***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
347s ***** error<nakacdf: function called with too few input arguments.> nakacdf ()
347s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
347s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
347s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
347s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
347s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
347s  nakacdf (ones (3), ones (2), ones (2))
347s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
347s  nakacdf (ones (2), ones (3), ones (2))
347s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
347s  nakacdf (ones (2), ones (2), ones (3))
347s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
347s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
347s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
347s 19 tests, 19 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/normcdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/normcdf.m
347s ***** demo
347s  ## Plot various CDFs from the normal distribution
347s  x = -5:0.01:5;
347s  p1 = normcdf (x, 0, 0.5);
347s  p2 = normcdf (x, 0, 1);
347s  p3 = normcdf (x, 0, 2);
347s  p4 = normcdf (x, -2, 0.8);
347s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
347s  grid on
347s  xlim ([-5, 5])
347s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
347s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
347s  title ("Normal CDF")
347s  xlabel ("values in x")
347s  ylabel ("probability")
347s ***** shared x, y
347s  x = [-Inf 1 2 Inf];
347s  y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
347s ***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
347s ***** assert (normcdf (x, 1, ones (1,4)), y)
347s ***** assert (normcdf (x, ones (1,4), 1), y)
347s ***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
347s ***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
347s ***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
347s ***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
347s ***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
347s ***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
347s ***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
347s ***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
347s ***** error<normcdf: invalid number of input arguments.> normcdf ()
347s ***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
347s ***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
347s ***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
347s  normcdf (ones (3), ones (2), ones (2))
347s ***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
347s ***** error<normcdf: covariance matrix is required for confidence bounds.> ...
347s  [p, plo, pup] = normcdf (1, 2, 3)
347s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
347s  normcdf (1, 2, 3, [1, 0; 0, 1], 0)
347s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
347s  normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
347s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
347s  normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
347s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
347s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
347s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
347s ***** error<normcdf: bad covariance matrix.> ...
347s  [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
347s 24 tests, 24 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/wblinv.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wblinv.m
347s ***** demo
347s  ## Plot various iCDFs from the Weibull distribution
347s  p = 0.001:0.001:0.999;
347s  x1 = wblinv (p, 1, 0.5);
347s  x2 = wblinv (p, 1, 1);
347s  x3 = wblinv (p, 1, 1.5);
347s  x4 = wblinv (p, 1, 5);
347s  plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
347s  ylim ([0, 2.5])
347s  grid on
347s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
347s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
347s  title ("Weibull iCDF")
347s  xlabel ("probability")
347s  ylabel ("x")
347s ***** shared p
347s  p = [-1 0 0.63212055882855778 1 2];
347s ***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
347s ***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
347s ***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
347s ***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
347s ***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
347s ***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
347s ***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
347s ***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
347s ***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
347s ***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
347s ***** error<wblinv: invalid number of input arguments.> wblinv ()
347s ***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
347s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
347s  wblinv (ones (3), ones (2), ones (2))
347s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
347s  wblinv (ones (2), ones (3), ones (2))
347s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
347s  wblinv (ones (2), ones (2), ones (3))
347s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
347s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
347s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
347s 18 tests, 18 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/bisarnd.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bisarnd.m
347s ***** assert (size (bisarnd (1, 1)), [1 1])
347s ***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
347s ***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
347s ***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
347s ***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
347s ***** assert (size (bisarnd (1, 1, 3)), [3, 3])
347s ***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
347s ***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
347s ***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
347s ***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
347s ***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
347s ***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
347s ***** assert (class (bisarnd (1, 1)), "double")
347s ***** assert (class (bisarnd (1, single (1))), "single")
347s ***** assert (class (bisarnd (1, single ([1, 1]))), "single")
347s ***** assert (class (bisarnd (single (1), 1)), "single")
347s ***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
347s ***** error<bisarnd: function called with too few input arguments.> bisarnd ()
347s ***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
347s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
347s  bisarnd (ones (3), ones (2))
347s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
347s  bisarnd (ones (2), ones (3))
347s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
347s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
347s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  bisarnd (1, 2, -1)
347s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  bisarnd (1, 2, 1.2)
347s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  bisarnd (1, 2, ones (2))
347s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  bisarnd (1, 2, [2 -1 2])
347s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  bisarnd (1, 2, [2 0 2.5])
347s ***** error<bisarnd: dimensions must be non-negative integers.> ...
347s  bisarnd (1, 2, 2, -1, 5)
347s ***** error<bisarnd: dimensions must be non-negative integers.> ...
347s  bisarnd (1, 2, 2, 1.5, 5)
347s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
347s  bisarnd (2, ones (2), 3)
347s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
347s  bisarnd (2, ones (2), [3, 2])
347s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
347s  bisarnd (2, ones (2), 3, 2)
347s 33 tests, 33 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/betapdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/betapdf.m
347s ***** demo
347s  ## Plot various PDFs from the Beta distribution
347s  x = 0.001:0.001:0.999;
347s  y1 = betapdf (x, 0.5, 0.5);
347s  y2 = betapdf (x, 5, 1);
347s  y3 = betapdf (x, 1, 3);
347s  y4 = betapdf (x, 2, 2);
347s  y5 = betapdf (x, 2, 5);
347s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
347s  grid on
347s  ylim ([0, 2.5])
347s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
347s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
347s  title ("Beta PDF")
347s  xlabel ("values in x")
347s  ylabel ("density")
347s ***** shared x, y
347s  x = [-1 0 0.5 1 2];
347s  y = [0 2 1 0 0];
347s ***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
347s ***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
347s ***** assert (betapdf (x, ones (1, 5), 2), y)
347s ***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
347s ***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
347s ***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
347s ***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
347s ***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
347s ***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
347s ***** test
347s  x = rand (10,1);
347s  y = 1 ./ (pi * sqrt (x .* (1 - x)));
347s  assert (betapdf (x, 1/2, 1/2), y, 1e-12);
347s ***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
347s ***** error<betapdf: function called with too few input arguments.> betapdf ()
347s ***** error<betapdf: function called with too few input arguments.> betapdf (1)
347s ***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
347s ***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
347s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
347s  betapdf (ones (3), ones (2), ones (2))
347s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
347s  betapdf (ones (2), ones (3), ones (2))
347s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
347s  betapdf (ones (2), ones (2), ones (3))
347s ***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
347s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
347s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
347s 21 tests, 21 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/vmpdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/vmpdf.m
347s ***** demo
347s  ## Plot various PDFs from the von Mises distribution
347s  x1 = [-pi:0.1:pi];
347s  y1 = vmpdf (x1, 0, 0.5);
347s  y2 = vmpdf (x1, 0, 1);
347s  y3 = vmpdf (x1, 0, 2);
347s  y4 = vmpdf (x1, 0, 4);
347s  plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
347s  grid on
347s  xlim ([-pi, pi])
347s  ylim ([0, 0.8])
347s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
347s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
347s  title ("Von Mises PDF")
347s  xlabel ("values in x")
347s  ylabel ("density")
347s ***** shared x, y0, y1
347s  x = [-pi:pi/2:pi];
347s  y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
347s  y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
347s ***** assert (vmpdf (x, 0, 1), y0, 1e-5)
347s ***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
347s ***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
347s ***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
347s ***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
347s ***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
347s ***** error<vmpdf: function called with too few input arguments.> vmpdf ()
347s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
347s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
347s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
347s  vmpdf (ones (3), ones (2), ones (2))
347s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
347s  vmpdf (ones (2), ones (3), ones (2))
347s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
347s  vmpdf (ones (2), ones (2), ones (3))
347s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
347s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
347s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
347s 15 tests, 15 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/cauchypdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/cauchypdf.m
347s ***** demo
347s  ## Plot various PDFs from the Cauchy distribution
347s  x = -5:0.01:5;
347s  y1 = cauchypdf (x, 0, 0.5);
347s  y2 = cauchypdf (x, 0, 1);
347s  y3 = cauchypdf (x, 0, 2);
347s  y4 = cauchypdf (x, -2, 1);
347s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
347s  grid on
347s  xlim ([-5, 5])
347s  ylim ([0, 0.7])
347s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
347s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
347s  title ("Cauchy PDF")
347s  xlabel ("values in x")
347s  ylabel ("density")
347s ***** shared x, y
347s  x = [-1 0 0.5 1 2];
347s  y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
347s ***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
347s ***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
347s ***** assert (cauchypdf (x, ones (1,5), 2), y)
347s ***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
347s ***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
347s ***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
347s ***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
347s ***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
347s ***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
347s ***** test
347s  x = rand (10, 1);
347s  assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
347s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
347s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
347s ***** error<cauchypdf: function called with too few input arguments.> ...
347s  cauchypdf (1, 2)
347s ***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
347s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
347s  cauchypdf (ones (3), ones (2), ones(2))
347s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
347s  cauchypdf (ones (2), ones (3), ones(2))
347s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
347s  cauchypdf (ones (2), ones (2), ones(3))
347s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
347s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
347s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
347s 20 tests, 20 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/nakarnd.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nakarnd.m
347s ***** assert (size (nakarnd (1, 1)), [1 1])
347s ***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
347s ***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
347s ***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
347s ***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
347s ***** assert (size (nakarnd (1, 1, 3)), [3, 3])
347s ***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
347s ***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
347s ***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
347s ***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
347s ***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
347s ***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
347s ***** assert (class (nakarnd (1, 1)), "double")
347s ***** assert (class (nakarnd (1, single (1))), "single")
347s ***** assert (class (nakarnd (1, single ([1, 1]))), "single")
347s ***** assert (class (nakarnd (single (1), 1)), "single")
347s ***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
347s ***** error<nakarnd: function called with too few input arguments.> nakarnd ()
347s ***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
347s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
347s  nakarnd (ones (3), ones (2))
347s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
347s  nakarnd (ones (2), ones (3))
347s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
347s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
347s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  nakarnd (1, 2, -1)
347s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  nakarnd (1, 2, 1.2)
347s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  nakarnd (1, 2, ones (2))
347s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  nakarnd (1, 2, [2 -1 2])
347s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  nakarnd (1, 2, [2 0 2.5])
347s ***** error<nakarnd: dimensions must be non-negative integers.> ...
347s  nakarnd (1, 2, 2, -1, 5)
347s ***** error<nakarnd: dimensions must be non-negative integers.> ...
347s  nakarnd (1, 2, 2, 1.5, 5)
347s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
347s  nakarnd (2, ones (2), 3)
347s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
347s  nakarnd (2, ones (2), [3, 2])
347s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
347s  nakarnd (2, ones (2), 3, 2)
347s 33 tests, 33 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/hygernd.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hygernd.m
347s ***** assert (size (hygernd (4,2,2)), [1, 1])
347s ***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
347s ***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
347s ***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
347s ***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
347s ***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
347s ***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
347s ***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
347s ***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
347s ***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
347s ***** assert (class (hygernd (4,2,2)), "double")
347s ***** assert (class (hygernd (single (4),2,2)), "single")
347s ***** assert (class (hygernd (single ([4 4]),2,2)), "single")
347s ***** assert (class (hygernd (4,single (2),2)), "single")
347s ***** assert (class (hygernd (4,single ([2 2]),2)), "single")
347s ***** assert (class (hygernd (4,2,single (2))), "single")
347s ***** assert (class (hygernd (4,2,single ([2 2]))), "single")
347s ***** error<hygernd: function called with too few input arguments.> hygernd ()
347s ***** error<hygernd: function called with too few input arguments.> hygernd (1)
347s ***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
347s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
347s  hygernd (ones (3), ones (2), ones (2))
347s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
347s  hygernd (ones (2), ones (3), ones (2))
347s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
347s  hygernd (ones (2), ones (2), ones (3))
347s ***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
347s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
347s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
347s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  hygernd (1, 2, 3, -1)
347s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  hygernd (1, 2, 3, 1.2)
347s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  hygernd (1, 2, 3, ones (2))
347s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  hygernd (1, 2, 3, [2 -1 2])
347s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
347s  hygernd (1, 2, 3, [2 0 2.5])
347s ***** error<hygernd: dimensions must be non-negative integers.> ...
347s  hygernd (1, 2, 3, 2, -1, 5)
347s ***** error<hygernd: dimensions must be non-negative integers.> ...
347s  hygernd (1, 2, 3, 2, 1.5, 5)
347s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
347s  hygernd (2, ones (2), 2, 3)
347s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
347s  hygernd (2, ones (2), 2, [3, 2])
347s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
347s  hygernd (2, ones (2), 2, 3, 2)
347s 36 tests, 36 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/copulapdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/copulapdf.m
347s ***** test
347s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
347s  theta = [1; 2];
347s  y = copulapdf ("Clayton", x, theta);
347s  expected_p = [0.9872; 0.7295];
347s  assert (y, expected_p, 0.001);
347s ***** test
347s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
347s  y = copulapdf ("Gumbel", x, 2);
347s  expected_p = [0.9468; 0.9468];
347s  assert (y, expected_p, 0.001);
347s ***** test
347s  x = [0.2, 0.6; 0.2, 0.6];
347s  theta = [1; 2];
347s  y = copulapdf ("Frank", x, theta);
347s  expected_p = [0.9378; 0.8678];
347s  assert (y, expected_p, 0.001);
347s ***** test
347s  x = [0.2, 0.6; 0.2, 0.6];
347s  theta = [0.3; 0.7];
347s  y = copulapdf ("AMH", x, theta);
347s  expected_p = [0.9540; 0.8577];
347s  assert (y, expected_p, 0.001);
347s 4 tests, 4 passed, 0 known failure, 0 skipped
347s [inst/dist_fun/gpcdf.m]
347s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gpcdf.m
347s ***** demo
347s  ## Plot various CDFs from the generalized Pareto distribution
347s  x = 0:0.001:5;
347s  p1 = gpcdf (x, 1, 1, 0);
347s  p2 = gpcdf (x, 5, 1, 0);
347s  p3 = gpcdf (x, 20, 1, 0);
347s  p4 = gpcdf (x, 1, 2, 0);
347s  p5 = gpcdf (x, 5, 2, 0);
347s  p6 = gpcdf (x, 20, 2, 0);
347s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
347s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
347s  grid on
347s  xlim ([0, 5])
347s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
347s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
347s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
347s          "location", "northwest")
347s  title ("Generalized Pareto CDF")
347s  xlabel ("values in x")
347s  ylabel ("probability")
347s ***** shared x, y1, y1u, y2, y2u, y3, y3u
347s  x = [-Inf, -1, 0, 1/2, 1, Inf];
347s  y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
347s  y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
347s  y2 = [0, 0, 0, 1/3, 1/2, 1];
347s  y2u = [1, 1, 1, 2/3, 1/2, 0];
347s  y3 = [0, 0, 0, 1/2, 1, 1];
347s  y3u = [1, 1, 1, 1/2, 0, 0];
347s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
347s ***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
347s ***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
347s ***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
347s ***** assert (gpcdf (x, 0, 1, 0), y1, eps)
347s ***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
347s ***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
347s ***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
347s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
347s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
347s ***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
347s ***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
347s ***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
347s ***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
347s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
347s ***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
347s ***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
347s ***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
347s ***** assert (gpcdf (x, 1, 1, 0), y2, eps)
347s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
347s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
347s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
347s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
347s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
347s ***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
348s ***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
348s ***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
348s ***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
348s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
348s                         [y2u(1:3), NaN, y2u(5:6)], eps)
348s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
348s                         [y2u(1:3), NaN, y2u(5:6)], eps)
348s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
348s                         [y2u(1:3), NaN, y2u(5:6)], eps)
348s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
348s                         [y2u(1:3), NaN, y2u(5:6)], eps)
348s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
348s ***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
348s ***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
348s ***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
348s ***** assert (gpcdf (x, -1, 1, 0), y3, eps)
348s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
348s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
348s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
348s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
348s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
348s ***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
348s ***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
348s ***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
348s ***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
348s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
348s                          [y3u(1:3), NaN, y3u(5:6)], eps)
348s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
348s                           [y3u(1:3), NaN, y3u(5:6)], eps)
348s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
348s                           [y3u(1:3), NaN, y3u(5:6)], eps)
348s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
348s                           [y3u(1:3), NaN, y3u(5:6)], eps)
348s ***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
348s ***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
348s ***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
348s ***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
348s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
348s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
348s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
348s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
348s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
348s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
348s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpcdf (ones (3), ones (2), ones(2), ones(2))
348s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpcdf (ones (2), ones (3), ones(2), ones(2))
348s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpcdf (ones (2), ones (2), ones(3), ones(2))
348s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpcdf (ones (2), ones (2), ones(2), ones(3))
348s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
348s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
348s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
348s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
348s 76 tests, 76 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/hygeinv.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hygeinv.m
348s ***** demo
348s  ## Plot various iCDFs from the hypergeometric distribution
348s  p = 0.001:0.001:0.999;
348s  x1 = hygeinv (p, 500, 50, 100);
348s  x2 = hygeinv (p, 500, 60, 200);
348s  x3 = hygeinv (p, 500, 70, 300);
348s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
348s  grid on
348s  ylim ([0, 60])
348s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
348s           "m = 500, k = 70, n = 300"}, "location", "northwest")
348s  title ("Hypergeometric iCDF")
348s  xlabel ("probability")
348s  ylabel ("values in p (number of successes)")
348s ***** shared p
348s  p = [-1 0 0.5 1 2];
348s ***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
348s ***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
348s ***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
348s ***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
348s ***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
348s ***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
348s ***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
348s ***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
348s ***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
348s ***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
348s ***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
348s ***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
348s ***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
348s ***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
348s ***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
348s ***** error<hygeinv: function called with too few input arguments.> hygeinv ()
348s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
348s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
348s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
348s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
348s  hygeinv (ones (2), ones (3), 1, 1)
348s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
348s  hygeinv (1, ones (2), ones (3), 1)
348s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
348s  hygeinv (1, 1, ones (2), ones (3))
348s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
348s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
348s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
348s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
348s 26 tests, 26 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/normrnd.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/normrnd.m
348s ***** assert (size (normrnd (1, 1)), [1 1])
348s ***** assert (size (normrnd (1, ones (2,1))), [2, 1])
348s ***** assert (size (normrnd (1, ones (2,2))), [2, 2])
348s ***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
348s ***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
348s ***** assert (size (normrnd (1, 1, 3)), [3, 3])
348s ***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
348s ***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
348s ***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
348s ***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
348s ***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
348s ***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
348s ***** assert (class (normrnd (1, 1)), "double")
348s ***** assert (class (normrnd (1, single (1))), "single")
348s ***** assert (class (normrnd (1, single ([1, 1]))), "single")
348s ***** assert (class (normrnd (single (1), 1)), "single")
348s ***** assert (class (normrnd (single ([1, 1]), 1)), "single")
348s ***** error<normrnd: function called with too few input arguments.> normrnd ()
348s ***** error<normrnd: function called with too few input arguments.> normrnd (1)
348s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
348s  normrnd (ones (3), ones (2))
348s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
348s  normrnd (ones (2), ones (3))
348s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
348s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
348s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  normrnd (1, 2, -1)
348s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  normrnd (1, 2, 1.2)
348s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  normrnd (1, 2, ones (2))
348s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  normrnd (1, 2, [2 -1 2])
348s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  normrnd (1, 2, [2 0 2.5])
348s ***** error<normrnd: dimensions must be non-negative integers.> ...
348s  normrnd (1, 2, 2, -1, 5)
348s ***** error<normrnd: dimensions must be non-negative integers.> ...
348s  normrnd (1, 2, 2, 1.5, 5)
348s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  normrnd (2, ones (2), 3)
348s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  normrnd (2, ones (2), [3, 2])
348s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  normrnd (2, ones (2), 3, 2)
348s 33 tests, 33 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/gumbelcdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gumbelcdf.m
348s ***** demo
348s  ## Plot various CDFs from the Gumbel distribution
348s  x = -5:0.01:20;
348s  p1 = gumbelcdf (x, 0.5, 2);
348s  p2 = gumbelcdf (x, 1.0, 2);
348s  p3 = gumbelcdf (x, 1.5, 3);
348s  p4 = gumbelcdf (x, 3.0, 4);
348s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
348s  grid on
348s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
348s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
348s  title ("Gumbel CDF")
348s  xlabel ("values in x")
348s  ylabel ("probability")
348s ***** shared x, y
348s  x = [-Inf, 1, 2, Inf];
348s  y = [0, 0.3679, 0.6922, 1];
348s ***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
348s ***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
348s ***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
348s ***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
348s ***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
348s ***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
348s ***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
348s ***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
348s ***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
348s ***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
348s ***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
348s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
348s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
348s ***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
348s ***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
348s  gumbelcdf (ones (3), ones (2), ones (2))
348s ***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
348s ***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
348s  [p, plo, pup] = gumbelcdf (1, 2, 3)
348s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
348s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
348s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
348s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
348s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
348s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
348s ***** error<gumbelcdf: bad covariance matrix.> ...
348s  [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
348s 24 tests, 24 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/poissrnd.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/poissrnd.m
348s ***** assert (size (poissrnd (2)), [1, 1])
348s ***** assert (size (poissrnd (ones (2,1))), [2, 1])
348s ***** assert (size (poissrnd (ones (2,2))), [2, 2])
348s ***** assert (size (poissrnd (1, 3)), [3, 3])
348s ***** assert (size (poissrnd (1, [4 1])), [4, 1])
348s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
348s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
348s ***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
348s ***** assert (size (poissrnd (1, 0, 1)), [0, 1])
348s ***** assert (size (poissrnd (1, 1, 0)), [1, 0])
348s ***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
348s ***** assert (poissrnd (0, 1, 1), 0)
348s ***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
348s ***** assert (class (poissrnd (2)), "double")
348s ***** assert (class (poissrnd (single (2))), "single")
348s ***** assert (class (poissrnd (single ([2 2]))), "single")
348s ***** error<poissrnd: function called with too few input arguments.> poissrnd ()
348s ***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (1, -1)
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (1, 1.2)
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (1, ones (2))
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (1, [2 -1 2])
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (1, [2 0 2.5])
348s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
348s  poissrnd (ones (2), ones (2))
348s ***** error<poissrnd: dimensions must be non-negative integers.> ...
348s  poissrnd (1, 2, -1, 5)
348s ***** error<poissrnd: dimensions must be non-negative integers.> ...
348s  poissrnd (1, 2, 1.5, 5)
348s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
348s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
348s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
348s 29 tests, 29 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/evcdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/evcdf.m
348s ***** demo
348s  ## Plot various CDFs from the extreme value distribution
348s  x = -10:0.01:10;
348s  p1 = evcdf (x, 0.5, 2);
348s  p2 = evcdf (x, 1.0, 2);
348s  p3 = evcdf (x, 1.5, 3);
348s  p4 = evcdf (x, 3.0, 4);
348s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
348s  grid on
348s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
348s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
348s  title ("Extreme value CDF")
348s  xlabel ("values in x")
348s  ylabel ("probability")
348s ***** shared x, y
348s  x = [-Inf, 1, 2, Inf];
348s  y = [0, 0.6321, 0.9340, 1];
348s ***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
348s ***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
348s ***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
348s ***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
348s ***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
348s ***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
348s ***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
348s ***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
348s ***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
348s ***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
348s ***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
348s ***** error<evcdf: invalid number of input arguments.> evcdf ()
348s ***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
348s ***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
348s ***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
348s  evcdf (ones (3), ones (2), ones (2))
348s ***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
348s ***** error<evcdf: covariance matrix is required for confidence bounds.> ...
348s  [p, plo, pup] = evcdf (1, 2, 3)
348s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  evcdf (1, 2, 3, [1, 0; 0, 1], 0)
348s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
348s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
348s  evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
348s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
348s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
348s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
348s ***** error<evcdf: bad covariance matrix.> ...
348s  [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
348s 24 tests, 24 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/bisacdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bisacdf.m
348s ***** demo
348s  ## Plot various CDFs from the Birnbaum-Saunders distribution
348s  x = 0.01:0.01:4;
348s  p1 = bisacdf (x, 1, 0.5);
348s  p2 = bisacdf (x, 1, 1);
348s  p3 = bisacdf (x, 1, 2);
348s  p4 = bisacdf (x, 1, 5);
348s  p5 = bisacdf (x, 1, 10);
348s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
348s  grid on
348s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
348s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
348s  title ("Birnbaum-Saunders CDF")
348s  xlabel ("values in x")
348s  ylabel ("probability")
348s ***** demo
348s  ## Plot various CDFs from the Birnbaum-Saunders distribution
348s  x = 0.01:0.01:6;
348s  p1 = bisacdf (x, 1, 0.3);
348s  p2 = bisacdf (x, 2, 0.3);
348s  p3 = bisacdf (x, 1, 0.5);
348s  p4 = bisacdf (x, 3, 0.5);
348s  p5 = bisacdf (x, 5, 0.5);
348s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
348s  grid on
348s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
348s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
348s  title ("Birnbaum-Saunders CDF")
348s  xlabel ("values in x")
348s  ylabel ("probability")
348s ***** shared x, y
348s  x = [-1, 0, 1, 2, Inf];
348s  y = [0, 0, 1/2, 0.76024993890652337, 1];
348s ***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
348s ***** assert (bisacdf (x, 1, 1), y, eps)
348s ***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
348s ***** assert (bisacdf (x, ones (1,5), 1), y, eps)
348s ***** assert (bisacdf (x, 1, 1), y, eps)
348s ***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
348s ***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
348s ***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
348s ***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
348s ***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
348s ***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
348s ***** error<bisacdf: function called with too few input arguments.> bisacdf ()
348s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
348s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
348s ***** error<bisacdf: function called with too many inputs> ...
348s  bisacdf (1, 2, 3, 4, 5)
348s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
348s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
348s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
348s  bisacdf (ones (3), ones (2), ones(2))
348s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
348s  bisacdf (ones (2), ones (3), ones(2))
348s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
348s  bisacdf (ones (2), ones (2), ones(3))
348s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
348s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
348s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
348s 23 tests, 23 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/unidinv.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unidinv.m
348s ***** demo
348s  ## Plot various iCDFs from the discrete uniform distribution
348s  p = 0.001:0.001:0.999;
348s  x1 = unidinv (p, 5);
348s  x2 = unidinv (p, 9);
348s  plot (p, x1, "-b", p, x2, "-g")
348s  grid on
348s  xlim ([0, 1])
348s  ylim ([0, 10])
348s  legend ({"N = 5", "N = 9"}, "location", "northwest")
348s  title ("Discrete uniform iCDF")
348s  xlabel ("probability")
348s  ylabel ("values in x")
348s ***** shared p
348s  p = [-1 0 0.5 1 2];
348s ***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
348s ***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
348s ***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
348s ***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
348s ***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
348s ***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
348s ***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
348s ***** error<unidinv: function called with too few input arguments.> unidinv ()
348s ***** error<unidinv: function called with too few input arguments.> unidinv (1)
348s ***** error<unidinv: P and N must be of common size or scalars.> ...
348s  unidinv (ones (3), ones (2))
348s ***** error<unidinv: P and N must be of common size or scalars.> ...
348s  unidinv (ones (2), ones (3))
348s ***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
348s ***** error<unidinv: P and N must not be complex.> unidinv (2, i)
348s 13 tests, 13 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/hninv.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hninv.m
348s ***** demo
348s  ## Plot various iCDFs from the half-normal distribution
348s  p = 0.001:0.001:0.999;
348s  x1 = hninv (p, 0, 1);
348s  x2 = hninv (p, 0, 2);
348s  x3 = hninv (p, 0, 3);
348s  x4 = hninv (p, 0, 5);
348s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
348s  grid on
348s  ylim ([0, 10])
348s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
348s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
348s  title ("Half-normal iCDF")
348s  xlabel ("probability")
348s  ylabel ("x")
348s ***** shared p, x
348s  p = [0, 0.3829, 0.6827, 1];
348s  x = [0, 1/2, 1, Inf];
348s ***** assert (hninv (p, 0, 1), x, 1e-4);
348s ***** assert (hninv (p, 5, 1), x + 5, 1e-4);
348s ***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
348s ***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
348s ***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
348s ***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
348s ***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
348s ***** error<hninv: function called with too few input arguments.> hninv (1)
348s ***** error<hninv: function called with too few input arguments.> hninv (1, 2)
348s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
348s  hninv (1, ones (2), ones (3))
348s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
348s  hninv (ones (2), 1, ones (3))
348s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
348s  hninv (ones (2), ones (3), 1)
348s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
348s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
348s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
348s 15 tests, 15 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/mvnpdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvnpdf.m
348s ***** demo
348s  mu = [1, -1];
348s  sigma = [0.9, 0.4; 0.4, 0.3];
348s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
348s  x = [X1(:), X2(:)];
348s  p = mvnpdf (x, mu, sigma);
348s  surf (X1, X2, reshape (p, 25, 25));
348s ***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
348s ***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
348s ***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
348s ***** error<mvnpdf: columns in X and MU mismatch.> ...
348s  y = mvnpdf (ones (10, 2), [4, 2, 3]);
348s ***** error<mvnpdf: rows in X and MU mismatch.> ...
348s  y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
348s ***** error<mvnpdf: wrong size of MU.> ...
348s  y = mvnpdf (ones (10, 2), ones (3, 3, 3));
348s ***** shared x, mu, sigma
348s  x = [1, 2, 5, 4, 6];
348s  mu = [2, 0, -1, 1, 4];
348s  sigma = [2, 2, 2, 2, 2];
348s ***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
348s ***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
348s ***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
348s 9 tests, 9 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/loglrnd.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/loglrnd.m
348s ***** assert (size (loglrnd (1, 1)), [1 1])
348s ***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
348s ***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
348s ***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
348s ***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
348s ***** assert (size (loglrnd (1, 1, 3)), [3, 3])
348s ***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
348s ***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
348s ***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
348s ***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
348s ***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
348s ***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
348s ***** assert (class (loglrnd (1, 1)), "double")
348s ***** assert (class (loglrnd (1, single (1))), "single")
348s ***** assert (class (loglrnd (1, single ([1, 1]))), "single")
348s ***** assert (class (loglrnd (single (1), 1)), "single")
348s ***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
348s ***** error<loglrnd: function called with too few input arguments.> loglrnd ()
348s ***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
348s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
348s  loglrnd (ones (3), ones (2))
348s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
348s  loglrnd (ones (2), ones (3))
348s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
348s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
348s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
348s  loglrnd (1, 2, -1)
348s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
348s  loglrnd (1, 2, 1.2)
348s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
348s  loglrnd (1, 2, ones (2))
348s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
348s  loglrnd (1, 2, [2 -1 2])
348s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
348s  loglrnd (1, 2, [2 0 2.5])
348s ***** error<loglrnd: dimensions must be non-negative integers.> ...
348s  loglrnd (1, 2, 2, -1, 5)
348s ***** error<loglrnd: dimensions must be non-negative integers.> ...
348s  loglrnd (1, 2, 2, 1.5, 5)
348s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  loglrnd (2, ones (2), 3)
348s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  loglrnd (2, ones (2), [3, 2])
348s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
348s  loglrnd (2, ones (2), 3, 2)
348s 33 tests, 33 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/gpinv.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gpinv.m
348s ***** demo
348s  ## Plot various iCDFs from the generalized Pareto distribution
348s  p = 0.001:0.001:0.999;
348s  x1 = gpinv (p, 1, 1, 0);
348s  x2 = gpinv (p, 5, 1, 0);
348s  x3 = gpinv (p, 20, 1, 0);
348s  x4 = gpinv (p, 1, 2, 0);
348s  x5 = gpinv (p, 5, 2, 0);
348s  x6 = gpinv (p, 20, 2, 0);
348s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
348s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
348s  grid on
348s  ylim ([0, 5])
348s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
348s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
348s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
348s          "location", "southeast")
348s  title ("Generalized Pareto iCDF")
348s  xlabel ("probability")
348s  ylabel ("values in x")
348s ***** shared p, y1, y2, y3
348s  p = [-1, 0, 1/2, 1, 2];
348s  y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
348s  y2 = [NaN, 0, 1, Inf, NaN];
348s  y3 = [NaN, 0, 1/2, 1, NaN];
348s ***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
348s ***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
348s ***** assert (gpinv (p, 0, ones (1,5), 0), y1)
348s ***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
348s ***** assert (gpinv (p, 0, 1, 0), y1)
348s ***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
348s ***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
348s ***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
348s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
348s ***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
348s ***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
348s ***** assert (gpinv (p, 1, ones (1,5), 0), y2)
348s ***** assert (gpinv (p, ones (1,5), 1, 0), y2)
348s ***** assert (gpinv (p, 1, 1, 0), y2)
348s ***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
348s ***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
348s ***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
348s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
348s ***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
348s ***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
348s ***** assert (gpinv (p, -1, ones (1,5), 0), y3)
348s ***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
348s ***** assert (gpinv (p, -1, 1, 0), y3)
348s ***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
348s ***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
348s ***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
348s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
348s ***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
348s ***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
348s ***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
348s ***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
348s ***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
348s ***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
348s ***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
348s ***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
348s ***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
348s ***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
348s ***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
348s ***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
348s ***** error<gpinv: function called with too few input arguments.> gpinv ()
348s ***** error<gpinv: function called with too few input arguments.> gpinv (1)
348s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
348s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
348s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpinv (ones (3), ones (2), ones(2), ones(2))
348s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpinv (ones (2), ones (3), ones(2), ones(2))
348s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpinv (ones (2), ones (2), ones(3), ones(2))
348s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
348s  gpinv (ones (2), ones (2), ones(2), ones(3))
348s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
348s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
348s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
348s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
348s 51 tests, 51 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/mvtpdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvtpdf.m
348s ***** demo
348s  ## Compute the pdf of a multivariate t distribution with correlation
348s  ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.
348s 
348s  rho = [1, 0.4; 0.4, 1];
348s  df = 2;
348s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
348s  X = [X1(:), X2(:)];
348s  y = mvtpdf (X, rho, df);
348s  surf (X1, X2, reshape (y, 25, 25));
348s  title ("Bivariate Student's t probability density function");
348s ***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
348s ***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
348s ***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
348s  [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
348s  [0.04713313 0.03722421 0.02069011]', 1E-7)
348s 3 tests, 3 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/geopdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/geopdf.m
348s ***** demo
348s  ## Plot various PDFs from the geometric distribution
348s  x = 0:10;
348s  y1 = geopdf (x, 0.2);
348s  y2 = geopdf (x, 0.5);
348s  y3 = geopdf (x, 0.7);
348s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
348s  grid on
348s  ylim ([0, 0.8])
348s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
348s  title ("Geometric PDF")
348s  xlabel ("values in x (number of failures)")
348s  ylabel ("density")
348s ***** shared x, y
348s  x = [-1 0 1 Inf];
348s  y = [0, 1/2, 1/4, NaN];
348s ***** assert (geopdf (x, 0.5*ones (1,4)), y)
348s ***** assert (geopdf (x, 0.5), y)
348s ***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
348s ***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
348s ***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
348s ***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
348s ***** error geopdf ()
348s ***** error geopdf (1)
348s ***** error geopdf (1,2,3)
348s ***** error geopdf (ones (3), ones (2))
348s ***** error geopdf (ones (2), ones (3))
348s ***** error geopdf (i, 2)
348s ***** error geopdf (2, i)
348s 13 tests, 13 passed, 0 known failure, 0 skipped
348s [inst/dist_fun/loglcdf.m]
348s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/loglcdf.m
348s ***** demo
348s  ## Plot various CDFs from the log-logistic distribution
348s  x = 0:0.001:2;
348s  p1 = loglcdf (x, log (1), 1/0.5);
348s  p2 = loglcdf (x, log (1), 1);
348s  p3 = loglcdf (x, log (1), 1/2);
348s  p4 = loglcdf (x, log (1), 1/4);
348s  p5 = loglcdf (x, log (1), 1/8);
348s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
348s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
348s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
348s  grid on
348s  title ("Log-logistic CDF")
348s  xlabel ("values in x")
348s  ylabel ("probability")
348s  text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
349s ***** shared out1, out2
349s  out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
349s  out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
349s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
349s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
349s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
349s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
349s ***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
349s ***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
349s ***** assert (class (loglcdf (single (1), 2, 3)), "single")
349s ***** assert (class (loglcdf (1, single (2), 3)), "single")
349s ***** assert (class (loglcdf (1, 2, single (3))), "single")
349s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
349s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
349s ***** error<loglcdf: invalid argument for upper tail.> ...
349s  loglcdf (1, 2, 3, 4)
349s ***** error<loglcdf: invalid argument for upper tail.> ...
349s  loglcdf (1, 2, 3, "uper")
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (1, ones (2), ones (3))
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (1, ones (2), ones (3), "upper")
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (ones (2), 1, ones (3))
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (ones (2), 1, ones (3), "upper")
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (ones (2), ones (3), 1)
349s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
349s  loglcdf (ones (2), ones (3), 1, "upper")
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
349s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
349s 25 tests, 25 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/logirnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logirnd.m
349s ***** assert (size (logirnd (1, 1)), [1 1])
349s ***** assert (size (logirnd (1, ones (2,1))), [2, 1])
349s ***** assert (size (logirnd (1, ones (2,2))), [2, 2])
349s ***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
349s ***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
349s ***** assert (size (logirnd (1, 1, 3)), [3, 3])
349s ***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (logirnd (1, 1)), "double")
349s ***** assert (class (logirnd (1, single (1))), "single")
349s ***** assert (class (logirnd (1, single ([1, 1]))), "single")
349s ***** assert (class (logirnd (single (1), 1)), "single")
349s ***** assert (class (logirnd (single ([1, 1]), 1)), "single")
349s ***** error<logirnd: function called with too few input arguments.> logirnd ()
349s ***** error<logirnd: function called with too few input arguments.> logirnd (1)
349s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
349s  logirnd (ones (3), ones (2))
349s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
349s  logirnd (ones (2), ones (3))
349s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
349s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
349s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  logirnd (1, 2, -1)
349s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  logirnd (1, 2, 1.2)
349s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  logirnd (1, 2, ones (2))
349s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  logirnd (1, 2, [2 -1 2])
349s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  logirnd (1, 2, [2 0 2.5])
349s ***** error<logirnd: dimensions must be non-negative integers.> ...
349s  logirnd (1, 2, 2, -1, 5)
349s ***** error<logirnd: dimensions must be non-negative integers.> ...
349s  logirnd (1, 2, 2, 1.5, 5)
349s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  logirnd (2, ones (2), 3)
349s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  logirnd (2, ones (2), [3, 2])
349s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  logirnd (2, ones (2), 3, 2)
349s 33 tests, 33 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/nctcdf.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nctcdf.m
349s ***** demo
349s  ## Plot various CDFs from the noncentral Τ distribution
349s  x = -5:0.01:5;
349s  p1 = nctcdf (x, 1, 0);
349s  p2 = nctcdf (x, 4, 0);
349s  p3 = nctcdf (x, 1, 2);
349s  p4 = nctcdf (x, 4, 2);
349s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
349s  grid on
349s  xlim ([-5, 5])
349s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
349s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
349s  title ("Noncentral Τ CDF")
349s  xlabel ("values in x")
349s  ylabel ("probability")
349s ***** demo
349s  ## Compare the noncentral T CDF with MU = 1 to the T CDF
349s  ## with the same number of degrees of freedom (10).
349s 
349s  x = -5:0.1:5;
349s  p1 = nctcdf (x, 10, 1);
349s  p2 = tcdf (x, 10);
349s  plot (x, p1, "-", x, p2, "-")
349s  grid on
349s  xlim ([-5, 5])
349s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
349s  title ("Noncentral T vs T CDFs")
349s  xlabel ("values in x")
349s  ylabel ("probability")
349s ***** test
349s  x = -2:0.1:2;
349s  p = nctcdf (x, 10, 1);
349s  assert (p(1), 0.003302485766631558, 1e-14);
349s  assert (p(2), 0.004084668193532631, 1e-14);
349s  assert (p(3), 0.005052800319478737, 1e-14);
349s  assert (p(41), 0.8076115625303751, 1e-14);
349s ***** test
349s  p = nctcdf (12, 10, 3);
349s  assert (p, 0.9997719343243797, 1e-14);
349s ***** test
349s  p = nctcdf (2, 3, 2);
349s  assert (p, 0.4430757822176028, 1e-14);
349s ***** test
349s  p = nctcdf (2, 3, 2, "upper");
349s  assert (p, 0.5569242177823971, 1e-14);
349s ***** test
349s  p = nctcdf ([3, 6], 3, 2, "upper");
349s  assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
349s ***** error<nctcdf: function called with too few input arguments.> nctcdf ()
349s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
349s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
349s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
349s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
349s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
349s  nctcdf (ones (3), ones (2), ones (2))
349s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
349s  nctcdf (ones (2), ones (3), ones (2))
349s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
349s  nctcdf (ones (2), ones (2), ones (3))
349s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
349s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
349s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
349s 16 tests, 16 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/binornd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/binornd.m
349s ***** assert (size (binornd (2, 1/2)), [1 1])
349s ***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
349s ***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
349s ***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
349s ***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
349s ***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
349s ***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
349s ***** assert (size (binornd (2, 1/2, 3)), [3, 3])
349s ***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (binornd (1, 1)), "double")
349s ***** assert (class (binornd (1, single (0))), "single")
349s ***** assert (class (binornd (1, single ([0, 0]))), "single")
349s ***** assert (class (binornd (1, single (1), 2)), "single")
349s ***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
349s ***** assert (class (binornd (single (1), 1, 2)), "single")
349s ***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
349s ***** error<binornd: function called with too few input arguments.> binornd ()
349s ***** error<binornd: function called with too few input arguments.> binornd (1)
349s ***** error<binornd: N and PS must be of common size or scalars.> ...
349s  binornd (ones (3), ones (2))
349s ***** error<binornd: N and PS must be of common size or scalars.> ...
349s  binornd (ones (2), ones (3))
349s ***** error<binornd: N and PS must not be complex.> binornd (i, 2)
349s ***** error<binornd: N and PS must not be complex.> binornd (1, i)
349s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  binornd (1, 1/2, -1)
349s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  binornd (1, 1/2, 1.2)
349s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  binornd (1, 1/2, ones (2))
349s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  binornd (1, 1/2, [2 -1 2])
349s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  binornd (1, 1/2, [2 0 2.5])
349s ***** error<binornd: dimensions must be non-negative integers.> ...
349s  binornd (1, 1/2, 2, -1, 5)
349s ***** error<binornd: dimensions must be non-negative integers.> ...
349s  binornd (1, 1/2, 2, 1.5, 5)
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2, 1/2 * ones (2), 3)
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2, 1/2 * ones (2), [3, 2])
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2, 1/2 * ones (2), 3, 2)
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2 * ones (2), 1/2, 3)
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2 * ones (2), 1/2, [3, 2])
349s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
349s  binornd (2 * ones (2), 1/2, 3, 2)
349s 40 tests, 40 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/mnrnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mnrnd.m
349s ***** test
349s  n = 10;
349s  pk = [0.2, 0.5, 0.3];
349s  r = mnrnd (n, pk);
349s  assert (size (r), size (pk));
349s  assert (all (r >= 0));
349s  assert (all (round (r) == r));
349s  assert (sum (r) == n);
349s ***** test
349s  n = 10 * ones (3, 1);
349s  pk = [0.2, 0.5, 0.3];
349s  r = mnrnd (n, pk);
349s  assert (size (r), [length(n), length(pk)]);
349s  assert (all (r >= 0));
349s  assert (all (round (r) == r));
349s  assert (all (sum (r, 2) == n));
349s ***** test
349s  n = (1:2)';
349s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
349s  r = mnrnd (n, pk);
349s  assert (size (r), size (pk));
349s  assert (all (r >= 0));
349s  assert (all (round (r) == r));
349s  assert (all (sum (r, 2) == n));
349s 3 tests, 3 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/nakainv.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nakainv.m
349s ***** demo
349s  ## Plot various iCDFs from the Nakagami distribution
349s  p = 0.001:0.001:0.999;
349s  x1 = nakainv (p, 0.5, 1);
349s  x2 = nakainv (p, 1, 1);
349s  x3 = nakainv (p, 1, 2);
349s  x4 = nakainv (p, 1, 3);
349s  x5 = nakainv (p, 2, 1);
349s  x6 = nakainv (p, 2, 2);
349s  x7 = nakainv (p, 5, 1);
349s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
349s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
349s  grid on
349s  ylim ([0, 3])
349s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
349s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
349s           "μ = 5, ω = 1"}, "location", "northwest")
349s  title ("Nakagami iCDF")
349s  xlabel ("probability")
349s  ylabel ("values in x")
349s ***** shared p, y
349s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
349s  y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
349s ***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
349s ***** assert (nakainv (p, 1, 1), y, eps)
349s ***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
349s ***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
349s ***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
349s ***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
349s ***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
349s ***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
349s ***** error<nakainv: function called with too few input arguments.> nakainv ()
349s ***** error<nakainv: function called with too few input arguments.> nakainv (1)
349s ***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
349s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
349s  nakainv (ones (3), ones (2), ones(2))
349s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
349s  nakainv (ones (2), ones (3), ones(2))
349s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
349s  nakainv (ones (2), ones (2), ones(3))
349s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
349s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
349s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
349s 17 tests, 17 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/evrnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/evrnd.m
349s ***** assert (size (evrnd (1, 1)), [1 1])
349s ***** assert (size (evrnd (1, ones (2,1))), [2, 1])
349s ***** assert (size (evrnd (1, ones (2,2))), [2, 2])
349s ***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
349s ***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
349s ***** assert (size (evrnd (1, 1, 3)), [3, 3])
349s ***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (evrnd (1, 1)), "double")
349s ***** assert (class (evrnd (1, single (1))), "single")
349s ***** assert (class (evrnd (1, single ([1, 1]))), "single")
349s ***** assert (class (evrnd (single (1), 1)), "single")
349s ***** assert (class (evrnd (single ([1, 1]), 1)), "single")
349s ***** error<evrnd: function called with too few input arguments.> evrnd ()
349s ***** error<evrnd: function called with too few input arguments.> evrnd (1)
349s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
349s  evrnd (ones (3), ones (2))
349s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
349s  evrnd (ones (2), ones (3))
349s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
349s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
349s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  evrnd (1, 2, -1)
349s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  evrnd (1, 2, 1.2)
349s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  evrnd (1, 2, ones (2))
349s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  evrnd (1, 2, [2 -1 2])
349s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  evrnd (1, 2, [2 0 2.5])
349s ***** error<evrnd: dimensions must be non-negative integers.> ...
349s  evrnd (1, 2, 2, -1, 5)
349s ***** error<evrnd: dimensions must be non-negative integers.> ...
349s  evrnd (1, 2, 2, 1.5, 5)
349s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  evrnd (2, ones (2), 3)
349s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  evrnd (2, ones (2), [3, 2])
349s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
349s  evrnd (2, ones (2), 3, 2)
349s 33 tests, 33 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/wishrnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wishrnd.m
349s ***** assert(size (wishrnd (1,2)), [1, 1]);
349s ***** assert(size (wishrnd (1,2,[])), [1, 1]);
349s ***** assert(size (wishrnd (1,2,1)), [1, 1]);
349s ***** assert(size (wishrnd ([],2,1)), [1, 1]);
349s ***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
349s ***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
349s ***** error wishrnd ()
349s ***** error wishrnd (1)
349s ***** error wishrnd ([1; 1], 2)
349s 9 tests, 9 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/chi2pdf.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/chi2pdf.m
349s ***** demo
349s  ## Plot various PDFs from the chi-squared distribution
349s  x = 0:0.01:8;
349s  y1 = chi2pdf (x, 1);
349s  y2 = chi2pdf (x, 2);
349s  y3 = chi2pdf (x, 3);
349s  y4 = chi2pdf (x, 4);
349s  y5 = chi2pdf (x, 6);
349s  y6 = chi2pdf (x, 9);
349s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
349s        x, y4, "-c", x, y5, "-m", x, y6, "-y")
349s  grid on
349s  xlim ([0, 8])
349s  ylim ([0, 0.5])
349s  legend ({"df = 1", "df = 2", "df = 3", ...
349s           "df = 4", "df = 6", "df = 9"}, "location", "northeast")
349s  title ("Chi-squared PDF")
349s  xlabel ("values in x")
349s  ylabel ("density")
349s ***** shared x, y
349s  x = [-1 0 0.5 1 Inf];
349s  y = [0, 1/2 * exp(-x(2:5)/2)];
349s ***** assert (chi2pdf (x, 2*ones (1,5)), y)
349s ***** assert (chi2pdf (x, 2), y)
349s ***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
349s ***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
349s ***** assert (chi2pdf (2, Inf), 0)
349s ***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
349s ***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
349s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
349s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
349s ***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
349s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
349s  chi2pdf (ones (3), ones (2))
349s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
349s  chi2pdf (ones (2), ones (3))
349s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
349s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
349s 14 tests, 14 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/gumbelrnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gumbelrnd.m
349s ***** assert (size (gumbelrnd (1, 1)), [1 1])
349s ***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
349s ***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
349s ***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
349s ***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
349s ***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
349s ***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (gumbelrnd (1, 1)), "double")
349s ***** assert (class (gumbelrnd (1, single (1))), "single")
349s ***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
349s ***** assert (class (gumbelrnd (single (1), 1)), "single")
349s ***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
349s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
349s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
349s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
349s  gumbelrnd (ones (3), ones (2))
349s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
349s  gumbelrnd (ones (2), ones (3))
349s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
349s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
349s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gumbelrnd (1, 2, -1)
349s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gumbelrnd (1, 2, 1.2)
349s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gumbelrnd (1, 2, ones (2))
349s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gumbelrnd (1, 2, [2 -1 2])
349s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gumbelrnd (1, 2, [2 0 2.5])
349s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
349s  gumbelrnd (1, 2, 2, -1, 5)
349s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
349s  gumbelrnd (1, 2, 2, 1.5, 5)
349s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
349s  gumbelrnd (2, ones (2), 3)
349s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
349s  gumbelrnd (2, ones (2), [3, 2])
349s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
349s  gumbelrnd (2, ones (2), 3, 2)
349s 33 tests, 33 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/gampdf.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gampdf.m
349s ***** demo
349s  ## Plot various PDFs from the Gamma distribution
349s  x = 0:0.01:20;
349s  y1 = gampdf (x, 1, 2);
349s  y2 = gampdf (x, 2, 2);
349s  y3 = gampdf (x, 3, 2);
349s  y4 = gampdf (x, 5, 1);
349s  y5 = gampdf (x, 9, 0.5);
349s  y6 = gampdf (x, 7.5, 1);
349s  y7 = gampdf (x, 0.5, 1);
349s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
349s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
349s  grid on
349s  ylim ([0,0.5])
349s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
349s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
349s           "α = 0.5, β = 1"}, "location", "northeast")
349s  title ("Gamma PDF")
349s  xlabel ("values in x")
349s  ylabel ("density")
349s ***** shared x, y
349s  x = [-1 0 0.5 1 Inf];
349s  y = [0 exp(-x(2:end))];
349s ***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
349s ***** assert (gampdf (x, 1, ones (1,5)), y)
349s ***** assert (gampdf (x, ones (1,5), 1), y)
349s ***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN 0 y(5)])
349s ***** assert (gampdf (x, [0 Inf NaN Inf 1], 1), [NaN 0 NaN 0 y(5)])
349s ***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
349s ***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
349s ***** assert (gampdf (2, Inf, 4), 0)
349s ***** assert (gampdf (2, 4, Inf), 0)
349s ***** assert (gampdf (2, Inf, Inf), 0)
349s ***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
349s ***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
349s ***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
349s ***** error<gampdf: function called with too few input arguments.> gampdf ()
349s ***** error<gampdf: function called with too few input arguments.> gampdf (1)
349s ***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
349s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
349s  gampdf (ones (3), ones (2), ones (2))
349s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
349s  gampdf (ones (2), ones (3), ones (2))
349s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
349s  gampdf (ones (2), ones (2), ones (3))
349s ***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
349s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
349s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
349s 22 tests, 22 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/gamrnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gamrnd.m
349s ***** assert (size (gamrnd (1, 1)), [1 1])
349s ***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
349s ***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
349s ***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
349s ***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
349s ***** assert (size (gamrnd (1, 1, 3)), [3, 3])
349s ***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (gamrnd (1, 1)), "double")
349s ***** assert (class (gamrnd (1, single (1))), "single")
349s ***** assert (class (gamrnd (1, single ([1, 1]))), "single")
349s ***** assert (class (gamrnd (single (1), 1)), "single")
349s ***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
349s ***** error<gamrnd: function called with too few input arguments.> gamrnd ()
349s ***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
349s ***** error<gamrnd: A and B must be of common size or scalars.> ...
349s  gamrnd (ones (3), ones (2))
349s ***** error<gamrnd: A and B must be of common size or scalars.> ...
349s  gamrnd (ones (2), ones (3))
349s ***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
349s ***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
349s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gamrnd (1, 2, -1)
349s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gamrnd (1, 2, 1.2)
349s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gamrnd (1, 2, ones (2))
349s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gamrnd (1, 2, [2 -1 2])
349s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  gamrnd (1, 2, [2 0 2.5])
349s ***** error<gamrnd: dimensions must be non-negative integers.> ...
349s  gamrnd (1, 2, 2, -1, 5)
349s ***** error<gamrnd: dimensions must be non-negative integers.> ...
349s  gamrnd (1, 2, 2, 1.5, 5)
349s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
349s  gamrnd (2, ones (2), 3)
349s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
349s  gamrnd (2, ones (2), [3, 2])
349s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
349s  gamrnd (2, ones (2), 3, 2)
349s 33 tests, 33 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/hygepdf.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hygepdf.m
349s ***** demo
349s  ## Plot various PDFs from the hypergeometric distribution
349s  x = 0:60;
349s  y1 = hygepdf (x, 500, 50, 100);
349s  y2 = hygepdf (x, 500, 60, 200);
349s  y3 = hygepdf (x, 500, 70, 300);
349s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
349s  grid on
349s  xlim ([0, 60])
349s  ylim ([0, 0.18])
349s  legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
349s           "m = 500, k = 70, μ = 300"}, "location", "northeast")
349s  title ("Hypergeometric PDF")
349s  xlabel ("values in x (number of successes)")
349s  ylabel ("density")
349s ***** shared x, y
349s  x = [-1 0 1 2 3];
349s  y = [0 1/6 4/6 1/6 0];
349s ***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
349s ***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
349s ***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
349s ***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
349s ***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
349s ***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
349s ***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
349s ***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
349s ***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
349s ***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
349s ***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
349s ***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
349s ***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
349s ***** test
349s  z = zeros(3,5);
349s  z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
349s  assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
349s  assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
349s  assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
349s  assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
349s  assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
349s  assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
349s ***** error<hygepdf: function called with too few input arguments.> hygepdf ()
349s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
349s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
349s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
349s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
349s  hygepdf (1, ones (3), ones (2), ones (2))
349s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
349s  hygepdf (1, ones (2), ones (3), ones (2))
349s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
349s  hygepdf (1, ones (2), ones (2), ones (3))
349s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
349s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
349s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
349s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
349s 25 tests, 25 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/copulacdf.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/copulacdf.m
349s ***** test
349s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
349s  theta = [1; 2];
349s  p = copulacdf ("Clayton", x, theta);
349s  expected_p = [0.1395; 0.1767];
349s  assert (p, expected_p, 0.001);
349s ***** test
349s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
349s  p = copulacdf ("Gumbel", x, 2);
349s  expected_p = [0.1464; 0.1464];
349s  assert (p, expected_p, 0.001);
349s ***** test
349s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
349s  theta = [1; 2];
349s  p = copulacdf ("Frank", x, theta);
349s  expected_p = [0.0699; 0.0930];
349s  assert (p, expected_p, 0.001);
349s ***** test
349s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
349s  theta = [0.3; 0.7];
349s  p = copulacdf ("AMH", x, theta);
349s  expected_p = [0.0629; 0.0959];
349s  assert (p, expected_p, 0.001);
349s ***** test
349s  x = [0.2:0.2:0.6; 0.2:0.1:0.4];
349s  theta = [0.2, 0.1, 0.1, 0.05];
349s  p = copulacdf ("FGM", x, theta);
349s  expected_p = [0.0558; 0.0293];
349s  assert (p, expected_p, 0.001);
349s 5 tests, 5 passed, 0 known failure, 0 skipped
349s [inst/dist_fun/betarnd.m]
349s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/betarnd.m
349s ***** assert (size (betarnd (2, 1/2)), [1 1])
349s ***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
349s ***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
349s ***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
349s ***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
349s ***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
349s ***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
349s ***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
349s ***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
349s ***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
349s ***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
349s ***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
349s ***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
349s ***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
349s ***** assert (class (betarnd (1, 1)), "double")
349s ***** assert (class (betarnd (1, single (0))), "single")
349s ***** assert (class (betarnd (1, single ([0, 0]))), "single")
349s ***** assert (class (betarnd (1, single (1), 2)), "single")
349s ***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
349s ***** assert (class (betarnd (single (1), 1, 2)), "single")
349s ***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
349s ***** error<betarnd: function called with too few input arguments.> betarnd ()
349s ***** error<betarnd: function called with too few input arguments.> betarnd (1)
349s ***** error<betarnd: A and B must be of common size or scalars.> ...
349s  betarnd (ones (3), ones (2))
349s ***** error<betarnd: A and B must be of common size or scalars.> ...
349s  betarnd (ones (2), ones (3))
349s ***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
349s ***** error<betarnd: A and B must not be complex.> betarnd (1, i)
349s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  betarnd (1, 1/2, -1)
349s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  betarnd (1, 1/2, 1.2)
349s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  betarnd (1, 1/2, ones (2))
349s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  betarnd (1, 1/2, [2 -1 2])
349s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
349s  betarnd (1, 1/2, [2 0 2.5])
349s ***** error<betarnd: dimensions must be non-negative integers.> ...
349s  betarnd (1, 1/2, 2, -1, 5)
349s ***** error<betarnd: dimensions must be non-negative integers.> ...
349s  betarnd (1, 1/2, 2, 1.5, 5)
349s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
349s  betarnd (2, 1/2 * ones (2), 3)
350s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
350s  betarnd (2, 1/2 * ones (2), [3, 2])
350s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
350s  betarnd (2, 1/2 * ones (2), 3, 2)
350s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
350s  betarnd (2 * ones (2), 1/2, 3)
350s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
350s  betarnd (2 * ones (2), 1/2, [3, 2])
350s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
350s  betarnd (2 * ones (2), 1/2, 3, 2)
350s 40 tests, 40 passed, 0 known failure, 0 skipped
350s [inst/dist_fun/chi2cdf.m]
350s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/chi2cdf.m
350s ***** demo
350s  ## Plot various CDFs from the chi-squared distribution
350s  x = 0:0.01:8;
350s  p1 = chi2cdf (x, 1);
350s  p2 = chi2cdf (x, 2);
350s  p3 = chi2cdf (x, 3);
350s  p4 = chi2cdf (x, 4);
350s  p5 = chi2cdf (x, 6);
350s  p6 = chi2cdf (x, 9);
350s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
350s        x, p4, "-c", x, p5, "-m", x, p6, "-y")
350s  grid on
350s  xlim ([0, 8])
350s  legend ({"df = 1", "df = 2", "df = 3", ...
350s           "df = 4", "df = 6", "df = 9"}, "location", "southeast")
350s  title ("Chi-squared CDF")
350s  xlabel ("values in x")
350s  ylabel ("probability")
350s ***** shared x, p, u
350s  x = [-1, 0, 0.5, 1, 2];
350s  p = [0, (1 - exp (-x(2:end) / 2))];
350s  u = [1, 0, NaN, 0.606530659712633, 0.367879441171442];
350s ***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
350s ***** assert (chi2cdf (x, 2), p, eps)
350s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [0, 1, NaN, p(4:5)], eps)
350s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), u, 3 * eps)
350s ***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
350s ***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
350s ***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
350s ***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
350s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
350s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
350s ***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
350s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
350s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
350s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
350s  chi2cdf (ones (3), ones (2))
350s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
350s  chi2cdf (ones (2), ones (3))
350s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
350s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
350s 17 tests, 17 passed, 0 known failure, 0 skipped
350s [inst/dist_fun/mvtcdf.m]
350s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvtcdf.m
350s ***** demo
350s  ## Compute the cdf of a multivariate Student's t distribution with
350s  ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.
350s 
350s  rho = [1, 0.4; 0.4, 1];
350s  df = 2;
350s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
350s  X = [X1(:), X2(:)];
350s  p = mvtcdf (X, rho, df);
350s  surf (X1, X2, reshape (p, 25, 25));
350s  title ("Bivariate Student's t cumulative distribution function");
350s ***** test
350s  x = [1, 2];
350s  rho = [1, 0.5; 0.5, 1];
350s  df = 4;
350s  a = [-1, 0];
350s  assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
350s ***** test
350s  x = [1, 2;2, 4;1, 5];
350s  rho = [1, 0.5; 0.5, 1];
350s  df = 4;
350s  p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
350s  assert (mvtcdf(x, rho, df), p, 1e-14);
350s ***** test
350s  x = [1, 2, 2, 4, 1, 5];
350s  rho = eye (6);
350s  rho(rho == 0) = 0.5;
350s  df = 4;
350s  assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
362s ***** error mvtcdf (1)
362s ***** error mvtcdf (1, 2)
362s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
362s  mvtcdf (1, [2, 3; 3, 2], 1)
362s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
362s  mvtcdf ([2, 3, 4], ones (2), 1)
362s ***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
362s  mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
362s ***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
362s  mvtcdf ([2, 3], ones (2), [1, 2, 3])
362s ***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
362s  mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
362s 10 tests, 10 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/nakapdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nakapdf.m
362s ***** demo
362s  ## Plot various PDFs from the Nakagami distribution
362s  x = 0:0.01:3;
362s  y1 = nakapdf (x, 0.5, 1);
362s  y2 = nakapdf (x, 1, 1);
362s  y3 = nakapdf (x, 1, 2);
362s  y4 = nakapdf (x, 1, 3);
362s  y5 = nakapdf (x, 2, 1);
362s  y6 = nakapdf (x, 2, 2);
362s  y7 = nakapdf (x, 5, 1);
362s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
362s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
362s  grid on
362s  xlim ([0, 3])
362s  ylim ([0, 2])
362s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
362s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
362s           "μ = 5, ω = 1"}, "location", "northeast")
362s  title ("Nakagami PDF")
362s  xlabel ("values in x")
362s  ylabel ("density")
362s ***** shared x, y
362s  x = [-1, 0, 1, 2, Inf];
362s  y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
362s ***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
362s ***** assert (nakapdf (x, 1, 1), y, eps)
362s ***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
362s ***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
362s ***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
362s ***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
362s ***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
362s ***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
362s ***** error<nakapdf: function called with too few input arguments.> nakapdf ()
362s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
362s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
362s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
362s  nakapdf (ones (3), ones (2), ones(2))
362s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
362s  nakapdf (ones (2), ones (3), ones(2))
362s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
362s  nakapdf (ones (2), ones (2), ones(3))
362s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
362s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
362s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
362s 17 tests, 17 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/ncfpdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncfpdf.m
362s ***** demo
362s  ## Plot various PDFs from the noncentral F distribution
362s  x = 0:0.01:5;
362s  y1 = ncfpdf (x, 2, 5, 1);
362s  y2 = ncfpdf (x, 2, 5, 2);
362s  y3 = ncfpdf (x, 5, 10, 1);
362s  y4 = ncfpdf (x, 10, 20, 10);
362s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
362s  grid on
362s  xlim ([0, 5])
362s  ylim ([0, 0.8])
362s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
362s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
362s          "location", "northeast")
362s  title ("Noncentral F PDF")
362s  xlabel ("values in x")
362s  ylabel ("density")
362s ***** demo
362s  ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
362s  ## same number of numerator and denominator degrees of freedom (5, 20)
362s 
362s  x = 0.01:0.1:10.01;
362s  y1 = ncfpdf (x, 5, 20, 10);
362s  y2 = fpdf (x, 5, 20);
362s  plot (x, y1, "-", x, y2, "-");
362s  grid on
362s  xlim ([0, 10])
362s  ylim ([0, 0.8])
362s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
362s  title ("Noncentral F vs F PDFs")
362s  xlabel ("values in x")
362s  ylabel ("density")
362s ***** shared x1, df1, df2, lambda
362s  x1 = [-Inf, 2, NaN, 4, Inf];
362s  df1 = [2, 0, -1, 1, 4];
362s  df2 = [2, 4, 5, 6, 8];
362s  lambda = [1, NaN, 3, -1, 2];
362s ***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
362s ***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
362s                                    0.05607937264237208, NaN], 1e-14);
362s ***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
362s                                    0.080125760971946518, NaN], 1e-14);
362s ***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
362s                                    0.0715902008258656, NaN], 1e-14);
362s ***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
362s ***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
362s                                       NaN, 0.2152571783045893], 1e-14);
362s ***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
362s                                       NaN, 0.05560846335398539], 1e-14);
362s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
362s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
362s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
362s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
362s  ncfpdf (ones (3), ones (2), ones (2), ones (2))
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
362s  ncfpdf (ones (2), ones (3), ones (2), ones (2))
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
362s  ncfpdf (ones (2), ones (2), ones (3), ones (2))
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
362s  ncfpdf (ones (2), ones (2), ones (2), ones (3))
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
362s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
362s 19 tests, 19 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/unidrnd.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unidrnd.m
362s ***** assert (size (unidrnd (2)), [1, 1])
362s ***** assert (size (unidrnd (ones (2,1))), [2, 1])
362s ***** assert (size (unidrnd (ones (2,2))), [2, 2])
362s ***** assert (size (unidrnd (1, 3)), [3, 3])
362s ***** assert (size (unidrnd (1, [4 1])), [4, 1])
362s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
362s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
362s ***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
362s ***** assert (size (unidrnd (1, 0, 1)), [0, 1])
362s ***** assert (size (unidrnd (1, 1, 0)), [1, 0])
362s ***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
362s ***** assert (unidrnd (0, 1, 1), NaN)
362s ***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
362s ***** assert (class (unidrnd (2)), "double")
362s ***** assert (class (unidrnd (single (2))), "single")
362s ***** assert (class (unidrnd (single ([2 2]))), "single")
362s ***** error<unidrnd: function called with too few input arguments.> unidrnd ()
362s ***** error<unidrnd: N must not be complex.> unidrnd (i)
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (1, -1)
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (1, 1.2)
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (1, ones (2))
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (1, [2 -1 2])
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (1, [2 0 2.5])
362s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unidrnd (ones (2), ones (2))
362s ***** error<unidrnd: dimensions must be non-negative integers.> ...
362s  unidrnd (1, 2, -1, 5)
362s ***** error<unidrnd: dimensions must be non-negative integers.> ...
362s  unidrnd (1, 2, 1.5, 5)
362s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
362s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
362s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
362s 29 tests, 29 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/mvnrnd.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvnrnd.m
362s ***** error<mvnrnd: too few input arguments.> mvnrnd ()
362s ***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
362s ***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
362s ***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
362s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
362s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
362s 6 tests, 6 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/binoinv.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/binoinv.m
362s ***** demo
362s  ## Plot various iCDFs from the binomial distribution
362s  p = 0.001:0.001:0.999;
362s  x1 = binoinv (p, 20, 0.5);
362s  x2 = binoinv (p, 20, 0.7);
362s  x3 = binoinv (p, 40, 0.5);
362s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
362s  grid on
362s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
362s           "n = 40, ps = 0.5"}, "location", "southeast")
362s  title ("Binomial iCDF")
362s  xlabel ("probability")
362s  ylabel ("values in x (number of successes)")
362s ***** shared p
362s  p = [-1 0 0.5 1 2];
362s ***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
362s ***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
362s ***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
362s ***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
362s ***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
362s ***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
362s ***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
362s ***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
362s ***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
362s ***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
362s ***** shared x, tol
362s  x = magic (3) + 1;
362s  tol = 1;
362s ***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
362s ***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
362s ***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
362s ***** error<binoinv: function called with too few input arguments.> binoinv ()
362s ***** error<binoinv: function called with too few input arguments.> binoinv (1)
362s ***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
362s ***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
362s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
362s  binoinv (ones (3), ones (2), ones (2))
362s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
362s  binoinv (ones (2), ones (3), ones (2))
362s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
362s  binoinv (ones (2), ones (2), ones (3))
362s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
362s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
362s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
362s 23 tests, 23 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/ncx2cdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncx2cdf.m
362s ***** demo
362s  ## Plot various CDFs from the noncentral chi-squared distribution
362s  x = 0:0.1:10;
362s  p1 = ncx2cdf (x, 2, 1);
362s  p2 = ncx2cdf (x, 2, 2);
362s  p3 = ncx2cdf (x, 2, 3);
362s  p4 = ncx2cdf (x, 4, 1);
362s  p5 = ncx2cdf (x, 4, 2);
362s  p6 = ncx2cdf (x, 4, 3);
362s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
362s        x, p4, "-m", x, p5, "-c", x, p6, "-y")
362s  grid on
362s  xlim ([0, 10])
362s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
362s           "df = 2, λ = 3", "df = 4, λ = 1", ...
362s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
362s  title ("Noncentral chi-squared CDF")
362s  xlabel ("values in x")
362s  ylabel ("probability")
362s ***** demo
362s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
362s  ## chi-squared CDF with the same number of degrees of freedom (4).
362s 
362s  x = 0:0.1:10;
362s  p1 = ncx2cdf (x, 4, 2);
362s  p2 = chi2cdf (x, 4);
362s  plot (x, p1, "-", x, p2, "-")
362s  grid on
362s  xlim ([0, 10])
362s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
362s  title ("Noncentral chi-squared vs chi-squared CDFs")
362s  xlabel ("values in x")
362s  ylabel ("probability")
362s ***** test
362s  x = -2:0.1:2;
362s  p = ncx2cdf (x, 10, 1);
362s  assert (p([1:21]), zeros (1, 21), 3e-84);
362s  assert (p(22), 1.521400636466575e-09, 1e-14);
362s  assert (p(30), 6.665480510026046e-05, 1e-14);
362s  assert (p(41), 0.002406447308399836, 1e-14);
362s ***** test
362s  p = ncx2cdf (12, 10, 3);
362s  assert (p, 0.4845555602398649, 1e-14);
362s ***** test
362s  p = ncx2cdf (2, 3, 2);
362s  assert (p, 0.2207330870741212, 1e-14);
362s ***** test
362s  p = ncx2cdf (2, 3, 2, "upper");
362s  assert (p, 0.7792669129258789, 1e-14);
362s ***** test
362s  p = ncx2cdf ([3, 6], 3, 2, "upper");
362s  assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
362s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
362s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
362s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
362s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
362s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
362s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
362s  ncx2cdf (ones (3), ones (2), ones (2))
362s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
362s  ncx2cdf (ones (2), ones (3), ones (2))
362s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
362s  ncx2cdf (ones (2), ones (2), ones (3))
362s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
362s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
362s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
362s 16 tests, 16 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/jsupdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/jsupdf.m
362s ***** error jsupdf ()
362s ***** error jsupdf (1, 2, 3, 4)
362s ***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
362s  jsupdf (1, ones (2), ones (3))
362s 3 tests, 3 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/unifrnd.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unifrnd.m
362s ***** assert (size (unifrnd (1, 1)), [1 1])
362s ***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
362s ***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
362s ***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
362s ***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
362s ***** assert (size (unifrnd (1, 1, 3)), [3, 3])
362s ***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
362s ***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
362s ***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
362s ***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
362s ***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
362s ***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
362s ***** assert (class (unifrnd (1, 1)), "double")
362s ***** assert (class (unifrnd (1, single (1))), "single")
362s ***** assert (class (unifrnd (1, single ([1, 1]))), "single")
362s ***** assert (class (unifrnd (single (1), 1)), "single")
362s ***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
362s ***** error<unifrnd: function called with too few input arguments.> unifrnd ()
362s ***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
362s ***** error<unifrnd: A and B must be of common size or scalars.> ...
362s  unifrnd (ones (3), ones (2))
362s ***** error<unifrnd: A and B must be of common size or scalars.> ...
362s  unifrnd (ones (2), ones (3))
362s ***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
362s ***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
362s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unifrnd (1, 2, -1)
362s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unifrnd (1, 2, 1.2)
362s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unifrnd (1, 2, ones (2))
362s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unifrnd (1, 2, [2 -1 2])
362s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
362s  unifrnd (1, 2, [2 0 2.5])
362s ***** error<unifrnd: dimensions must be non-negative integers.> ...
362s  unifrnd (1, 2, 2, -1, 5)
362s ***** error<unifrnd: dimensions must be non-negative integers.> ...
362s  unifrnd (1, 2, 2, 1.5, 5)
362s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
362s  unifrnd (2, ones (2), 3)
362s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
362s  unifrnd (2, ones (2), [3, 2])
362s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
362s  unifrnd (2, ones (2), 3, 2)
362s 33 tests, 33 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/tcdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tcdf.m
362s ***** demo
362s  ## Plot various CDFs from the Student's T distribution
362s  x = -5:0.01:5;
362s  p1 = tcdf (x, 1);
362s  p2 = tcdf (x, 2);
362s  p3 = tcdf (x, 5);
362s  p4 = tcdf (x, Inf);
362s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
362s  grid on
362s  xlim ([-5, 5])
362s  ylim ([0, 1])
362s  legend ({"df = 1", "df = 2", ...
362s           "df = 5", 'df = \infty'}, "location", "southeast")
362s  title ("Student's T CDF")
362s  xlabel ("values in x")
362s  ylabel ("probability")
362s ***** shared x,y
362s  x = [-Inf 0 1 Inf];
362s  y = [0 1/2 3/4 1];
362s ***** assert (tcdf (x, ones (1,4)), y, eps)
362s ***** assert (tcdf (x, 1), y, eps)
362s ***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
362s ***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
362s ***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
362s ***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
362s ***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
362s ***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
362s ***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
362s ***** error<tcdf: function called with too few input arguments.> tcdf ()
362s ***** error<tcdf: function called with too few input arguments.> tcdf (1)
362s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
362s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
362s ***** error<tcdf: X and DF must be of common size or scalars.> ...
362s  tcdf (ones (3), ones (2))
362s ***** error<tcdf: X and DF must be of common size or scalars.> ...
362s  tcdf (ones (3), ones (2))
362s ***** error<tcdf: X and DF must be of common size or scalars.> ...
362s  tcdf (ones (3), ones (2), "upper")
362s ***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
362s ***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
362s ***** shared tol_rel
362s  tol_rel = 10 * eps;
362s ***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
362s ***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
362s ***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
362s ***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
362s ***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
362s ***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
362s ***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
362s ***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
362s ***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
362s ***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
362s ***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
362s ***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
362s 30 tests, 30 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/gamcdf.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gamcdf.m
362s ***** demo
362s  ## Plot various CDFs from the Gamma distribution
362s  x = 0:0.01:20;
362s  p1 = gamcdf (x, 1, 2);
362s  p2 = gamcdf (x, 2, 2);
362s  p3 = gamcdf (x, 3, 2);
362s  p4 = gamcdf (x, 5, 1);
362s  p5 = gamcdf (x, 9, 0.5);
362s  p6 = gamcdf (x, 7.5, 1);
362s  p7 = gamcdf (x, 0.5, 1);
362s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
362s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
362s  grid on
362s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
362s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
362s           "α = 0.5, β = 1"}, "location", "southeast")
362s  title ("Gamma CDF")
362s  xlabel ("values in x")
362s  ylabel ("probability")
362s ***** shared x, y, u
362s  x = [-1, 0, 0.5, 1, 2, Inf];
362s  y = [0, gammainc(x(2:end), 1)];
362s  u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
362s ***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
362s ***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
362s ***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
362s ***** assert (gamcdf (x, ones (1,6), 1), y, eps)
362s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
362s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
362s ***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
362s ***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
362s ***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
362s ***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
362s ***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
362s ***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
362s ***** error<gamcdf: invalid number of input arguments.> gamcdf ()
362s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
362s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
362s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
362s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
362s ***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
362s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
362s  [p, plo, pup] = gamcdf (1, 2, 3)
362s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
362s  [p, plo, pup] = gamcdf (1, 2, 3, "upper")
362s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
362s  gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
362s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
362s  gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
362s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
362s  gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
362s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
362s  gamcdf (ones (3), ones (2), ones (2))
362s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
362s  gamcdf (ones (2), ones (3), ones (2))
362s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
362s  gamcdf (ones (2), ones (2), ones (3))
362s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
362s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
362s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
362s ***** error<gamcdf: bad covariance matrix.> ...
362s  [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
362s 30 tests, 30 passed, 0 known failure, 0 skipped
362s [inst/dist_fun/ncx2inv.m]
362s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncx2inv.m
362s ***** demo
362s  ## Plot various iCDFs from the noncentral chi-squared distribution
362s  p = 0.001:0.001:0.999;
362s  x1 = ncx2inv (p, 2, 1);
362s  x2 = ncx2inv (p, 2, 2);
362s  x3 = ncx2inv (p, 2, 3);
362s  x4 = ncx2inv (p, 4, 1);
362s  x5 = ncx2inv (p, 4, 2);
362s  x6 = ncx2inv (p, 4, 3);
362s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
362s        p, x4, "-m", p, x5, "-c", p, x6, "-y")
362s  grid on
362s  ylim ([0, 10])
362s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
362s           "df = 2, λ = 3", "df = 4, λ = 1", ...
362s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
362s  title ("Noncentral chi-squared iCDF")
362s  xlabel ("probability")
362s  ylabel ("values in x")
362s ***** demo
362s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
362s  ## chi-squared CDF with the same number of degrees of freedom (4).
362s 
362s  p = 0.001:0.001:0.999;
362s  x1 = ncx2inv (p, 4, 2);
362s  x2 = chi2inv (p, 4);
362s  plot (p, x1, "-", p, x2, "-");
362s  grid on
362s  ylim ([0, 10])
362s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
362s  title ("Noncentral chi-squared vs chi-squared quantile functions")
362s  xlabel ("probability")
362s  ylabel ("values in x")
362s ***** test
362s  x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
362s  assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
362s ***** test
362s  x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
362s  assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
362s ***** test
362s  x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
362s  assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
363s ***** test
363s  x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
363s  assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
363s ***** test
363s  x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
363s  assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
363s ***** test
363s  x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
363s  assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
363s ***** test
363s  assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
364s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
364s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
364s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
364s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
364s  ncx2inv (ones (3), ones (2), ones (2))
364s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
364s  ncx2inv (ones (2), ones (3), ones (2))
364s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
364s  ncx2inv (ones (2), ones (2), ones (3))
364s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
364s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
364s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
364s 16 tests, 16 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/chi2inv.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/chi2inv.m
364s ***** demo
364s  ## Plot various iCDFs from the chi-squared distribution
364s  p = 0.001:0.001:0.999;
364s  x1 = chi2inv (p, 1);
364s  x2 = chi2inv (p, 2);
364s  x3 = chi2inv (p, 3);
364s  x4 = chi2inv (p, 4);
364s  x5 = chi2inv (p, 6);
364s  x6 = chi2inv (p, 9);
364s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
364s        p, x4, "-c", p, x5, "-m", p, x6, "-y")
364s  grid on
364s  ylim ([0, 8])
364s  legend ({"df = 1", "df = 2", "df = 3", ...
364s           "df = 4", "df = 6", "df = 9"}, "location", "northwest")
364s  title ("Chi-squared iCDF")
364s  xlabel ("probability")
364s  ylabel ("values in x")
364s ***** shared p
364s  p = [-1 0 0.3934693402873666 1 2];
364s ***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
364s ***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
364s ***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
364s ***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
364s ***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
364s ***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
364s ***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
364s ***** error<chi2inv: function called with too few input arguments.> chi2inv ()
364s ***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
364s ***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
364s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
364s  chi2inv (ones (3), ones (2))
364s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
364s  chi2inv (ones (2), ones (3))
364s ***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
364s ***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
364s 14 tests, 14 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/raylinv.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/raylinv.m
364s ***** demo
364s  ## Plot various iCDFs from the Rayleigh distribution
364s  p = 0.001:0.001:0.999;
364s  x1 = raylinv (p, 0.5);
364s  x2 = raylinv (p, 1);
364s  x3 = raylinv (p, 2);
364s  x4 = raylinv (p, 3);
364s  x5 = raylinv (p, 4);
364s  plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
364s  grid on
364s  ylim ([0, 10])
364s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
364s           "σ = 3", "σ = 4"}, "location", "northwest")
364s  title ("Rayleigh iCDF")
364s  xlabel ("probability")
364s  ylabel ("values in x")
364s ***** test
364s  p = 0:0.1:0.5;
364s  sigma = 1:6;
364s  x = raylinv (p, sigma);
364s  expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
364s  assert (x, expected_x, 0.001);
364s ***** test
364s  p = 0:0.1:0.5;
364s  x = raylinv (p, 0.5);
364s  expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
364s  assert (x, expected_x, 0.001);
364s ***** error<raylinv: function called with too few input arguments.> raylinv ()
364s ***** error<raylinv: function called with too few input arguments.> raylinv (1)
364s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
364s  raylinv (ones (3), ones (2))
364s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
364s  raylinv (ones (2), ones (3))
364s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
364s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
364s 8 tests, 8 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/expcdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/expcdf.m
364s ***** demo
364s  ## Plot various CDFs from the exponential distribution
364s  x = 0:0.01:5;
364s  p1 = expcdf (x, 2/3);
364s  p2 = expcdf (x, 1.0);
364s  p3 = expcdf (x, 2.0);
364s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
364s  grid on
364s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
364s  title ("Exponential CDF")
364s  xlabel ("values in x")
364s  ylabel ("probability")
364s ***** shared x, p
364s  x = [-1 0 0.5 1 Inf];
364s  p = [0, 1 - exp(-x(2:end)/2)];
364s ***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
364s ***** assert (expcdf (x, 2), p, 1e-16)
364s ***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
364s ***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
364s ***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
364s ***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
364s ***** test
364s  [p, plo, pup] = expcdf (1, 2, 3);
364s  assert (p, 0.39346934028737, 1e-14);
364s  assert (plo, 0.08751307220484, 1e-14);
364s  assert (pup, 0.93476821257933, 1e-14);
364s ***** test
364s  [p, plo, pup] = expcdf (1, 2, 2, 0.1);
364s  assert (p, 0.39346934028737, 1e-14);
364s  assert (plo, 0.14466318041675, 1e-14);
364s  assert (pup, 0.79808291849140, 1e-14);
364s ***** test
364s  [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
364s  assert (p, 0.60653065971263, 1e-14);
364s  assert (plo, 0.20191708150860, 1e-14);
364s  assert (pup, 0.85533681958325, 1e-14);
364s ***** error<expcdf: invalid number of input arguments.> expcdf ()
364s ***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
364s ***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
364s ***** error<expcdf: X and MU must be of common size or scalars.> ...
364s  expcdf (ones (3), ones (2))
364s ***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
364s  expcdf (2, 3, [1, 2])
364s ***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
364s  [p, plo, pup] = expcdf (1, 2)
364s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
364s  expcdf (1, 2, 3, 0)
364s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
364s  expcdf (1, 2, 3, 1.22)
364s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
364s  expcdf (1, 2, 3, "alpha", "upper")
364s ***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
364s ***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
364s ***** error<expcdf: variance, PCOV, cannot be negative.> ...
364s  [p, plo, pup] = expcdf (1, 2, -1, 0.04)
364s 21 tests, 21 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/evpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/evpdf.m
364s ***** demo
364s  ## Plot various PDFs from the Extreme value distribution
364s  x = -10:0.001:10;
364s  y1 = evpdf (x, 0.5, 2);
364s  y2 = evpdf (x, 1.0, 2);
364s  y3 = evpdf (x, 1.5, 3);
364s  y4 = evpdf (x, 3.0, 4);
364s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
364s  grid on
364s  ylim ([0, 0.2])
364s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
364s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
364s  title ("Extreme value PDF")
364s  xlabel ("values in x")
364s  ylabel ("density")
364s ***** shared x, y0, y1
364s  x = [-5, 0, 1, 2, 3];
364s  y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
364s  y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
364s ***** assert (evpdf (x), y0, 1e-4)
364s ***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
364s ***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
364s ***** error<evpdf: function called with too few input arguments.> evpdf ()
364s ***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  evpdf (ones (3), ones (2), ones (2))
364s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
364s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
364s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
364s 8 tests, 8 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/normpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/normpdf.m
364s ***** demo
364s  ## Plot various PDFs from the normal distribution
364s  x = -5:0.01:5;
364s  y1 = normpdf (x, 0, 0.5);
364s  y2 = normpdf (x, 0, 1);
364s  y3 = normpdf (x, 0, 2);
364s  y4 = normpdf (x, -2, 0.8);
364s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
364s  grid on
364s  xlim ([-5, 5])
364s  ylim ([0, 0.9])
364s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
364s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
364s  title ("Normal PDF")
364s  xlabel ("values in x")
364s  ylabel ("density")
364s ***** shared x, y
364s  x = [-Inf, 1, 2, Inf];
364s  y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
364s ***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
364s ***** assert (normpdf (x, 1, ones (1,4)), y, eps)
364s ***** assert (normpdf (x, ones (1,4), 1), y, eps)
364s ***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
364s ***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
364s ***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
364s ***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
364s ***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
364s ***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
364s ***** error<normpdf: function called with too few input arguments.> normpdf ()
364s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  normpdf (ones (3), ones (2), ones (2))
364s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  normpdf (ones (2), ones (3), ones (2))
364s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  normpdf (ones (2), ones (2), ones (3))
364s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
364s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
364s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
364s 16 tests, 16 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/bvtcdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bvtcdf.m
364s ***** test
364s  x = [1, 2];
364s  rho = [1, 0.5; 0.5, 1];
364s  df = 4;
364s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
364s ***** test
364s  x = [3, 2;2, 4;1, 5];
364s  rho = [1, 0.5; 0.5, 1];
364s  df = 4;
364s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
364s 2 tests, 2 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/logicdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logicdf.m
364s ***** demo
364s  ## Plot various CDFs from the logistic distribution
364s  x = -5:0.01:20;
364s  p1 = logicdf (x, 5, 2);
364s  p2 = logicdf (x, 9, 3);
364s  p3 = logicdf (x, 9, 4);
364s  p4 = logicdf (x, 6, 2);
364s  p5 = logicdf (x, 2, 1);
364s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
364s  grid on
364s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
364s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
364s  title ("Logistic CDF")
364s  xlabel ("values in x")
364s  ylabel ("probability")
364s ***** shared x, y
364s  x = [-Inf -log(3) 0 log(3) Inf];
364s  y = [0, 1/4, 1/2, 3/4, 1];
364s ***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
364s ***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1], eps)
364s ***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
364s ***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
364s ***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
364s ***** error<logicdf: function called with too few input arguments.> logicdf ()
364s ***** error<logicdf: function called with too few input arguments.> logicdf (1)
364s ***** error<logicdf: function called with too few input arguments.> ...
364s  logicdf (1, 2)
364s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
364s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
364s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  logicdf (1, ones (2), ones (3))
364s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  logicdf (ones (2), 1, ones (3))
364s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  logicdf (ones (2), ones (3), 1)
364s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
364s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
364s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
364s 16 tests, 16 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/wishpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wishpdf.m
364s ***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
364s ***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
364s ***** assert(wishpdf([6 2 5; 2 10 -5; 5 -5 25], [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.474865e-10, 1E-15);
364s ***** error wishpdf ()
364s ***** error wishpdf (1, 2)
364s ***** error wishpdf (1, 2, 0)
364s ***** error wishpdf (1, 2)
364s 7 tests, 7 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/hygecdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hygecdf.m
364s ***** demo
364s  ## Plot various CDFs from the hypergeometric distribution
364s  x = 0:60;
364s  p1 = hygecdf (x, 500, 50, 100);
364s  p2 = hygecdf (x, 500, 60, 200);
364s  p3 = hygecdf (x, 500, 70, 300);
364s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
364s  grid on
364s  xlim ([0, 60])
364s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
364s           "m = 500, k = 70, n = 300"}, "location", "southeast")
364s  title ("Hypergeometric CDF")
364s  xlabel ("values in x (number of successes)")
364s  ylabel ("probability")
364s ***** shared x, y
364s  x = [-1 0 1 2 3];
364s  y = [0 1/6 5/6 1 1];
364s ***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
364s ***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
364s ***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
364s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
364s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
364s  [y(5) NaN NaN NaN y(1)], 5*eps)
364s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
364s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
364s  [y(5) NaN NaN NaN y(1)], 5*eps)
364s ***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
364s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
364s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
364s  [y(5) NaN NaN NaN y(1)], 5*eps)
364s ***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
364s ***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
364s ***** test
364s  p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
364s  assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
364s ***** test
364s  p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
364s  assert (p, [1, 34/90, 0, 0, 0], 10*eps);
364s ***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
364s ***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
364s  eps ("single"))
364s ***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
364s  eps ("single"))
364s ***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
364s  eps ("single"))
364s ***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
364s  eps ("single"))
364s ***** error<hygecdf: function called with too few input arguments.> hygecdf ()
364s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
364s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
364s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
364s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
364s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
364s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
364s  hygecdf (ones (2), ones (3), 1, 1)
364s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
364s  hygecdf (1, ones (2), ones (3), 1)
364s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
364s  hygecdf (1, 1, ones (2), ones (3))
364s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
364s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
364s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
364s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
364s 32 tests, 32 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/nbininv.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nbininv.m
364s ***** demo
364s  ## Plot various iCDFs from the negative binomial distribution
364s  p = 0.001:0.001:0.999;
364s  x1 = nbininv (p, 2, 0.15);
364s  x2 = nbininv (p, 5, 0.2);
364s  x3 = nbininv (p, 4, 0.4);
364s  x4 = nbininv (p, 10, 0.3);
364s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
364s  grid on
364s  ylim ([0, 40])
364s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
364s           "r = 10, ps = 0.3"}, "location", "northwest")
364s  title ("Negative binomial iCDF")
364s  xlabel ("probability")
364s  ylabel ("values in x (number of failures)")
364s ***** shared p
364s  p = [-1 0 3/4 1 2];
364s ***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
364s ***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
364s ***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
364s ***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
364s ***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
364s ***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
364s ***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
364s ***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
364s ***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
364s ***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
364s ***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
364s ***** shared y, tol
364s  y = magic (3) + 1;
364s  tol = 1;
364s ***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
364s ***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
364s ***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
364s ***** error<nbininv: function called with too few input arguments.> nbininv ()
364s ***** error<nbininv: function called with too few input arguments.> nbininv (1)
364s ***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
364s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
364s  nbininv (ones (3), ones (2), ones (2))
364s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
364s  nbininv (ones (2), ones (3), ones (2))
364s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
364s  nbininv (ones (2), ones (2), ones (3))
364s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
364s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
364s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
364s 23 tests, 23 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/binocdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/binocdf.m
364s ***** demo
364s  ## Plot various CDFs from the binomial distribution
364s  x = 0:40;
364s  p1 = binocdf (x, 20, 0.5);
364s  p2 = binocdf (x, 20, 0.7);
364s  p3 = binocdf (x, 40, 0.5);
364s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
364s  grid on
364s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
364s           "n = 40, ps = 0.5"}, "location", "southeast")
364s  title ("Binomial CDF")
364s  xlabel ("values in x (number of successes)")
364s  ylabel ("probability")
364s ***** shared x, p, p1
364s  x = [-1 0 1 2 3];
364s  p = [0 1/4 3/4 1 1];
364s  p1 = 1 - p;
364s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
364s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
364s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
364s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
364s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
364s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
364s ***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
364s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
364s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
364s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
364s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
364s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
364s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
364s ***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
364s ***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
364s ***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
364s ***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
364s ***** error<binocdf: function called with too few input arguments.> binocdf ()
364s ***** error<binocdf: function called with too few input arguments.> binocdf (1)
364s ***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
364s ***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
364s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
364s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
364s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
364s  binocdf (ones (3), ones (2), ones (2))
364s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
364s  binocdf (ones (2), ones (3), ones (2))
364s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
364s  binocdf (ones (2), ones (2), ones (3))
364s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
364s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
364s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
364s 29 tests, 29 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/wblpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wblpdf.m
364s ***** demo
364s  ## Plot various PDFs from the Weibul distribution
364s  x = 0:0.001:2.5;
364s  y1 = wblpdf (x, 1, 0.5);
364s  y2 = wblpdf (x, 1, 1);
364s  y3 = wblpdf (x, 1, 1.5);
364s  y4 = wblpdf (x, 1, 5);
364s  plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
364s  grid on
364s  ylim ([0, 2.5])
364s  legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
364s           "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
364s  title ("Weibul PDF")
364s  xlabel ("values in x")
364s  ylabel ("density")
364s ***** shared x,y
364s  x = [-1 0 0.5 1 Inf];
364s  y = [0, exp(-x(2:4)), NaN];
364s ***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
364s ***** assert (wblpdf (x, 1, ones (1,5)), y)
364s ***** assert (wblpdf (x, ones (1,5), 1), y)
364s ***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
364s ***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
364s ***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
364s ***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
364s ***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
364s ***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
364s ***** error wblpdf ()
364s ***** error wblpdf (1,2,3,4)
364s ***** error wblpdf (ones (3), ones (2), ones (2))
364s ***** error wblpdf (ones (2), ones (3), ones (2))
364s ***** error wblpdf (ones (2), ones (2), ones (3))
364s ***** error wblpdf (i, 2, 2)
364s ***** error wblpdf (2, i, 2)
364s ***** error wblpdf (2, 2, i)
364s 17 tests, 17 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/hnpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/hnpdf.m
364s ***** demo
364s  ## Plot various PDFs from the half-normal distribution
364s  x = 0:0.001:10;
364s  y1 = hnpdf (x, 0, 1);
364s  y2 = hnpdf (x, 0, 2);
364s  y3 = hnpdf (x, 0, 3);
364s  y4 = hnpdf (x, 0, 5);
364s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
364s  grid on
364s  xlim ([0, 10])
364s  ylim ([0, 0.9])
364s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
364s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
364s  title ("Half-normal PDF")
364s  xlabel ("values in x")
364s  ylabel ("density")
364s ***** demo
364s  ## Plot half-normal against normal probability density function
364s  x = -5:0.001:5;
364s  y1 = hnpdf (x, 0, 1);
364s  y2 = normpdf (x);
364s  plot (x, y1, "-b", x, y2, "-g")
364s  grid on
364s  xlim ([-5, 5])
364s  ylim ([0, 0.9])
364s  legend ({"half-normal with μ = 0, σ = 1", ...
364s           "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
364s  title ("Half-normal against standard normal PDF")
364s  xlabel ("values in x")
364s  ylabel ("density")
364s ***** shared x, y
364s  x = [-Inf, -1, 0, 1/2, 1, Inf];
364s  y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
364s ***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
364s ***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
364s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
364s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
364s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
364s ***** error<hnpdf: function called with too few input arguments.> hnpdf ()
364s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
364s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
364s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  hnpdf (1, ones (2), ones (3))
364s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  hnpdf (ones (2), 1, ones (3))
364s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
364s  hnpdf (ones (2), ones (3), 1)
364s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
364s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
364s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
364s 14 tests, 14 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/iwishpdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/iwishpdf.m
364s ***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
364s ***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
364s ***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
364s  [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
364s ***** error iwishpdf ()
364s ***** error iwishpdf (1, 2)
364s ***** error iwishpdf (1, 2, 0)
364s 6 tests, 6 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/ncfcdf.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncfcdf.m
364s ***** demo
364s  ## Plot various CDFs from the noncentral F distribution
364s  x = 0:0.01:5;
364s  p1 = ncfcdf (x, 2, 5, 1);
364s  p2 = ncfcdf (x, 2, 5, 2);
364s  p3 = ncfcdf (x, 5, 10, 1);
364s  p4 = ncfcdf (x, 10, 20, 10);
364s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
364s  grid on
364s  xlim ([0, 5])
364s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
364s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
364s          "location", "southeast")
364s  title ("Noncentral F CDF")
364s  xlabel ("values in x")
364s  ylabel ("probability")
364s ***** demo
364s  ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
364s  ## same number of numerator and denominator degrees of freedom (5, 20)
364s 
364s  x = 0.01:0.1:10.01;
364s  p1 = ncfcdf (x, 5, 20, 10);
364s  p2 = fcdf (x, 5, 20);
364s  plot (x, p1, "-", x, p2, "-");
364s  grid on
364s  xlim ([0, 10])
364s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
364s  title ("Noncentral F vs F CDFs")
364s  xlabel ("values in x")
364s  ylabel ("probability")
364s ***** test
364s  x = -2:0.1:2;
364s  p = ncfcdf (x, 10, 1, 3);
364s  assert (p([1:21]), zeros (1, 21), 1e-76);
364s  assert (p(22), 0.004530737275319753, 1e-14);
364s  assert (p(30), 0.255842099135669, 1e-14);
364s  assert (p(41), 0.4379890998457305, 1e-14);
364s ***** test
364s  p = ncfcdf (12, 10, 3, 2);
364s  assert (p, 0.9582287900447416, 1e-14);
364s ***** test
364s  p = ncfcdf (2, 3, 2, 1);
364s  assert (p, 0.5731985522994989, 1e-14);
364s ***** test
364s  p = ncfcdf (2, 3, 2, 1, "upper");
364s  assert (p, 0.4268014477004823, 1e-14);
364s ***** test
364s  p = ncfcdf ([3, 6], 3, 2, 5, "upper");
364s  assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
364s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
364s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
364s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
364s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
364s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
364s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
364s  ncfcdf (ones (3), ones (2), ones (2), ones (2))
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
364s  ncfcdf (ones (2), ones (3), ones (2), ones (2))
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
364s  ncfcdf (ones (2), ones (2), ones (3), ones (2))
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
364s  ncfcdf (ones (2), ones (2), ones (2), ones (3))
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
364s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
364s 19 tests, 19 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/copularnd.m]
364s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/copularnd.m
364s ***** test
364s  theta = 0.5;
364s  r = copularnd ("Gaussian", theta);
364s  assert (size (r), [1, 2]);
364s  assert (all ((r >= 0) & (r <= 1)));
364s ***** test
364s  theta = 0.5;
364s  df = 2;
364s  r = copularnd ("t", theta, df);
364s  assert (size (r), [1, 2]);
364s  assert (all ((r >= 0) & (r <= 1)));
364s ***** test
364s  theta = 0.5;
364s  r = copularnd ("Clayton", theta);
364s  assert (size (r), [1, 2]);
364s  assert (all ((r >= 0) & (r <= 1)));
364s ***** test
364s  theta = 0.5;
364s  n = 2;
364s  r = copularnd ("Clayton", theta, n);
364s  assert (size (r), [n, 2]);
364s  assert (all ((r >= 0) & (r <= 1)));
364s ***** test
364s  theta = [1; 2];
364s  n = 2;
364s  d = 3;
364s  r = copularnd ("Clayton", theta, n, d);
364s  assert (size (r), [n, d]);
364s  assert (all ((r >= 0) & (r <= 1)));
364s 5 tests, 5 passed, 0 known failure, 0 skipped
364s [inst/dist_fun/wblcdf.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/wblcdf.m
365s ***** demo
365s  ## Plot various CDFs from the Weibull distribution
365s  x = 0:0.001:2.5;
365s  p1 = wblcdf (x, 1, 0.5);
365s  p2 = wblcdf (x, 1, 1);
365s  p3 = wblcdf (x, 1, 1.5);
365s  p4 = wblcdf (x, 1, 5);
365s  plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
365s  grid on
365s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
365s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
365s  title ("Weibull CDF")
365s  xlabel ("values in x")
365s  ylabel ("probability")
365s ***** shared x, y
365s  x = [-1 0 0.5 1 Inf];
365s  y = [0, 1-exp(-x(2:4)), 1];
365s ***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
365s ***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
365s ***** assert (wblcdf (x, "upper"), 1 - y)
365s ***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
365s ***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
365s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
365s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
365s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
365s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
365s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
365s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
365s ***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
365s ***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
365s ***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
365s ***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
365s ***** error<wblcdf: invalid number of input arguments.> wblcdf ()
365s ***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
365s ***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
365s ***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
365s  wblcdf (ones (3), ones (2), ones (2))
365s ***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
365s ***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
365s  [p, plo, pup] = wblcdf (1, 2, 3)
365s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
365s  wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
365s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
365s  wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
365s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
365s  wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
365s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
365s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
365s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
365s ***** error<wblcdf: bad covariance matrix.> ...
365s  [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
365s 28 tests, 28 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/bisainv.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bisainv.m
365s ***** demo
365s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = bisainv (p, 1, 0.5);
365s  x2 = bisainv (p, 1, 1);
365s  x3 = bisainv (p, 1, 2);
365s  x4 = bisainv (p, 1, 5);
365s  x5 = bisainv (p, 1, 10);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
365s  grid on
365s  ylim ([0, 10])
365s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
365s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
365s  title ("Birnbaum-Saunders iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x")
365s ***** demo
365s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = bisainv (p, 1, 0.3);
365s  x2 = bisainv (p, 2, 0.3);
365s  x3 = bisainv (p, 1, 0.5);
365s  x4 = bisainv (p, 3, 0.5);
365s  x5 = bisainv (p, 5, 0.5);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
365s  grid on
365s  ylim ([0, 10])
365s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
365s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
365s  title ("Birnbaum-Saunders iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x")
365s ***** shared p, y, f
365s  f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
365s  p = [-1, 0, 1/4, 1/2, 1, 2];
365s  y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
365s ***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
365s ***** assert (bisainv (p, 1, ones (1,6)), y)
365s ***** assert (bisainv (p, ones (1,6), 1), y)
365s ***** assert (bisainv (p, 1, 1), y)
365s ***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
365s ***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
365s ***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
365s ***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
365s ***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
365s ***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
365s ***** error<bisainv: function called with too few input arguments.> bisainv ()
365s ***** error<bisainv: function called with too few input arguments.> bisainv (1)
365s ***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
365s ***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
365s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
365s  bisainv (ones (3), ones (2), ones(2))
365s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
365s  bisainv (ones (2), ones (3), ones(2))
365s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
365s  bisainv (ones (2), ones (2), ones(3))
365s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
365s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
365s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
365s 20 tests, 20 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/geocdf.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/geocdf.m
365s ***** demo
365s  ## Plot various CDFs from the geometric distribution
365s  x = 0:10;
365s  p1 = geocdf (x, 0.2);
365s  p2 = geocdf (x, 0.5);
365s  p3 = geocdf (x, 0.7);
365s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
365s  grid on
365s  xlim ([0, 10])
365s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
365s  title ("Geometric CDF")
365s  xlabel ("values in x (number of failures)")
365s  ylabel ("probability")
365s ***** test
365s  p = geocdf ([1, 2, 3, 4], 0.25);
365s  assert (p(1), 0.4375000000, 1e-14);
365s  assert (p(2), 0.5781250000, 1e-14);
365s  assert (p(3), 0.6835937500, 1e-14);
365s  assert (p(4), 0.7626953125, 1e-14);
365s ***** test
365s  p = geocdf ([1, 2, 3, 4], 0.25, "upper");
365s  assert (p(1), 0.5625000000, 1e-14);
365s  assert (p(2), 0.4218750000, 1e-14);
365s  assert (p(3), 0.3164062500, 1e-14);
365s  assert (p(4), 0.2373046875, 1e-14);
365s ***** shared x, p
365s  x = [-1 0 1 Inf];
365s  p = [0 0.5 0.75 1];
365s ***** assert (geocdf (x, 0.5*ones (1,4)), p)
365s ***** assert (geocdf (x, 0.5), p)
365s ***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
365s ***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
365s ***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
365s ***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
365s ***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
365s ***** error<geocdf: function called with too few input arguments.> geocdf ()
365s ***** error<geocdf: function called with too few input arguments.> geocdf (1)
365s ***** error<geocdf: X and PS must be of common size or scalars.> ...
365s  geocdf (ones (3), ones (2))
365s ***** error<geocdf: X and PS must be of common size or scalars.> ...
365s  geocdf (ones (2), ones (3))
365s ***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
365s ***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
365s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
365s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
365s 17 tests, 17 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/lognpdf.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/lognpdf.m
365s ***** demo
365s  ## Plot various PDFs from the log-normal distribution
365s  x = 0:0.01:5;
365s  y1 = lognpdf (x, 0, 1);
365s  y2 = lognpdf (x, 0, 0.5);
365s  y3 = lognpdf (x, 0, 0.25);
365s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
365s  grid on
365s  ylim ([0, 2])
365s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
365s           "location", "northeast")
365s  title ("Log-normal PDF")
365s  xlabel ("values in x")
365s  ylabel ("density")
365s ***** shared x, y
365s  x = [-1 0 e Inf];
365s  y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
365s ***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
365s ***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
365s ***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
365s ***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
365s ***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
365s ***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
365s ***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
365s ***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
365s ***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
365s ***** error lognpdf ()
365s ***** error lognpdf (1,2,3,4)
365s ***** error lognpdf (ones (3), ones (2), ones (2))
365s ***** error lognpdf (ones (2), ones (3), ones (2))
365s ***** error lognpdf (ones (2), ones (2), ones (3))
365s ***** error lognpdf (i, 2, 2)
365s ***** error lognpdf (2, i, 2)
365s ***** error lognpdf (2, 2, i)
365s 17 tests, 17 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/laplacernd.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/laplacernd.m
365s ***** assert (size (laplacernd (1, 1)), [1 1])
365s ***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
365s ***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
365s ***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
365s ***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
365s ***** assert (size (laplacernd (1, 1, 3)), [3, 3])
365s ***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
365s ***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
365s ***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
365s ***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
365s ***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
365s ***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
365s ***** assert (class (laplacernd (1, 1)), "double")
365s ***** assert (class (laplacernd (1, single (1))), "single")
365s ***** assert (class (laplacernd (1, single ([1, 1]))), "single")
365s ***** assert (class (laplacernd (single (1), 1)), "single")
365s ***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
365s ***** error<laplacernd: function called with too few input arguments.> laplacernd ()
365s ***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
365s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
365s  laplacernd (ones (3), ones (2))
365s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
365s  laplacernd (ones (2), ones (3))
365s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
365s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
365s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  laplacernd (1, 2, -1)
365s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  laplacernd (1, 2, 1.2)
365s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  laplacernd (1, 2, ones (2))
365s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  laplacernd (1, 2, [2 -1 2])
365s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  laplacernd (1, 2, [2 0 2.5])
365s ***** error<laplacernd: dimensions must be non-negative integers.> ...
365s  laplacernd (1, 2, 2, -1, 5)
365s ***** error<laplacernd: dimensions must be non-negative integers.> ...
365s  laplacernd (1, 2, 2, 1.5, 5)
365s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
365s  laplacernd (2, ones (2), 3)
365s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
365s  laplacernd (2, ones (2), [3, 2])
365s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
365s  laplacernd (2, ones (2), 3, 2)
365s 33 tests, 33 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/laplacepdf.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/laplacepdf.m
365s ***** demo
365s  ## Plot various PDFs from the Laplace distribution
365s  x = -10:0.01:10;
365s  y1 = laplacepdf (x, 0, 1);
365s  y2 = laplacepdf (x, 0, 2);
365s  y3 = laplacepdf (x, 0, 4);
365s  y4 = laplacepdf (x, -5, 4);
365s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
365s  grid on
365s  xlim ([-10, 10])
365s  ylim ([0, 0.6])
365s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
365s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
365s  title ("Laplace PDF")
365s  xlabel ("values in x")
365s  ylabel ("density")
365s ***** shared x, y
365s  x = [-Inf -log(2) 0 log(2) Inf];
365s  y = [0, 1/4, 1/2, 1/4, 0];
365s ***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
365s ***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
365s ***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
365s ***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
365s ***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
365s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
365s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
365s ***** error<laplacepdf: function called with too few input arguments.> ...
365s  laplacepdf (1, 2)
365s ***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
365s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
365s  laplacepdf (1, ones (2), ones (3))
365s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
365s  laplacepdf (ones (2), 1, ones (3))
365s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
365s  laplacepdf (ones (2), ones (3), 1)
365s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
365s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
365s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
365s 15 tests, 15 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/ncx2rnd.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncx2rnd.m
365s ***** assert (size (ncx2rnd (1, 1)), [1 1])
365s ***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
365s ***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
365s ***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
365s ***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
365s ***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
365s ***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
365s ***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
365s ***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
365s ***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
365s ***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
365s ***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
365s ***** assert (class (ncx2rnd (1, 1)), "double")
365s ***** assert (class (ncx2rnd (1, single (1))), "single")
365s ***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
365s ***** assert (class (ncx2rnd (single (1), 1)), "single")
365s ***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
365s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
365s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
365s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
365s  ncx2rnd (ones (3), ones (2))
365s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
365s  ncx2rnd (ones (2), ones (3))
365s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
365s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
365s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  ncx2rnd (1, 2, -1)
365s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  ncx2rnd (1, 2, 1.2)
365s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  ncx2rnd (1, 2, ones (2))
365s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  ncx2rnd (1, 2, [2 -1 2])
365s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  ncx2rnd (1, 2, [2 0 2.5])
365s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
365s  ncx2rnd (1, 2, 2, -1, 5)
365s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
365s  ncx2rnd (1, 2, 2, 1.5, 5)
365s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
365s  ncx2rnd (2, ones (2), 3)
365s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
365s  ncx2rnd (2, ones (2), [3, 2])
365s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
365s  ncx2rnd (2, ones (2), 3, 2)
365s 33 tests, 33 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/geornd.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/geornd.m
365s ***** assert (size (geornd (0.5)), [1, 1])
365s ***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
365s ***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
365s ***** assert (size (geornd (0.5, 3)), [3, 3])
365s ***** assert (size (geornd (0.5, [4 1])), [4, 1])
365s ***** assert (size (geornd (0.5, 4, 1)), [4, 1])
365s ***** assert (class (geornd (0.5)), "double")
365s ***** assert (class (geornd (single (0.5))), "single")
365s ***** assert (class (geornd (single ([0.5 0.5]))), "single")
365s ***** assert (class (geornd (single (0))), "single")
365s ***** assert (class (geornd (single (1))), "single")
365s ***** error<geornd: function called with too few input arguments.> geornd ()
365s ***** error<geornd: PS must not be complex.> geornd (i)
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (1, -1)
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (1, 1.2)
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (1, ones (2))
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (1, [2 -1 2])
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (1, [2 0 2.5])
365s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  geornd (ones (2), ones (2))
365s ***** error<geornd: dimensions must be non-negative integers.> ...
365s  geornd (1, 2, -1, 5)
365s ***** error<geornd: dimensions must be non-negative integers.> ...
365s  geornd (1, 2, 1.5, 5)
365s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
365s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
365s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
365s 24 tests, 24 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/unifcdf.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unifcdf.m
365s ***** demo
365s  ## Plot various CDFs from the continuous uniform distribution
365s  x = 0:0.1:10;
365s  p1 = unifcdf (x, 2, 5);
365s  p2 = unifcdf (x, 3, 9);
365s  plot (x, p1, "-b", x, p2, "-g")
365s  grid on
365s  xlim ([0, 10])
365s  ylim ([0, 1])
365s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
365s  title ("Continuous uniform CDF")
365s  xlabel ("values in x")
365s  ylabel ("probability")
365s ***** shared x, y
365s  x = [-1 0 0.5 1 2] + 1;
365s  y = [0 0 0.5 1 1];
365s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
365s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
365s ***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
365s ***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
365s ***** assert (unifcdf (x, ones (1,5), 2), y)
365s ***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
365s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
365s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
365s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
365s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
365s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
365s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
365s ***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
365s ***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
365s ***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
365s ***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
365s ***** error<unifcdf: function called with too few input arguments.> unifcdf ()
365s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
365s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
365s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
365s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
365s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
365s  unifcdf (ones (3), ones (2), ones (2))
365s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
365s  unifcdf (ones (2), ones (3), ones (2))
365s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
365s  unifcdf (ones (2), ones (2), ones (3))
365s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
365s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
365s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
365s 27 tests, 27 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/poissinv.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/poissinv.m
365s ***** demo
365s  ## Plot various iCDFs from the Poisson distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = poissinv (p, 13);
365s  x2 = poissinv (p, 4);
365s  x3 = poissinv (p, 10);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
365s  grid on
365s  ylim ([0, 20])
365s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
365s  title ("Poisson iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x (number of occurences)")
365s ***** shared p
365s  p = [-1 0 0.5 1 2];
365s ***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
365s ***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
365s ***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
365s ***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
365s ***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
365s ***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
365s ***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
365s ***** error<poissinv: function called with too few input arguments.> poissinv ()
365s ***** error<poissinv: function called with too few input arguments.> poissinv (1)
365s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
365s  poissinv (ones (3), ones (2))
365s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
365s  poissinv (ones (2), ones (3))
365s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
365s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
365s 13 tests, 13 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/norminv.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/norminv.m
365s ***** demo
365s  ## Plot various iCDFs from the normal distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = norminv (p, 0, 0.5);
365s  x2 = norminv (p, 0, 1);
365s  x3 = norminv (p, 0, 2);
365s  x4 = norminv (p, -2, 0.8);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
365s  grid on
365s  ylim ([-5, 5])
365s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
365s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
365s  title ("Normal iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x")
365s ***** shared p
365s  p = [-1 0 0.5 1 2];
365s ***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
365s ***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
365s ***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
365s ***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
365s ***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
365s ***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
365s ***** assert (norminv (p), probit (p))
365s ***** assert (norminv (0.31254), probit (0.31254))
365s ***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
365s ***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
365s ***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
365s ***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
365s ***** error<norminv: function called with too few input arguments.> norminv ()
365s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
365s  norminv (ones (3), ones (2), ones (2))
365s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
365s  norminv (ones (2), ones (3), ones (2))
365s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
365s  norminv (ones (2), ones (2), ones (3))
365s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
365s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
365s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
365s 19 tests, 19 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/gumbelinv.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gumbelinv.m
365s ***** demo
365s  ## Plot various iCDFs from the Gumbel distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = gumbelinv (p, 0.5, 2);
365s  x2 = gumbelinv (p, 1.0, 2);
365s  x3 = gumbelinv (p, 1.5, 3);
365s  x4 = gumbelinv (p, 3.0, 4);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
365s  grid on
365s  ylim ([-5, 20])
365s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
365s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
365s  title ("Gumbel iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x")
365s ***** shared p, x
365s  p = [0, 0.05, 0.5 0.95];
365s  x = [-Inf, -1.0972, 0.3665, 2.9702];
365s ***** assert (gumbelinv (p), x, 1e-4)
365s ***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
365s ***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
365s ***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
365s ***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
365s ***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
365s ***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
365s ***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
365s ***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
365s ***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
365s ***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
365s  p = [0.05, 0.5, 0.95];
365s  x = gumbelinv(p);
365s ***** assert (gumbelcdf(x), p, 1e-4)
365s ***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
365s ***** error gumbelinv (1,2,3,4,5,6)
365s ***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
365s  gumbelinv (ones (3), ones (2), ones (2))
365s ***** error<gumbelinv: invalid size of covariance matrix.> ...
365s  [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
365s ***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
365s  [p, plo, pup] = gumbelinv (1, 2, 3)
365s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
365s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
365s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
365s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
365s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
365s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
365s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
365s ***** error<gumbelinv: bad covariance matrix.> ...
365s  [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
365s 23 tests, 23 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/invgrnd.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/invgrnd.m
365s ***** assert (size (invgrnd (1, 1, 1)), [1, 1])
365s ***** assert (size (invgrnd (1, 1, 2)), [2, 2])
365s ***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
365s ***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
365s ***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
365s ***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
365s ***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
365s ***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
365s ***** assert (size (invgrnd (1, 1, 3)), [3, 3])
365s ***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
365s ***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
365s ***** test
365s  r =  invgrnd (1, [1, 0, -1]);
365s  assert (r([2:3]), [NaN, NaN])
365s ***** assert (class (invgrnd (1, 0)), "double")
365s ***** assert (class (invgrnd (1, single (0))), "single")
365s ***** assert (class (invgrnd (1, single ([0 0]))), "single")
365s ***** assert (class (invgrnd (1, single (1))), "single")
365s ***** assert (class (invgrnd (1, single ([1 1]))), "single")
365s ***** assert (class (invgrnd (single (1), 1)), "single")
365s ***** assert (class (invgrnd (single ([1 1]), 1)), "single")
365s ***** error<invgrnd: function called with too few input arguments.> invgrnd ()
365s ***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
365s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
365s  invgrnd (ones (3), ones (2))
365s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
365s  invgrnd (ones (2), ones (3))
365s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
365s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
365s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  invgrnd (1, 2, -1)
365s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  invgrnd (1, 2, 1.2)
365s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  invgrnd (1, 2, ones (2))
365s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  invgrnd (1, 2, [2 -1 2])
365s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
365s  invgrnd (1, 2, [2 0 2.5])
365s ***** error<invgrnd: dimensions must be non-negative integers.> ...
365s  invgrnd (1, 2, 2, -1, 5)
365s ***** error<invgrnd: dimensions must be non-negative integers.> ...
365s  invgrnd (1, 2, 2, 1.5, 5)
365s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
365s  invgrnd (2, ones (2), 3)
365s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
365s  invgrnd (2, ones (2), [3, 2])
365s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
365s  invgrnd (2, ones (2), 3, 2)
365s 35 tests, 35 passed, 0 known failure, 0 skipped
365s [inst/dist_fun/finv.m]
365s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/finv.m
365s ***** demo
365s  ## Plot various iCDFs from the F distribution
365s  p = 0.001:0.001:0.999;
365s  x1 = finv (p, 1, 1);
365s  x2 = finv (p, 2, 1);
365s  x3 = finv (p, 5, 2);
365s  x4 = finv (p, 10, 1);
365s  x5 = finv (p, 100, 100);
365s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
365s  grid on
365s  ylim ([0, 4])
365s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
365s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
365s           "df1 = 100, df2 = 100"}, "location", "northwest")
365s  title ("F iCDF")
365s  xlabel ("probability")
365s  ylabel ("values in x")
365s ***** shared p
365s  p = [-1 0 0.5 1 2];
365s ***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
365s ***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
365s ***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
365s ***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN Inf NaN])
365s ***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN Inf NaN])
365s ***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
365s ***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
365s ***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
365s ***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
365s ***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
365s ***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
365s ***** test
365s  x = finv (0.35, Inf, 4);
365s  assert (x, 0.9014, 1e-4)
365s ***** test
365s  x = finv (0, Inf, 4);
365s  assert (x, 0)
365s ***** test
365s  x = finv (1, Inf, 4);
365s  assert (x, Inf)
365s ***** test
365s  x = finv (0.35, 4, Inf);
365s  assert (x, 0.6175, 1e-4)
365s ***** test
365s  x = finv (0, 4, Inf);
365s  assert (x, 0)
365s ***** test
365s  x = finv (1, 4, Inf);
365s  assert (x, Inf)
366s ***** test
366s  x = finv ([0, 0.000001, 0.35, 1, 1.2], Inf, Inf);
366s  assert (x, [0, 1, 1, 1, NaN]);
366s ***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
366s ***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
366s ***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
366s ***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
366s ***** error<finv: function called with too few input arguments.> finv ()
366s ***** error<finv: function called with too few input arguments.> finv (1)
366s ***** error<finv: function called with too few input arguments.> finv (1,2)
366s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
366s  finv (ones (3), ones (2), ones (2))
366s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
366s  finv (ones (2), ones (3), ones (2))
366s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
366s  finv (ones (2), ones (2), ones (3))
366s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
366s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
366s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
366s 31 tests, 31 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/fcdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/fcdf.m
366s ***** demo
366s  ## Plot various CDFs from the F distribution
366s  x = 0.01:0.01:4;
366s  p1 = fcdf (x, 1, 2);
366s  p2 = fcdf (x, 2, 1);
366s  p3 = fcdf (x, 5, 2);
366s  p4 = fcdf (x, 10, 1);
366s  p5 = fcdf (x, 100, 100);
366s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
366s  grid on
366s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
366s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
366s           "df1 = 100, df2 = 100"}, "location", "southeast")
366s  title ("F CDF")
366s  xlabel ("values in x")
366s  ylabel ("probability")
366s ***** shared x, y
366s  x = [-1, 0, 0.5, 1, 2, Inf];
366s  y = [0, 0, 1/3, 1/2, 2/3, 1];
366s ***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
366s ***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
366s ***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
366s ***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
366s ***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
366s ***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
366s ***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
366s ***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
366s ***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
366s ***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
366s ***** error<fcdf: function called with too few input arguments.> fcdf ()
366s ***** error<fcdf: function called with too few input arguments.> fcdf (1)
366s ***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
366s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
366s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
366s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
366s  fcdf (ones (3), ones (2), ones (2))
366s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
366s  fcdf (ones (2), ones (3), ones (2))
366s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
366s  fcdf (ones (2), ones (2), ones (3))
366s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
366s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
366s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
366s 21 tests, 21 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/raylcdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/raylcdf.m
366s ***** demo
366s  ## Plot various CDFs from the Rayleigh distribution
366s  x = 0:0.01:10;
366s  p1 = raylcdf (x, 0.5);
366s  p2 = raylcdf (x, 1);
366s  p3 = raylcdf (x, 2);
366s  p4 = raylcdf (x, 3);
366s  p5 = raylcdf (x, 4);
366s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
366s  grid on
366s  ylim ([0, 1])
366s  legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
366s           "σ = 3", "σ = 4"}, "location", "southeast")
366s  title ("Rayleigh CDF")
366s  xlabel ("values in x")
366s  ylabel ("probability")
366s ***** test
366s  x = 0:0.5:2.5;
366s  sigma = 1:6;
366s  p = raylcdf (x, sigma);
366s  expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
366s  assert (p, expected_p, 0.001);
366s ***** test
366s  x = 0:0.5:2.5;
366s  p = raylcdf (x, 0.5);
366s  expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
366s  assert (p, expected_p, 0.001);
366s ***** shared x, p
366s  x = [-1, 0, 1, 2, Inf];
366s  p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
366s ***** assert (raylcdf (x, 1), p, 1e-14)
366s ***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
366s ***** error<raylcdf: function called with too few input arguments.> raylcdf ()
366s ***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
366s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
366s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
366s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
366s  raylcdf (ones (3), ones (2))
366s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
366s  raylcdf (ones (2), ones (3))
366s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
366s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
366s 12 tests, 12 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/chi2rnd.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/chi2rnd.m
366s ***** assert (size (chi2rnd (2)), [1, 1])
366s ***** assert (size (chi2rnd (ones (2,1))), [2, 1])
366s ***** assert (size (chi2rnd (ones (2,2))), [2, 2])
366s ***** assert (size (chi2rnd (1, 3)), [3, 3])
366s ***** assert (size (chi2rnd (1, [4 1])), [4, 1])
366s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
366s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
366s ***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
366s ***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
366s ***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
366s ***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
366s ***** assert (class (chi2rnd (2)), "double")
366s ***** assert (class (chi2rnd (single (2))), "single")
366s ***** assert (class (chi2rnd (single ([2 2]))), "single")
366s ***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
366s ***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (1, -1)
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (1, 1.2)
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (1, ones (2))
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (1, [2 -1 2])
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (1, [2 0 2.5])
366s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  chi2rnd (ones (2), ones (2))
366s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
366s  chi2rnd (1, 2, -1, 5)
366s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
366s  chi2rnd (1, 2, 1.5, 5)
366s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
366s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
366s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
366s 27 tests, 27 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/tripdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/tripdf.m
366s ***** demo
366s  ## Plot various CDFs from the triangular distribution
366s  x = 0.001:0.001:10;
366s  y1 = tripdf (x, 3, 4, 6);
366s  y2 = tripdf (x, 1, 2, 5);
366s  y3 = tripdf (x, 2, 3, 9);
366s  y4 = tripdf (x, 2, 5, 9);
366s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
366s  grid on
366s  xlim ([0, 10])
366s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
366s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
366s          "location", "northeast")
366s  title ("Triangular CDF")
366s  xlabel ("values in x")
366s  ylabel ("probability")
366s ***** shared x, y, deps
366s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
366s  y = [0, 0, 0.4, 2, 0.4, 0, 0];
366s  deps = 2*eps;
366s ***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
366s ***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
366s ***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
366s ***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
366s ***** assert (tripdf (x, 1, 1.5, 2), y, deps)
366s ***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
366s ***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
366s ***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
366s ***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
366s ***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
366s ***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
366s ***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
366s ***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
366s ***** error<tripdf: function called with too few input arguments.> tripdf ()
366s ***** error<tripdf: function called with too few input arguments.> tripdf (1)
366s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
366s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
366s ***** error<tripdf: function called with too many inputs> ...
366s  tripdf (1, 2, 3, 4, 5)
366s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
366s  tripdf (ones (3), ones (2), ones(2), ones(2))
366s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
366s  tripdf (ones (2), ones (3), ones(2), ones(2))
366s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
366s  tripdf (ones (2), ones (2), ones(3), ones(2))
366s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
366s  tripdf (ones (2), ones (2), ones(2), ones(3))
366s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
366s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
366s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
366s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
366s 26 tests, 26 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/loglinv.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/loglinv.m
366s ***** demo
366s  ## Plot various iCDFs from the log-logistic distribution
366s  p = 0.001:0.001:0.999;
366s  x1 = loglinv (p, log (1), 1/0.5);
366s  x2 = loglinv (p, log (1), 1);
366s  x3 = loglinv (p, log (1), 1/2);
366s  x4 = loglinv (p, log (1), 1/4);
366s  x5 = loglinv (p, log (1), 1/8);
366s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
366s  ylim ([0, 20])
366s  grid on
366s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
366s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
366s  title ("Log-logistic iCDF")
366s  xlabel ("probability")
366s  ylabel ("x")
366s  text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
366s ***** shared p, out1, out2
366s  p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
366s  out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
366s  out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
366s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
366s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
366s ***** assert (loglinv (p, 1, 3), out2, 1e-6)
366s ***** assert (class (loglinv (single (1), 2, 3)), "single")
366s ***** assert (class (loglinv (1, single (2), 3)), "single")
366s ***** assert (class (loglinv (1, 2, single (3))), "single")
366s ***** error<loglinv: function called with too few input arguments.> loglinv (1)
366s ***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
366s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
366s  loglinv (1, ones (2), ones (3))
366s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
366s  loglinv (ones (2), 1, ones (3))
366s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
366s  loglinv (ones (2), ones (3), 1)
366s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
366s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
366s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
366s 14 tests, 14 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/jsucdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/jsucdf.m
366s ***** error jsucdf ()
366s ***** error jsucdf (1, 2, 3, 4)
366s ***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
366s  jsucdf (1, ones (2), ones (3))
366s 3 tests, 3 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/invgpdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/invgpdf.m
366s ***** demo
366s  ## Plot various PDFs from the inverse Gaussian distribution
366s  x = 0:0.001:3;
366s  y1 = invgpdf (x, 1, 0.2);
366s  y2 = invgpdf (x, 1, 1);
366s  y3 = invgpdf (x, 1, 3);
366s  y4 = invgpdf (x, 3, 0.2);
366s  y5 = invgpdf (x, 3, 1);
366s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
366s  grid on
366s  xlim ([0, 3])
366s  ylim ([0, 3])
366s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
366s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
366s  title ("Inverse Gaussian PDF")
366s  xlabel ("values in x")
366s  ylabel ("density")
366s ***** shared x, y
366s  x = [-Inf, -1, 0, 1/2, 1, Inf];
366s  y = [0, 0, 0, 0.8788, 0.3989, 0];
366s ***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
366s ***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
366s ***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
366s ***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
366s ***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
366s ***** error<invgpdf: function called with too few input arguments.> invgpdf ()
366s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
366s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
366s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
366s  invgpdf (1, ones (2), ones (3))
366s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
366s  invgpdf (ones (2), 1, ones (3))
366s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
366s  invgpdf (ones (2), ones (3), 1)
366s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
366s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
366s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
366s 14 tests, 14 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/gprnd.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gprnd.m
366s ***** assert (size (gprnd (0, 1, 0)), [1, 1])
366s ***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
366s ***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
366s ***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
366s ***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
366s ***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
366s ***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
366s ***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
366s ***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
366s ***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
366s ***** assert (size (gprnd (1,1,0)), [1, 1])
366s ***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
366s ***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
366s ***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
366s ***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
366s ***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
366s ***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
366s ***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
366s ***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
366s ***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
366s ***** assert (size (gprnd (-1, 1, 0)), [1, 1])
366s ***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
366s ***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
366s ***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
366s ***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
366s ***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
366s ***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
366s ***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
366s ***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
366s ***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
366s ***** assert (class (gprnd (0, 1, 0)), "double")
366s ***** assert (class (gprnd (0, 1, single (0))), "single")
366s ***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
366s ***** assert (class (gprnd (0, single (1),0)), "single")
366s ***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
366s ***** assert (class (gprnd (single (0), 1, 0)), "single")
366s ***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
366s ***** error<gprnd: function called with too few input arguments.> gprnd ()
366s ***** error<gprnd: function called with too few input arguments.> gprnd (1)
366s ***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
366s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
366s  gprnd (ones (3), ones (2), ones (2))
366s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
366s  gprnd (ones (2), ones (3), ones (2))
366s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
366s  gprnd (ones (2), ones (2), ones (3))
366s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
366s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
366s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
366s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  gprnd (1, 2, 3, -1)
366s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  gprnd (1, 2, 3, 1.2)
366s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  gprnd (1, 2, 3, ones (2))
366s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  gprnd (1, 2, 3, [2 -1 2])
366s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  gprnd (1, 2, 3, [2 0 2.5])
366s ***** error<gprnd: dimensions must be non-negative integers.> ...
366s  gprnd (1, 2, 3, 2, -1, 5)
366s ***** error<gprnd: dimensions must be non-negative integers.> ...
366s  gprnd (1, 2, 3, 2, 1.5, 5)
366s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
366s  gprnd (2, ones (2), 2, 3)
366s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
366s  gprnd (2, ones (2), 2, [3, 2])
366s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
366s  gprnd (2, ones (2), 2, 3, 2)
366s 56 tests, 56 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/raylrnd.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/raylrnd.m
366s ***** assert (size (raylrnd (2)), [1, 1])
366s ***** assert (size (raylrnd (ones (2,1))), [2, 1])
366s ***** assert (size (raylrnd (ones (2,2))), [2, 2])
366s ***** assert (size (raylrnd (1, 3)), [3, 3])
366s ***** assert (size (raylrnd (1, [4 1])), [4, 1])
366s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
366s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
366s ***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
366s ***** assert (size (raylrnd (1, 0, 1)), [0, 1])
366s ***** assert (size (raylrnd (1, 1, 0)), [1, 0])
366s ***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
366s ***** assert (raylrnd (0, 1, 1), NaN)
366s ***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
366s ***** assert (class (raylrnd (2)), "double")
366s ***** assert (class (raylrnd (single (2))), "single")
366s ***** assert (class (raylrnd (single ([2 2]))), "single")
366s ***** error<raylrnd: function called with too few input arguments.> raylrnd ()
366s ***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (1, -1)
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (1, 1.2)
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (1, ones (2))
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (1, [2 -1 2])
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (1, [2 0 2.5])
366s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
366s  raylrnd (ones (2), ones (2))
366s ***** error<raylrnd: dimensions must be non-negative integers.> ...
366s  raylrnd (1, 2, -1, 5)
366s ***** error<raylrnd: dimensions must be non-negative integers.> ...
366s  raylrnd (1, 2, 1.5, 5)
366s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
366s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
366s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
366s 29 tests, 29 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/raylpdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/raylpdf.m
366s ***** demo
366s  ## Plot various PDFs from the Rayleigh distribution
366s  x = 0:0.01:10;
366s  y1 = raylpdf (x, 0.5);
366s  y2 = raylpdf (x, 1);
366s  y3 = raylpdf (x, 2);
366s  y4 = raylpdf (x, 3);
366s  y5 = raylpdf (x, 4);
366s  plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
366s  grid on
366s  ylim ([0, 1.25])
366s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
366s           "σ = 3", "σ = 4"}, "location", "northeast")
366s  title ("Rayleigh PDF")
366s  xlabel ("values in x")
366s  ylabel ("density")
366s ***** test
366s  x = 0:0.5:2.5;
366s  sigma = 1:6;
366s  y = raylpdf (x, sigma);
366s  expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
366s  assert (y, expected_y, 0.001);
366s ***** test
366s  x = 0:0.5:2.5;
366s  y = raylpdf (x, 0.5);
366s  expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
366s  assert (y, expected_y, 0.001);
366s ***** error<raylpdf: function called with too few input arguments.> raylpdf ()
366s ***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
366s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
366s  raylpdf (ones (3), ones (2))
366s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
366s  raylpdf (ones (2), ones (3))
366s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
366s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
366s 8 tests, 8 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/betacdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/betacdf.m
366s ***** demo
366s  ## Plot various CDFs from the Beta distribution
366s  x = 0:0.005:1;
366s  p1 = betacdf (x, 0.5, 0.5);
366s  p2 = betacdf (x, 5, 1);
366s  p3 = betacdf (x, 1, 3);
366s  p4 = betacdf (x, 2, 2);
366s  p5 = betacdf (x, 2, 5);
366s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
366s  grid on
366s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
366s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
366s  title ("Beta CDF")
366s  xlabel ("values in x")
366s  ylabel ("probability")
366s ***** shared x, y, x1, x2
366s  x = [-1 0 0.5 1 2];
366s  y = [0 0 0.75 1 1];
366s ***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
366s ***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
366s ***** assert (betacdf (x, ones (1, 5), 2), y)
366s ***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
366s ***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
366s ***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
366s  x1 = [0.1:0.2:0.9];
366s ***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
366s ***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
366s         1e-14);
366s  x2 = [1, 2, 3];
366s ***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
366s ***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
366s ***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
366s ***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
366s ***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
366s ***** error<betacdf: function called with too few input arguments.> betacdf ()
366s ***** error<betacdf: function called with too few input arguments.> betacdf (1)
366s ***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
366s ***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
366s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
366s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
366s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
366s  betacdf (ones (3), ones (2), ones (2))
366s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
366s  betacdf (ones (2), ones (3), ones (2))
366s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
366s  betacdf (ones (2), ones (2), ones (3))
366s ***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
366s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
366s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
366s 25 tests, 25 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/plpdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/plpdf.m
366s ***** demo
366s  ## Plot various PDFs from the Piecewise linear distribution
366s  data = 0:0.01:10;
366s  x1 = [0, 1, 3, 4, 7, 10];
366s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
366s  x2 = [0, 2, 5, 6, 7, 8];
366s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
366s  y1 = plpdf (data, x1, Fx1);
366s  y2 = plpdf (data, x2, Fx2);
366s  plot (data, y1, "-b", data, y2, "g")
366s  grid on
366s  ylim ([0, 0.6])
366s  xlim ([0, 10])
366s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
366s  title ("Piecewise linear CDF")
366s  xlabel ("values in data")
366s  ylabel ("density")
366s ***** shared x, Fx
366s  x = [0, 1, 3, 4, 7, 10];
366s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
366s ***** assert (plpdf (0.5, x, Fx), 0.2, eps);
366s ***** assert (plpdf (1.5, x, Fx), 0.15, eps);
366s ***** assert (plpdf (3.5, x, Fx), 0.1, eps);
366s ***** assert (plpdf (5, x, Fx), 0.1/3, eps);
366s ***** assert (plpdf (8, x, Fx), 0.1, eps);
366s ***** error<plpdf: function called with too few input arguments.> plpdf ()
366s ***** error<plpdf: function called with too few input arguments.> plpdf (1)
366s ***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
366s ***** error<plpdf: X and FX must be vectors of equal size.> ...
366s  plpdf (1, [0, 1, 2], [0, 1])
366s ***** error<plpdf: X and FX must be at least two-elements long.> ...
366s  plpdf (1, [0], [1])
366s ***** error<plpdf: FX must be bounded in the range> ...
366s  plpdf (1, [0, 1, 2], [0, 1, 1.5])
366s ***** error<plpdf: FX must be bounded in the range> ...
366s  plpdf (1, [0, 1, 2], [0, i, 1])
366s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
366s  plpdf (i, [0, 1, 2], [0, 0.5, 1])
366s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
366s  plpdf (1, [0, i, 2], [0, 0.5, 1])
366s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
366s  plpdf (1, [0, 1, 2], [0, 0.5i, 1])
366s 15 tests, 15 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/nbinpdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/nbinpdf.m
366s ***** demo
366s  ## Plot various PDFs from the negative binomial distribution
366s  x = 0:40;
366s  y1 = nbinpdf (x, 2, 0.15);
366s  y2 = nbinpdf (x, 5, 0.2);
366s  y3 = nbinpdf (x, 4, 0.4);
366s  y4 = nbinpdf (x, 10, 0.3);
366s  plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
366s  grid on
366s  xlim ([0, 40])
366s  ylim ([0, 0.12])
366s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
366s           "r = 10, ps = 0.3"}, "location", "northeast")
366s  title ("Negative binomial PDF")
366s  xlabel ("values in x (number of failures)")
366s  ylabel ("density")
366s ***** shared x, y
366s  x = [-1 0 1 2 Inf];
366s  y = [0 1/2 1/4 1/8 NaN];
366s ***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
366s ***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
366s ***** assert (nbinpdf (x, ones (1,5), 0.5), y)
366s ***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
366s ***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
366s ***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
366s ***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
366s ***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
366s ***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
366s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
366s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
366s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
366s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
366s  nbinpdf (ones (3), ones (2), ones (2))
366s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
366s  nbinpdf (ones (2), ones (3), ones (2))
366s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
366s  nbinpdf (ones (2), ones (2), ones (3))
366s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
366s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
366s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
366s 18 tests, 18 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/cauchyinv.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/cauchyinv.m
366s ***** demo
366s  ## Plot various iCDFs from the Cauchy distribution
366s  p = 0.001:0.001:0.999;
366s  x1 = cauchyinv (p, 0, 0.5);
366s  x2 = cauchyinv (p, 0, 1);
366s  x3 = cauchyinv (p, 0, 2);
366s  x4 = cauchyinv (p, -2, 1);
366s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
366s  grid on
366s  ylim ([-5, 5])
366s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
366s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
366s  title ("Cauchy iCDF")
366s  xlabel ("probability")
366s  ylabel ("values in x")
366s ***** shared p
366s  p = [-1 0 0.5 1 2];
366s ***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
366s ***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
366s ***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
366s ***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
366s ***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
366s ***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
366s ***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
366s ***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
366s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
366s ***** assert (cauchyinv ([p, NaN], single (1), 2), ...
366s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
366s ***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
366s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
366s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
366s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
366s ***** error<cauchyinv: function called with too few input arguments.> ...
366s  cauchyinv (1, 2)
366s ***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
366s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
366s  cauchyinv (ones (3), ones (2), ones(2))
366s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
366s  cauchyinv (ones (2), ones (3), ones(2))
366s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
366s  cauchyinv (ones (2), ones (2), ones(3))
366s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
366s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
366s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
366s 20 tests, 20 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/bvncdf.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/bvncdf.m
366s ***** demo
366s  mu = [1, -1];
366s  sigma = [0.9, 0.4; 0.4, 0.3];
366s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
366s  x = [X1(:), X2(:)];
366s  p = bvncdf (x, mu, sigma);
366s  Z = reshape (p, 25, 25);
366s  surf (X1, X2, Z);
366s  title ("Bivariate Normal Distribution");
366s  ylabel "X1"
366s  xlabel "X2"
366s ***** test
366s  mu = [1, -1];
366s  sigma = [0.9, 0.4; 0.4, 0.3];
366s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
366s  x = [X1(:), X2(:)];
366s  p = bvncdf (x, mu, sigma);
366s  p_out = [0.00011878988774500, 0.00034404112322371, ...
366s           0.00087682502191813, 0.00195221905058185, ...
366s           0.00378235566873474, 0.00638175749734415, ...
366s           0.00943764224329656, 0.01239164888125426, ...
366s           0.01472750274376648, 0.01623228313374828]';
366s  assert (p([1:10]), p_out, 1e-16);
366s ***** test
366s  mu = [1, -1];
366s  sigma = [0.9, 0.4; 0.4, 0.3];
366s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
366s  x = [X1(:), X2(:)];
366s  p = bvncdf (x, mu, sigma);
366s  p_out = [0.8180695783608276, 0.8854485749482751, ...
366s           0.9308108777385832, 0.9579855743025508, ...
366s           0.9722897881414742, 0.9788150170059926, ...
366s           0.9813597788804785, 0.9821977956568989, ...
366s           0.9824283794464095, 0.9824809345614861]';
366s  assert (p([616:625]), p_out, 3e-16);
366s ***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
366s ***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
366s ***** error bvncdf (randn (25,2), [], ones (3, 2));
366s 5 tests, 5 passed, 0 known failure, 0 skipped
366s [inst/dist_fun/trirnd.m]
366s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/trirnd.m
366s ***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
366s ***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
366s ***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
366s ***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
366s ***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
366s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
366s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
366s ***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
367s ***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
367s ***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
367s ***** assert (class (trirnd (1, 1.5, 2)), "double")
367s ***** assert (class (trirnd (single (1), 1.5, 2)), "single")
367s ***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
367s ***** assert (class (trirnd (1, single (1.5), 2)), "single")
367s ***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
367s ***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
367s ***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
367s ***** error<trirnd: function called with too few input arguments.> trirnd ()
367s ***** error<trirnd: function called with too few input arguments.> trirnd (1)
367s ***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
367s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
367s  trirnd (ones (3), 5 * ones (2), ones (2))
367s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
367s  trirnd (ones (2), 5 * ones (3), ones (2))
367s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
367s  trirnd (ones (2), 5 * ones (2), ones (3))
367s ***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
367s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
367s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
367s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
367s  trirnd (1, 5, 3, -1)
367s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
367s  trirnd (1, 5, 3, 1.2)
367s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
367s  trirnd (1, 5, 3, ones (2))
367s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
367s  trirnd (1, 5, 3, [2 -1 2])
367s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
367s  trirnd (1, 5, 3, [2 0 2.5])
367s ***** error<trirnd: dimensions must be non-negative integers.> ...
367s  trirnd (1, 5, 3, 2, -1, 5)
367s ***** error<trirnd: dimensions must be non-negative integers.> ...
367s  trirnd (1, 5, 3, 2, 1.5, 5)
367s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
367s  trirnd (2, 5 * ones (2), 2, 3)
367s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
367s  trirnd (2, 5 * ones (2), 2, [3, 2])
367s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
367s  trirnd (2, 5 * ones (2), 2, 3, 2)
367s 36 tests, 36 passed, 0 known failure, 0 skipped
367s [inst/dist_fun/poisspdf.m]
367s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/poisspdf.m
367s ***** demo
367s  ## Plot various PDFs from the Poisson distribution
367s  x = 0:20;
367s  y1 = poisspdf (x, 1);
367s  y2 = poisspdf (x, 4);
367s  y3 = poisspdf (x, 10);
367s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
367s  grid on
367s  ylim ([0, 0.4])
367s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
367s  title ("Poisson PDF")
367s  xlabel ("values in x (number of occurences)")
367s  ylabel ("density")
367s ***** shared x, y
367s  x = [-1 0 1 2 Inf];
367s  y = [0, exp(-1)*[1 1 0.5], 0];
367s ***** assert (poisspdf (x, ones (1,5)), y, eps)
367s ***** assert (poisspdf (x, 1), y, eps)
367s ***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
367s ***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
367s ***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
367s ***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
367s ***** error<poisspdf: function called with too few input arguments.> poisspdf ()
367s ***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
367s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
367s  poisspdf (ones (3), ones (2))
367s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
367s  poisspdf (ones (2), ones (3))
367s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
367s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
367s 12 tests, 12 passed, 0 known failure, 0 skipped
367s [inst/dist_fun/ricecdf.m]
367s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ricecdf.m
367s ***** demo
367s  ## Plot various CDFs from the Rician distribution
367s  x = 0:0.01:10;
367s  p1 = ricecdf (x, 0, 1);
367s  p2 = ricecdf (x, 0.5, 1);
367s  p3 = ricecdf (x, 1, 1);
367s  p4 = ricecdf (x, 2, 1);
367s  p5 = ricecdf (x, 4, 1);
367s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
367s  grid on
367s  ylim ([0, 1])
367s  xlim ([0, 8])
367s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
367s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
367s  title ("Rician CDF")
367s  xlabel ("values in x")
367s  ylabel ("probability")
367s ***** demo
367s  ## Plot various CDFs from the Rician distribution
367s  x = 0:0.01:10;
367s  p1 = ricecdf (x, 0, 0.5);
367s  p2 = ricecdf (x, 0, 2);
367s  p3 = ricecdf (x, 0, 3);
367s  p4 = ricecdf (x, 2, 2);
367s  p5 = ricecdf (x, 4, 2);
367s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
367s  grid on
367s  ylim ([0, 1])
367s  xlim ([0, 8])
367s  legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
367s           "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
367s  title ("Rician CDF")
367s  xlabel ("values in x")
367s  ylabel ("probability")
367s ***** test
367s  x = 0:0.5:2.5;
367s  s = 1:6;
367s  p = ricecdf (x, s, 1);
367s  expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
367s  assert (p, expected_p, 0.001);
367s ***** test
367s  x = 0:0.5:2.5;
367s  sigma = 1:6;
367s  p = ricecdf (x, 1, sigma);
367s  expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
367s  assert (p, expected_p, 0.001);
367s ***** test
367s  x = 0:0.5:2.5;
367s  p = ricecdf (x, 0, 1);
367s  expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
367s  assert (p, expected_p, 0.001);
367s ***** test
367s  x = 0:0.5:2.5;
367s  p = ricecdf (x, 1, 1);
367s  expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
367s  assert (p, expected_p, 0.001);
367s ***** shared x, p
367s  x = [-1, 0, 1, 2, Inf];
367s  p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
367s ***** assert (ricecdf (x, 1, 1), p, 1e-14)
367s ***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
367s ***** error<ricecdf: function called with too few input arguments.> ricecdf ()
367s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
367s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
367s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
367s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
367s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
367s  ricecdf (ones (3), ones (2), ones (2))
367s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
367s  ricecdf (ones (2), ones (3), ones (2))
367s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
367s  ricecdf (ones (2), ones (2), ones (3))
367s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
367s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
367s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
367s 17 tests, 17 passed, 0 known failure, 0 skipped
367s [inst/dist_fun/vmcdf.m]
367s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/vmcdf.m
367s ***** demo
367s  ## Plot various CDFs from the von Mises distribution
367s  x1 = [-pi:0.1:pi];
367s  p1 = vmcdf (x1, 0, 0.5);
367s  p2 = vmcdf (x1, 0, 1);
367s  p3 = vmcdf (x1, 0, 2);
367s  p4 = vmcdf (x1, 0, 4);
367s  plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
367s  grid on
367s  xlim ([-pi, pi])
367s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
367s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
367s  title ("Von Mises CDF")
367s  xlabel ("values in x")
367s  ylabel ("probability")
367s ***** shared x, p0, p1
367s  x = [-pi:pi/2:pi];
367s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
367s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
367s ***** assert (vmcdf (x, 0, 1), p0, 1e-5)
367s ***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
367s ***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
367s ***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
367s ***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
367s ***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
367s ***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
367s ***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
367s ***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
367s ***** error<vmcdf: function called with too few input arguments.> vmcdf ()
367s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
367s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
367s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
367s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
367s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
367s  vmcdf (ones (3), ones (2), ones (2))
367s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
367s  vmcdf (ones (2), ones (3), ones (2))
367s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
367s  vmcdf (ones (2), ones (2), ones (3))
367s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
367s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
367s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
367s 20 tests, 20 passed, 0 known failure, 0 skipped
367s [inst/dist_fun/unifpdf.m]
367s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/unifpdf.m
367s ***** demo
367s  ## Plot various PDFs from the continuous uniform distribution
367s  x = 0:0.001:10;
367s  y1 = unifpdf (x, 2, 5);
367s  y2 = unifpdf (x, 3, 9);
367s  plot (x, y1, "-b", x, y2, "-g")
367s  grid on
367s  xlim ([0, 10])
367s  ylim ([0, 0.4])
367s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
367s  title ("Continuous uniform PDF")
367s  xlabel ("values in x")
367s  ylabel ("density")
367s ***** shared x, y
367s  x = [-1 0 0.5 1 2] + 1;
367s  y = [0 1 1 1 0];
367s ***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
367s ***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
367s ***** assert (unifpdf (x, ones (1,5), 2), y)
367s ***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
367s ***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
367s ***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
367s ***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
367s ***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
367s ***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
367s ***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
367s ***** error<unifpdf: function called with too few input arguments.> unifpdf ()
367s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
367s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
367s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
367s  unifpdf (ones (3), ones (2), ones (2))
367s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
367s  unifpdf (ones (2), ones (3), ones (2))
367s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
367s  unifpdf (ones (2), ones (2), ones (3))
367s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
367s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
367s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
367s 19 tests, 19 passed, 0 known failure, 0 skipped
367s [inst/dist_fun/expinv.m]
367s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/expinv.m
367s ***** demo
367s  ## Plot various iCDFs from the exponential distribution
367s  p = 0.001:0.001:0.999;
367s  x1 = expinv (p, 2/3);
367s  x2 = expinv (p, 1.0);
367s  x3 = expinv (p, 2.0);
367s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
367s  grid on
367s  ylim ([0, 5])
367s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
367s  title ("Exponential iCDF")
367s  xlabel ("probability")
367s  ylabel ("values in x")
367s ***** shared p
367s  p = [-1 0 0.3934693402873666 1 2];
367s ***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
367s ***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
367s ***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
367s ***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
367s ***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
367s ***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
367s ***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
367s ***** error<expinv: invalid number of input arguments.> expinv ()
367s ***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
367s ***** error<expinv: P and MU must be of common size or scalars.> ...
367s  expinv (ones (3), ones (2))
367s ***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
367s  expinv (2, 3, [1, 2])
367s ***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
367s  [x, xlo, xup] = expinv (1, 2)
367s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
367s  expinv (1, 2, 3, 0)
367s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
367s  expinv (1, 2, 3, 1.22)
368s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
368s  expinv (1, 2, 3, [0.05, 0.1])
368s ***** error<expinv: P and MU must not be complex.> expinv (i, 2)
368s ***** error<expinv: P and MU must not be complex.> expinv (2, i)
368s ***** error<expinv: variance, PCOV, cannot be negative.> ...
368s  [x, xlo, xup] = expinv (1, 2, -1, 0.04)
368s 18 tests, 18 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/binopdf.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/binopdf.m
368s ***** demo
368s  ## Plot various PDFs from the binomial distribution
368s  x = 0:40;
368s  y1 = binopdf (x, 20, 0.5);
368s  y2 = binopdf (x, 20, 0.7);
368s  y3 = binopdf (x, 40, 0.5);
368s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
368s  grid on
368s  ylim ([0, 0.25])
368s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
368s           "n = 40, ps = 0.5"}, "location", "northeast")
368s  title ("Binomial PDF")
368s  xlabel ("values in x (number of successes)")
368s  ylabel ("density")
368s ***** shared x, y
368s  x = [-1 0 1 2 3];
368s  y = [0 1/4 1/2 1/4 0];
368s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
368s ***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
368s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
368s ***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
368s ***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
368s ***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
368s ***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
368s ***** assert (binopdf (1, 1, 1), 1)
368s ***** assert (binopdf (0, 3, 0), 1)
368s ***** assert (binopdf (2, 2, 1), 1)
368s ***** assert (binopdf (1, 2, 1), 0)
368s ***** assert (binopdf (0, 1.1, 0), NaN)
368s ***** assert (binopdf (1, 2, -1), NaN)
368s ***** assert (binopdf (1, 2, 1.5), NaN)
368s ***** assert (binopdf ([], 1, 1), [])
368s ***** assert (binopdf (1, [], 1), [])
368s ***** assert (binopdf (1, 1, []), [])
368s ***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
368s ***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
368s ***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
368s ***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
368s ***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
368s ***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
368s ***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
368s ***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
368s ***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
368s ***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
368s ***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
368s ***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
368s ***** error<binopdf: function called with too few input arguments.> binopdf ()
368s ***** error<binopdf: function called with too few input arguments.> binopdf (1)
368s ***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
368s ***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
368s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
368s  binopdf (ones (3), ones (2), ones (2))
368s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
368s  binopdf (ones (2), ones (3), ones (2))
368s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
368s  binopdf (ones (2), ones (2), ones (3))
368s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
368s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
368s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
368s 39 tests, 39 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/mvtcdfqmc.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvtcdfqmc.m
368s ***** error mvtcdfqmc (1, 2, 3);
368s ***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
368s 2 tests, 2 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/gumbelpdf.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gumbelpdf.m
368s ***** demo
368s  ## Plot various PDFs from the Extreme value distribution
368s  x = -5:0.001:20;
368s  y1 = gumbelpdf (x, 0.5, 2);
368s  y2 = gumbelpdf (x, 1.0, 2);
368s  y3 = gumbelpdf (x, 1.5, 3);
368s  y4 = gumbelpdf (x, 3.0, 4);
368s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
368s  grid on
368s  ylim ([0, 0.2])
368s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
368s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
368s  title ("Extreme value PDF")
368s  xlabel ("values in x")
368s  ylabel ("density")
368s ***** shared x, y0, y1
368s  x = [-5, 0, 1, 2, 3];
368s  y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
368s  y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
368s ***** assert (gumbelpdf (x), y0, 1e-4)
368s ***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
368s ***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
368s ***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
368s ***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
368s  gumbelpdf (ones (3), ones (2), ones (2))
368s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
368s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
368s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
368s 8 tests, 8 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/mvtrnd.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/mvtrnd.m
368s ***** test
368s  rho = [1, 0.5; 0.5, 1];
368s  df = 3;
368s  n = 10;
368s  r = mvtrnd (rho, df, n);
368s  assert (size (r), [10, 2]);
368s ***** test
368s  rho = [1, 0.5; 0.5, 1];
368s  df = [2; 3];
368s  n = 2;
368s  r = mvtrnd (rho, df, 2);
368s  assert (size (r), [2, 2]);
368s 2 tests, 2 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/ncfinv.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/ncfinv.m
368s ***** demo
368s  ## Plot various iCDFs from the noncentral F distribution
368s  p = 0.001:0.001:0.999;
368s  x1 = ncfinv (p, 2, 5, 1);
368s  x2 = ncfinv (p, 2, 5, 2);
368s  x3 = ncfinv (p, 5, 10, 1);
368s  x4 = ncfinv (p, 10, 20, 10);
368s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
368s  grid on
368s  ylim ([0, 5])
368s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
368s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
368s          "location", "northwest")
368s  title ("Noncentral F iCDF")
368s  xlabel ("probability")
368s  ylabel ("values in x")
368s ***** demo
368s  ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
368s  ## same number of numerator and denominator degrees of freedom (5, 20)
368s 
368s  p = 0.001:0.001:0.999;
368s  x1 = ncfinv (p, 5, 20, 10);
368s  x2 = finv (p, 5, 20);
368s  plot (p, x1, "-", p, x2, "-");
368s  grid on
368s  ylim ([0, 10])
368s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
368s  title ("Noncentral F vs F quantile functions")
368s  xlabel ("probability")
368s  ylabel ("values in x")
368s ***** test
368s  x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
368s  assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
368s ***** test
368s  x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
368s  assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
368s ***** test
368s  x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
368s  assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
368s ***** test
368s  x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
368s  assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
368s ***** test
368s  x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
368s  assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
368s ***** test
368s  x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
368s  assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
368s ***** test
368s  assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
368s ***** error<ncfinv: function called with too few input arguments.> ncfinv ()
368s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
368s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
368s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
368s  ncfinv (ones (3), ones (2), ones (2), ones (2))
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
368s  ncfinv (ones (2), ones (3), ones (2), ones (2))
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
368s  ncfinv (ones (2), ones (2), ones (3), ones (2))
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
368s  ncfinv (ones (2), ones (2), ones (2), ones (3))
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
368s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
368s 19 tests, 19 passed, 0 known failure, 0 skipped
368s [inst/dist_fun/gppdf.m]
368s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/gppdf.m
368s ***** demo
368s  ## Plot various PDFs from the generalized Pareto distribution
368s  x = 0:0.001:5;
368s  y1 = gppdf (x, 1, 1, 0);
368s  y2 = gppdf (x, 5, 1, 0);
368s  y3 = gppdf (x, 20, 1, 0);
368s  y4 = gppdf (x, 1, 2, 0);
368s  y5 = gppdf (x, 5, 2, 0);
368s  y6 = gppdf (x, 20, 2, 0);
368s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
368s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
368s  grid on
368s  xlim ([0, 5])
368s  ylim ([0, 1])
368s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
368s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
368s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
368s          "location", "northeast")
368s  title ("Generalized Pareto PDF")
368s  xlabel ("values in x")
368s  ylabel ("density")
368s ***** shared x, y1, y2, y3
368s  x = [-Inf, -1, 0, 1/2, 1, Inf];
368s  y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
368s  y2 = [0, 0, 1, 4/9, 1/4, 0];
368s  y3 = [0, 0, 1, 1, 1, 0];
368s ***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
368s ***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
368s ***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
368s ***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
368s ***** assert (gppdf (x, 0, 1, 0), y1, eps)
368s ***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
368s ***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
368s ***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
368s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
368s ***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
368s ***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
368s ***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
368s ***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
368s ***** assert (gppdf (x, 1, 1, 0), y2, eps)
368s ***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
368s ***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
370s ***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
370s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
370s ***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
370s ***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
370s ***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
370s ***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
370s ***** assert (gppdf (x, -1, 1, 0), y3, eps)
370s ***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
370s ***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
370s ***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
370s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
370s ***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
370s ***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
370s ***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
370s ***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
370s ***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
370s ***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
370s ***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
370s ***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
370s ***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
370s ***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
370s ***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
370s ***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
370s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
370s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
370s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
370s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
370s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
370s  gpcdf (ones (3), ones (2), ones(2), ones(2))
370s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
370s  gpcdf (ones (2), ones (3), ones(2), ones(2))
370s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
370s  gpcdf (ones (2), ones (2), ones(3), ones(2))
370s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
370s  gpcdf (ones (2), ones (2), ones(2), ones(3))
370s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
370s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
370s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
370s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
370s 51 tests, 51 passed, 0 known failure, 0 skipped
370s [inst/dist_fun/geoinv.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/geoinv.m
370s ***** demo
370s  ## Plot various iCDFs from the geometric distribution
370s  p = 0.001:0.001:0.999;
370s  x1 = geoinv (p, 0.2);
370s  x2 = geoinv (p, 0.5);
370s  x3 = geoinv (p, 0.7);
370s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
370s  grid on
370s  ylim ([0, 10])
370s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
370s  title ("Geometric iCDF")
370s  xlabel ("probability")
370s  ylabel ("values in x (number of failures)")
370s ***** shared p
370s  p = [-1 0 0.75 1 2];
370s ***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
370s ***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
370s ***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
370s ***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
370s ***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
370s ***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
370s ***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
370s ***** error<geoinv: function called with too few input arguments.> geoinv ()
370s ***** error<geoinv: function called with too few input arguments.> geoinv (1)
370s ***** error<geoinv: P and PS must be of common size or scalars.> ...
370s  geoinv (ones (3), ones (2))
370s ***** error<geoinv: P and PS must be of common size or scalars.> ...
370s  geoinv (ones (2), ones (3))
370s ***** error<geoinv: P and PS must not be complex.> ...
370s  geoinv (i, 2)
370s ***** error<geoinv: P and PS must not be complex.> ...
370s  geoinv (2, i)
370s 13 tests, 13 passed, 0 known failure, 0 skipped
370s [inst/dist_fun/logninv.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/logninv.m
370s ***** demo
370s  ## Plot various iCDFs from the log-normal distribution
370s  p = 0.001:0.001:0.999;
370s  x1 = logninv (p, 0, 1);
370s  x2 = logninv (p, 0, 0.5);
370s  x3 = logninv (p, 0, 0.25);
370s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
370s  grid on
370s  ylim ([0, 3])
370s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
370s          "location", "northwest")
370s  title ("Log-normal iCDF")
370s  xlabel ("probability")
370s  ylabel ("values in x")
370s ***** shared p
370s  p = [-1 0 0.5 1 2];
370s ***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
370s ***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
370s ***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN], 2*eps)
370s ***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
370s ***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
370s ***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
370s ***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN], 2*eps)
370s ***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
370s ***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
370s ***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
370s ***** error logninv ()
370s ***** error logninv (1,2,3,4)
370s ***** error logninv (ones (3), ones (2), ones (2))
370s ***** error logninv (ones (2), ones (3), ones (2))
370s ***** error logninv (ones (2), ones (2), ones (3))
370s ***** error logninv (i, 2, 2)
370s ***** error logninv (2, i, 2)
370s ***** error logninv (2, 2, i)
370s 18 tests, 18 passed, 0 known failure, 0 skipped
370s [inst/dist_fun/betainv.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/betainv.m
370s ***** demo
370s  ## Plot various iCDFs from the Beta distribution
370s  p = 0.001:0.001:0.999;
370s  x1 = betainv (p, 0.5, 0.5);
370s  x2 = betainv (p, 5, 1);
370s  x3 = betainv (p, 1, 3);
370s  x4 = betainv (p, 2, 2);
370s  x5 = betainv (p, 2, 5);
370s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
370s  grid on
370s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
370s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
370s  title ("Beta iCDF")
370s  xlabel ("probability")
370s  ylabel ("values in x")
370s ***** shared p
370s  p = [-1 0 0.75 1 2];
370s ***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
370s ***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
370s ***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
370s ***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
370s ***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
370s ***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
370s ***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
370s ***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
370s ***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
370s ***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
370s ***** error<betainv: function called with too few input arguments.> betainv ()
370s ***** error<betainv: function called with too few input arguments.> betainv (1)
370s ***** error<betainv: function called with too few input arguments.> betainv (1,2)
370s ***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
370s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
370s  betainv (ones (3), ones (2), ones (2))
370s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
370s  betainv (ones (2), ones (3), ones (2))
370s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
370s  betainv (ones (2), ones (2), ones (3))
370s ***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
370s ***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
370s ***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
370s 20 tests, 20 passed, 0 known failure, 0 skipped
370s [inst/dist_fun/cauchyrnd.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/cauchyrnd.m
370s ***** assert (size (cauchyrnd (1, 1)), [1 1])
370s ***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
370s ***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
370s ***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
370s ***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
370s ***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
370s ***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
370s ***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
370s ***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
370s ***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
370s ***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
370s ***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
370s ***** assert (class (cauchyrnd (1, 1)), "double")
370s ***** assert (class (cauchyrnd (1, single (1))), "single")
370s ***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
370s ***** assert (class (cauchyrnd (single (1), 1)), "single")
370s ***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
370s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
370s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
370s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
370s  cauchyrnd (ones (3), ones (2))
370s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
370s  cauchyrnd (ones (2), ones (3))
370s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
370s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
370s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
370s  cauchyrnd (1, 2, -1)
370s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
370s  cauchyrnd (1, 2, 1.2)
370s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
370s  cauchyrnd (1, 2, ones (2))
370s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
370s  cauchyrnd (1, 2, [2 -1 2])
370s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
370s  cauchyrnd (1, 2, [2 0 2.5])
370s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
370s  cauchyrnd (1, 2, 2, -1, 5)
370s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
370s  cauchyrnd (1, 2, 2, 1.5, 5)
370s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
370s  cauchyrnd (2, ones (2), 3)
370s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
370s  cauchyrnd (2, ones (2), [3, 2])
370s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
370s  cauchyrnd (2, ones (2), 3, 2)
370s 33 tests, 33 passed, 0 known failure, 0 skipped
370s [inst/dist_fun/vminv.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fun/vminv.m
370s ***** demo
370s  ## Plot various iCDFs from the von Mises distribution
370s  p1 = [0,0.005,0.01:0.01:0.1,0.15,0.2:0.1:0.8,0.85,0.9:0.01:0.99,0.995,1];
370s  x1 = vminv (p1, 0, 0.5);
370s  x2 = vminv (p1, 0, 1);
370s  x3 = vminv (p1, 0, 2);
370s  x4 = vminv (p1, 0, 4);
370s  plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
370s  grid on
370s  ylim ([-pi, pi])
370s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
370s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
370s  title ("Von Mises iCDF")
370s  xlabel ("probability")
370s  ylabel ("values in x")
370s ***** shared x, p0, p1
370s  x = [-pi:pi/2:pi];
370s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
370s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
370s ***** assert (vminv (p0, 0, 1), x, 5e-5)
370s ***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
370s ***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
370s ***** error<vminv: function called with too few input arguments.> vminv ()
370s ***** error<vminv: function called with too few input arguments.> vminv (1)
370s ***** error<vminv: function called with too few input arguments.> vminv (1, 2)
370s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
370s  vminv (ones (3), ones (2), ones (2))
370s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
370s  vminv (ones (2), ones (3), ones (2))
370s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
370s  vminv (ones (2), ones (2), ones (3))
370s ***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
370s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
370s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
370s 12 tests, 12 passed, 0 known failure, 0 skipped
370s [inst/canoncorr.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/canoncorr.m
370s ***** shared X,Y,A,B,r,U,V,k
370s  k = 10;
370s  X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
370s  [A,B,r,U,V,stats] = canoncorr (X,Y);
370s ***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
370s ***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
370s ***** assert (r, [0.99590   0.26754], 1E-5);
370s ***** assert (U, center(X) * A, 10*eps);
370s ***** assert (V, center(Y) * B, 10*eps);
370s ***** assert (cov(U), eye(size(U, 2)), 10*eps);
370s ***** assert (cov(V), eye(size(V, 2)), 10*eps);
370s  rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
370s ***** assert (r, ones(1, 5), 10*eps);
370s 8 tests, 8 passed, 0 known failure, 0 skipped
370s [inst/princomp.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/princomp.m
370s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
370s ***** test
370s  x=[7 4 3
370s     4 1 8
370s     6 3 5
370s     8 6 1
370s     8 5 7
370s     7 2 9
370s     5 3 3
370s     9 5 8
370s     7 4 5
370s     8 2 2];
370s  R = corrcoef (x);
370s  [V, lambda] = eig (R);
370s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
370s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
370s  ## contribution of first 2 PCs to each original variable
370s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
370s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
370s  F = zscore(x)*B;
370s  [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
370s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
370s ***** test
370s  x=[1,2,3;2,1,3]';
370s  [COEFF,SCORE,latent,tsquare] = princomp(x);
370s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
370s  m(:,1) = m(:,1)*sign(COEFF(1,1));
370s  m(:,2) = m(:,2)*sign(COEFF(1,2));
370s ***** assert(COEFF,m(1:2,:),10*eps);
370s ***** assert(SCORE,-m,10*eps);
370s ***** assert(latent,[1.5;.5],10*eps);
370s ***** assert(tsquare,[4;4;4]/3,10*eps);
370s ***** test
370s  x=x';
370s  [COEFF,SCORE,latent,tsquare] = princomp(x);
370s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
370s  m(:,1) = m(:,1)*sign(COEFF(1,1));
370s  m(:,2) = m(:,2)*sign(COEFF(1,2));
370s  m(:,3) = m(:,3)*sign(COEFF(3,3));
370s ***** assert(COEFF,m,10*eps);
370s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
370s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
370s ***** assert(latent,[1;0;0],10*eps);
370s ***** assert(tsquare,[0.5;0.5],10*eps)
370s ***** test
370s  [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
370s ***** assert(COEFF,m(:, 1),10*eps);
370s ***** assert(SCORE,-m(1:2,1),10*eps);
370s ***** assert(latent,[1],10*eps);
370s ***** assert(tsquare,[0.5;0.5],10*eps)
370s 19 tests, 19 passed, 0 known failure, 0 skipped
370s [inst/bartlett_test.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/bartlett_test.m
370s ***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
370s ***** error<bartlett_test: invalid number of input arguments.> ...
370s  bartlett_test (1, 2, 3, 4);
370s ***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
370s ***** error<bartlett_test: GROUP and X mismatch.> ...
370s  bartlett_test (randn (50, 2), [1, 2, 3]);
370s ***** error<bartlett_test: GROUP and X mismatch.> ...
370s  bartlett_test (randn (50, 1), ones (55, 1));
370s ***** error<bartlett_test: invalid second input argument.> ...
370s  bartlett_test (randn (50, 1), ones (50, 2));
370s ***** error<bartlett_test: wrong value for alpha.> ...
370s  bartlett_test (randn (50, 2), [], 1.2);
370s ***** error<bartlett_test: wrong value for alpha.> ...
370s  bartlett_test (randn (50, 2), [], "alpha");
370s ***** error<bartlett_test: wrong value for alpha.> ...
370s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
370s ***** error<bartlett_test: wrong value for alpha.> ...
370s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
370s ***** warning<bartlett_test: GROUP> ...
370s  bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
370s ***** test
370s  load examgrades
370s  [h, pval, chisq, df] = bartlett_test (grades);
370s  assert (h, 1);
370s  assert (pval, 7.908647337018238e-08, 1e-14);
370s  assert (chisq, 38.73324, 1e-5);
370s  assert (df, 4);
370s ***** test
370s  load examgrades
370s  [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
370s  assert (h, 1);
370s  assert (pval, 0.01172, 1e-5);
370s  assert (chisq, 8.89274, 1e-5);
370s  assert (df, 2);
370s ***** test
370s  load examgrades
370s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
370s  assert (h, 0);
370s  assert (pval, 0.88118, 1e-5);
370s  assert (chisq, 0.02234, 1e-5);
370s  assert (df, 1);
370s ***** test
370s  load examgrades
370s  grades = [grades; nan(10, 5)];
370s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
370s  assert (h, 0);
370s  assert (pval, 0.88118, 1e-5);
370s  assert (chisq, 0.02234, 1e-5);
370s  assert (df, 1);
370s ***** test
370s  load examgrades
370s  [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
370s  assert (h, 0);
370s  assert (pval, 0.01791, 1e-5);
370s  assert (chisq, 5.60486, 1e-5);
370s  assert (df, 1);
370s 16 tests, 16 passed, 0 known failure, 0 skipped
370s [inst/ismissing.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ismissing.m
370s ***** assert (ismissing ([1,NaN,3]), [false,true,false])
370s ***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
370s ***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
370s ***** assert (ismissing ({'x','','y'}), [false,true,false])
370s ***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
370s ***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
370s ***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
370s ***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
370s ***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
370s ***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
370s ***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
370s ***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
370s ***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
370s ***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
370s ***** assert (ismissing (double (NaN)), true)
370s ***** assert (ismissing (single (NaN)), true)
370s ***** assert (ismissing (' '), true)
370s ***** assert (ismissing ({''}), true)
370s ***** assert (ismissing ({' '}), false)
370s ***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
370s ***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
370s ***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
370s ***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
370s ***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
370s ***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
370s ***** assert (ismissing ({'123', '', 123}), [false false false])
370s ***** assert (ismissing (logical ([1 0 1])), [false false false])
370s ***** assert (ismissing (int32 ([1 2 3])), [false false false])
370s ***** assert (ismissing (uint32 ([1 2 3])), [false false false])
370s ***** assert (ismissing ({1, 2, 3}), [false false false])
370s ***** assert (ismissing ([struct struct struct]), [false false false])
370s ***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
370s ***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
370s ***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
370s ***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
370s ***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
370s ***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
370s ***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
370s ***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
370s ***** assert (ismissing ([]), logical([]))
370s ***** assert (ismissing (''), logical([]))
370s ***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
370s ***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
370s ***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
370s ***** error ismissing ()
370s ***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
370s ***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
370s ***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
370s ***** error <indicators not supported for data type> ismissing (struct, 1)
370s 49 tests, 49 passed, 0 known failure, 0 skipped
370s [inst/pdist2.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/pdist2.m
370s ***** shared x, y, xx
370s  x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
370s  y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
370s  xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
370s ***** test
370s  d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
370s  assert (pdist2 (x, y), d);
370s ***** test
370s  d = [5.1962, 2.2361, 3.3166, 6.7082; ...
370s       3.4641, 2.2361, 3.3166, 5.4772];
370s  i = [3, 1, 1, 1; 2, 3, 3, 2];
370s  [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  d = [1.7321, 1.4142, 2.8284, 4.5826; ...
370s       3.4641, 2.2361, 3.3166, 5.4772];
370s  i = [1, 2, 2, 3;2, 1, 1, 2];
370s  [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  yy = [1 2 3;5 6 7;9 5 1];
370s  d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
370s       3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
370s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
370s  [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  yy = [1 2 3;5 6 7;9 5 1];
370s  d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
370s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
370s  [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  yy = [1 2 3;5 6 7;9 5 1];
370s  d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
370s       1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
370s  i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
370s  [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
370s       3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
370s  assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
370s ***** test
370s  xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
370s  d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
370s  i = [2, 2, 2; 3, 4, 4];
370s  [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
370s  i = [1, 1, 3; 4, 3, 1];
370s  [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
370s  assert ({D, I}, {d, i}, 1e-4);
370s ***** test
370s  d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
370s  assert (pdist2 (x, y, "cityblock"), d);
370s ***** test
370s  d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
370s  assert (pdist2 (x, y, "chebychev"), d);
370s ***** test
370s  d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
370s  assert (pdist2 (x, y, "cosine"), d, 1e-4);
370s ***** test
370s  yy = [1 2 3;5 6 7;9 5 1];
370s  d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
370s  i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
370s  [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
370s  assert ({D, I}, {d, i}, eps);
370s  [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
370s  assert ({D, I}, {d, i}, eps);
370s ***** test
370s  d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
370s  i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
370s  [D, I] = pdist2 (x, y, "hamming", "largest", 4);
370s  assert ({D, I}, {d, i}, eps);
370s  [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
370s  assert ({D, I}, {d, i}, eps);
370s ***** test
370s  xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
370s  yy = [1, 2, 2, 3; 2, 3, 3, 4];
370s  [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
370s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
370s  [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
370s  assert ({D, I}, {d, i});
370s ***** warning<pdist2: matrix is close to singular> ...
370s  pdist2 (xx, xx, "mahalanobis");
370s ***** error<pdist2: too few input arguments.> pdist2 (1)
370s ***** error<pdist2: X and Y must have equal number of columns.> ...
370s  pdist2 (ones (4, 5), ones (4))
370s ***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
370s  pdist2 (ones (4, 2, 3), ones (3, 2))
370s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
370s  pdist2 (ones (3), ones (3), "euclidean", "Largest")
370s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
370s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
370s ***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
370s  pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
370s ***** error<pdist2: you can only use either Smallest or Largest.> ...
370s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
370s ***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
370s  [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
370s ***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
370s  pdist2 (ones (3), ones (3), "seuclidean", 3)
370s ***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
370s  pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
370s ***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
370s  pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
370s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
370s  pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
370s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
370s  pdist2 (ones (3), eye (3), "minkowski", 0)
370s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
370s  pdist2 (ones (3), eye (3), "minkowski", -5)
370s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
370s  pdist2 (ones (3), eye (3), "minkowski", [1, 2])
370s ***** error<pdist2: invalid function handle for distance metric.> ...
370s  pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
370s ***** error<pdist2: custom distance function produces wrong output size.> ...
370s  pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
370s 33 tests, 33 passed, 0 known failure, 0 skipped
370s [inst/pcacov.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/pcacov.m
370s ***** demo
370s  x = [ 7    26     6    60;
370s        1    29    15    52;
370s       11    56     8    20;
370s       11    31     8    47;
370s        7    52     6    33;
370s       11    55     9    22;
370s        3    71    17     6;
370s        1    31    22    44;
370s        2    54    18    22;
370s       21    47     4    26;
370s        1    40    23    34;
370s       11    66     9    12;
370s       10    68     8    12
370s      ];
370s  Kxx = cov (x);
370s  [coeff, latent, explained] = pcacov (Kxx)
370s ***** test
370s  load hald
370s  Kxx = cov (ingredients);
370s  [coeff,latent,explained] = pcacov(Kxx);
370s  c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
370s           -0.6785, -0.0200, -0.5440, 0.4933; ...
370s            0.0290,  0.7553,  0.4036, 0.5156; ...
370s            0.7309, -0.1085, -0.4684, 0.4844];
370s  l_out = [517.7969; 67.4964; 12.4054; 0.2372];
370s  e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
370s  assert (coeff, c_out, 1e-4);
370s  assert (latent, l_out, 1e-4);
370s  assert (explained, e_out, 1e-4);
370s ***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
370s ***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
370s 3 tests, 3 passed, 0 known failure, 0 skipped
370s [inst/plsregress.m]
370s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/plsregress.m
370s ***** demo
370s  ## Perform Partial Least-Squares Regression
370s 
370s  ## Load the spectra data set and use the near infrared (NIR) spectral
370s  ## intensities (NIR) as the predictor and the corresponding octave
370s  ## ratings (octave) as the response.
370s  load spectra
370s 
370s  ## Perform PLS regression with 10 components
370s  [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);
370s 
370s  ## Plot the percentage of explained variance in the response variable
370s  ## (PCTVAR) as a function of the number of components.
370s  plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
370s  xlim ([1, 10]);
370s  xlabel ("Number of PLS components");
370s  ylabel ("Percentage of Explained Variance in octane");
370s  title ("Explained Variance per PLS components");
370s 
370s  ## Compute the fitted response and display the residuals.
370s  octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
370s  residuals = octane - octane_fitted;
370s  figure
370s  stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
370s  xlabel ("Observations");
370s  ylabel ("Residuals");
370s  title ("Residuals in octane's fitted response");
370s ***** demo
370s  ## Calculate Variable Importance in Projection (VIP) for PLS Regression
370s 
370s  ## Load the spectra data set and use the near infrared (NIR) spectral
370s  ## intensities (NIR) as the predictor and the corresponding octave
370s  ## ratings (octave) as the response.  Variables with a VIP score greater than
370s  ## 1 are considered important for the projection of the PLS regression model.
370s  load spectra
370s 
370s  ## Perform PLS regression with 10 components
370s  [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
370s                                                  plsregress (NIR, octane, 10);
370s 
370s  ## Calculate the normalized PLS weights
370s  W0 = stats.W ./ sqrt(sum(stats.W.^2,1));
370s 
370s  ## Calculate the VIP scores for 10 components
370s  nobs = size (xload, 1);
370s  SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
370s  VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));
370s 
370s  ## Find variables with a VIP score greater than or equal to 1
370s  VIPidx = find (VIPscore >= 1);
370s 
370s  ## Plot the VIP scores
370s  scatter (1:length (VIPscore), VIPscore, "xb");
370s  hold on
370s  scatter (VIPidx, VIPscore (VIPidx), "xr");
370s  plot ([1, length(VIPscore)], [1, 1], "--k");
370s  hold off
370s  axis ("tight");
370s  xlabel ("Predictor Variables");
370s  ylabel ("VIP scores");
370s  title ("VIP scores for each predictor variable with 10 components");
370s ***** test
370s  load spectra
370s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
370s  xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
370s                -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
370s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
370s               0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
370s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
370s                 -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
370s  yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
370s                  -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
370s  assert (xload(1,:), xload1_out, 1e-4);
370s  assert (yload, yload_out, 1e-4);
370s  assert (xscore(1,:), xscore1_out, 1e-4);
370s  assert (yscore(1,:), yscore1_out, 1e-4);
370s ***** test
370s  load spectra
370s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
370s  xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
370s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
370s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
370s  yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
370s  assert (xload(1,:), xload1_out, 1e-4);
370s  assert (yload, yload_out, 1e-4);
370s  assert (xscore(1,:), xscore1_out, 1e-4);
370s  assert (yscore(1,:), yscore1_out, 1e-4);
370s ***** error<plsregress: function called with too few input arguments.>
370s  plsregress (1)
370s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
370s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
370s ***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
370s ***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
370s ***** error<plsregress: X and Y observations mismatch.> ...
370s  plsregress (ones (20,3), ones (15,1))
370s ***** error<plsregress: invalid value for NCOMP.> ...
370s  plsregress (ones (20,3), ones (20,1), 0)
370s ***** error<plsregress: invalid value for NCOMP.> ...
370s  plsregress (ones (20,3), ones (20,1), -5)
370s ***** error<plsregress: invalid value for NCOMP.> ...
370s  plsregress (ones (20,3), ones (20,1), 3.2)
370s ***** error<plsregress: invalid value for NCOMP.> ...
370s  plsregress (ones (20,3), ones (20,1), [2, 3])
370s ***** error<plsregress: NCOMP exceeds maximum components for X.> ...
370s  plsregress (ones (20,3), ones (20,1), 4)
371s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
371s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
371s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
371s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
371s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
371s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
371s ***** error<plsregress: invalid NAME argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
371s ***** error<plsregress: invalid NAME argument.> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
371s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
371s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
371s  plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
371s ***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
371s 24 tests, 24 passed, 0 known failure, 0 skipped
371s [inst/harmmean.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/harmmean.m
371s ***** test
371s  x = [0:10];
371s  y = [x;x+5;x+10];
371s  assert (harmmean (x), 0);
371s  m = [0 8.907635160795225 14.30854471766802];
371s  assert (harmmean (y, 2), m', 4e-14);
371s  assert (harmmean (y, "all"), 0);
371s  y(2,4) = NaN;
371s  m(2) = 9.009855936313949;
371s  assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
371s  assert (harmmean (y', "omitnan"), m, 4e-14);
371s  z = y + 20;
371s  assert (harmmean (z, "all"), NaN);
371s  assert (harmmean (z, "all", "includenan"), NaN);
371s  assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
371s  m = [24.59488458841874 NaN 34.71244385944397];
371s  assert (harmmean (z'), m, 4e-14);
371s  assert (harmmean (z', "includenan"), m, 4e-14);
371s  m(2) = 29.84104075528277;
371s  assert (harmmean (z', "omitnan"), m, 4e-14);
371s  assert (harmmean (z, 2, "omitnan"), m', 4e-14);
371s ***** test
371s  x = repmat ([1:20;6:25], [5 2 6 3]);
371s  assert (size (harmmean (x, [3 2])), [10 1 1 3]);
371s  assert (size (harmmean (x, [1 2])), [1 1 6 3]);
371s  assert (size (harmmean (x, [1 2 4])), [1 1 6]);
371s  assert (size (harmmean (x, [1 4 3])), [1 40]);
371s  assert (size (harmmean (x, [1 2 3 4])), [1 1]);
371s ***** test
371s  x = repmat ([1:20;6:25], [5 2 6 3]);
371s  m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
371s  assert (harmmean (x, [3 2]), m, 4e-14);
371s  x(2,5,6,3) = NaN;
371s  m(2,3) = NaN;
371s  assert (harmmean (x, [3 2]), m, 4e-14);
371s  m(2,3) = 13.06617961315406;
371s  assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
371s ***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
371s ***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
371s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
371s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
371s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
371s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
371s ***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
371s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
371s 8 tests, 8 passed, 0 known failure, 0 skipped
371s [inst/grp2idx.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/grp2idx.m
371s ***** test
371s  in = [true false false true];
371s  out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
371s  assert (nthargout (1:3, @grp2idx, in), out)
371s  assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
371s ***** test
371s  assert (nthargout (1:3, @grp2idx, [false, true]),
371s          {[1; 2] {"0"; "1"} [false; true]});
371s  assert (nthargout (1:3, @grp2idx, [true, false]),
371s          {[1; 2] {"1"; "0"} [true; false]});
371s ***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
371s         {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
371s ***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
371s         {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
371s ***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
371s         {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
371s          [1; -3; -2; 2; -1; 3]});
371s ***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
371s         {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
371s ***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
371s         {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
371s ***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
371s         {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
371s ***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
371s         {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
371s ***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
371s         {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
371s 10 tests, 10 passed, 0 known failure, 0 skipped
371s [inst/fitrgam.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitrgam.m
371s ***** demo
371s  # Train a RegressionGAM Model for synthetic values
371s 
371s  f1 = @(x) cos (3 *x);
371s  f2 = @(x) x .^ 3;
371s 
371s  # generate x1 and x2 for f1 and f2
371s  x1 = 2 * rand (50, 1) - 1;
371s  x2 = 2 * rand (50, 1) - 1;
371s 
371s  # calculate y
371s  y = f1(x1) + f2(x2);
371s 
371s  # add noise
371s  y = y + y .* 0.2 .* rand (50,1);
371s  X = [x1, x2];
371s 
371s  # create an object
371s  a = fitrgam (X, y, "tol", 1e-3)
371s ***** test
371s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
371s  y = [1; 2; 3; 4];
371s  a = fitrgam (x, y);
371s  assert ({a.X, a.Y}, {x, y})
371s  assert ({a.BaseModel.Intercept}, {2.5000})
371s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
371s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
371s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
371s  assert ({a.Formula}, {[]})
371s ***** test
371s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
371s  y = [1; 2; 3; 4];
371s  pnames = {"A", "B", "C", "D"};
371s  formula = "Y ~ A + B + C + D + A:C";
371s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
371s  a = fitrgam (x, y, "predictors", pnames, "formula", formula);
371s  assert ({a.IntMatrix}, {intMat})
371s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
371s  assert ({a.Formula}, {formula})
371s ***** error<fitrgam: too few arguments.> fitrgam ()
371s ***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
371s ***** error<fitrgam: Name-Value arguments must be in pairs.>
371s  fitrgam (ones (4,2), ones (4, 1), "K")
371s ***** error<fitrgam: number of rows in X and Y must be equal.>
371s  fitrgam (ones (4,2), ones (3, 1))
371s ***** error<fitrgam: number of rows in X and Y must be equal.>
371s  fitrgam (ones (4,2), ones (3, 1), "K", 2)
371s 7 tests, 7 passed, 0 known failure, 0 skipped
371s [inst/crossval.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/crossval.m
371s ***** demo
371s  ## Determine the optimal number of clusters using cross-validation
371s 
371s  ## Declare a function to compute the sum of squared distances
371s  ## between data points and a varying number of clusters.
371s  function D = dist2clusters (X, Y, k)
371s    [Z, Zmu, Zstd] = zscore (X);
371s    [~, C] = kmeans (Z, k);
371s    ZY = (Y - Zmu) ./ Zstd;
371s    d = pdist2 (C, ZY, 'euclidean', 'Smallest', 1);
371s    D = sum (d .^ 2);
371s  endfunction
371s 
371s  load fisheriris
371s  for k = 1:8
371s    fcn = @(X, Y) dist2clusters (X, Y, k);
371s    distances = crossval (fcn, meas);
371s    cvdist(k) = sum (distances);
371s  endfor
371s 
371s  plot(cvdist)
371s  xlabel('Number of Clusters')
371s  ylabel('CV Sum of Squared Distances')
371s  xlim ([1,8]);
371s ***** test
371s  function yfit = regf (Xtrain, ytrain, Xtest)
371s    b = regress (ytrain, Xtrain);
371s    yfit = Xtest * b;
371s  endfunction
371s 
371s  load carsmall
371s  data = [Acceleration Horsepower Weight MPG];
371s  data(any(isnan(data),2),:) = [];
371s 
371s  y = data(:,4);
371s  X = [ones(length(y),1) data(:,1:3)];
371s  rand ("seed", 3);
371s  cvMSE = crossval('mse',X,y,'Predfun',@regf);
371s  assert (cvMSE, 18.720, 1e-3);
371s ***** error <crossval: criterion must be 'mse' or 'mcr'.> ...
371s  crossval ('fe', rand (10, 1), rand (10, 1), 1);
371s ***** error <crossval: prediction function handle is required for error evaluation.>  ...
371s  crossval ('mse', rand (10, 1), rand (10, 1), 1);
371s ***** error <crossval: X and Y are required for error evaluation.> ...
371s  crossval ('mse', rand (10, 1), 'Predfun', @(x,y) x + y);
371s ***** error <crossval: bad prediction function handle for error evaluation.> ...
371s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y) sum (x + y));
371s ***** error <crossval: prediction function handle must return a column vector with the same rows as XTest.> ...
371s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y,z) sum (x + y));
371s ***** error <crossval: X is required for values evaluation.> crossval (@(x) x);
371s ***** error <crossval: bad function handle to cross-validate.> ...
371s  crossval (@(x) x, rand (10, 3), rand (10, 1));
371s ***** error <crossval: function handle must return a scalar or a row vector.> ...
371s  crossval (@(x,y) [x, y], rand (10, 3), rand (10, 1));
371s ***** error <crossval: invalid first input argument.> crossval ({1}, 1, 1);
371s ***** error <crossval: you can only set one cvpartition type in paired arguments.> ...
371s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Holdout', 0.1, 'Leaveout', true)
371s ***** error <crossval: you cannot specify both 'Partition' and 'Stratify'.> ...
371s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Partition', cvpartition (10, 'Leaveout'), 'Stratify', true)
371s 12 tests, 12 passed, 0 known failure, 0 skipped
371s [inst/cl_multinom.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cl_multinom.m
371s ***** demo
371s  CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
371s ***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
371s ***** error cl_multinom (1, 2, 3, 4, 5);
371s ***** error<cl_multinom: argument method must be a string.> ...
371s  cl_multinom (1, 2, 3, 4);
371s ***** error<cl_multinom: unknown calculation type.> ...
371s  cl_multinom (1, 2, 3, "some string");
371s 4 tests, 4 passed, 0 known failure, 0 skipped
371s [inst/glmfit.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/glmfit.m
371s ***** demo
371s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
371s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
371s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
371s  b = glmfit (x, [y n], "binomial", "Link", "probit");
371s  yfit = glmval (b, x, "probit", "Size", n);
371s  plot (x, y./n, 'o', x, yfit ./ n, '-')
371s ***** demo
371s  load fisheriris
371s  X = meas (51:end, :);
371s  y = strcmp ("versicolor", species(51:end));
371s  b = glmfit (X, y, "binomial", "link", "logit")
371s ***** test
371s  load fisheriris;
371s  X = meas(51:end,:);
371s  y = strcmp ("versicolor", species(51:end));
371s  b = glmfit (X, y, "binomial", "link", "logit");
371s  assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
371s ***** test
371s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
371s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
371s  [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
371s  b_matlab = [-0.7631; 0.1113];
371s  dev_matlab = 0.0111;
371s  assert (Bnew, b_matlab, 0.001);
371s  assert (dev, dev_matlab, 0.001);
371s ***** test
371s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
371s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
371s  p_input = 1;
371s  [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
371s  b_matlab = [0.3813; 0.0950];
371s  dev_matlab = 0.0051;
371s  assert (Bnew, b_matlab, 0.001);
371s  assert (dev, dev_matlab, 0.001);
371s ***** error <glmfit: too few input arguments.> glmfit ()
371s ***** error <glmfit: too few input arguments.> glmfit (1)
371s ***** error <glmfit: too few input arguments.> glmfit (1, 2)
371s ***** error <glmfit: Name-Value arguments must be in pairs.> ...
371s  glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
371s ***** error <glmfit: X must be a numeric matrix.> ...
371s  glmfit ('abc', rand (6, 1), 'poisson')
371s ***** error <glmfit: X must be a numeric matrix.> ...
371s  glmfit ([], rand (6, 1), 'poisson')
371s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
371s  glmfit (rand (5, 2), 'abc', 'poisson')
371s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
371s  glmfit (rand (5, 2), [], 'poisson')
371s ***** error <glmfit: X and Y must have the same number of observations.> ...
371s  glmfit (rand (5, 2), rand (6, 1), 'poisson')
371s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
371s  glmfit (rand (6, 2), rand (6, 1), 3)
371s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
371s  glmfit (rand (6, 2), rand (6, 1), {'poisson'})
371s ***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
371s  glmfit (rand (5, 2), rand (5, 3), 'binomial')
371s ***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
371s  glmfit (rand (2, 2), [true, true; false, false], 'binomial')
371s ***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
371s  glmfit (rand (5, 2), rand (5, 2), 'normal')
371s ***** error <glmfit: unsupported distribution.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'chebychev')
371s ***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
371s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
371s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
371s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
371s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
371s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
371s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
371s ***** error <glmfit: structure with custom link functions must be a scalar.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
371s ***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
371s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
371s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
371s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
371s ***** error <glmfit: invalid custom 'Link' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
371s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
371s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
371s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
371s ***** error <glmfit: invalid custom 'Derivative' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
371s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
371s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
371s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
371s ***** error <glmfit: invalid custom 'Inverse' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
371s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
371s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
371s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
371s ***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
371s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
371s ***** error <glmfit: invalid custom 'Link' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
371s ***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
371s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
371s ***** error <glmfit: invalid custom 'Derivative' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
371s ***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
371s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
371s ***** error <glmfit: invalid custom 'Inverse' function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
371s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
371s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
371s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
371s ***** error <glmfit: canonical link function name must be a character vector.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
371s ***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
371s ***** error <glmfit: invalid value for custom link function.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
371s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
371s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
371s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
371s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
371s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
371s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
371s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
371s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
371s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
371s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
371s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
371s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
371s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
371s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
371s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
371s 70 tests, 70 passed, 0 known failure, 0 skipped
371s [inst/kmeans.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/kmeans.m
371s ***** demo
371s  ## Generate a two-cluster problem
371s  randn ("seed", 31)  # for reproducibility
371s  C1 = randn (100, 2) + 1;
371s  randn ("seed", 32)  # for reproducibility
371s  C2 = randn (100, 2) - 1;
371s  data = [C1; C2];
371s 
371s  ## Perform clustering
371s  rand ("seed", 1)  # for reproducibility
371s  [idx, centers] = kmeans (data, 2);
371s 
371s  ## Plot the result
371s  figure;
371s  plot (data (idx==1, 1), data (idx==1, 2), "ro");
371s  hold on;
371s  plot (data (idx==2, 1), data (idx==2, 2), "bs");
371s  plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
371s  title ("A simple two-clusters example");
371s  hold off;
371s ***** demo
371s  ## Cluster data using k-means clustering, then plot the cluster regions
371s  ## Load Fisher's iris data set and use the petal lengths and widths as
371s  ## predictors
371s 
371s  load fisheriris
371s  X = meas(:,3:4);
371s 
371s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
371s  title ("Fisher's Iris Data");
371s  xlabel ("Petal Lengths (cm)");
371s  ylabel ("Petal Widths (cm)");
371s 
371s  ## Cluster the data. Specify k = 3 clusters
371s  rand ("seed", 1)  # for reproducibility
371s  [idx, C] = kmeans (X, 3);
371s  x1 = min (X(:,1)):0.01:max (X(:,1));
371s  x2 = min (X(:,2)):0.01:max (X(:,2));
371s  [x1G, x2G] = meshgrid (x1, x2);
371s  XGrid = [x1G(:), x2G(:)];
371s 
371s  idx2Region = kmeans (XGrid, 3, "MaxIter", 10, "Start", C);
371s  figure;
371s  gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
371s            [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
371s  hold on;
371s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
371s  title ("Fisher's Iris Data");
371s  xlabel ("Petal Lengths (cm)");
371s  ylabel ("Petal Widths (cm)");
371s  legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
371s  hold off
371s ***** demo
371s  ## Partition Data into Two Clusters
371s 
371s  randn ("seed", 1)  # for reproducibility
371s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
371s  randn ("seed", 2)  # for reproducibility
371s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
371s  X = [r1; r2];
371s 
371s  plot (X(:,1), X(:,2), ".");
371s  title ("Randomly Generated Data");
371s  rand ("seed", 1)  # for reproducibility
371s  [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
371s                           "Replicates", 5, "Display", "final");
371s  figure;
371s  plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
371s  hold on
371s  plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
371s  plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
371s  legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
371s  title ("Cluster Assignments and Centroids");
371s  hold off
371s ***** demo
371s  ## Assign New Data to Existing Clusters
371s 
371s  ## Generate a training data set using three distributions
371s  randn ("seed", 5)  # for reproducibility
371s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
371s  randn ("seed", 7)  # for reproducibility
371s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
371s  randn ("seed", 9)  # for reproducibility
371s  r3 = randn (100, 2) * 0.75;
371s  X = [r1; r2; r3];
371s 
371s  ## Partition the training data into three clusters by using kmeans
371s 
371s  rand ("seed", 1)  # for reproducibility
371s  [idx, C] = kmeans (X, 3);
371s 
371s  ## Plot the clusters and the cluster centroids
371s 
371s  gscatter (X(:,1), X(:,2), idx, "bgm", "***");
371s  hold on
371s  plot (C(:,1), C(:,2), "kx");
371s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")
371s 
371s  ## Generate a test data set
371s  randn ("seed", 25)  # for reproducibility
371s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
371s  randn ("seed", 27)  # for reproducibility
371s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
371s  randn ("seed", 29)  # for reproducibility
371s  r3 = randn (100, 2) * 0.75;
371s  Xtest = [r1; r2; r3];
371s 
371s  ## Classify the test data set using the existing clusters
371s  ## Find the nearest centroid from each test data point by using pdist2
371s 
371s  D = pdist2 (C, Xtest, "euclidean");
371s  [group, ~] = find (D == min (D));
371s 
371s  ## Plot the test data and label the test data using idx_test with gscatter
371s 
371s  gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
371s  box on;
371s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
371s          "Data classified to Cluster 1", "Data classified to Cluster 2", ...
371s          "Data classified to Cluster 3", "Location", "NorthWest");
371s  title ("Assign New Data to Existing Clusters");
371s ***** test
371s  samples = 4;
371s  dims = 3;
371s  k = 2;
371s  [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
371s                           "emptyAction", "singleton");
371s  assert (size (cls), [samples, 1]);
371s  assert (size (c), [k, dims]);
371s  assert (size (d), [k, 1]);
371s  assert (size (z), [samples, k]);
371s ***** test
371s  samples = 4;
371s  dims = 3;
371s  k = 2;
371s  [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
371s                           "emptyAction", "singleton");
371s  assert (size (cls), [samples, 1]);
371s  assert (size (c), [k, dims]);
371s  assert (size (d), [k, 1]);
371s  assert (size (z), [samples, k]);
371s ***** test
371s  [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
371s  assert (cls, [1; 1]);
371s  assert (c, [1.5, 0; NA, NA]);
371s ***** test
371s  kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
371s          "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
371s ***** test
371s  kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
371s ***** test
371s  kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
371s ***** error kmeans (rand (3,2), 4);
371s ***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
371s ***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
371s ***** error kmeans (rand (4,3), 2, "start", rand (2,2));
371s ***** error kmeans (rand (4,3), 2, "distance", "manhattan");
371s ***** error kmeans (rand (3,4), 2, "start", "normal");
371s ***** error kmeans (rand (4,3), 2, "replicates", i);
371s ***** error kmeans (rand (4,3), 2, "replicates", -1);
371s ***** error kmeans (rand (4,3), 2, "replicates", []);
371s ***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
371s ***** error kmeans (rand (4,3), 2, "replicates", "one");
371s ***** error kmeans (rand (4,3), 2, "MAXITER", i);
371s ***** error kmeans (rand (4,3), 2, "MaxIter", -1);
371s ***** error kmeans (rand (4,3), 2, "maxiter", []);
371s ***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
371s ***** error kmeans (rand (4,3), 2, "maxiter", "one");
371s ***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
371s 31 tests, 31 passed, 0 known failure, 0 skipped
371s [inst/dcov.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dcov.m
371s ***** demo
371s  base=@(x) (x- min(x))./(max(x)-min(x));
371s  N = 5e2;
371s  x = randn (N,1); x = base (x);
371s  z = randn (N,1); z = base (z);
371s  # Linear relations
371s  cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
371s  ly = x .* cy;
371s  ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
371s  # Correlated Gaussian
371s  cz = 1 - abs (cy);
371s  gy = base ( ly + cz.*z);
371s  # Shapes
371s  sx      = repmat (x,1,7);
371s  sy      = zeros (size (ly));
371s  v       = 2 * rand (size(x,1),2) - 1;
371s  sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
371s  R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
371s  tmp     = R(35) * v.';
371s  sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
371s  tmp     = R(45) * v.';
371s  sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
371s  sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
371s  sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
371s  sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
371s  sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
371s  sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
371s  sy      = base (sy);
371s  sx      = base (sx);
371s  # scaled shape
371s  sc  = 1/3;
371s  ssy = (sy-0.5) * sc + 0.5;
371s  n = size (ly,2);
371s  ym = 1.2;
371s  xm = 0.5;
371s  fmt={'horizontalalignment','center'};
371s  ff = "% .2f";
371s  figure (1)
371s  for i=1:n
371s    subplot(4,n,i);
371s    plot (x, gy(:,i), '.b');
371s    axis tight
371s    axis off
371s    text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})
371s 
371s    subplot(4,n,i+n);
371s    plot (x, ly(:,i), '.b');
371s    axis tight
371s    axis off
371s    text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})
371s 
371s    subplot(4,n,i+2*n);
371s    plot (sx(:,i), sy(:,i), '.b');
371s    axis tight
371s    axis off
371s    text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
371s    v = axis ();
371s 
371s    subplot(4,n,i+3*n);
371s    plot (sx(:,i), ssy(:,i), '.b');
371s    axis (v)
371s    axis off
371s    text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
371s  endfor
371s ***** error dcov (randn (30, 5), randn (25,5))
371s 1 test, 1 passed, 0 known failure, 0 skipped
371s [inst/mahal.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/mahal.m
371s ***** error mahal ()
371s ***** error mahal (1, 2, 3)
371s ***** error mahal ("A", "B")
371s ***** error <must be numeric> mahal ([1, 2], ["A", "B"])
371s ***** error mahal (ones (2, 2, 2))
371s ***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
371s ***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
371s ***** test
371s  X = [1 0; 0 1; 1 1; 0 0];
371s  assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
371s  assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
371s ***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
371s 9 tests, 9 passed, 0 known failure, 0 skipped
371s [inst/ttest.m]
371s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ttest.m
371s ***** test
371s  x = 8:0.1:12;
371s  [h, pval, ci] = ttest (x, 10);
371s  assert (h, 0)
371s  assert (pval, 1, 10*eps)
371s  assert (ci, [9.6219 10.3781], 1E-5)
371s  [h, pval, ci0] = ttest (x, 0);
371s  assert (h, 1)
371s  assert (pval, 0)
371s  assert (ci0, ci, 2e-15)
371s  [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
371s  assert (h, 0)
371s  assert (pval, 0.5, 10*eps)
371s  assert (ci, [9.68498 Inf], 1E-5)
372s ***** error ttest ([8:0.1:12], 10, "tail", "invalid");
372s ***** error ttest ([8:0.1:12], 10, "tail", 25);
372s 3 tests, 3 passed, 0 known failure, 0 skipped
372s [inst/hmmgenerate.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hmmgenerate.m
372s ***** test
372s  len = 25;
372s  transprob = [0.8, 0.2; 0.4, 0.6];
372s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
372s  [sequence, states] = hmmgenerate (len, transprob, outprob);
372s  assert (length (sequence), len);
372s  assert (length (states), len);
372s  assert (min (sequence) >= 1);
372s  assert (max (sequence) <= columns (outprob));
372s  assert (min (states) >= 1);
372s  assert (max (states) <= rows (transprob));
372s ***** test
372s  len = 25;
372s  transprob = [0.8, 0.2; 0.4, 0.6];
372s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
372s  symbols = {"A", "B", "C"};
372s  statenames = {"One", "Two"};
372s  [sequence, states] = hmmgenerate (len, transprob, outprob, ...
372s                       "symbols", symbols, "statenames", statenames);
372s  assert (length (sequence), len);
372s  assert (length (states), len);
372s  assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
372s                                   strcmp (sequence, "C") == ones (1, len));
372s  assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
372s 2 tests, 2 passed, 0 known failure, 0 skipped
372s [inst/cophenet.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cophenet.m
372s ***** demo
372s  randn ("seed", 5)  # for reproducibility
372s  X = randn (10,2);
372s  y = pdist (X);
372s  Z = linkage (y, "average");
372s  cophenet (Z, y)
372s ***** error<cophenet: function called with too few input arguments.> cophenet ()
372s ***** error<cophenet: function called with too few input arguments.> cophenet (1)
372s ***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
372s  cophenet (ones (2,2), 1)
372s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
372s  cophenet ([1 2 1], "a")
372s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
372s  cophenet ([1 2 1], [1 2])
372s 5 tests, 5 passed, 0 known failure, 0 skipped
372s [inst/gscatter.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/gscatter.m
372s ***** demo
372s  load fisheriris;
372s  X = meas(:,3:4);
372s  cidcs = kmeans (X, 3, "Replicates", 5);
372s  gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
372s  title ("Fisher's iris data");
372s ***** shared visibility_setting
372s  visibility_setting = get (0, "DefaultFigureVisible");
372s ***** test
372s  hf = figure ("visible", "off");
372s  unwind_protect
372s    load fisheriris;
372s    X = meas(:,3:4);
372s    cidcs = kmeans (X, 3, "Replicates", 5);
372s    gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
372s    title ("Fisher's iris data");
372s  unwind_protect_cleanup
372s    close (hf);
372s  end_unwind_protect
372s warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
372s ***** error gscatter ();
372s ***** error gscatter ([1]);
372s ***** error gscatter ([1], [2]);
372s ***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
372s ***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
372s ***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
372s ***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
372s ***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
372s 9 tests, 9 passed, 0 known failure, 0 skipped
372s [inst/chi2gof.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/chi2gof.m
372s ***** demo
372s  x = normrnd (50, 5, 100, 1);
372s  [h, p, stats] = chi2gof (x)
372s  [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
372s  [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
372s ***** demo
372s  x = rand (100,1 );
372s  n = length (x);
372s  binedges = linspace (0, 1, 11);
372s  expectedCounts = n * diff (binedges);
372s  [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
372s ***** demo
372s  bins = 0:5;
372s  obsCounts = [6 16 10 12 4 2];
372s  n = sum(obsCounts);
372s  lambdaHat = sum(bins.*obsCounts) / n;
372s  expCounts = n * poisspdf(bins,lambdaHat);
372s  [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
372s                           "expected", expCounts, "nparams",1)
372s ***** error chi2gof ()
372s ***** error chi2gof ([2,3;3,4])
372s ***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
372s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
372s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
372s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
372s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
372s ***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
372s ***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
372s ***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
372s ***** test
372s  x = [1 2 1 3 2 4 3 2 4 3 2 2];
372s  [h, p, stats] = chi2gof (x);
372s  assert (h, 0);
372s  assert (p, NaN);
372s  assert (stats.chi2stat, 0.1205375022748029, 1e-14);
372s  assert (stats.df, 0);
372s  assert (stats.edges, [1, 2.5, 4], 1e-14);
372s  assert (stats.O, [7, 5], 1e-14);
372s  assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
372s 11 tests, 11 passed, 0 known failure, 0 skipped
372s [inst/combnk.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/combnk.m
372s ***** demo
372s  c = combnk (1:5, 2);
372s  disp ("All pairs of integers between 1 and 5:");
372s  disp (c);
372s ***** test
372s  c = combnk (1:3, 2);
372s  assert (c, [1, 2; 1, 3; 2, 3]);
372s ***** test
372s  c = combnk (1:3, 6);
372s  assert (isempty (c));
372s ***** test
372s  c = combnk ({1, 2, 3}, 2);
372s  assert (c, {1, 2; 1, 3; 2, 3});
372s ***** test
372s  c = combnk ("hello", 2);
372s  assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
372s 4 tests, 4 passed, 0 known failure, 0 skipped
372s [inst/nansum.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/nansum.m
372s ***** demo
372s  ## Find the column sums for a matrix with missing values.,
372s 
372s  x = magic (3);
372s  x([1, 4, 7:9]) = NaN
372s  s = nansum (x)
372s ***** demo
372s  ## Find the row sums for a matrix with missing values.,
372s 
372s  x = magic (3);
372s  x([1, 4, 7:9]) = NaN
372s  s = nansum (x, 2)
372s ***** demo
372s  ## Find the sum of all the values in a multidimensional array
372s  ## with missing values.
372s 
372s  x = reshape (1:30, [2, 5, 3]);
372s  x([10:12, 25]) = NaN
372s  s = nansum (x, "all")
372s ***** demo
372s  ## Find the sum of a multidimensional array with missing values over
372s  ## multiple dimensions.
372s 
372s  x = reshape (1:30, [2, 5, 3]);
372s  x([10:12, 25]) = NaN
372s  s = nansum (x, [2, 3])
372s ***** assert (nansum ([]), 0)
372s ***** assert (nansum (NaN), 0)
372s ***** assert (nansum (NaN(3)), [0, 0, 0])
372s ***** assert (nansum ([2 4 NaN 7]), 13)
372s ***** assert (nansum ([2 4 NaN Inf]), Inf)
372s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
372s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
372s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
372s ***** test
372s  x = magic(3);
372s  x([1 6:9]) = NaN;
372s  assert (nansum (x), [7, 6, 0])
372s  assert (nansum (x, 2), [1; 8; 4])
372s ***** test
372s  x = reshape(1:24, [2, 4, 3]);
372s  x([5:6, 20]) = NaN;
372s  assert (nansum (x, "all"), 269)
372s ***** test
372s  x = reshape(1:24,[2, 4, 3]);
372s  x([5:6, 20]) = NaN;
372s  assert (squeeze (nansum (x, [1, 2])), [25; 100; 144])
372s  assert (nansum (x, [2, 3]), [139; 130])
372s ***** error <nansum: X must be numeric.> nansum ({3})
372s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 0)
372s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
372s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
372s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
372s  nansum (ones (3, 3, 3), [2, 2.5])
372s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
372s  nansum (ones (3, 3, 3), [-1, 2])
372s ***** error <nansum: VECDIM must contain non-repeating positive integers.> ...
372s  nansum (ones (3, 3, 3), [2, 2, 3])
372s 18 tests, 18 passed, 0 known failure, 0 skipped
372s [inst/cdfplot.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cdfplot.m
372s ***** demo
372s  x = randn(100,1);
372s  cdfplot (x);
372s ***** test
372s  hf = figure ("visible", "off");
372s  unwind_protect
372s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
372s    [hCDF, stats] = cdfplot (x);
372s    assert (stats.min, 2);
372s    assert (stats.max, 6);
372s    assert (stats.median, 3.5);
372s    assert (stats.std, 1.35400640077266, 1e-14);
372s  unwind_protect_cleanup
372s    close (hf);
372s  end_unwind_protect
372s ***** test
372s  hf = figure ("visible", "off");
372s  unwind_protect
372s    x = randn(100,1);
372s    cdfplot (x);
372s  unwind_protect_cleanup
372s    close (hf);
372s  end_unwind_protect
372s ***** error cdfplot ();
372s ***** error cdfplot ([x',x']);
372s ***** error cdfplot ([NaN, NaN, NaN, NaN]);
372s 5 tests, 5 passed, 0 known failure, 0 skipped
372s [inst/slicesample.m]
372s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/slicesample.m
372s ***** demo
372s  ## Define function to sample
372s  d = 2;
372s  mu = [-1; 2];
372s  rand ("seed", 5)  # for reproducibility
372s  Sigma = rand (d);
372s  Sigma = (Sigma + Sigma');
372s  Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
372s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
372s 
372s  ## Inputs
372s  start = ones (1,2);
372s  nsamples = 500;
372s  K = 500;
372s  m = 10;
372s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
372s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
372s  figure;
372s  hold on;
372s  plot (smpl(:,1), smpl(:,2), 'x');
372s  [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
372s  z = reshape (pdf ([x(:), y(:)]), size(x));
372s  mesh (x, y, z, "facecolor", "None");
372s 
372s  ## Using sample points to find the volume of half a sphere with radius of .5
372s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
372s  int = mean (f (smpl) ./ pdf (smpl));
372s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
372s  trueerr = abs (2/3*pi*.25^(3/2)-int);
372s  fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
372s  fprintf ("Monte Carlo integral error estimate %f\n", errest);
372s  fprintf ("The actual error %f\n", trueerr);
372s  mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
372s ***** demo
372s  ## Integrate truncated normal distribution to find normalization constant
372s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
372s  nsamples = 1e3;
372s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
372s  [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
372s  f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
372s  x = linspace (-3, 3, 1000);
372s  area (x, f(x));
372s  xlabel ("x");
372s  ylabel ("f(x)");
372s  int = mean (f (smpl) ./ pdf (smpl));
372s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
372s  trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
372s  fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
372s  fprintf("Monte Carlo integral error estimate %f\n", errest);
372s  fprintf("The actual error %f\n", trueerr);
372s ***** test
372s  start = 0.5;
372s  nsamples = 1e3;
372s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
372s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
372s  assert (mean (smpl, 1), 1, .15);
372s  assert (var (smpl, 1), 1, .25);
373s ***** error slicesample ();
373s ***** error slicesample (1);
373s ***** error slicesample (1, 1);
373s 4 tests, 4 passed, 0 known failure, 0 skipped
373s [inst/confusionmat.m]
373s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/confusionmat.m
373s ***** test
373s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
373s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
373s  C  = [0 1 1 0 0 0 0 0; 0 0 0 0 1 0 0 0; 0 1 0 0 0 0 1 0; 0 0 0 1 0 1 0 0; ...
373s        0 0 0 0 3 0 0 0; 0 0 0 1 0 1 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 2];
373s  assert (confusionmat (Yt, Yp), C)
373s 1 test, 1 passed, 0 known failure, 0 skipped
373s [inst/ridge.m]
373s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ridge.m
373s ***** demo
373s  ## Perform ridge regression for a range of ridge parameters and observe
373s  ## how the coefficient estimates change based on the acetylene dataset.
373s 
373s  load acetylene
373s 
373s  X = [x1, x2, x3];
373s 
373s  x1x2 = x1 .* x2;
373s  x1x3 = x1 .* x3;
373s  x2x3 = x2 .* x3;
373s 
373s  D = [x1, x2, x3, x1x2, x1x3, x2x3];
373s 
373s  k = 0:1e-5:5e-3;
373s 
373s  b = ridge (y, D, k);
373s 
373s  figure
373s  plot (k, b, "LineWidth", 2)
373s  ylim ([-100, 100])
373s  grid on
373s  xlabel ("Ridge Parameter")
373s  ylabel ("Standardized Coefficient")
373s  title ("Ridge Trace")
373s  legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")
373s 
373s ***** demo
373s 
373s  load carbig
373s  X = [Acceleration Weight Displacement Horsepower];
373s  y = MPG;
373s 
373s  n = length(y);
373s 
373s  rand("seed",1); % For reproducibility
373s 
373s  c = cvpartition(n,'HoldOut',0.3);
373s  idxTrain = training(c,1);
373s  idxTest = ~idxTrain;
373s 
373s  idxTrain = training(c,1);
373s  idxTest = ~idxTrain;
373s 
373s  k = 5;
373s  b = ridge(y(idxTrain),X(idxTrain,:),k,0);
373s 
373s  % Predict MPG values for the test data using the model.
373s  yhat = b(1) + X(idxTest,:)*b(2:end);
373s  scatter(y(idxTest),yhat)
373s 
373s  hold on
373s  plot(y(idxTest),y(idxTest),"r")
373s  xlabel('Actual MPG')
373s  ylabel('Predicted MPG')
373s  hold off
373s 
373s ***** test
373s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
373s  assert (b, [0.5533; 0.5533], 1e-4);
373s ***** test
373s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
373s  assert (b, [0.4841; 0.4841], 1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0);
373s  assert (b,[10.2273;1.97128;-0.601818],1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0.0005);
373s  assert (b,[10.2233;1.9712;-0.6056],1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0.001);
373s  assert (b,[10.2194;1.9711;-0.6094],1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0.002);
373s  assert (b,[10.2116;1.9709;-0.6169],1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0.005);
373s  assert (b,[10.1882;1.9704;-0.6393],1e-4);
373s ***** test
373s  load acetylene
373s  x = [x1, x2, x3];
373s  b = ridge (y, x, 0.01);
373s  assert (b,[10.1497;1.9695;-0.6761],1e-4);
373s ***** error<ridge: function called with too few input arguments.> ridge (1)
373s ***** error<ridge: function called with too few input arguments.> ridge (1, 2)
373s ***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
373s ***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
373s ***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
373s ***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
373s ***** error<ridge: Y and X must contain the same number of rows.> ...
373s  ridge ([1; 2; 3; 4; 5], ones (3), 3)
373s ***** error<ridge: wrong value for SCALED argument.> ...
373s  ridge ([1; 2; 3], ones (3), 3, 2)
373s ***** error<ridge: wrong value for SCALED argument.> ...
373s  ridge ([1; 2; 3], ones (3), 3, "some")
373s 17 tests, 17 passed, 0 known failure, 0 skipped
373s [inst/hotelling_t2test.m]
373s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hotelling_t2test.m
373s ***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
373s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
373s  hotelling_t2test (1);
373s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
373s  hotelling_t2test (ones(2,2,2));
373s ***** error<hotelling_t2test: invalid value for alpha.> ...
373s  hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
373s ***** error<hotelling_t2test: invalid value for alpha.> ...
373s  hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
373s ***** error<hotelling_t2test: invalid value for alpha.> ...
373s  hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
373s ***** error<hotelling_t2test: invalid value for alpha.> ...
373s  hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
373s ***** error<hotelling_t2test: invalid Name argument.> ...
373s  hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
373s ***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
373s  hotelling_t2test (ones(20,1), [0, 0]);
373s ***** error<hotelling_t2test: X must have more rows than columns.> ...
373s  hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
373s ***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
373s  hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
373s ***** test
373s  randn ("seed", 1);
373s  x = randn (50000, 5);
373s  [h, pval, stats] = hotelling_t2test (x);
373s  assert (h, 0);
373s  assert (stats.df1, 5);
373s  assert (stats.df2, 49995);
373s ***** test
373s  randn ("seed", 1);
373s  x = randn (50000, 5);
373s  [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
373s  assert (h, 1);
373s  assert (stats.df1, 5);
373s  assert (stats.df2, 49995);
373s 13 tests, 13 passed, 0 known failure, 0 skipped
373s [inst/violin.m]
373s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/violin.m
373s ***** demo
373s  clf
373s  x = zeros (9e2, 10);
373s  for i=1:10
373s    x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
373s  endfor
373s  h = violin (x, "color", "c");
373s  axis tight
373s  set (h.violin, "linewidth", 2);
373s  set (gca, "xgrid", "on");
373s  xlabel ("Variables")
373s  ylabel ("Values")
373s ***** demo
373s  clf
373s  data = {randn(100,1)*5+140, randn(130,1)*8+135};
373s  subplot (1,2,1)
373s  title ("Grade 3 heights - vertical");
373s  set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
373s  violin (data, "Nbins", 10);
373s  axis tight
373s 
373s  subplot(1,2,2)
373s  title ("Grade 3 heights - horizontal");
373s  set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
373s  violin (data, "horizontal", "Nbins", 10);
373s  axis tight
373s ***** demo
373s  clf
373s  data = exprnd (0.1, 500,4);
373s  violin (data, "nbins", {5,10,50,100});
373s  axis ([0 5 0 max(data(:))])
373s ***** demo
373s  clf
373s  data = exprnd (0.1, 500,4);
373s  violin (data, "color", jet(4));
373s  axis ([0 5 0 max(data(:))])
373s ***** demo
373s  clf
373s  data = repmat(exprnd (0.1, 500,1), 1, 4);
373s  violin (data, "width", linspace (0.1,0.5,4));
373s  axis ([0 5 0 max(data(:))])
373s ***** demo
373s  clf
373s  data = repmat(exprnd (0.1, 500,1), 1, 4);
373s  violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
373s  axis ([0 5 0 max(data(:))])
373s ***** test
373s  hf = figure ("visible", "off");
373s  unwind_protect
373s    data = exprnd (0.1, 500,4);
373s    violin (data, "color", jet(4));
373s    axis ([0 5 0 max(data(:))])
373s  unwind_protect_cleanup
373s    close (hf);
373s  end_unwind_protect
374s ***** test
374s  hf = figure ("visible", "off");
374s  unwind_protect
374s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
374s    subplot (1,2,1)
374s    title ("Grade 3 heights - vertical");
374s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
374s    violin (data, "Nbins", 10);
374s    axis tight
374s  unwind_protect_cleanup
374s    close (hf);
374s  end_unwind_protect
374s ***** test
374s  hf = figure ("visible", "off");
374s  unwind_protect
374s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
374s    subplot (1,2,1)
374s    title ("Grade 3 heights - vertical");
374s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
374s    violin (data, "Nbins", 10);
374s    axis tight
374s    subplot(1,2,2)
374s    title ("Grade 3 heights - horizontal");
374s    set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
374s    violin (data, "horizontal", "Nbins", 10);
374s    axis tight
374s  unwind_protect_cleanup
374s    close (hf);
374s  end_unwind_protect
374s ***** test
374s  hf = figure ("visible", "off");
374s  unwind_protect
374s    data = repmat(exprnd (0.1, 500,1), 1, 4);
374s    violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
374s    axis ([0 5 0 max(data(:))])
374s  unwind_protect_cleanup
374s    close (hf);
374s  end_unwind_protect
374s ***** test
374s  hf = figure ("visible", "off");
374s  unwind_protect
374s    data = repmat(exprnd (0.1, 500,1), 1, 4);
374s    violin (data, "width", linspace (0.1,0.5,4));
374s    axis ([0 5 0 max(data(:))])
374s  unwind_protect_cleanup
374s    close (hf);
374s  end_unwind_protect
375s 5 tests, 5 passed, 0 known failure, 0 skipped
375s [inst/jackknife.m]
375s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/jackknife.m
375s ***** demo
375s  for k = 1:1000
375s    rand ("seed", k);  # for reproducibility
375s    x = rand (10, 1);
375s    s(k) = std (x);
375s    jackstat = jackknife (@std, x);
375s    j(k) = 10 * std (x) - 9 * mean (jackstat);
375s  endfor
375s  figure();
375s  hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
375s ***** demo
375s  for k = 1:1000
375s    randn ("seed", k); # for reproducibility
375s    x = randn (1, 50);
375s    rand ("seed", k);  # for reproducibility
375s    y = rand (1, 50);
375s    jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
375s    j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
375s    v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
375s  endfor
375s  t = (j - sqrt (1 / 12)) ./ sqrt (v);
375s  figure();
375s  plot (sort (tcdf (t, 49)), ...
375s        "-;Almost linear mapping indicates good fit with t-distribution.;")
375s ***** test
375s  ##Example from Quenouille, Table 1
375s  d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
375s  jackstat = jackknife ( @(x) 1/mean(x), d );
375s  assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
375s 1 test, 1 passed, 0 known failure, 0 skipped
375s [inst/mhsample.m]
375s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/mhsample.m
375s ***** demo
375s  ## Define function to sample
375s  d = 2;
375s  mu = [-1; 2];
375s  rand ("seed", 5)  # for reproducibility
375s  Sigma = rand (d);
375s  Sigma = (Sigma + Sigma');
375s  Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
375s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
375s  ## Inputs
375s  start = ones (1, 2);
375s  nsamples = 500;
375s  sym = true;
375s  K = 500;
375s  m = 10;
375s  rand ("seed", 8)  # for reproducibility
375s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
375s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
375s                             "symmetric", sym, "burnin", K, "thin", m);
375s  figure;
375s  hold on;
375s  plot (smpl(:, 1), smpl(:, 2), 'x');
375s  [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
375s  z = reshape (pdf ([x(:), y(:)]), size(x));
375s  mesh (x, y, z, "facecolor", "None");
375s  ## Using sample points to find the volume of half a sphere with radius of .5
375s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
375s  int = mean (f (smpl) ./ pdf (smpl));
375s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
375s  trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
375s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
375s  printf ("Monte Carlo integral error estimate %f\n", errest);
375s  printf ("The actual error %f\n", trueerr);
375s  mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
375s ***** demo
375s  ## Integrate truncated normal distribution to find normalization constant
375s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
375s  nsamples = 1e3;
375s  rand ("seed", 5)  # for reproducibility
375s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
375s  [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
375s                             "symmetric", true, "thin", 4);
375s  f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
375s  x = linspace (-3, 3, 1000);
375s  area(x, f(x));
375s  xlabel ('x');
375s  ylabel ('f(x)');
375s  int = mean (f (smpl) ./ pdf (smpl));
375s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
375s  trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
375s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
375s  printf ("Monte Carlo integral error estimate %f\n", errest);
375s  printf ("The actual error %f\n", trueerr);
375s ***** test
375s  nchain = 1e4;
375s  start = rand (nchain, 1);
375s  nsamples = 1e3;
375s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
375s  proppdf = @(x, y) 1/3;
375s  proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
375s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
375s                             "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
375s                             "burnin", 0);
375s  assert (mean (mean (smpl, 1), 3), 1, .01);
375s  assert (mean (var (smpl, 1), 3), 1, .01)
379s ***** error mhsample ();
379s ***** error mhsample (1);
379s ***** error mhsample (1, 1);
379s ***** error mhsample (1, 1, "pdf", @(x)x);
379s ***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
379s 6 tests, 6 passed, 0 known failure, 0 skipped
379s [inst/probit.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/probit.m
379s ***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
379s ***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
379s ***** error probit ()
379s ***** error probit (1, 2)
379s 4 tests, 4 passed, 0 known failure, 0 skipped
379s [inst/inconsistent.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/inconsistent.m
379s ***** error inconsistent ()
379s ***** error inconsistent ([1 2 1], 2, 3)
379s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
379s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
379s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
379s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
379s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
379s ***** test
379s  load fisheriris;
379s  Z = linkage(meas, 'average', 'chebychev');
379s  assert (cond (inconsistent (Z)), 39.9, 1e-3);
379s 8 tests, 8 passed, 0 known failure, 0 skipped
379s [inst/loadmodel.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/loadmodel.m
379s ***** error<loadmodel: too few arguments.> loadmodel ()
379s ***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
379s  loadmodel ("fisheriris.mat")
379s ***** error<loadmodel: 'ClassificationModel' is not supported.> ...
379s  loadmodel ("fail_loadmodel.mdl")
379s ***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
379s  loadmodel ("fail_load_model.mdl")
379s 4 tests, 4 passed, 0 known failure, 0 skipped
379s [inst/ztest2.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ztest2.m
379s ***** error ztest2 ();
379s ***** error ztest2 (1);
379s ***** error ztest2 (1, 2);
379s ***** error ztest2 (1, 2, 3);
379s ***** error ztest2 (1, 2, 3, 2);
379s ***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
379s  ztest2 (1, 2, 3, 4, "alpha")
379s ***** error<ztest2: invalid VALUE for alpha.> ...
379s  ztest2 (1, 2, 3, 4, "alpha", 0);
379s ***** error<ztest2: invalid VALUE for alpha.> ...
379s  ztest2 (1, 2, 3, 4, "alpha", 1.2);
379s ***** error<ztest2: invalid VALUE for alpha.> ...
379s  ztest2 (1, 2, 3, 4, "alpha", "val");
379s ***** error<ztest2: invalid VALUE for tail.>  ...
379s  ztest2 (1, 2, 3, 4, "tail", "val");
379s ***** error<ztest2: invalid VALUE for tail.>  ...
379s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
379s ***** error<ztest2: invalid NAME for optional arguments.> ...
379s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
379s 12 tests, 12 passed, 0 known failure, 0 skipped
379s [inst/nanmax.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/nanmax.m
379s ***** demo
379s  ## Find the column maximum values and their indices
379s  ## for matrix data with missing values.
379s 
379s  x = magic (3);
379s  x([1, 6:9]) = NaN
379s  [y, ind] = nanmax (x)
379s ***** demo
379s  ## Find the maximum of all the values in an array, ignoring missing values.
379s  ## Create a 2-by-5-by-3 array x with some missing values.
379s 
379s  x = reshape (1:30, [2, 5, 3]);
379s  x([10:12, 25]) = NaN
379s 
379s  ## Find the maximum of the elements of x.
379s 
379s  y = nanmax (x, [], 'all')
379s ***** assert (nanmax ([2, 4, NaN, 7]), 7)
379s ***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
379s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
379s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
379s ***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
379s ***** shared x, y
379s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
379s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
379s  y = x;
379s  y(2,3,1) = 0.51;
379s ***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
379s ***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
379s ***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
379s ***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
379s ***** assert (nanmax (x, [], 'all'), 6.77)
379s ***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
379s ***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
379s ***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
379s ***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
379s ***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
379s ***** test
379s  xx = repmat ([1:20;6:25], [5 2 6 3]);
379s  assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
379s  assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
379s  assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
379s  assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
379s  assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
379s ***** assert (nanmax (ones (2), [], 3), ones (2, 2))
379s ***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
379s ***** assert (nanmax (magic (3), [], 3), magic (3))
379s ***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
379s ***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
379s ***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
379s ***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
379s  nanmax (y, [], [1, 1, 2])
379s ***** error <nanmax: a second output is not supported with this syntax.> ...
379s  [v, idx] = nanmax(x, y, [1 2])
379s 24 tests, 24 passed, 0 known failure, 0 skipped
379s [inst/fitcnet.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitcnet.m
379s ***** demo
379s  ## Train a Neural Network on the Fisher's Iris data set and display
379s  ## a confusion chart with the classification results.
379s 
379s  load fisheriris
379s  Mdl = fitcnet (meas, species);
379s  pred_species = resubPredict (Mdl);
379s  confusionchart (species, pred_species, "Title", ...
379s  "Fully Connected Neural Network classification on Fisher's Iris dataset");
379s ***** test
379s  load fisheriris
379s  x = meas;
379s  y = grp2idx (species);
379s  Mdl = fitcnet (x, y, "IterationLimit", 50);
379s  assert (class (Mdl), "ClassificationNeuralNetwork");
379s  assert (numel (Mdl.ModelParameters.LayerWeights), 2);
379s  assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
379s  assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
379s ***** error<fitcnet: too few arguments.> fitcnet ()
379s ***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
379s ***** error<fitcnet: Name-Value arguments must be in pairs.>
379s  fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
379s ***** error<fitcnet: number of rows in X and Y must be equal.>
379s  fitcnet (ones (4,2), ones (3, 1))
379s ***** error<fitcnet: number of rows in X and Y must be equal.>
379s  fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
379s 6 tests, 6 passed, 0 known failure, 0 skipped
379s [inst/levene_test.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/levene_test.m
379s ***** error<levene_test: invalid number of input arguments.> levene_test ()
379s ***** error<levene_test: invalid number of input arguments.> ...
379s  levene_test (1, 2, 3, 4, 5);
379s ***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
379s ***** error<levene_test: GROUP and X mismatch.> ...
379s  levene_test (randn (50, 2), [1, 2, 3]);
379s ***** error<levene_test: GROUP and X mismatch.> ...
379s  levene_test (randn (50, 1), ones (55, 1));
379s ***** error<levene_test: invalid second input argument.> ...
379s  levene_test (randn (50, 1), ones (50, 2));
379s ***** error<levene_test: wrong value for alpha.> ...
379s  levene_test (randn (50, 2), [], 1.2);
379s ***** error<levene_test: GROUP and X mismatch.> ...
379s  levene_test (randn (50, 2), "some_string");
379s ***** error<levene_test: invalid third input argument.> ...
379s  levene_test (randn (50, 2), [], "alpha");
379s ***** error<levene_test: wrong value for alpha.> ...
379s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
379s ***** error<levene_test: invalid third input argument.> ...
379s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
379s ***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
379s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
379s ***** warning<levene_test: GROUP> ...
379s  levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades);
379s  assert (h, 1);
379s  assert (pval, 9.523239714592791e-07, 1e-14);
379s  assert (W, 8.59529, 1e-5);
379s  assert (df, [4, 595]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades, [], "quadratic");
379s  assert (h, 1);
379s  assert (pval, 9.523239714592791e-07, 1e-14);
379s  assert (W, 8.59529, 1e-5);
379s  assert (df, [4, 595]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades, [], "median");
379s  assert (h, 1);
379s  assert (pval, 1.312093241723211e-06, 1e-14);
379s  assert (W, 8.415969, 1e-6);
379s  assert (df, [4, 595]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades(:,[1:3]));
379s  assert (h, 1);
379s  assert (pval, 0.004349390980463497, 1e-14);
379s  assert (W, 5.52139, 1e-5);
379s  assert (df, [2, 357]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
379s  assert (h, 1);
379s  assert (pval, 0.004355216763951453, 1e-14);
379s  assert (W, 5.52001, 1e-5);
379s  assert (df, [2, 357]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
379s  assert (h, 0);
379s  assert (pval, 0.1807494957440653, 2e-14);
379s  assert (W, 1.80200, 1e-5);
379s  assert (df, [1, 238]);
379s ***** test
379s  load examgrades
379s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
379s  assert (h, 0);
379s  assert (pval, 0.1978225622063785, 2e-14);
379s  assert (W, 1.66768, 1e-5);
379s  assert (df, [1, 238]);
379s 20 tests, 20 passed, 0 known failure, 0 skipped
379s [inst/kstest.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/kstest.m
379s ***** demo
379s  ## Use the stock return data set to test the null hypothesis that the data
379s  ## come from a standard normal distribution against the alternative
379s  ## hypothesis that the population CDF of the data is larger that the
379s  ## standard normal CDF.
379s 
379s  load stockreturns;
379s  x = stocks(:,2);
379s  [h, p, k, c] = kstest (x, "Tail", "larger")
379s 
379s  ## Compute the empirical CDF and plot against the standard normal CDF
379s  [f, x_values] = ecdf (x);
379s  h1 = plot (x_values, f);
379s  hold on;
379s  h2 = plot (x_values, normcdf (x_values), 'r--');
379s  set (h1, "LineWidth", 2);
379s  set (h2, "LineWidth", 2);
379s  legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
379s          "Location", "southeast");
379s  title ("Empirical CDF of stock return data against standard normal CDF")
379s ***** error<kstest: too few inputs.> kstest ()
379s ***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
379s ***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
379s ***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
379s ***** error<kstest: optional parameters must be in name/value pairs.> ...
379s  kstest ([2, 3, 4, 5, 6], "tail")
379s ***** error<kstest: alpha must be a numeric scalar in the range> ...
379s  kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
379s ***** error<kstest: alpha must be a numeric scalar in the range> ...
379s  kstest ([2, 3, 4, 5, 6], "alpha", NaN)
379s ***** error<kstest: tail argument must be a string.> ...
379s  kstest ([2, 3, 4, 5, 6], "tail", 0)
379s ***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
379s  kstest ([2,3,4,5,6], "tail", "whatever")
379s ***** error<kstest: invalid function handle.> ...
379s  kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
379s ***** error<kstest: 'somedist' is not a supported distribution.> ...
379s  kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
379s ***** error<kstest: 'CDF' must be a probability distribution object.> ...
379s  kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5, 'resubstitution'))
379s ***** error<kstest: numerical CDF should have only 2 columns.> ...
379s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
379s ***** error<kstest: numerical CDF should have at least one row.> ...
379s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
379s ***** error<kstest: non-incrementing numerical CDF.> ...
379s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
379s ***** error<kstest: wrong duplicates in numerical CDF.> ...
379s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
379s ***** error<kstest: invalid value parsed as CDF optional argument.> ...
379s  kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
379s ***** test
379s  load examgrades
379s  [h, p] = kstest (grades(:,1));
379s  assert (h, true);
379s  assert (p, 7.58603305206105e-107, 1e-14);
379s ***** test
379s  load examgrades
379s  [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
379s  assert (h, false);
379s  assert (p, 0.5612, 1e-4);
379s ***** test
379s  load examgrades
379s  x = grades(:,1);
379s  test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
379s  [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
379s  assert (h, true);
379s  assert (p, 0.0021, 1e-4);
379s ***** test
379s  load stockreturns
379s  x = stocks(:,3);
379s  [h,p,k,c] = kstest (x, "Tail", "larger");
379s  assert (h, true);
379s  assert (p, 5.085438806199252e-05, 1e-14);
379s  assert (k, 0.2197, 1e-4);
379s  assert (c, 0.1207, 1e-4);
379s 21 tests, 21 passed, 0 known failure, 0 skipped
379s [inst/Regression/RegressionGAM.m]
379s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Regression/RegressionGAM.m
379s ***** demo
379s  ## Train a RegressionGAM Model for synthetic values
379s  f1 = @(x) cos (3 * x);
379s  f2 = @(x) x .^ 3;
379s  x1 = 2 * rand (50, 1) - 1;
379s  x2 = 2 * rand (50, 1) - 1;
379s  y = f1(x1) + f2(x2);
379s  y = y + y .* 0.2 .* rand (50,1);
379s  X = [x1, x2];
379s  a = fitrgam (X, y, "tol", 1e-3)
379s ***** demo
379s  ## Declare two different functions
379s  f1 = @(x) cos (3 * x);
379s  f2 = @(x) x .^ 3;
379s 
379s  ## Generate 80 samples for f1 and f2
379s  x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
379s  X1 = f1 (x);
379s  X2 = f2 (x);
379s 
379s  ## Create a synthetic response by adding noise
379s  rand ("seed", 3);
379s  Ytrue = X1 + X2;
379s  Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);
379s 
379s  ## Assemble predictor data
379s  X = [X1, X2];
379s 
379s  ## Train the GAM and test on the same data
379s  a = fitrgam (X, Y, "order", [5, 5]);
379s  [ypred, ySDsd, yInt] = predict (a, X);
379s 
379s  ## Plot the results
379s  figure
379s  [sortedY, indY] = sort (Ytrue);
379s  plot (sortedY, "r-");
379s  xlim ([0, 80]);
379s  hold on
379s  plot (ypred(indY), "g+")
379s  plot (yInt(indY,1), "k:")
379s  plot (yInt(indY,2), "k:")
379s  xlabel ("Predictor samples");
379s  ylabel ("Response");
379s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
379s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
379s 
379s  ## Use 30% Holdout partitioning for training and testing data
379s  C = cvpartition (80, "HoldOut", 0.3);
379s  [ypred, ySDsd, yInt] = predict (a, X(test(C),:));
379s 
379s  ## Plot the results
379s  figure
379s  [sortedY, indY] = sort (Ytrue(test(C)));
379s  plot (sortedY, 'r-');
379s  xlim ([0, sum(test(C))]);
379s  hold on
379s  plot (ypred(indY), "g+")
379s  plot (yInt(indY,1),'k:')
379s  plot (yInt(indY,2),'k:')
379s  xlabel ("Predictor samples");
379s  ylabel ("Response");
379s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
379s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
379s ***** test
379s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
379s  y = [1; 2; 3; 4];
379s  a = RegressionGAM (x, y);
379s  assert ({a.X, a.Y}, {x, y})
379s  assert ({a.BaseModel.Intercept}, {2.5000})
379s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
379s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
379s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
379s  assert ({a.Formula}, {[]})
380s ***** test
380s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
380s  y = [1; 2; 3; 4];
380s  pnames = {"A", "B", "C", "D"};
380s  formula = "Y ~ A + B + C + D + A:C";
380s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
380s  a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
380s  assert ({a.IntMatrix}, {intMat})
380s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
380s  assert ({a.Formula}, {formula})
380s ***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
380s ***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
380s ***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
380s  RegressionGAM (ones(10,2), ones (5,1))
380s ***** error<RegressionGAM: invalid values in X.> ...
380s  RegressionGAM ([1;2;3;"a";4], ones (5,1))
380s ***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "some", "some")
380s ***** error<RegressionGAM: Formula must be a string.>
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
380s ***** error<RegressionGAM: Formula must be a string.>
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
380s ***** error<RegressionGAM: invalid syntax in Formula.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
380s ***** error<RegressionGAM: no predictor terms in Formula.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
380s ***** error<RegressionGAM: no predictor terms in Formula.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
380s ***** error<RegressionGAM: some predictors have not been identified> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
380s ***** error<RegressionGAM: invalid Interactions parameter.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
380s ***** error<RegressionGAM: invalid Interactions parameter.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
380s ***** error<RegressionGAM: invalid Interactions parameter.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
380s ***** error<RegressionGAM: number of interaction terms requested is larger than> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
380s ***** error<RegressionGAM: Formula has been already defined.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
380s ***** error<RegressionGAM: Interactions have been already defined.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
380s ***** error<RegressionGAM: invalid value for Knots.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
380s ***** error<RegressionGAM: DoF and Order have been set already.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
380s ***** error<RegressionGAM: invalid value for DoF.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
380s ***** error<RegressionGAM: Knots and Order have been set already.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
380s ***** error<RegressionGAM: invalid value for Order.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
380s ***** error<RegressionGAM: DoF and Knots have been set already.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
380s ***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
380s ***** error<RegressionGAM: ResponseName must be a char string.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
380s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
380s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
380s ***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
380s  RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
380s ***** error<RegressionGAM.predict: too few arguments.> ...
380s  predict (RegressionGAM (ones(10,1), ones(10,1)))
380s ***** error<RegressionGAM.predict: Xfit is empty.> ...
380s  predict (RegressionGAM (ones(10,1), ones(10,1)), [])
380s ***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
380s ***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
380s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
380s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
380s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
380s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
380s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
380s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
380s 39 tests, 39 passed, 0 known failure, 0 skipped
380s [inst/mcnemar_test.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/mcnemar_test.m
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
380s  assert (h, 1);
380s  assert (pval, 3.8151e-06, 1e-10);
380s  assert (chisq, 21.356, 1e-3);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
380s  assert (h, 1);
380s  assert (pval, 0.034690, 1e-6);
380s  assert (isempty (chisq), true);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
380s  assert (h, 0);
380s  assert (pval, 0.034690, 1e-6);
380s  assert (isempty (chisq), true);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
380s  assert (h, 1);
380s  assert (pval, 0.034690, 1e-6);
380s  assert (isempty (chisq), true);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
380s  assert (h, 1);
380s  assert (pval, 0.033006, 1e-6);
380s  assert (chisq, 4.5455, 1e-4);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
380s  assert (h, 0);
380s  assert (pval, 0.052479, 1e-6);
380s  assert (isempty (chisq), true);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
380s  assert (h, 0);
380s  assert (pval, 0.055009, 1e-6);
380s  assert (chisq, 3.6818, 1e-4);
380s ***** test
380s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
380s  assert (h, 1);
380s  assert (pval, 0.055009, 1e-6);
380s  assert (chisq, 3.6818, 1e-4);
380s ***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
380s ***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
380s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
380s  mcnemar_test ([59,6;16,-80])
380s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
380s  mcnemar_test ([59,6;16,4.5])
380s ***** error<mcnemar_test: invalid 2nd input argument.> ...
380s  mcnemar_test ([59,6;16,80], {""})
380s ***** error<mcnemar_test: invalid value for ALPHA.> ...
380s  mcnemar_test ([59,6;16,80], -0.2)
380s ***** error<mcnemar_test: invalid value for ALPHA.> ...
380s  mcnemar_test ([59,6;16,80], [0.05, 0.1])
380s ***** error<mcnemar_test: invalid value for ALPHA.> ...
380s  mcnemar_test ([59,6;16,80], 1)
380s ***** error<mcnemar_test: invalid value for TESTTYPE.> ...
380s  mcnemar_test ([59,6;16,80], "")
380s 17 tests, 17 passed, 0 known failure, 0 skipped
380s [inst/barttest.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/barttest.m
380s ***** error<barttest: invalid number of input arguments.> barttest ()
380s ***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
380s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
380s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
380s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
380s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
380s ***** error<barttest: not enough data in X.> barttest (ones (30, 1))
380s ***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
380s ***** test
380s  x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
380s  [ndim, pval, chisq] = barttest (x);
380s  assert (ndim, 2);
380s  assert (pval, 0);
380s  ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
380s ***** test
380s  x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
380s       1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
380s      -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
380s       0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
380s       0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
380s      -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
380s      -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
380s       0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
380s       3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
380s       2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
380s      -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
380s       3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
380s       0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
380s      -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
380s       0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
380s      -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
380s      -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
380s       1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
380s       1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
380s       1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
380s  [ndim, pval, chisq] = barttest (x);
380s  assert (ndim, 3);
380s  assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
380s  chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
380s  assert (chisq, chisq_out, 1e-4);
380s 10 tests, 10 passed, 0 known failure, 0 skipped
380s [inst/kruskalwallis.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/kruskalwallis.m
380s ***** demo
380s  x = meshgrid (1:6);
380s  x = x + normrnd (0, 1, 6, 6);
380s  kruskalwallis (x, [], 'off');
380s ***** demo
380s  x = meshgrid (1:6);
380s  x = x + normrnd (0, 1, 6, 6);
380s  [p, atab] = kruskalwallis(x);
380s ***** demo
380s  x = ones (30, 4) .* [-2, 0, 1, 5];
380s  x = x + normrnd (0, 2, 30, 4);
380s  group = {"A", "B", "C", "D"};
380s  kruskalwallis (x, group);
380s ***** test
380s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
380s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
380s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
380s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
380s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
380s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
380s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
380s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
380s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
380s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
380s  group = [1:10] .* ones (10,10);
380s  group = group(:);
380s  [p, tbl] = kruskalwallis (data, group, "off");
380s  assert (p, 0.048229, 1e-6);
380s  assert (tbl{2,5}, 17.03124, 1e-5);
380s  assert (tbl{2,3}, 9, 0);
380s  assert (tbl{4,2}, 82655.5, 1e-16);
380s  data = reshape (data, 10, 10);
380s  [p, tbl, stats] = kruskalwallis (data, [], "off");
380s  assert (p, 0.048229, 1e-6);
380s  assert (tbl{2,5}, 17.03124, 1e-5);
380s  assert (tbl{2,3}, 9, 0);
380s  assert (tbl{4,2}, 82655.5, 1e-16);
380s  means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
380s  N = 10 * ones (1, 10);
380s  assert (stats.meanranks, means, 1e-6);
380s  assert (length (stats.gnames), 10, 0);
380s  assert (stats.n, N, 0);
380s 1 test, 1 passed, 0 known failure, 0 skipped
380s [inst/datasample.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/datasample.m
380s ***** error datasample();
380s ***** error datasample(1);
380s ***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
380s ***** error <k must be a positive integer scalar> datasample([1 2], -1);
380s ***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
380s ***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
380s ***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
380s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
380s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
380s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
380s ***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
380s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
380s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
380s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
380s ***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
380s ***** test
380s  dat = randn (10, 4);
380s  assert (size (datasample (dat, 3, 1)), [3 4]);
380s ***** test
380s  dat = randn (10, 4);
380s  assert (size (datasample (dat, 3, 2)), [10 3]);
380s 17 tests, 17 passed, 0 known failure, 0 skipped
380s [inst/runstest.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/runstest.m
380s ***** test
380s  ## NIST beam deflection data
380s  ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
380s  data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
380s           194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
380s          -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
380s          -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
380s           131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
380s          -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
380s           162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
380s          -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
380s           -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
380s           134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
380s          -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
380s          -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
380s          -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
380s           103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
380s          -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
380s          -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
380s            15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
380s  [h, p, stats] = runstest (data, median (data));
380s  expected_h = 1;
380s  expected_p = 0.008562;
380s  expected_z = 2.6229;
380s  assert (h, expected_h);
380s  assert (p, expected_p, 1E-6);
380s  assert (stats.z, expected_z, 1E-4);
380s ***** shared x
380s  x = [45, -60, 1.225, 55.4, -9 27];
380s ***** test
380s  [h, p, stats] = runstest (x);
380s  assert (h, 0);
380s  assert (p, 0.6, 1e-14);
380s  assert (stats.nruns, 5);
380s  assert (stats.n1, 3);
380s  assert (stats.n0, 3);
380s  assert (stats.z, 0.456435464587638, 1e-14);
380s ***** test
380s  [h, p, stats] = runstest (x, [], "method", "approximate");
380s  assert (h, 0);
380s  assert (p, 0.6481, 1e-4);
380s  assert (stats.z, 0.456435464587638, 1e-14);
380s ***** test
380s  [h, p, stats] = runstest (x, [], "tail", "left");
380s  assert (h, 0);
380s  assert (p, 0.9, 1e-14);
380s  assert (stats.z, 1.369306393762915, 1e-14);
380s ***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
380s ***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
380s ***** error<runstest: V must be either a scalar number or> ...
380s  runstest ([2 3 4 3 2 3 4], "updown")
380s ***** error<runstest: invalid value for alpha.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
380s ***** error<runstest: invalid value for alpha.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
380s ***** error<runstest: invalid value for alpha.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
380s ***** error<runstest: invalid value for alpha.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
380s ***** error<runstest: invalid value for method.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "method", "some")
380s ***** error<runstest: invalid value for tail.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "tail", "some")
380s ***** error<runstest: invalid optional argument.> ...
380s  runstest ([2 3 4 3 2 3 4], [], "option", "some")
380s 14 tests, 14 passed, 0 known failure, 0 skipped
380s [inst/normplot.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/normplot.m
380s ***** demo
380s  h = normplot([1:20]);
380s ***** demo
380s  h = normplot([1:20;5:2:44]');
380s ***** demo
380s  ax = newplot();
380s  h = normplot(ax, [1:20]);
380s  ax = gca;
380s  h = normplot(ax, [-10:10]);
380s  set (ax, "xlim", [-11, 21]);
380s ***** error normplot ();
380s ***** error normplot (23);
380s ***** error normplot (23, [1:20]);
380s ***** error normplot (ones(3,4,5));
380s ***** test
380s  hf = figure ("visible", "off");
380s  unwind_protect
380s    ax = newplot (hf);
380s    h = normplot (ax, [1:20]);
380s    ax = gca;
380s    h = normplot(ax, [-10:10]);
380s    set (ax, "xlim", [-11, 21]);
380s  unwind_protect_cleanup
380s    close (hf);
380s  end_unwind_protect
380s ***** test
380s  hf = figure ("visible", "off");
380s  unwind_protect
380s    h = normplot([1:20;5:2:44]');
380s  unwind_protect_cleanup
380s    close (hf);
380s  end_unwind_protect
380s 6 tests, 6 passed, 0 known failure, 0 skipped
380s [inst/bar3.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/bar3.m
380s ***** demo
380s  ## Plotting 5 bars in the same series.
380s 
380s  z = [50; 40; 30; 20; 10];
380s  bar3 (z);
380s ***** demo
380s  ## Plotting 5 bars in different groups.
380s 
380s  z = [50, 40, 30, 20, 10];
380s  bar3 (z);
380s ***** demo
380s  ## A 3D bar graph with each series corresponding to a column in z.
380s 
380s  z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
380s  bar3 (z);
380s ***** demo
380s  ## Specify y-axis locations as tick names. y must be a column vector!
380s 
380s  y = [1950, 1960, 1970, 1980, 1990]';
380s  z = [16, 8, 4, 2, 1]';
380s  bar3 (y, z);
380s ***** demo
380s  ## Plot 3 series as a grouped plot without any space between the grouped bars
380s 
380s  z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
380s  bar3 (z, 1, 'grouped');
380s ***** demo
380s  ## Plot a stacked style 3D bar graph
380s 
380s  z = [19, 30, 21, 30; 40, 16, 32, 12];
380s  b = bar3 (z, 0.5, 'stacked');
380s ***** error <bar3: Z must be numeric.> bar3 ("A")
380s ***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
380s ***** error <bar3: inconsistent size in Y and Z input arguments.> ...
380s  bar3 ([1,2,3]', ones (2))
380s ***** error <bar3: WIDTH must be a scalar in the range> ...
380s  bar3 ([1:5], 1.2)
380s ***** error <bar3: WIDTH must be a scalar in the range> ...
380s  bar3 ([1:5]', ones (5), 1.2)
380s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
380s  bar3 ([1:5]', ones (5), [0.8, 0.7])
380s ***** error <bar3: missing value for optional argument 'width'.> ...
380s  bar3 (ones (5), 'width')
380s ***** error <bar3: invalid value for optional argument 'width'.> ...
380s  bar3 (ones (5), 'width', 1.2)
380s ***** error <bar3: invalid value for optional argument 'width'.> ...
380s  bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
380s ***** error <bar3: missing value for optional argument 'color'.> ...
380s  bar3 (ones (5), 'color')
380s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
380s  bar3 (ones (5), 'color', [0.8, 0.8])
380s ***** error <bar3: invalid value for optional argument 'color'.> ...
380s  bar3 (ones (5), 'color', "brown")
380s ***** error <bar3: invalid value for optional argument 'color'.> ...
380s  bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
380s ***** error <bar3: missing value for optional argument 'xlabel'.> ...
380s  bar3 (ones (5), 'xlabel')
380s ***** error <bar3: invalid value for optional argument 'xlabel'.> ...
380s  bar3 (ones (5), 'xlabel', 4)
380s ***** error <bar3: missing value for optional argument 'ylabel'.> ...
380s  bar3 (ones (5), 'ylabel')
380s ***** error <bar3: invalid value for optional argument 'ylabel'.> ...
380s  bar3 (ones (5), 'ylabel', 4)
380s ***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
380s ***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
380s  bar3 (ones (5), 'xlabel', {"A", "B", "C"})
380s ***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
380s  bar3 (ones (5), 'ylabel', {"A", "B", "C"})
380s 20 tests, 20 passed, 0 known failure, 0 skipped
380s [inst/fullfact.m]
380s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fullfact.m
380s ***** demo
380s  ## Full factorial design with 3 ordinal variables
380s  fullfact ([2, 3, 4])
380s ***** error<fullfact: one input argument is required.> fullfact ();
380s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
380s  fullfact (Inf);
380s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
380s  fullfact (NaN);
380s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
380s  fullfact (ones (2));
380s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
380s  fullfact ([1, 2, NaN]);
380s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
380s  fullfact ([1, 2, Inf]);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact (2.5);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact (0);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact (-3);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact (3+2i);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([1, 2, -3]);
380s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([0, 1, 2]);
380s ***** test
380s  A = fullfact (1);
380s  assert (A, 1);
380s ***** test
380s  A = fullfact (2);
380s  assert (A, [1; 2]);
380s ***** test
380s ***** test
380s  A = fullfact (3);
380s  assert (A, [1; 2; 3]);
380s ***** test
380s  A = fullfact ([1, 2, 4]);
380s  A_out = [1, 1, 1; 1, 2, 1; 1, 1, 2; 1, 2, 2; ...
380s           1, 1, 3; 1, 2, 3; 1, 1, 4; 1, 2, 4];
380s  assert (A, A_out);
381s ***** test
381s  A = fullfact ([2, 2]);
381s  assert (A, [1, 1; 2, 1; 1, 2; 2, 2]);
381s ***** test
381s  A = fullfact ([2, 2, 4]);
381s  A_out = [1, 1, 1; 2, 1, 1; 1, 2, 1; 2, 2, 1; ...
381s           1, 1, 2; 2, 1, 2; 1, 2, 2; 2, 2, 2; ...
381s           1, 1, 3; 2, 1, 3; 1, 2, 3; 2, 2, 3; ...
381s           1, 1, 4; 2, 1, 4; 1, 2, 4; 2, 2, 4];
381s  assert (A, A_out);
381s ***** test
381s  A = fullfact ([3, 2, 4]);
381s  A_out = [1, 1, 1; 2, 1, 1; 3, 1, 1; 1, 2, 1; 2, 2, 1; 3, 2, 1; ...
381s           1, 1, 2; 2, 1, 2; 3, 1, 2; 1, 2, 2; 2, 2, 2; 3, 2, 2; ...
381s           1, 1, 3; 2, 1, 3; 3, 1, 3; 1, 2, 3; 2, 2, 3; 3, 2, 3; ...
381s           1, 1, 4; 2, 1, 4; 3, 1, 4; 1, 2, 4; 2, 2, 4; 3, 2, 4];
381s  assert (A, A_out);
381s ***** test
381s  A = fullfact ([4, 2]);
381s  assert (A, [1, 1; 2, 1; 3, 1; 4, 1; 1, 2; 2, 2; 3, 2; 4, 2]);
381s 21 tests, 21 passed, 0 known failure, 0 skipped
381s [inst/bar3h.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/bar3h.m
381s ***** demo
381s  ## Plotting 5 bars in the same series.
381s 
381s  y = [50; 40; 30; 20; 10];
381s  bar3h (y);
381s ***** demo
381s  ## Plotting 5 bars in different groups.
381s 
381s  y = [50, 40, 30, 20, 10];
381s  bar3h (y);
381s ***** demo
381s  ## A 3D bar graph with each series corresponding to a column in y.
381s 
381s  y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
381s  bar3h (y);
381s ***** demo
381s  ## Specify z-axis locations as tick names. z must be a column vector!
381s 
381s  z = [1950, 1960, 1970, 1980, 1990]';
381s  y = [16, 8, 4, 2, 1]';
381s  bar3h (z, y);
381s ***** demo
381s  ## Plot 3 series as a grouped plot without any space between the grouped bars
381s 
381s  y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
381s  bar3h (y, 1, 'grouped');
381s ***** demo
381s  ## Plot a stacked style 3D bar graph
381s 
381s  y = [19, 30, 21, 30; 40, 16, 32, 12];
381s  b = bar3h (y, 0.5, 'stacked');
381s ***** error <bar3h: Z must be numeric.> bar3h ("A")
381s ***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
381s ***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
381s  bar3h ([1,2,3]', ones (2))
381s ***** error <bar3h: WIDTH must be a scalar in the range> ...
381s  bar3h ([1:5], 1.2)
381s ***** error <bar3h: WIDTH must be a scalar in the range> ...
381s  bar3h ([1:5]', ones (5), 1.2)
381s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
381s  bar3h ([1:5]', ones (5), [0.8, 0.7])
381s ***** error <bar3h: missing value for optional argument 'width'.> ...
381s  bar3h (ones (5), 'width')
381s ***** error <bar3h: invalid value for optional argument 'width'.> ...
381s  bar3h (ones (5), 'width', 1.2)
381s ***** error <bar3h: invalid value for optional argument 'width'.> ...
381s  bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
381s ***** error <bar3h: missing value for optional argument 'color'.> ...
381s  bar3h (ones (5), 'color')
381s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
381s  bar3h (ones (5), 'color', [0.8, 0.8])
381s ***** error <bar3h: invalid value for optional argument 'color'.> ...
381s  bar3h (ones (5), 'color', "brown")
381s ***** error <bar3h: invalid value for optional argument 'color'.> ...
381s  bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
381s ***** error <bar3h: missing value for optional argument 'xlabel'.> ...
381s  bar3h (ones (5), 'xlabel')
381s ***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
381s  bar3h (ones (5), 'xlabel', 4)
381s ***** error <bar3h: missing value for optional argument 'zlabel'.> ...
381s  bar3h (ones (5), 'zlabel')
381s ***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
381s  bar3h (ones (5), 'zlabel', 4)
381s ***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
381s ***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
381s  bar3h (ones (5), 'xlabel', {"A", "B", "C"})
381s ***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
381s  bar3h (ones (5), 'zlabel', {"A", "B", "C"})
381s 20 tests, 20 passed, 0 known failure, 0 skipped
381s [inst/adtest.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/adtest.m
381s ***** error<Invalid call to adtest.  Correct usage is:> adtest ();
381s ***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
381s ***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
381s ***** error<adtest: invalid distribution family in char string.> ...
381s  adtest (ones (20,1), "Distribution", "normal");
381s ***** error<adtest: invalid distribution family in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"normal", 5, 3});
381s ***** error<adtest: invalid distribution parameters in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"norm", 5});
381s ***** error<adtest: invalid distribution parameters in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"exp", 5, 4});
381s ***** error<adtest: invalid distribution parameters in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"ev", 5});
381s ***** error<adtest: invalid distribution parameters in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
381s ***** error<adtest: invalid distribution parameters in cell array.> ...
381s  adtest (rand (20,1), "Distribution", {"Weibull", 5});
381s ***** error<adtest: invalid distribution option.> ...
381s  adtest (rand (20,1), "Distribution", 35);
381s ***** error<adtest: invalid Name argument.> ...
381s  adtest (rand (20,1), "Name", "norm");
381s ***** error<adtest: invalid Name argument.> ...
381s  adtest (rand (20,1), "Name", {"norm", 75, 10});
381s ***** error<adtest: asymptotic option is not valid for the composite> ...
381s  adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
381s ***** error<adtest: asymptotic option is not valid for the composite> ...
381s  adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
381s ***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
381s  adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
381s          "Asymptotic", true);
381s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
381s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
381s                                  "Alpha", 0.000000001);
381s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
381s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
381s                                  "Alpha", 0.999999999);
381s ***** error<adtest: not enough data for composite testing.> ...
381s  adtest (10);
381s ***** warning<adtest: out of range min p-value:> ...
381s  randn ("seed", 34);
381s  adtest (ones (20,1), "Alpha", 0.000001);
381s ***** warning<adtest: alpha not within the lookup table.> ...
381s  randn ("seed", 34);
381s  adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
381s ***** warning<adtest: alpha not within the lookup table.> ...
381s  randn ("seed", 34);
381s  adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
381s ***** test
381s  load examgrades
381s  x = grades(:,1);
381s  [h, pval, adstat, cv] = adtest (x);
381s  assert (h, false);
381s  assert (pval, 0.1854, 1e-4);
381s  assert (adstat, 0.5194, 1e-4);
381s  assert (cv, 0.7470, 1e-4);
381s ***** test
381s  load examgrades
381s  x = grades(:,1);
381s  [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
381s  assert (h, false);
381s  assert (pval, 0.071363, 1e-6);
381s ***** test
381s  load examgrades
381s  x = grades(:,1);
381s  [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
381s  assert (h, false);
381s  assert (pval, 0.4687, 1e-4);
381s 25 tests, 25 passed, 0 known failure, 0 skipped
381s [inst/stepwisefit.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/stepwisefit.m
381s ***** test
381s  % Sample data from Draper and Smith (n = 13, k = 4)
381s  X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
381s      26 29 56 31 52 55 71 31 54 47 40 66 68; ...
381s      6 15 8 8 6 9 17 22 18 4 23 9 8; ...
381s      60 52 20 47 33 22 6 44 22 26 34 12 12]';
381s  y = [78.5 74.3 104.3 87.6 95.9 109.2 102.7 72.5 93.1 115.9 83.8 113.3 109.4]';
381s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
381s  assert(X_use, [4 1])
381s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
381s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
381s  assert(X_use, [4 1])
381s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
381s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
381s  assert(X_use, [4 1])
381s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
381s 1 test, 1 passed, 0 known failure, 0 skipped
381s [inst/vartest.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/vartest.m
381s ***** error<vartest: too few input arguments.> vartest ();
381s ***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
381s ***** error<vartest: invalid value for alpha.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 0);
381s ***** error<vartest: invalid value for alpha.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
381s ***** error<vartest: invalid value for alpha.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", "val");
381s ***** error<vartest: invalid value for tail.>  ...
381s  vartest ([1, 2, 3, 4], 1, "tail", "val");
381s ***** error<vartest: invalid value for tail.>  ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
381s ***** error<vartest: invalid value for operating dimension.> ...
381s  vartest ([1, 2, 3, 4], 1, "dim", 3);
381s ***** error<vartest: invalid value for operating dimension.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
381s ***** error<vartest: invalid name for optional arguments.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
381s ***** error<vartest: optional arguments must be in name/value pairs.> ...
381s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
381s ***** test
381s  load carsmall
381s  [h, pval, ci] = vartest (MPG, 7^2);
381s  assert (h, 1);
381s  assert (pval, 0.04335086742174443, 1e-14);
381s  assert (ci, [49.397; 88.039], 1e-3);
381s ***** test
381s  load carsmall
381s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "left");
381s  assert (h, 0);
381s  assert (pval, 0.978324566289128, 1e-14);
381s  assert (ci, [0; 83.685], 1e-3);
381s ***** test
381s  load carsmall
381s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "right");
381s  assert (h, 1);
381s  assert (pval, 0.021675433710872, 1e-14);
381s  assert (ci, [51.543; Inf], 1e-3);
381s 14 tests, 14 passed, 0 known failure, 0 skipped
381s [inst/fishertest.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fishertest.m
381s ***** demo
381s  ## A Fisher's exact test example
381s 
381s  x = [3, 1; 1, 3]
381s  [h, p, stats] = fishertest(x)
381s ***** assert (fishertest ([3, 4; 5, 7]), false);
381s ***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
381s ***** test
381s  [h, pval, stats] = fishertest ([3, 4; 5, 7]);
381s  assert (pval, 1, 1e-14);
381s  assert (stats.OddsRatio, 1.05);
381s  CI = [0.159222057151289, 6.92429189601808];
381s  assert (stats.ConfidenceInterval, CI, 1e-14)
381s ***** test
381s  [h, pval, stats] = fishertest ([3, 4; 5, 0]);
381s  assert (pval, 0.08080808080808080, 1e-14);
381s  assert (stats.OddsRatio, 0);
381s  assert (stats.ConfidenceInterval, [-Inf, Inf])
381s ***** error fishertest ();
381s ***** error fishertest (1, 2, 3, 4, 5, 6);
381s ***** error<fishertest: X must be a 2-dimensional matrix.> ...
381s  fishertest (ones (2, 2, 2));
381s ***** error<fishertest: X must contain only non-negative real integers.> ...
381s  fishertest ([1, 2; -3, 4]);
381s ***** error<fishertest: X must contain only non-negative real integers.> ...
381s  fishertest ([1, 2; 3, 4+i]);
381s ***** error<fishertest: X must contain only non-negative real integers.> ...
381s  fishertest ([1, 2; 3, 4.2]);
381s ***** error<fishertest: X must contain only non-negative real integers.> ...
381s  fishertest ([NaN, 2; 3, 4]);
381s ***** error<fishertest: X must contain only non-negative real integers.> ...
381s  fishertest ([1, Inf; 3, 4]);
381s ***** error<fishertest: cannot handle large entries.> ...
381s  fishertest (ones (2) * 1e8);
381s ***** error<fishertest: invalid value for alpha.> ...
381s  fishertest ([1, 2; 3, 4], "alpha", 0);
381s ***** error<fishertest: invalid value for alpha.> ...
381s  fishertest ([1, 2; 3, 4], "alpha", 1.2);
381s ***** error<fishertest: invalid value for alpha.> ...
381s  fishertest ([1, 2; 3, 4], "alpha", "val");
381s ***** error<fishertest: invalid value for tail.>  ...
381s  fishertest ([1, 2; 3, 4], "tail", "val");
381s ***** error<fishertest: invalid value for tail.>  ...
381s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
381s ***** error<fishertest: invalid name for optional arguments.> ...
381s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
381s 19 tests, 19 passed, 0 known failure, 0 skipped
381s [inst/ppplot.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ppplot.m
381s ***** test
381s  hf = figure ("visible", "off");
381s  unwind_protect
381s    ppplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
381s  unwind_protect_cleanup
381s    close (hf);
381s  end_unwind_protect
381s ***** error ppplot ()
381s ***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
381s ***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
381s ***** error <ppplot: DIST must be a string> ppplot ([1 2 3 4], 2)
381s 5 tests, 5 passed, 0 known failure, 0 skipped
381s [inst/procrustes.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/procrustes.m
381s ***** demo
381s  ## Create some random points in two dimensions
381s  n = 10;
381s  randn ("seed", 1);
381s  X = normrnd (0, 1, [n, 2]);
381s 
381s  ## Those same points, rotated, scaled, translated, plus some noise
381s  S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
381s  Y = normrnd (0.5*X*S + 2, 0.05, n, 2);
381s 
381s  ## Conform Y to X, plot original X and Y, and transformed Y
381s  [d, Z] = procrustes (X, Y);
381s  plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
381s ***** demo
381s  ## Find Procrustes distance and plot superimposed shape
381s 
381s  X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
381s  Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
381s  plot (X(:,1),X(:,2),"x");
381s  hold on
381s  plot (Y(:,1),Y(:,2),"o");
381s  xlim ([0 100]);
381s  ylim ([0 100]);
381s  legend ("Target shape (X)", "Source shape (Y)");
381s  [d, Z] = procrustes (X, Y)
381s  plot (Z(:,1), Z(:,2), "s");
381s  legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
381s  hold off
381s ***** demo
381s  ## Apply Procrustes transformation to larger set of points
381s 
381s  ## Create matrices with landmark points for two triangles
381s  X = [5, 0; 5, 5; 8, 5];   # target
381s  Y = [0, 0; 1, 0; 1, 1];   # source
381s 
381s  ## Create a matrix with more points on the source triangle
381s  Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
381s          linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
381s          linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];
381s 
381s  ## Plot both shapes, including the larger set of points for the source shape
381s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
381s  hold on
381s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
381s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
381s  xlim ([-1 10]);
381s  ylim ([-1 6]);
381s  legend ("Target shape (X)", "Source shape (Y)", ...
381s          "More points on Y", "Location", "northwest");
381s  hold off
381s 
381s  ## Obtain the Procrustes transformation
381s  [d, Z, transform] = procrustes (X, Y)
381s 
381s  ## Use the Procrustes transformation to superimpose the more points (Y_mp)
381s  ## on the source shape onto the target shape, and then visualize the results.
381s  Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
381s  figure
381s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
381s  hold on
381s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
381s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
381s  xlim ([-1 10]);
381s  ylim ([-1 6]);
381s  plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
381s  plot (Z_mp(:,1),Z_mp(:,2),"ks");
381s  legend ("Target shape (X)", "Source shape (Y)", ...
381s          "More points on Y", "Transformed source shape (Z)", ...
381s          "Transformed additional points", "Location", "northwest");
381s  hold off
381s ***** demo
381s  ## Compare shapes without reflection
381s 
381s  T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
381s       28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
381s  S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
381s       52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
381s  plot (T(:,1), T(:,2), "x-");
381s  hold on
381s  plot (S(:,1), S(:,2), "o-");
381s  legend ("Target shape (d)", "Source shape (b)");
381s  hold off
381s  d_false = procrustes (T, S, "reflection", false);
381s  printf ("Procrustes distance without reflection: %f\n", d_false);
381s  d_true = procrustes (T, S, "reflection", true);
381s  printf ("Procrustes distance with reflection: %f\n", d_true);
381s  d_best = procrustes (T, S, "reflection", "best");
381s  printf ("Procrustes distance with best fit: %f\n", d_true);
381s ***** error procrustes ();
381s ***** error procrustes (1, 2, 3, 4, 5, 6);
381s ***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
381s  procrustes (ones (2, 2, 2), ones (2, 2, 2));
381s ***** error<procrustes: values in X and Y must be real.> ...
381s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
381s ***** error<procrustes: values in X and Y must be real.> ...
381s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
381s ***** error<procrustes: values in X and Y must be real.> ...
381s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
381s ***** error<procrustes: X and Y must have equal number of rows.> ...
381s  procrustes (ones (10 ,3), ones (11, 3));
381s ***** error<procrustes: X must have at least as many columns as Y.> ...
381s  procrustes (ones (10 ,3), ones (10, 4));
381s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "reflection");
381s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), true);
381s ***** error<procrustes: invalid value for scaling.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
381s ***** error<procrustes: invalid value for scaling.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
381s ***** error<procrustes: invalid value for reflection.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
381s ***** error<procrustes: invalid value for reflection.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
381s ***** error<procrustes: invalid name for optional arguments.> ...
381s  procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
381s 15 tests, 15 passed, 0 known failure, 0 skipped
381s [inst/knnsearch.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/knnsearch.m
381s ***** demo
381s  ## find 10 nearest neighbour of a point using different distance metrics
381s  ## and compare the results by plotting
381s  load fisheriris
381s  X = meas(:,3:4);
381s  Y = species;
381s  point = [5, 1.45];
381s 
381s  ## calculate 10 nearest-neighbours by minkowski distance
381s  [id, d] = knnsearch (X, point, "K", 10);
381s 
381s  ## calculate 10 nearest-neighbours by minkowski distance
381s  [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);
381s 
381s  ## calculate 10 nearest-neighbours by chebychev distance
381s  [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");
381s 
381s  ## plotting the results
381s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
381s  title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
381s  xlabel("Petal length (cm)");
381s  ylabel("Petal width (cm)");
381s 
381s  line (point(1), point(2), "marker", "X", "color", "k", ...
381s        "linewidth", 2, "displayname", "query point")
381s  line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
381s        "linestyle", "none", "markersize", 10, "displayname", "euclidean")
381s  line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
381s        "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
381s  line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
381s        "linestyle", "none", "markersize", 10, "displayname", "chebychev")
381s  xlim ([4.5 5.5]);
381s  ylim ([1 2]);
381s  axis square;
381s ***** demo
381s  ## knnsearch on iris dataset using kdtree method
381s  load fisheriris
381s  X = meas(:,3:4);
381s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
381s  title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");
381s 
381s  ## new point to be predicted
381s  point = [5 1.45];
381s 
381s  line (point(1), point(2), "marker", "X", "color", "k", ...
381s        "linewidth", 2, "displayname", "query point")
381s 
381s  ## knnsearch using kdtree method
381s  [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");
381s 
381s  ## plotting predicted neighbours
381s  line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
381s        "linestyle", "none", "markersize", 10, ...
381s        "displayname", "nearest neighbour")
381s  xlim ([4 6])
381s  ylim ([1 3])
381s  axis square
381s  ## details of predicted labels
381s  tabulate (species(idx))
381s 
381s  ctr = point - d(end);
381s  diameter = 2 * d(end);
381s  ##  Draw a circle around the 10 nearest neighbors.
381s  h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);
381s 
381s  ## here only 8 neighbours are plotted instead of 10 since the dataset
381s  ## contains duplicate values
381s ***** shared X, Y
381s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
381s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * sqrt (2));
381s ***** test
381s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
381s  [idx, D] = knnsearch (X, Y, "Distance", eucldist);
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * sqrt (2));
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
381s  assert (iscell (idx), true);
381s  assert (iscell (D), true)
381s  assert (idx {1}, [1]);
381s  assert (idx {2}', [1, 2]);
381s  assert (D{1}, ones (1, 1) * sqrt (2));
381s  assert (D{2}', ones (1, 2) * sqrt (2));
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
381s  assert (idx, [1, 2; 1, 2]);
381s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * sqrt (2));
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
381s  assert (idx, [1, 2; 1, 2]);
381s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
381s ***** test
381s  xx = [1, 2; 1, 3; 2, 4; 3, 6];
381s  yy = [2, 4; 2, 6];
381s  [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
381s  assert (idx, [3; 2]);
381s  assert (D, [0; 3.162277660168377], 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * sqrt (2));
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
381s  assert (idx, [1; 1]);
381s  assert (D, [2; 2]);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
381s  assert (idx, [1; 1]);
381s  assert (D, [1; 1]);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "cosine");
381s  assert (idx, [2; 3]);
381s  assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "correlation");
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "spearman");
381s  assert (idx, [1; 1]);
381s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "hamming");
381s  assert (idx, [1; 1]);
381s  assert (D, [0.5; 0.5]);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
381s  assert (idx, [1; 1]);
381s  assert (D, [0.5; 0.5]);
381s ***** test
381s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
381s  assert (idx, [1, 2; 1, 2]);
381s  assert (D, [0.5, 1; 0.5, 0.5]);
381s ***** test
381s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
381s  b = [1, 1];
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
381s  assert (iscell (idx), true);
381s  assert (iscell (D), true)
381s  assert (cell2mat (idx)', [4, 2, 3, 6, 1, 5, 7, 9]);
381s  assert (cell2mat (D)', [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
381s ***** test
381s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
381s  b = [1, 1];
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
381s  assert (iscell (idx), true);
381s  assert (iscell (D), true)
381s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
381s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
381s ***** test
381s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
381s  b = [1, 1];
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
381s  assert (iscell (idx), false);
381s  assert (iscell (D), false)
381s  assert (idx, [4, 2, 3, 6, 1]);
381s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
381s ***** test
381s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
381s  b = [1, 1];
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
381s  assert (iscell (idx), false);
381s  assert (iscell (D), false)
381s  assert (idx, [4, 2, 3, 6, 1]);
381s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
381s ***** test
381s  load fisheriris
381s  a = meas;
381s  b = min(meas);
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
381s  assert (idx, [42, 9, 14, 39, 13]);
381s  assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874], 1e-4);
381s ***** test
381s  load fisheriris
381s  a = meas;
381s  b = mean(meas);
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
381s  assert (idx, [65, 83, 89, 72, 100]);
381s  assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625], 1e-4);
381s ***** test
381s  load fisheriris
381s  a = meas;
381s  b = max(meas);
381s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
381s  assert (idx, [118, 132, 110, 106, 136]);
381s  assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
381s 
381s ***** test
381s  load fisheriris
381s  a = meas;
381s  b = max(meas);
381s  [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
381s  assert (iscell (idx), true);
381s  assert (iscell (D), true);
381s  assert (cell2mat (idx)', [118, 132, 110, 106, 136]);
381s  assert (cell2mat (D)', [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
381s ***** error<knnsearch: too few input arguments.> knnsearch (1)
381s ***** error<knnsearch: number of columns in X and Y must match.> ...
381s  knnsearch (ones (4, 5), ones (4))
381s ***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
381s  knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
381s ***** error<knnsearch: only a single distance parameter can be defined.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
381s ***** error<knnsearch: invalid value of K.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "K", 0)
381s ***** error<knnsearch: invalid value of Minkowski Exponent.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "P", -2)
381s ***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
381s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
381s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
381s ***** error<knnsearch: invalid value of bucketsize.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
381s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
381s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
381s 42 tests, 42 passed, 0 known failure, 0 skipped
381s [inst/anova2.m]
381s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/anova2.m
381s ***** demo
381s 
381s  # Factorial (Crossed) Two-way ANOVA with Interaction
381s 
381s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
381s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
381s 
381s  [p, atab, stats] = anova2(popcorn, 3, "on");
381s ***** demo
381s 
381s  # One-way Repeated Measures ANOVA (Rows are a crossed random factor)
381s 
381s  data = [54, 43, 78, 111;
381s          23, 34, 37, 41;
381s          45, 65, 99, 78;
381s          31, 33, 36, 35;
381s          15, 25, 30, 26];
381s 
381s  [p, atab, stats] = anova2 (data, 1, "on", "linear");
381s ***** demo
381s 
381s  # Balanced Nested One-way ANOVA (Rows are a nested random factor)
381s 
381s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
381s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
381s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
381s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
381s 
381s  [p, atab, stats] = anova2 (data, 4, "on", "nested");
381s ***** test
381s  ## Test for anova2 ("interaction")
381s  ## comparison with results from Matlab for column effect
381s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
381s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
381s  [p, atab, stats] = anova2 (popcorn, 3, "off");
381s  assert (p(1), 7.678957383294716e-07, 1e-14);
381s  assert (p(2), 0.0001003738963050171, 1e-14);
381s  assert (p(3), 0.7462153966366274, 1e-14);
381s  assert (atab{2,5}, 56.700, 1e-14);
381s  assert (atab{2,3}, 2, 0);
381s  assert (atab{4,2}, 0.08333333333333348, 1e-14);
381s  assert (atab{5,4}, 0.1388888888888889, 1e-14);
381s  assert (atab{5,2}, 1.666666666666667, 1e-14);
381s  assert (atab{6,2}, 22);
381s  assert (stats.source, "anova2");
381s  assert (stats.colmeans, [6.25, 4.75, 4]);
381s  assert (stats.inter, 1, 0);
381s  assert (stats.pval, 0.7462153966366274, 1e-14);
381s  assert (stats.df, 12);
381s ***** test
381s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
381s  data = [54, 43, 78, 111;
381s          23, 34, 37, 41;
381s          45, 65, 99, 78;
381s          31, 33, 36, 35;
381s          15, 25, 30, 26];
381s  [p, atab, stats] = anova2 (data, 1, "off", "linear");
381s  assert (atab{2,2}, 2174.95, 1e-10);
381s  assert (atab{3,2}, 8371.7, 1e-10);
381s  assert (atab{4,2}, 2404.3, 1e-10);
381s  assert (atab{5,2}, 12950.95, 1e-10);
381s  assert (atab{2,4}, 724.983333333333, 1e-10);
381s  assert (atab{3,4}, 2092.925, 1e-10);
381s  assert (atab{4,4}, 200.358333333333, 1e-10);
381s  assert (atab{2,5}, 3.61843363972882, 1e-10);
381s  assert (atab{3,5}, 10.445909412303, 1e-10);
381s  assert (atab{2,6}, 0.087266112738617, 1e-10);
381s  assert (atab{3,6}, 0.000698397753556, 1e-10);
381s ***** test
381s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
381s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
381s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
381s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
381s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
381s  [p, atab, stats] = anova2 (data, 4, "off", "nested");
381s  assert (atab{2,2}, 745.360306290833, 1e-10);
381s  assert (atab{3,2}, 278.01854140125, 1e-10);
381s  assert (atab{4,2}, 180.180377467501, 1e-10);
381s  assert (atab{5,2}, 1203.55922515958, 1e-10);
381s  assert (atab{2,4}, 372.680153145417, 1e-10);
381s  assert (atab{3,4}, 92.67284713375, 1e-10);
381s  assert (atab{4,4}, 10.0100209704167, 1e-10);
381s  assert (atab{2,5}, 4.02146005730833, 1e-10);
381s  assert (atab{3,5}, 9.25800729165627, 1e-10);
381s  assert (atab{2,6}, 0.141597630656771, 1e-10);
381s  assert (atab{3,6}, 0.000636643812875719, 1e-10);
382s 3 tests, 3 passed, 0 known failure, 0 skipped
382s [inst/qrandn.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/qrandn.m
382s ***** demo
382s  z = qrandn (-5, 5e6);
382s  [c x] = hist (z,linspace(-1.5,1.5,200),1);
382s  figure(1)
382s  plot(x,c,"r."); axis tight; axis([-1.5,1.5]);
382s 
382s  z = qrandn (-0.14286, 5e6);
382s  [c x] = hist (z,linspace(-2,2,200),1);
382s  figure(2)
382s  plot(x,c,"r."); axis tight; axis([-2,2]);
382s 
382s  z = qrandn (2.75, 5e6);
382s  [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
382s  figure(3)
382s  semilogy(x,c,"r."); axis tight; axis([-100,100]);
382s 
382s  # ---------
382s  # Figures from the reference paper.
382s ***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
382s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
382s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
382s ***** error qrandn (2.5, 1, 2, 3)
382s ***** error qrandn (2.5)
382s ***** test
382s  q = 1.5;
382s  s = [2, 3];
382s  z = qrandn (q, s);
382s  assert (isnumeric (z) && isequal (size (z), s));
382s 6 tests, 6 passed, 0 known failure, 0 skipped
382s [inst/isoutlier.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/isoutlier.m
382s ***** demo
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  TF = isoutlier (A, "mean")
382s ***** demo
382s  ## Use a moving detection method to detect local outliers in a sine wave
382s 
382s  x = -2*pi:0.1:2*pi;
382s  A = sin(x);
382s  A(47) = 0;
382s  time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
382s  TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
382s  plot (time, A)
382s  hold on
382s  plot (time(TF), A(TF), "x")
382s  datetick ('x', 20, 'keepticks')
382s  legend ("Original Data", "Outlier Data")
382s ***** demo
382s  ## Locate an outlier in a vector of data and visualize the outlier
382s 
382s  x = 1:10;
382s  A = [60 59 49 49 58 100 61 57 48 58];
382s  [TF, L, U, C] = isoutlier (A);
382s  plot (x, A);
382s  hold on
382s  plot (x(TF), A(TF), "x");
382s  xlim ([1,10]);
382s  line ([1,10], [L, L], "Linestyle", ":");
382s  text (1.1, L-2, "Lower Threshold");
382s  line ([1,10], [U, U], "Linestyle", ":");
382s  text (1.1, U-2, "Upper Threshold");
382s  line ([1,10], [C, C], "Linestyle", ":");
382s  text (1.1, C-3, "Center Value");
382s  legend ("Original Data", "Outlier Data");
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
382s  assert (isoutlier (A, "median"), ...
382s  logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "mean");
382s  assert (L, -109.2459044922864, 1e-12)
382s  assert (U, 264.9792378256198, 1e-12)
382s  assert (C, 77.8666666666666, 1e-12)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "median");
382s  assert (L, 50.104386688966386, 1e-12)
382s  assert (U, 67.895613311033610, 1e-12)
382s  assert (C, 59)
382s ***** test
382s  A = magic(5) + diag(200*ones(1,5));
382s  T = logical (eye (5));
382s  assert (isoutlier (A, 2), T)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "movmedian", 5);
382s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
382s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
382s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
382s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
382s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
382s  assert (L, l, 1e-4)
382s  assert (U, u, 1e-4)
382s  assert (C, c)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
382s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
382s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
382s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
382s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
382s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
382s  assert (L, l, 1e-4)
382s  assert (U, u, 1e-4)
382s  assert (C, c)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "movmean", 5);
382s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
382s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
382s       52.5979, 51.0627];
382s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
382s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
382s       66.9373];
382s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
382s       60.6, 59.8, 59.25, 59];
382s  assert (L, l, 1e-4)
382s  assert (U, u, 1e-4)
382s  assert (C, c, 1e-4)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
382s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
382s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
382s       52.5979, 51.0627];
382s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
382s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
382s       66.9373];
382s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
382s       60.6, 59.8, 59.25, 59];
382s  assert (L, l, 1e-4)
382s  assert (U, u, 1e-4)
382s  assert (C, c, 1e-4)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "gesd");
382s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 34.235977035439944, 1e-12)
382s  assert (U, 89.764022964560060, 1e-12)
382s  assert (C, 62)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
382s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 31.489256770616173, 1e-12)
382s  assert (U, 92.510743229383820, 1e-12)
382s  assert (C, 62)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
382s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 23.976664158788935, 1e-12)
382s  assert (U, 100.02333584121110, 1e-12)
382s  assert (C, 62)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "grubbs");
382s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 54.642809574646606, 1e-12)
382s  assert (U, 63.511036579199555, 1e-12)
382s  assert (C, 59.076923076923080, 1e-12)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
382s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 54.216083184201850, 1e-12)
382s  assert (U, 63.937762969644310, 1e-12)
382s  assert (C, 59.076923076923080, 1e-12)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
382s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
382s  assert (L, 57)
382s  assert (U, 100)
382s  assert (C, 78.5)
382s ***** test
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s  [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
382s  assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
382s  assert (L, 57.5)
382s  assert (U, 62)
382s  assert (C, 59.75)
382s ***** shared A
382s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", 0);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", []);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", [2 3 4]);
382s ***** error<isoutlier: WINDOW must be a positive integer> ...
382s  isoutlier (A, "movmedian", 1.4);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", [0 1]);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", [2 -1]);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", {2 3});
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmedian", "char");
382s 
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", 0);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", []);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", [2 3 4]);
382s ***** error<isoutlier: WINDOW must be a positive integer> ...
382s  isoutlier (A, "movmean", 1.4);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", [0 1]);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", [2 -1]);
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", {2 3});
382s ***** error<isoutlier: WINDOW must be a positive scalar> ...
382s  isoutlier (A, "movmean", "char");
382s 
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [-1 90]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [10 -90]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [90]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [90 20]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [90 20]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", [10 20 90]);
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", {10 90});
382s ***** error<isoutlier: THRESHOLD must be a two-element> ...
382s  isoutlier (A, "percentiles", "char");
382s 
382s ***** error<isoutlier: sample points must be a vector.> ...
382s  isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
382s ***** error<isoutlier: sample points must be a vector.> ...
382s  isoutlier (A, "movmean", 5, "SamplePoints", 15);
382s ***** error<isoutlier: sample points must be unique.> ...
382s  isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
382s ***** error<isoutlier: sample points must be sorted.> ...
382s  isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
382s ***** error<isoutlier: sample points must have the same size> ...
382s  isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);
382s 
382s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
382s  isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
382s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
382s  isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
382s ***** error<isoutlier: threshold factor must must be in> ...
382s  isoutlier (A, "gesd", "ThresholdFactor", 3);
382s ***** error<isoutlier: threshold factor must must be in> ...
382s  isoutlier (A, "grubbs", "ThresholdFactor", 3);
382s 
382s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
382s  isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
382s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
382s  isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
382s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
382s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
382s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
382s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);
382s 
382s ***** error<isoutlier: invalid input argument.> ...
382s  isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
382s ***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
382s ***** error<isoutlier: invalid input argument.> isoutlier (A, true);
382s ***** error<isoutlier: invalid input argument.> isoutlier (A, false);
382s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
382s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
382s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
382s 59 tests, 59 passed, 0 known failure, 0 skipped
382s [inst/cmdscale.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cmdscale.m
382s ***** shared m, n, X, D
382s  m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
382s ***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
382s ***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
382s 2 tests, 2 passed, 0 known failure, 0 skipped
382s [inst/fitgmdist.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitgmdist.m
382s ***** demo
382s  ## Generate a two-cluster problem
382s  C1 = randn (100, 2) + 2;
382s  C2 = randn (100, 2) - 2;
382s  data = [C1; C2];
382s 
382s  ## Perform clustering
382s  GMModel = fitgmdist (data, 2);
382s 
382s  ## Plot the result
382s  figure
382s  [heights, bins] = hist3([C1; C2]);
382s  [xx, yy] = meshgrid(bins{1}, bins{2});
382s  bbins = [xx(:), yy(:)];
382s  contour (reshape (GMModel.pdf (bbins), size (heights)));
382s ***** demo
382s  Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
382s  nbOrientations = 2;
382s  initial_orientations = [38.0; 18.0];
382s  initial_weights = ones (1, nbOrientations) / nbOrientations;
382s  initial_Sigma = 10 * ones (1, 1, nbOrientations);
382s  start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
382s                  "ComponentProportion", initial_weights);
382s  GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
382s                             "RegularizationValue", 0.0001)
382s ***** test
382s  load fisheriris
382s  classes = unique (species);
382s  [~, score] = pca (meas, "NumComponents", 2);
382s  options.MaxIter = 1000;
382s  options.TolFun = 1e-6;
382s  options.Display = "off";
382s  GMModel = fitgmdist (score, 2, "Options", options);
382s  assert (isa (GMModel, "gmdistribution"), true);
382s  assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
382s 1 test, 1 passed, 0 known failure, 0 skipped
382s [inst/cluster.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cluster.m
382s ***** error cluster ()
382s ***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
382s ***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
382s ***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
382s ***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
382s ***** test
382s 6 tests, 6 passed, 0 known failure, 0 skipped
382s [inst/ttest2.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ttest2.m
382s ***** test
382s  a = 1:5;
382s  b = 6:10;
382s  b(5) = NaN;
382s  [h,p,ci,stats] = ttest2 (a,b);
382s  assert (h, 1);
382s  assert (p, 0.002535996080258229, 1e-14);
382s  assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
382s  assert (stats.tstat, -4.582575694955839, 1e-14);
382s  assert (stats.df, 7);
382s  assert (stats.sd, 1.4638501094228, 1e-13);
382s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
382s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
382s 3 tests, 3 passed, 0 known failure, 0 skipped
382s [inst/nanmin.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/nanmin.m
382s ***** demo
382s  ## Find the column minimum values and their indices
382s  ## for matrix data with missing values.
382s 
382s  x = magic (3);
382s  x([1, 6:9]) = NaN
382s  [y, ind] = nanmin (x)
382s ***** demo
382s  ## Find the minimum of all the values in an array, ignoring missing values.
382s  ## Create a 2-by-5-by-3 array x with some missing values.
382s 
382s  x = reshape (1:30, [2, 5, 3]);
382s  x([10:12, 25]) = NaN
382s 
382s  ## Find the minimum of the elements of x.
382s 
382s  y = nanmin (x, [], 'all')
382s ***** assert (nanmin ([2, 4, NaN, 7]), 2)
382s ***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
382s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
382s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
382s ***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
382s ***** shared x, y
382s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
382s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
382s  y = x;
382s  y(2,3,1) = 0.51;
382s ***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
382s ***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
382s ***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
382s ***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
382s ***** assert (nanmin (x, [], 'all'), -2.95)
382s ***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
382s ***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
382s ***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
382s ***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
382s ***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
382s ***** test
382s  xx = repmat ([1:20;6:25], [5 2 6 3]);
382s  assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
382s  assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
382s  assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
382s  assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
382s  assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
382s ***** assert (nanmin (ones (2), [], 3), ones (2, 2))
382s ***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
382s ***** assert (nanmin (magic (3), [], 3), magic (3))
382s ***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
382s ***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
382s ***** assert (nanmin (ones (2), 3), ones (2,2))
382s ***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
382s  nanmin (y, [], [1, 1, 2])
382s ***** error <nanmin: a second output is not supported with this syntax.> ...
382s  [v, idx] = nanmin(x, y, [1 2])
382s 24 tests, 24 passed, 0 known failure, 0 skipped
382s [inst/confusionchart.m]
382s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/confusionchart.m
382s ***** demo
382s  close all
382s  ## Setting the chart properties
382s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
382s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
382s  confusionchart (Yt, Yp, "Title", ...
382s    "Demonstration with summaries","Normalization",...
382s    "absolute","ColumnSummary", "column-normalized","RowSummary",...
382s    "row-normalized")
382s ***** demo
382s  close all
382s  ## Cellstr as inputs
382s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
382s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
382s  m = confusionmat (Yt, Yp);
382s  confusionchart (m, {"Positive", "Negative"});
382s  hold off
382s ***** demo
382s  close all
382s  ## Editing the object properties
382s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
382s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
382s  cm = confusionchart (Yt, Yp);
382s  cm.Title = "This is an example with a green diagonal";
382s  cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
382s  hold off
382s ***** demo
382s  close all
382s  ## Confusion chart in a uipanel
382s  h = uipanel ();
382s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
382s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
382s  cm = confusionchart (h, Yt, Yp);
382s  hold off
382s ***** demo
382s  close all
382s  ## Sorting classes
382s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
382s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
382s  cm = confusionchart (Yt, Yp, "Title", ...
382s    "Classes are sorted in ascending order");
382s  cm = confusionchart (Yt, Yp, "Title", ...
382s    "Classes are sorted according to clusters");
382s  sortClasses (cm, "cluster");
382s ***** shared visibility_setting
382s  visibility_setting = get (0, "DefaultFigureVisible");
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ()", "Invalid call");
382s  set (0, "DefaultFigureVisible", visibility_setting);
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
382s  set (0, "DefaultFigureVisible", visibility_setting);
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
382s  set (0, "DefaultFigureVisible", visibility_setting);
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
382s  set (0, "DefaultFigureVisible", visibility_setting);
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
382s        ".* YLabel .* string");
382s  set (0, "DefaultFigureVisible", visibility_setting);
382s ***** test
382s  set (0, "DefaultFigureVisible", "off");
382s  fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
382s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
383s        ".* FontName .* string");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
383s        ".* FontSize .* numeric");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
383s        ".* DiagonalColor .* color");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
383s        ".* OffDiagonalColor .* color");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
383s        ".* invalid .* Normalization");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
383s        ".* invalid .* ColumnSummary");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
383s        ".* invalid .* RowSummary");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
383s        ".* invalid .* GridVisible");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
383s        ".* invalid .* HandleVisibility");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
383s        ".* invalid .* OuterPosition");
383s  set (0, "DefaultFigureVisible", visibility_setting);
383s ***** test
383s  set (0, "DefaultFigureVisible", "off");
383s  fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
383s        ".* invalid .* Position");
383s  set (0, "DefaultFigureVisible", visibility_setting);
384s ***** test
384s  set (0, "DefaultFigureVisible", "off");
384s  fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
384s  set (0, "DefaultFigureVisible", visibility_setting);
384s 18 tests, 18 passed, 0 known failure, 0 skipped
384s [inst/squareform.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/squareform.m
384s ***** shared v, m
384s  v = 1:6;
384s  m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
384s ***** assert (squareform (v), m)
384s ***** assert (squareform (squareform (v)), v)
384s ***** assert (squareform (m), v)
384s ***** assert (squareform (v'), m)
384s ***** assert (squareform (1), [0 1;1 0])
384s ***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
384s ***** assert (squareform (0, "tovector"), zeros (1, 0))
384s ***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
384s ***** test
384s  for c = {@single, @double, @uint8, @uint32, @uint64}
384s    f = c{1};
384s    assert (squareform (f (v)), f (m))
384s    assert (squareform (f (m)), f (v))
384s  endfor
384s 9 tests, 9 passed, 0 known failure, 0 skipped
384s [inst/signrank.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/signrank.m
384s ***** test
384s  load gradespaired.mat
384s  [p, h, stats] = signrank (gradespaired(:,1), ...
384s                            gradespaired(:,2), 'tail', 'left');
384s  assert (p, 0.0047, 1e-4);
384s  assert (h, true);
384s  assert (stats.zval, -2.5982, 1e-4);
384s  assert (stats.signedrank, 2017.5);
384s ***** test
384s  load ('gradespaired.mat');
384s  [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
384s                            'tail', 'left', 'method', 'exact');
384s  assert (p, 0.0045, 1e-4);
384s  assert (h, true);
384s  assert (stats.zval, NaN);
384s  assert (stats.signedrank, 2017.5);
384s ***** test
384s  load mileage
384s  [p, h, stats] = signrank (mileage(:,2), 33);
384s  assert (p, 0.0312, 1e-4);
384s  assert (h, true);
384s  assert (stats.zval, NaN);
384s  assert (stats.signedrank, 21);
384s ***** test
384s  load mileage
384s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
384s  assert (p, 0.0156, 1e-4);
384s  assert (h, true);
384s  assert (stats.zval, NaN);
384s  assert (stats.signedrank, 21);
384s ***** test
384s  load mileage
384s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
384s                            'alpha', 0.01, 'method', 'approximate');
384s  assert (p, 0.0180, 1e-4);
384s  assert (h, false);
384s  assert (stats.zval, 2.0966, 1e-4);
384s  assert (stats.signedrank, 21);
384s ***** error <signrank: X must be a vector.> signrank (ones (2))
384s ***** error <signrank: Y must be either a scalar of a vector.> ...
384s  signrank ([1, 2, 3, 4], ones (2))
384s ***** error <signrank: X and Y vectors have different lengths.> ...
384s  signrank ([1, 2, 3, 4], [1, 2, 3])
384s ***** error <signrank: optional arguments must be in pairs.> ...
384s  signrank ([1, 2, 3, 4], [], 'tail')
384s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
384s  signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
384s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
384s  signrank ([1, 2, 3, 4], [], 'alpha', 0)
384s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
384s  signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
384s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
384s  signrank ([1, 2, 3, 4], [], 'alpha', "a")
384s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
384s  signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
384s ***** error <signrank: 'tail' argument must be a character vector.> ...
384s  signrank ([1, 2, 3, 4], [], 'tail', 0.01)
384s ***** error <signrank: 'tail' argument must be a character vector.> ...
384s  signrank ([1, 2, 3, 4], [], 'tail', {"both"})
384s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
384s  signrank ([1, 2, 3, 4], [], 'tail', "some")
384s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
384s  signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
384s ***** error <signrank: 'method' argument must be a character vector.> ...
384s  signrank ([1, 2, 3, 4], [], 'method', 0.01)
384s ***** error <signrank: 'method' argument must be a character vector.> ...
384s  signrank ([1, 2, 3, 4], [], 'method', {"exact"})
384s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
384s  signrank ([1, 2, 3, 4], [], 'method', "some")
384s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
384s  signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
384s 22 tests, 22 passed, 0 known failure, 0 skipped
384s [inst/hmmviterbi.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hmmviterbi.m
384s ***** test
384s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
384s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
384s  transprob = [0.8, 0.2; 0.4, 0.6];
384s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
384s  vpath = hmmviterbi (sequence, transprob, outprob);
384s  expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
384s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
384s  assert (vpath, expected);
384s ***** test
384s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
384s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
384s  transprob = [0.8, 0.2; 0.4, 0.6];
384s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
384s  symbols = {"A", "B", "C"};
384s  statenames = {"One", "Two"};
384s  vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
384s                                                    "statenames", statenames);
384s  expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
384s              "One", "One", "One", "One", "One", "One", "One", "Two", ...
384s              "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
384s  assert (vpath, expected);
384s 2 tests, 2 passed, 0 known failure, 0 skipped
384s [inst/manova1.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/manova1.m
384s ***** demo
384s  load carbig
384s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
384s ***** test
384s  load carbig
384s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
384s  assert (d, 3);
384s  assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
384s              0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
384s ***** test
384s  load carbig
384s  [d,p] = manova1([MPG, Acceleration, Weight], Origin);
384s  assert (d, 2);
384s  assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
384s             [1e-12, 1e-12, 1e-12]');
384s 2 tests, 2 passed, 0 known failure, 0 skipped
384s [inst/linkage.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/linkage.m
384s ***** shared x, t
384s  x = reshape (mod (magic (6),5), [], 3);
384s  t = 1e-6;
384s ***** assert (cond (linkage (pdist (x))),                   34.119045, t);
384s ***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
384s ***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
384s ***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
384s  lastwarn(); # Clear last warning before the test
384s ***** warning <cluster distances> linkage (pdist (x), "centroid");
384s ***** test
384s  warning off Octave:clustering
384s  assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
384s  warning on Octave:clustering
384s ***** warning <cluster distances> linkage (pdist (x), "median");
384s ***** test
384s  warning off Octave:clustering
384s  assert (cond (linkage (pdist (x), "median")),        27.683325, t);
384s  warning on Octave:clustering
384s ***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
384s ***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
384s ***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
384s ***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
384s 12 tests, 12 passed, 0 known failure, 0 skipped
384s [inst/binotest.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/binotest.m
384s ***** demo
384s  % flip a coin 1000 times, showing 475 heads
384s  % Hypothesis: coin is fair, i.e. p=1/2
384s  [h,p_val,ci] = binotest(475,1000,0.5)
384s  % Result: h = 0 : null hypothesis not rejected, coin could be fair
384s  %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
384s  %         0.444 <= p <= 0.506 with 95% confidence
384s ***** demo
384s  % flip a coin 100 times, showing 65 heads
384s  % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
384s  [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
384s  % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
384s  %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
384s  %         0 <= p <= 0.76 with 99% confidence
384s ***** test #example from https://en.wikipedia.org/wiki/Binomial_test
384s  [h,p_val,ci] = binotest (51,235,1/6);
384s  assert (p_val, 0.0437, 0.00005)
384s  [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
384s  assert (p_val, 0.027, 0.0005)
384s 1 test, 1 passed, 0 known failure, 0 skipped
384s [inst/normalise_distribution.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/normalise_distribution.m
384s ***** test
384s  v = normalise_distribution ([1 2 3], [], 1);
384s  assert (v, [0 0 0])
384s ***** test
384s  v = normalise_distribution ([1 2 3], [], 2);
384s  assert (v, norminv ([1 3 5] / 6), 3 * eps)
384s ***** test
384s  v = normalise_distribution ([1 2 3]', [], 2);
384s  assert (v, [0 0 0]')
384s ***** test
384s  v = normalise_distribution ([1 2 3]', [], 1);
384s  assert (v, norminv ([1 3 5]' / 6), 3 * eps)
384s ***** test
384s  v = normalise_distribution ([1 1 2 2 3 3], [], 2);
384s  assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
384s ***** test
384s  v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
384s  assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
384s ***** test
384s  A = randn ( 10 );
384s  N = normalise_distribution (A, @normcdf);
384s  assert (A, N, 10000 * eps)
384s ***** test
384s  A = exprnd (1, 100);
384s  N = normalise_distribution (A, @(x)(expcdf (x, 1)));
384s  assert (mean (vec (N)), 0, 0.1)
384s  assert (std (vec (N)), 1, 0.1)
384s ***** test
384s  A = rand (1000,1);
384s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
384s  assert (mean (vec (N)), 0, 0.2)
384s  assert (std (vec (N)), 1, 0.1)
384s ***** test
384s  A = [rand(1000,1), randn(1000, 1)];
384s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
384s  assert (mean (N), [0, 0], 0.2)
384s  assert (std (N), [1, 1], 0.1)
384s ***** test
384s  A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
384s  N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
384s  assert (mean (N, 2), [0, 0, 0]', 0.2);
384s  assert (std (N, [], 2), [1, 1, 1]', 0.1);
384s ***** xtest
384s  A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
384s  N = normalise_distribution (A);
384s  assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
384s  assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
384s ***** test
384s ***** error normalise_distribution (zeros (3, 4), ...
384s  {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
384s 14 tests, 14 passed, 0 known failure, 0 skipped
384s [inst/fitlm.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/fitlm.m
384s ***** demo
384s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
384s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
384s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
384s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
384s         25.694 ]';
384s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
384s 
384s  [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
384s ***** demo
384s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
384s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
384s  brands = {'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'};
384s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
384s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
384s 
384s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
384s                           "CategoricalVars",[1,2],"display","on");
384s ***** test
384s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
384s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
384s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
384s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
384s         25.694 ]';
384s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
384s  [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
384s  [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
384s  [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
384s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
384s  [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
384s  assert (TAB{2,2}, 10, 1e-04);
384s  assert (TAB{3,2}, 7.99999999999999, 1e-09);
384s  assert (TAB{4,2}, 8.99999999999999, 1e-09);
384s  assert (TAB{5,2}, 11.0001428571429, 1e-09);
384s  assert (TAB{6,2}, 19.0001111111111, 1e-09);
384s  assert (TAB{2,3}, 1.01775379540949, 1e-09);
384s  assert (TAB{3,3}, 1.64107868458008, 1e-09);
384s  assert (TAB{4,3}, 1.43932122062479, 1e-09);
384s  assert (TAB{5,3}, 1.48983900477565, 1e-09);
384s  assert (TAB{6,3}, 1.3987687997822, 1e-09);
384s  assert (TAB{2,6}, 9.82555903510687, 1e-09);
384s  assert (TAB{3,6}, 4.87484242844031, 1e-09);
384s  assert (TAB{4,6}, 6.25294748040552, 1e-09);
384s  assert (TAB{5,6}, 7.38344399756088, 1e-09);
384s  assert (TAB{6,6}, 13.5834536158296, 1e-09);
384s  assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
384s  assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
384s  assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
384s  assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
384s ***** test
384s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
384s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
384s  brands = bsxfun (@times, ones(6,1), [1,2,3]);
384s  popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));
384s 
384s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
384s                           "categoricalvars",[1,2],"display","off");
384s  assert (TAB{2,2}, 5.66666666666667, 1e-09);
384s  assert (TAB{3,2}, -1.33333333333333, 1e-09);
384s  assert (TAB{4,2}, -2.16666666666667, 1e-09);
384s  assert (TAB{5,2}, 1.16666666666667, 1e-09);
384s  assert (TAB{6,2}, -0.333333333333334, 1e-09);
384s  assert (TAB{7,2}, -0.166666666666667, 1e-09);
384s  assert (TAB{2,3}, 0.215165741455965, 1e-09);
384s  assert (TAB{3,3}, 0.304290309725089, 1e-09);
384s  assert (TAB{4,3}, 0.304290309725089, 1e-09);
384s  assert (TAB{5,3}, 0.304290309725089, 1e-09);
384s  assert (TAB{6,3}, 0.43033148291193, 1e-09);
384s  assert (TAB{7,3}, 0.43033148291193, 1e-09);
384s  assert (TAB{2,6}, 26.3362867542108, 1e-09);
384s  assert (TAB{3,6}, -4.38178046004138, 1e-09);
384s  assert (TAB{4,6}, -7.12039324756724, 1e-09);
384s  assert (TAB{5,6}, 3.83405790253621, 1e-09);
384s  assert (TAB{6,6}, -0.774596669241495, 1e-09);
384s  assert (TAB{7,6}, -0.387298334620748, 1e-09);
384s  assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
384s  assert (TAB{3,7}, 0.000893505495903642, 1e-09);
384s  assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
384s  assert (TAB{5,7}, 0.00237798044119407, 1e-09);
384s  assert (TAB{6,7}, 0.453570536021938, 1e-09);
384s  assert (TAB{7,7}, 0.705316781644046, 1e-09);
384s  ## Test with string ids for categorical variables
384s  brands = {'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'; ...
384s            'Gourmet', 'National', 'Generic'};
384s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
384s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
384s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
384s                           "categoricalvars",[1,2],"display","off");
384s ***** test
384s  load carsmall
384s  X = [Weight,Horsepower,Acceleration];
384s  [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
384s  [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
384s  assert (TAB{2,2}, 47.9767628118615, 1e-09);
384s  assert (TAB{3,2}, -0.00654155878851796, 1e-09);
384s  assert (TAB{4,2}, -0.0429433065881864, 1e-09);
384s  assert (TAB{5,2}, -0.0115826516894871, 1e-09);
384s  assert (TAB{2,3}, 3.87851641748551, 1e-09);
384s  assert (TAB{3,3}, 0.00112741016370336, 1e-09);
384s  assert (TAB{4,3}, 0.0243130608813806, 1e-09);
384s  assert (TAB{5,3}, 0.193325043113178, 1e-09);
384s  assert (TAB{2,6}, 12.369874881944, 1e-09);
384s  assert (TAB{3,6}, -5.80228828790225, 1e-09);
384s  assert (TAB{4,6}, -1.76626492228599, 1e-09);
384s  assert (TAB{5,6}, -0.0599128364487485, 1e-09);
384s  assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
384s  assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
384s  assert (TAB{4,7}, 0.0807803098213114, 1e-09);
384s  assert (TAB{5,7}, 0.952359384151778, 1e-09);
384s 3 tests, 3 passed, 0 known failure, 0 skipped
384s [inst/gmdistribution.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/gmdistribution.m
384s ***** test
384s  mu = eye(2);
384s  Sigma = eye(2);
384s  GM = gmdistribution (mu, Sigma);
384s  density = GM.pdf ([0 0; 1 1]);
384s  assert (density(1) - density(2), 0, 1e-6);
384s 
384s  [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
384s  assert (idx, [1; 2]);
384s  [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
384s  assert (nlogl - nlogl2, 0, 1e-6);
384s  [idx3,nlogl3,P3] = cluster (GM, eye(2));
384s  assert (P - P3, zeros (2), 1e-6);
384s  [idx4,nlogl4] = cluster (GM, eye(2));
384s  assert (size (nlogl4), [1 1]);
384s  idx5 = cluster (GM, eye(2));
384s  assert (idx - idx5, zeros (2,1));
384s 
384s  D = GM.mahal ([1;0]);
384s  assert (D - M(1,:), zeros (1,2), 1e-6);
384s 
384s  P = GM.posterior ([0 1]);
384s  assert (P - P2(2,:), zeros (1,2), 1e-6);
384s 
384s  R = GM.random(20);
384s  assert (size(R), [20, 2]);
384s 
384s  R = GM.random();
384s  assert (size(R), [1, 2]);
384s 1 test, 1 passed, 0 known failure, 0 skipped
384s [inst/friedman.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/friedman.m
384s ***** demo
384s  load popcorn;
384s  friedman (popcorn, 3);
384s ***** demo
384s  load popcorn;
384s  [p, atab] = friedman (popcorn, 3, "off");
384s  disp (p);
384s ***** test
384s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
384s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
384s  [p, atab] = friedman (popcorn, 3, "off");
384s  assert (p, 0.001028853354594794, 1e-14);
384s  assert (atab{2,2}, 99.75, 1e-14);
384s  assert (atab{2,3}, 2, 0);
384s  assert (atab{2,4}, 49.875, 1e-14);
384s  assert (atab{2,5}, 13.75862068965517, 1e-14);
384s  assert (atab{2,6}, 0.001028853354594794, 1e-14);
384s  assert (atab{3,2}, 0.08333333333333215, 1e-14);
384s  assert (atab{3,4}, 0.04166666666666607, 1e-14);
384s  assert (atab{4,3}, 12, 0);
384s ***** test
384s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
384s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
384s  [p, atab, stats] = friedman (popcorn, 3, "off");
384s  assert (atab{5,2}, 116, 0);
384s  assert (atab{5,3}, 17, 0);
384s  assert (stats.source, "friedman");
384s  assert (stats.n, 2);
384s  assert (stats.meanranks, [8, 4.75, 2.25], 0);
384s  assert (stats.sigma, 2.692582403567252, 1e-14);
384s 2 tests, 2 passed, 0 known failure, 0 skipped
384s [inst/mnrfit.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/mnrfit.m
384s ***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
384s ***** error<mnrfit: Predictors must be numeric.> ...
384s  mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
384s ***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
384s  mnrfit (ones (50, 4, 2), ones (50, 1))
384s ***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
384s  mnrfit (ones (50, 4), ones (50, 1, 3))
384s ***** error<mnrfit: Y must have the same number of rows as X.> ...
384s  mnrfit (ones (50, 4), ones (45,1))
384s ***** error<mnrfit: Response labels must be a character array, a cell vector> ...
384s  mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
384s ***** error<mnrfit: optional arguments must be in pairs.> ...
384s  mnrfit (ones (5, 4), ones (5, 1), "model")
384s ***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
384s  mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
384s ***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
384s  mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
384s ***** error<mnrfit: Y must contain positive integer category numbers.> ...
384s  mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
384s ***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
384s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
384s ***** error<mnrfit: fitting hierarchical responses not supported.> ...
384s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
384s ***** error<mnrfit: model type not recognised.> ...
384s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
384s 13 tests, 13 passed, 0 known failure, 0 skipped
384s [inst/regress_gp.m]
384s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/regress_gp.m
384s ***** demo
384s  ## Linear fitting of 1D Data
384s  rand ("seed", 125);
384s  X = 2 * rand (5, 1) - 1;
384s  randn ("seed", 25);
384s  Y = 2 * X - 1 + 0.3 * randn (5, 1);
384s 
384s  ## Points for interpolation/extrapolation
384s  Xfit = linspace (-2, 2, 10)';
384s 
384s  ## Fit regression model
384s  [Yfit, Yint, m] = regress_gp (X, Y, Xfit);
384s 
384s  ## Plot fitted data
384s  plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
384s  title ("Gaussian process regression with linear kernel");
384s ***** demo
384s  ## Linear fitting of 2D Data
384s  rand ("seed", 135);
384s  X = 2 * rand (4, 2) - 1;
384s  randn ("seed", 35);
384s  Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);
384s 
384s  ## Mesh for interpolation/extrapolation
384s  [x1, x2] = meshgrid (linspace (-1, 1, 10));
384s  Xfit = [x1(:), x2(:)];
384s 
384s  ## Fit regression model
384s  [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
384s  Ypred = reshape (Ypred, 10, 10);
384s  YintU = reshape (Yint(:,1), 10, 10);
384s  YintL = reshape (Yint(:,2), 10, 10);
384s 
384s  ## Plot fitted data
384s  plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
384s  hold on;
384s  h = mesh (x1, x2, Ypred, zeros (10, 10));
384s  set (h, "facecolor", "none", "edgecolor", "yellow");
384s  h = mesh (x1, x2, YintU, ones (10, 10));
384s  set (h, "facecolor", "none", "edgecolor", "cyan");
384s  h = mesh (x1, x2, YintL, ones (10, 10));
384s  set (h, "facecolor", "none", "edgecolor", "cyan");
384s  hold off
384s  axis tight
384s  view (75, 25)
384s  title ("Gaussian process regression with linear kernel");
384s ***** demo
384s  ## Projection over basis function with linear kernel
384s  pp = [2, 2, 0.3, 1];
384s  n = 10;
384s  rand ("seed", 145);
384s  X = 2 * rand (n, 1) - 1;
384s  randn ("seed", 45);
384s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
384s 
384s  ## Powers
384s  px = [sqrt(abs(X)), X, X.^2, X.^3];
384s 
384s  ## Points for interpolation/extrapolation
384s  Xfit = linspace (-1, 1, 100)';
384s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
384s 
384s  ## Define a prior covariance assuming that the sqrt component is not present
384s  Sp = 100 * eye (size (px, 2) + 1);
384s  Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present
384s 
384s  ## Fit regression model
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);
384s 
384s  ## Plot fitted data
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("Linear kernel over basis function with prior covariance");
384s ***** demo
384s  ## Projection over basis function with linear kernel
384s  pp = [2, 2, 0.3, 1];
384s  n = 10;
384s  rand ("seed", 145);
384s  X = 2 * rand (n, 1) - 1;
384s  randn ("seed", 45);
384s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
384s 
384s  ## Powers
384s  px = [sqrt(abs(X)), X, X.^2, X.^3];
384s 
384s  ## Points for interpolation/extrapolation
384s  Xfit = linspace (-1, 1, 100)';
384s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
384s 
384s  ## Fit regression model without any assumption on prior covariance
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);
384s 
384s  ## Plot fitted data
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("Linear kernel over basis function without prior covariance");
384s ***** demo
384s  ## Projection over basis function with rbf kernel
384s  pp = [2, 2, 0.3, 1];
384s  n = 10;
384s  rand ("seed", 145);
384s  X = 2 * rand (n, 1) - 1;
384s  randn ("seed", 45);
384s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
384s 
384s  ## Powers
384s  px = [sqrt(abs(X)), X, X.^2, X.^3];
384s 
384s  ## Points for interpolation/extrapolation
384s  Xfit = linspace (-1, 1, 100)';
384s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
384s 
384s  ## Fit regression model with RBF kernel (standard parameters)
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");
384s 
384s  ## Plot fitted data
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("RBF kernel over basis function with standard parameters");
384s  text (-0.5, 4, "theta = 5\n g = 0.01");
384s ***** demo
384s  ## Projection over basis function with rbf kernel
384s  pp = [2, 2, 0.3, 1];
384s  n = 10;
384s  rand ("seed", 145);
384s  X = 2 * rand (n, 1) - 1;
384s  randn ("seed", 45);
384s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
384s 
384s  ## Powers
384s  px = [sqrt(abs(X)), X, X.^2, X.^3];
384s 
384s  ## Points for interpolation/extrapolation
384s  Xfit = linspace (-1, 1, 100)';
384s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
384s 
384s  ## Fit regression model with RBF kernel with different parameters
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);
384s 
384s  ## Plot fitted data
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("GP regression with RBF kernel and non default parameters");
384s  text (-0.5, 4, "theta = 10\n g = 0.01");
384s 
384s  ## Fit regression model with RBF kernel with different parameters
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);
384s 
384s  ## Plot fitted data
384s  figure
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("GP regression with RBF kernel and non default parameters");
384s  text (-0.5, 4, "theta = 50\n g = 0.01");
384s 
384s  ## Fit regression model with RBF kernel with different parameters
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);
384s 
384s  ## Plot fitted data
384s  figure
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("GP regression with RBF kernel and non default parameters");
384s  text (-0.5, 4, "theta = 50\n g = 0.001");
384s 
384s  ## Fit regression model with RBF kernel with different parameters
384s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);
384s 
384s  ## Plot fitted data
384s  figure
384s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
384s                          Xfit, polyval (pp, Xfit), "g-;True;");
384s  axis tight
384s  axis manual
384s  hold on
384s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
384s  hold off
384s  title ("GP regression with RBF kernel and non default parameters");
384s  text (-0.5, 4, "theta = 50\n g = 0.05");
384s ***** demo
384s  ## RBF fitting on noiseless 1D Data
384s  x = [0:2*pi/7:2*pi]';
384s  y = 5 * sin (x);
384s 
384s  ## Predictive grid of 500 equally spaced locations
384s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
384s 
384s  ## Fit regression model with RBF kernel
384s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
384s 
384s  ## Plot fitted data
384s  r = mvnrnd (Yfit, diag (Ysd)', 50);
384s  plot (xi, r', "c-");
384s  hold on
384s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
384s  plot (x, y, ".k;Predictor points;", "markersize", 20)
384s  plot (xi, 5 * sin (xi), "-y;True Function;");
384s  xlim ([-0.5,2*pi+0.5]);
384s  ylim ([-10,10]);
384s  hold off
384s  title ("GP regression with RBF kernel on noiseless 1D data");
384s  text (0, -7, "theta = 5\n g = 0.01");
384s ***** demo
384s  ## RBF fitting on noisy 1D Data
384s  x = [0:2*pi/7:2*pi]';
384s  x = [x; x];
384s  y = 5 * sin (x) + randn (size (x));
384s 
384s  ## Predictive grid of 500 equally spaced locations
384s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
384s 
384s  ## Fit regression model with RBF kernel
384s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
384s 
384s  ## Plot fitted data
384s  r = mvnrnd (Yfit, diag (Ysd)', 50);
384s  plot (xi, r', "c-");
384s  hold on
384s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
384s  plot (x, y, ".k;Predictor points;", "markersize", 20)
384s  plot (xi, 5 * sin (xi), "-y;True Function;");
384s  xlim ([-0.5,2*pi+0.5]);
384s  ylim ([-10,10]);
384s  hold off
384s  title ("GP regression with RBF kernel on noisy 1D data");
384s  text (0, -7, "theta = 5\n g = 0.01");
384s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
384s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
384s ***** error<regress_gp: X must be a 2-D matrix.> ...
384s  regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
385s ***** error<regress_gp: Y must be a column vector.> ...
385s  regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
385s ***** error<regress_gp: rows in X must equal the length of Y.> ...
385s  regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
385s ***** error<regress_gp: X and XI must have the same number of columns.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
385s ***** error<regress_gp: invalid 4th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
385s ***** error<regress_gp: invalid 4th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
385s ***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
385s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
385s ***** error<regress_gp: invalid 5th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
385s ***** error<regress_gp: invalid 5th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
385s ***** error<regress_gp: invalid 5th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
385s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
385s ***** error<regress_gp: invalid 6th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
385s ***** error<regress_gp: invalid 6th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
385s ***** error<regress_gp: invalid 6th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
385s ***** error<regress_gp: invalid 6th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
385s ***** error<regress_gp: invalid 6th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
385s ***** error<regress_gp: invalid 7th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
385s ***** error<regress_gp: invalid 7th argument.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
385s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
385s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
385s 22 tests, 22 passed, 0 known failure, 0 skipped
385s [inst/hmmestimate.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hmmestimate.m
385s ***** test
385s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
385s              3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
385s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
385s              1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
385s  [transprobest, outprobest] = hmmestimate (sequence, states);
385s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
385s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
385s  assert (transprobest, expectedtransprob, 0.001);
385s  assert (outprobest, expectedoutprob, 0.001);
385s ***** test
385s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
385s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
385s  states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
385s            "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
385s            "Two", "One", "One", "One", "One", "One", "One"};
385s  symbols = {"A", "B", "C"};
385s  statenames = {"One", "Two"};
385s  [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
385s                                            symbols, "statenames", statenames);
385s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
385s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
385s  assert (transprobest, expectedtransprob, 0.001);
385s  assert (outprobest, expectedoutprob, 0.001);
385s ***** test
385s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
385s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
385s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
385s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
385s  pseudotransitions = [8, 2; 4, 6];
385s  pseudoemissions = [2, 4, 4; 7, 2, 1];
385s  [transprobest, outprobest] = hmmestimate (sequence, states, ...
385s   "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
385s  expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
385s  expectedoutprob = [0.178571, 0.357143, 0.464286; ...
385s                     0.823529, 0.117647, 0.058824];
385s  assert (transprobest, expectedtransprob, 0.001);
385s  assert (outprobest, expectedoutprob, 0.001);
385s 3 tests, 3 passed, 0 known failure, 0 skipped
385s [inst/monotone_smooth.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/monotone_smooth.m
385s ***** error<Invalid call to monotone_smooth.  Correct usage is> ...
385s  monotone_smooth (1)
385s ***** error<monotone_smooth: X must be a numeric vector.> ...
385s  monotone_smooth ("char", 1)
385s ***** error<monotone_smooth: X must be a numeric vector.> ...
385s  monotone_smooth ({1,2,3}, 1)
385s ***** error<monotone_smooth: X must be a numeric vector.> ...
385s  monotone_smooth (ones(20,3), 1)
385s ***** error<monotone_smooth: Y must be a numeric vector.> ...
385s  monotone_smooth (1, "char")
385s ***** error<monotone_smooth: Y must be a numeric vector.> ...
385s  monotone_smooth (1, {1,2,3})
385s ***** error<monotone_smooth: Y must be a numeric vector.> ...
385s  monotone_smooth (1, ones(20,3))
385s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
385s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
385s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
385s 10 tests, 10 passed, 0 known failure, 0 skipped
385s [inst/hotelling_t2test2.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/hotelling_t2test2.m
385s ***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
385s ***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
385s  hotelling_t2test2 ([2, 3, 4, 5, 6]);
385s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
385s  hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
385s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
385s  hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
385s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
385s  hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
385s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
385s  hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
385s ***** error<hotelling_t2test2: invalid value for alpha.> ...
385s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
385s ***** error<hotelling_t2test2: invalid value for alpha.> ...
385s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
385s ***** error<hotelling_t2test2: invalid value for alpha.> ...
385s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
385s ***** error<hotelling_t2test2: invalid value for alpha.> ...
385s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
385s ***** error<hotelling_t2test2: invalid Name argument.> ...
385s  hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
385s ***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
385s  hotelling_t2test2 (ones (20,1), ones (20,2));
385s ***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
385s  hotelling_t2test2 (ones (20,2), ones (25,3));
385s ***** test
385s  randn ("seed", 1);
385s  x1 = randn (60000, 5);
385s  randn ("seed", 5);
385s  x2 = randn (30000, 5);
385s  [h, pval, stats] = hotelling_t2test2 (x1, x2);
385s  assert (h, 0);
385s  assert (stats.df1, 5);
385s  assert (stats.df2, 89994);
385s 14 tests, 14 passed, 0 known failure, 0 skipped
385s [inst/ztest.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ztest.m
385s ***** error<ztest: too few input arguments.> ztest ();
385s ***** error<ztest: invalid value for standard deviation.> ...
385s  ztest ([1, 2, 3, 4], 2, -0.5);
385s ***** error<ztest: invalid VALUE for alpha.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
385s ***** error<ztest: invalid VALUE for alpha.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
385s ***** error<ztest: invalid VALUE for alpha.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
385s ***** error<ztest: invalid VALUE for tail.>  ...
385s  ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
385s ***** error<ztest: invalid VALUE for tail.>  ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
385s ***** error<ztest: invalid VALUE for operating dimension.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
385s ***** error<ztest: invalid VALUE for operating dimension.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
385s ***** error<ztest: invalid NAME for optional arguments.> ...
385s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
385s ***** test
385s  load carsmall
385s  [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
385s  assert (h, 0);
385s  assert (pval, 1, 1e-14);
385s  assert (ci, [22.094; 25.343], 1e-3);
385s ***** test
385s  load carsmall
385s  [h, pval, ci] = ztest (MPG, 26, 8);
385s  assert (h, 1);
385s  assert (pval, 0.00568359158544743, 1e-14);
385s  assert (ci, [22.101; 25.335], 1e-3);
385s ***** test
385s  load carsmall
385s  [h, pval, ci] = ztest (MPG, 26, 4);
385s  assert (h, 1);
385s  assert (pval, 3.184168011941316e-08, 1e-14);
385s  assert (ci, [22.909; 24.527], 1e-3);
385s ***** test
385s  x = normrnd (10, 2, 100, 1);
385s  [h, pval, ci] = ztest (x, 10, 2, "tail", "right");
385s  assert (isnan (pval), false);
385s  assert (pval >= 0 && pval <= 1, true);
385s ***** test
385s  x = normrnd (10, 2, 100, 1);
385s  [h, pval, ci] = ztest (x, 10, 2, "tail", "left");
385s  assert (isnan (pval), false);
385s  assert (pval >= 0 && pval <= 1, true);
385s ***** test
385s  load fisheriris;
385s  x = meas(:,1);
385s  m = 5.8;
385s  sigma = 0.8;
385s  [h, pval, ci] = ztest (x, m, sigma, "tail", "right");
385s  assert (h, 0)
385s  assert (pval, 0.2535, 1e-4)
385s  assert (ci, [5.7359; Inf], 1e-5)
385s ***** test
385s  load fisheriris;
385s  x = meas(:,1);
385s  m = 5.8;
385s  sigma = 0.8;
385s  [h, pval, ci] = ztest (x, m, sigma, "tail", "left");
385s  assert (h, 0)
385s  assert (pval, 0.7465, 1e-4)
385s  assert (ci, [-Inf; 5.9508], 1e-4)    
385s 17 tests, 17 passed, 0 known failure, 0 skipped
385s [inst/ff2n.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/ff2n.m
385s ***** error ff2n ();
385s ***** error ff2n (2, 5);
385s ***** error ff2n (2.5);
385s ***** error ff2n (0);
385s ***** error ff2n (-3);
385s ***** error ff2n (3+2i);
385s ***** error ff2n (Inf);
385s ***** error ff2n (NaN);
385s ***** test
385s  A = ff2n (3);
385s  assert (A, [0, 0, 0; 0, 0, 1; 0, 1, 0; 0, 1, 1; ...
385s              1, 0, 0; 1, 0, 1; 1, 1, 0; 1, 1, 1]);
385s ***** test
385s  A = ff2n (2);
385s  assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
385s 10 tests, 10 passed, 0 known failure, 0 skipped
385s [inst/trimmean.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/trimmean.m
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  out = trimmean (x, 10, [1, 2]);
385s  assert (out(1,1,1), 10.3889, 1e-4);
385s  assert (out(1,1,2), 29.6111, 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  out = trimmean (x, 10, 1);
385s  assert (out(:,:,1), [-17.6, 8, 13, 18]);
385s  assert (out(:,:,2), [23, 28, 33, 10.6]);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, 1);
385s  assert (out(:,:,1), [-23, 8, 13, 18]);
385s  assert (out(:,:,2), [23, 28, 33, 3.75]);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  out = trimmean (x, 10, 2);
385s  assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
385s  assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, 2);
385s  assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
385s  assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  out = trimmean (x, 10, [1, 2, 3]);
385s  assert (out, trimmean (x, 10, "all"));
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, [1, 2]);
385s  assert (out(1,1,1), 10.7647, 1e-4);
385s  assert (out(1,1,2), 29.1176, 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, [1, 3]);
385s  assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, [2, 3]);
385s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, [1, 2, 3]);
385s  assert (out, trimmean (x, 10, "all"));
385s ***** test
385s  x = reshape (1:40, [5, 4, 2]);
385s  x([3, 37]) = -100;
385s  x([4, 38]) = NaN;
385s  out = trimmean (x, 10, [2, 3, 5]);
385s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
385s ***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
385s ***** assert (trimmean ([], 10), NaN)
385s ***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
385s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
385s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
385s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
385s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
385s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
385s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
385s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
385s  trimmean ([1 2 3 4], 10, -1)
385s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
385s  trimmean ([1 2 3 4], 10, "floor", -1)
385s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
385s  trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
385s ***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
385s  trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
385s 26 tests, 26 passed, 0 known failure, 0 skipped
385s [inst/dist_wrap/icdf.m]
385s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/icdf.m
385s ***** shared p
385s  p = [0.05:0.05:0.5];
385s ***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
385s ***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
385s ***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
385s ***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
385s ***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
385s ***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
385s ***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
385s ***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
385s ***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
385s ***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
385s ***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
385s ***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
385s ***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
385s ***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
385s ***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
385s ***** assert (icdf ("exp", p, 5), expinv (p, 5))
385s ***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
385s ***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
385s ***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
385s ***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
385s ***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
385s ***** assert (icdf ("geo", p, 5), geoinv (p, 5))
385s ***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
385s ***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
385s ***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
385s ***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
385s ***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
385s ***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
385s ***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
385s ***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
385s ***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
385s ***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
385s ***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
385s ***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
385s ***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
385s ***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
385s ***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
385s ***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
385s ***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
385s ***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
385s ***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
385s ***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
385s ***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
385s ***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
385s ***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
385s ***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
385s ***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
385s ***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
386s ***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
386s ***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
386s ***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
387s ***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
387s ***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
387s ***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
387s ***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
387s ***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
387s ***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
387s ***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
387s ***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
388s ***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
389s ***** assert (icdf ("Student T", p, 5), tinv (p, 5))
389s ***** assert (icdf ("t", p, 5), tinv (p, 5))
389s ***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
389s ***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
389s ***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
389s ***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
389s ***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
389s ***** assert (icdf ("unid", p, 5), unidinv (p, 5))
389s ***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
389s ***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
389s ***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
394s ***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
399s ***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
399s ***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
399s ***** error<icdf: distribution NAME must be a char string.> icdf (1)
399s ***** error<icdf: distribution NAME must be a char string.> icdf ({"beta"})
399s ***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
399s ***** error<icdf: P must be numeric.> icdf ("beta", "text")
399s ***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
399s ***** error<icdf: distribution parameters must be numeric.> ...
399s  icdf ("Beta", p, "a", 2)
399s ***** error<icdf: distribution parameters must be numeric.> ...
399s  icdf ("Beta", p, 5, "")
399s ***** error<icdf: distribution parameters must be numeric.> ...
399s  icdf ("Beta", p, 5, {2})
399s ***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
399s ***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
399s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
399s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
399s 86 tests, 86 passed, 0 known failure, 0 skipped
399s [inst/dist_wrap/random.m]
399s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/random.m
399s ***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
399s ***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
399s ***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
399s ***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
399s ***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
399s ***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
399s ***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
399s ***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
399s ***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
399s ***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
399s ***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
399s ***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
399s ***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
399s ***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
399s ***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
399s ***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
399s ***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
399s ***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
399s ***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
399s ***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
399s ***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
399s ***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
399s ***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
399s ***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
399s ***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
399s ***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
399s ***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
399s ***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
399s ***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
399s ***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
399s ***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
399s ***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
399s ***** error<random: distribution NAME must be a char string.> random (1)
399s ***** error<random: distribution NAME must be a char string.> random ({"beta"})
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", "a", 2)
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", 5, "")
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", 5, {2})
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", "a", 2, 2, 10)
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", 5, "", 2, 10)
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", 5, {2}, 2, 10)
399s ***** error<random: distribution parameters must be numeric.> ...
399s  random ("Beta", 5, "", 2, 10)
399s ***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
399s ***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
399s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
399s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
399s 87 tests, 87 passed, 0 known failure, 0 skipped
399s [inst/dist_wrap/fitdist.m]
399s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/fitdist.m
399s ***** test
399s  x = betarnd (1, 1, 100, 1);
399s  pd = fitdist (x, "Beta");
399s  [phat, pci] = betafit (x);
399s  assert ([pd.a, pd.b], phat);
399s  assert (paramci (pd), pci);
399s ***** test
399s  x1 = betarnd (1, 1, 100, 1);
399s  x2 = betarnd (5, 2, 100, 1);
399s  pd = fitdist ([x1; x2], "Beta", "By", [ones(100, 1); 2*ones(100, 1)]);
399s  [phat, pci] = betafit (x1);
399s  assert ([pd{1}.a, pd{1}.b], phat);
399s  assert (paramci (pd{1}), pci);
399s  [phat, pci] = betafit (x2);
399s  assert ([pd{2}.a, pd{2}.b], phat);
399s  assert (paramci (pd{2}), pci);
399s ***** warning <fitdist: no data in group '2' to fit a 'Beta' distribution.> ...
399s  fitdist ([betarnd(1, 1, 100, 1); nan(100, 1)], "Beta", ...
399s           "By", [ones(100, 1); 2*ones(100, 1)]);
399s ***** test
399s  N = 1;
399s  x = binornd (N, 0.5, 100, 1);
399s  pd = fitdist (x, "binomial");
399s  [phat, pci] = binofit (sum (x), numel (x));
399s  assert ([pd.N, pd.p], [N, phat]);
399s  assert (paramci (pd), pci);
399s ***** test
399s  N = 3;
399s  x = binornd (N, 0.4, 100, 1);
399s  pd = fitdist (x, "binomial", "ntrials", N);
399s  [phat, pci] = binofit (sum (x), numel (x) * N);
399s  assert ([pd.N, pd.p], [N, phat]);
399s  assert (paramci (pd), pci);
399s ***** test
399s  N = 1;
399s  x1 = binornd (N, 0.5, 100, 1);
399s  x2 = binornd (N, 0.7, 100, 1);
399s  pd = fitdist ([x1; x2], "binomial", "By", [ones(100, 1); 2*ones(100, 1)]);
399s  [phat, pci] = binofit (sum (x1), numel (x1));
399s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
399s  assert (paramci (pd{1}), pci);
399s  [phat, pci] = binofit (sum (x2), numel (x2));
399s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
399s  assert (paramci (pd{2}), pci);
399s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
399s  fitdist ([binornd(1, 0.5, 100, 1); nan(100, 1)], "binomial", ...
399s           "By", [ones(100, 1); 2*ones(100, 1)]);
399s ***** test
399s  N = 5;
399s  x1 = binornd (N, 0.5, 100, 1);
399s  x2 = binornd (N, 0.8, 100, 1);
399s  pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
399s                "By", [ones(100, 1); 2*ones(100, 1)]);
399s  [phat, pci] = binofit (sum (x1), numel (x1) * N);
399s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
399s  assert (paramci (pd{1}), pci);
399s  [phat, pci] = binofit (sum (x2), numel (x2) * N);
399s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
399s  assert (paramci (pd{2}), pci);
399s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
399s  fitdist ([binornd(5, 0.5, 100, 1); nan(100, 1)], "binomial", "ntrials", 5, ...
399s           "By", [ones(100, 1); 2*ones(100, 1)]);
399s ***** test
399s  x = bisarnd (1, 1, 100, 1);
399s  pd = fitdist (x, "BirnbaumSaunders");
399s  [phat, pci] = bisafit (x);
399s  assert ([pd.beta, pd.gamma], phat);
399s  assert (paramci (pd), pci);
399s ***** test
399s  x1 = bisarnd (1, 1, 100, 1);
399s  x2 = bisarnd (5, 2, 100, 1);
399s  pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
399s  [phat, pci] = bisafit (x1);
399s  assert ([pd{1}.beta, pd{1}.gamma], phat);
399s  assert (paramci (pd{1}), pci);
399s  [phat, pci] = bisafit (x2);
399s  assert ([pd{2}.beta, pd{2}.gamma], phat);
399s  assert (paramci (pd{2}), pci);
400s ***** warning <fitdist: no data in group '2' to fit a 'bisa' distribution.> ...
400s  fitdist ([bisarnd(1, 1, 100, 1); nan(100, 1)], "bisa", ...
400s           "By", [ones(100, 1); 2*ones(100, 1)]);
400s ***** test
400s  x = burrrnd (1, 2, 1, 100, 1);
400s  pd = fitdist (x, "Burr");
400s  [phat, pci] = burrfit (x);
400s  assert ([pd.alpha, pd.c, pd.k], phat);
400s  assert (paramci (pd), pci);
400s ***** test
400s  rand ("seed", 4);   # for reproducibility
400s  x1 = burrrnd (1, 2, 1, 100, 1);
400s  rand ("seed", 3);   # for reproducibility
400s  x2 = burrrnd (1, 0.5, 2, 100, 1);
400s  pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
400s  [phat, pci] = burrfit (x1);
400s  assert ([pd{1}.alpha, pd{1}.c, pd{1}.k], phat);
400s  assert (paramci (pd{1}), pci);
400s  [phat, pci] = burrfit (x2);
400s  assert ([pd{2}.alpha, pd{2}.c, pd{2}.k], phat);
400s  assert (paramci (pd{2}), pci);
400s ***** warning <fitdist: no data in group '2' to fit a 'burr' distribution.> ...
400s  fitdist ([burrrnd(1, 2, 1, 100, 1); nan(100, 1)], "burr", ...
400s           "By", [ones(100, 1); 2*ones(100, 1)]);
401s ***** test
401s  x = exprnd (1, 100, 1);
401s  pd = fitdist (x, "exponential");
401s  [muhat, muci] = expfit (x);
401s  assert ([pd.mu], muhat);
401s  assert (paramci (pd), muci);
401s ***** test
401s  x1 = exprnd (1, 100, 1);
401s  x2 = exprnd (5, 100, 1);
401s  pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
401s  [muhat, muci] = expfit (x1);
401s  assert ([pd{1}.mu], muhat);
401s  assert (paramci (pd{1}), muci);
401s  [muhat, muci] = expfit (x2);
401s  assert ([pd{2}.mu], muhat);
401s  assert (paramci (pd{2}), muci);
401s ***** warning <fitdist: no data in group '2' to fit a 'exponential' distribution.> ...
401s  fitdist ([exprnd(1, 100, 1); nan(100, 1)], "exponential", ...
401s           "By", [ones(100, 1); 2*ones(100, 1)]);
401s ***** test
401s  x = evrnd (1, 1, 100, 1);
401s  pd = fitdist (x, "ev");
401s  [phat, pci] = evfit (x);
401s  assert ([pd.mu, pd.sigma], phat);
401s  assert (paramci (pd), pci);
401s ***** test
401s  x1 = evrnd (1, 1, 100, 1);
401s  x2 = evrnd (5, 2, 100, 1);
401s  pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
401s  [phat, pci] = evfit (x1);
401s  assert ([pd{1}.mu, pd{1}.sigma], phat);
401s  assert (paramci (pd{1}), pci);
401s  [phat, pci] = evfit (x2);
401s  assert ([pd{2}.mu, pd{2}.sigma], phat);
401s  assert (paramci (pd{2}), pci);
401s ***** warning <fitdist: no data in group '2' to fit a 'extremevalue' distribution.> ...
401s  fitdist ([evrnd(1, 1, 100, 1); nan(100, 1)], "extremevalue", ...
401s           "By", [ones(100, 1); 2*ones(100, 1)]);
401s ***** test
401s  x = gamrnd (1, 1, 100, 1);
401s  pd = fitdist (x, "Gamma");
401s  [phat, pci] = gamfit (x);
401s  assert ([pd.a, pd.b], phat);
401s  assert (paramci (pd), pci);
401s ***** test
401s  x1 = gamrnd (1, 1, 100, 1);
401s  x2 = gamrnd (5, 2, 100, 1);
401s  pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
401s  [phat, pci] = gamfit (x1);
401s  assert ([pd{1}.a, pd{1}.b], phat);
401s  assert (paramci (pd{1}), pci);
401s  [phat, pci] = gamfit (x2);
401s  assert ([pd{2}.a, pd{2}.b], phat);
401s  assert (paramci (pd{2}), pci);
401s ***** warning <fitdist: no data in group '2' to fit a 'Gamma' distribution.> ...
401s  fitdist ([gamrnd(1, 1, 100, 1); nan(100, 1)], "Gamma", ...
401s           "By", [ones(100, 1); 2*ones(100, 1)]);
401s ***** test
401s  rand ("seed", 4);   # for reproducibility
401s  x = gevrnd (-0.5, 1, 2, 1000, 1);
401s  pd = fitdist (x, "generalizedextremevalue");
401s  [phat, pci] = gevfit (x);
401s  assert ([pd.k, pd.sigma, pd.mu], phat);
401s  assert (paramci (pd), pci);
401s ***** test
401s  rand ("seed", 5);   # for reproducibility
401s  x1 = gevrnd (-0.5, 1, 2, 1000, 1);
401s  rand ("seed", 9);   # for reproducibility
401s  x2 = gevrnd (0, 1, -4, 1000, 1);
401s  pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
401s  [phat, pci] = gevfit (x1);
401s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.mu], phat);
401s  assert (paramci (pd{1}), pci);
401s  [phat, pci] = gevfit (x2);
401s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.mu], phat);
401s  assert (paramci (pd{2}), pci);
402s ***** warning <fitdist: no data in group '2' to fit a 'gev' distribution.> ...
402s  fitdist ([gevrnd(-0.5, 1, 2, 1000, 1); nan(1000, 1)], "gev", ...
402s           "By", [ones(1000, 1); 2*ones(1000, 1)]);
402s ***** test
402s  x = gprnd (1, 1, 1, 100, 1);
402s  pd = fitdist (x, "GeneralizedPareto");
402s  [phat, pci] = gpfit (x, 1);
402s  assert ([pd.k, pd.sigma, pd.theta], phat);
402s  assert (paramci (pd), pci);
402s ***** test
402s  x = gprnd (1, 1, 2, 100, 1);
402s  pd = fitdist (x, "GeneralizedPareto", "theta", 2);
402s  [phat, pci] = gpfit (x, 2);
402s  assert ([pd.k, pd.sigma, pd.theta], phat);
402s  assert (paramci (pd), pci);
402s ***** test
402s  x1 = gprnd (1, 1, 1, 100, 1);
402s  x2 = gprnd (0, 2, 1, 100, 1);
402s  pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
402s  [phat, pci] = gpfit (x1, 1);
402s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
402s  assert (paramci (pd{1}), pci);
402s  [phat, pci] = gpfit (x2, 1);
402s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
402s  assert (paramci (pd{2}), pci);
402s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
402s  fitdist ([gprnd(1, 1, 1, 100, 1); nan(100, 1)], "gp", ...
402s           "By", [ones(100, 1); 2*ones(100, 1)]);
402s ***** test
402s  x1 = gprnd (3, 2, 2, 100, 1);
402s  x2 = gprnd (2, 3, 2, 100, 1);
402s  pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
402s                "By", [ones(100,1); 2*ones(100,1)]);
402s  [phat, pci] = gpfit (x1, 2);
402s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
402s  assert (paramci (pd{1}), pci);
402s  [phat, pci] = gpfit (x2, 2);
402s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
402s  assert (paramci (pd{2}), pci);
402s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
402s  fitdist ([gprnd(3, 2, 2, 100, 1); nan(100, 1)], "gp", "theta", 2, ...
402s           "By", [ones(100, 1); 2*ones(100, 1)]);
402s ***** test
402s  x = hnrnd (0, 1, 100, 1);
402s  pd = fitdist (x, "HalfNormal");
402s  [phat, pci] = hnfit (x, 0);
402s  assert ([pd.mu, pd.sigma], phat);
402s  assert (paramci (pd), pci);
403s ***** test
403s  x = hnrnd (1, 1, 100, 1);
403s  pd = fitdist (x, "HalfNormal", "mu", 1);
403s  [phat, pci] = hnfit (x, 1);
403s  assert ([pd.mu, pd.sigma], phat);
403s  assert (paramci (pd), pci);
403s ***** test
403s  x1 = hnrnd (0, 1, 100, 1);
403s  x2 = hnrnd (0, 2, 100, 1);
403s  pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
403s  [phat, pci] = hnfit (x1, 0);
403s  assert ([pd{1}.mu, pd{1}.sigma], phat);
403s  assert (paramci (pd{1}), pci);
403s  [phat, pci] = hnfit (x2, 0);
403s  assert ([pd{2}.mu, pd{2}.sigma], phat);
403s  assert (paramci (pd{2}), pci);
403s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
403s  fitdist ([hnrnd(0, 1, 100, 1); nan(100, 1)], "HalfNormal", ...
403s           "By", [ones(100, 1); 2*ones(100, 1)]);
403s ***** test
403s  x1 = hnrnd (2, 1, 100, 1);
403s  x2 = hnrnd (2, 2, 100, 1);
403s  pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
403s                "By", [ones(100,1); 2*ones(100,1)]);
403s  [phat, pci] = hnfit (x1, 2);
403s  assert ([pd{1}.mu, pd{1}.sigma], phat);
403s  assert (paramci (pd{1}), pci);
403s  [phat, pci] = hnfit (x2, 2);
403s  assert ([pd{2}.mu, pd{2}.sigma], phat);
403s  assert (paramci (pd{2}), pci);
403s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
403s  fitdist ([hnrnd(2, 1, 100, 1); nan(100, 1)], "HalfNormal", "mu", 2, ...
403s           "By", [ones(100, 1); 2*ones(100, 1)]);
403s ***** test
403s  x = invgrnd (1, 1, 100, 1);
403s  pd = fitdist (x, "InverseGaussian");
403s  [phat, pci] = invgfit (x);
403s  assert ([pd.mu, pd.lambda], phat);
403s  assert (paramci (pd), pci);
403s ***** test
403s  x1 = invgrnd (1, 1, 100, 1);
403s  x2 = invgrnd (5, 2, 100, 1);
403s  pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
403s  [phat, pci] = invgfit (x1);
403s  assert ([pd{1}.mu, pd{1}.lambda], phat);
403s  assert (paramci (pd{1}), pci);
403s  [phat, pci] = invgfit (x2);
403s  assert ([pd{2}.mu, pd{2}.lambda], phat);
403s  assert (paramci (pd{2}), pci);
403s ***** warning <fitdist: no data in group '2' to fit a 'InverseGaussian' distribution.> ...
403s  fitdist ([invgrnd(1, 1, 100, 1); nan(100, 1)], "InverseGaussian", ...
403s           "By", [ones(100, 1); 2*ones(100, 1)]);
403s ***** test
403s  x = logirnd (1, 1, 100, 1);
403s  pd = fitdist (x, "logistic");
403s  [phat, pci] = logifit (x);
403s  assert ([pd.mu, pd.sigma], phat);
403s  assert (paramci (pd), pci);
403s ***** test
403s  x1 = logirnd (1, 1, 100, 1);
403s  x2 = logirnd (5, 2, 100, 1);
403s  pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
403s  [phat, pci] = logifit (x1);
403s  assert ([pd{1}.mu, pd{1}.sigma], phat);
403s  assert (paramci (pd{1}), pci);
403s  [phat, pci] = logifit (x2);
403s  assert ([pd{2}.mu, pd{2}.sigma], phat);
403s  assert (paramci (pd{2}), pci);
404s ***** warning <fitdist: no data in group '2' to fit a 'logistic' distribution.> ...
404s  fitdist ([logirnd(1, 1, 100, 1); nan(100, 1)], "logistic", ...
404s           "By", [ones(100, 1); 2*ones(100, 1)]);
404s ***** test
404s  x = loglrnd (1, 1, 100, 1);
404s  pd = fitdist (x, "loglogistic");
404s  [phat, pci] = loglfit (x);
404s  assert ([pd.mu, pd.sigma], phat);
404s  assert (paramci (pd), pci);
404s ***** test
404s  x1 = loglrnd (1, 1, 100, 1);
404s  x2 = loglrnd (5, 2, 100, 1);
404s  pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
404s  [phat, pci] = loglfit (x1);
404s  assert ([pd{1}.mu, pd{1}.sigma], phat);
404s  assert (paramci (pd{1}), pci);
404s  [phat, pci] = loglfit (x2);
404s  assert ([pd{2}.mu, pd{2}.sigma], phat);
404s  assert (paramci (pd{2}), pci);
404s ***** warning <fitdist: no data in group '2' to fit a 'loglogistic' distribution.> ...
404s  fitdist ([loglrnd(1, 1, 100, 1); nan(100, 1)], "loglogistic", ...
404s           "By", [ones(100, 1); 2*ones(100, 1)]);
404s ***** test
404s  x = lognrnd (1, 1, 100, 1);
404s  pd = fitdist (x, "lognormal");
404s  [phat, pci] = lognfit (x);
404s  assert ([pd.mu, pd.sigma], phat);
404s  assert (paramci (pd), pci);
404s ***** test
404s  x1 = lognrnd (1, 1, 100, 1);
404s  x2 = lognrnd (5, 2, 100, 1);
404s  pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
404s  [phat, pci] = lognfit (x1);
404s  assert ([pd{1}.mu, pd{1}.sigma], phat);
404s  assert (paramci (pd{1}), pci);
404s  [phat, pci] = lognfit (x2);
404s  assert ([pd{2}.mu, pd{2}.sigma], phat);
404s  assert (paramci (pd{2}), pci);
405s ***** warning <fitdist: no data in group '2' to fit a 'lognormal' distribution.> ...
405s  fitdist ([lognrnd(1, 1, 100, 1); nan(100, 1)], "lognormal", ...
405s           "By", [ones(100, 1); 2*ones(100, 1)]);
405s ***** test
405s  x = nakarnd (2, 0.5, 100, 1);
405s  pd = fitdist (x, "Nakagami");
405s  [phat, pci] = nakafit (x);
405s  assert ([pd.mu, pd.omega], phat);
405s  assert (paramci (pd), pci);
405s ***** test
405s  x1 = nakarnd (2, 0.5, 100, 1);
405s  x2 = nakarnd (5, 0.8, 100, 1);
405s  pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
405s  [phat, pci] = nakafit (x1);
405s  assert ([pd{1}.mu, pd{1}.omega], phat);
405s  assert (paramci (pd{1}), pci);
405s  [phat, pci] = nakafit (x2);
405s  assert ([pd{2}.mu, pd{2}.omega], phat);
405s  assert (paramci (pd{2}), pci);
405s ***** warning <fitdist: no data in group '2' to fit a 'Nakagami' distribution.> ...
405s  fitdist ([nakarnd(2, 0.5, 100, 1); nan(100, 1)], "Nakagami", ...
405s           "By", [ones(100, 1); 2*ones(100, 1)]);
405s ***** test
405s  randp ("seed", 123);
405s  randg ("seed", 321);
405s  x = nbinrnd (2, 0.5, 100, 1);
405s  pd = fitdist (x, "negativebinomial");
405s  [phat, pci] = nbinfit (x);
405s  assert ([pd.R, pd.P], phat);
405s  assert (paramci (pd), pci);
405s ***** test
405s  randp ("seed", 345);
405s  randg ("seed", 543);
405s  x1 = nbinrnd (2, 0.5, 100, 1);
405s  randp ("seed", 432);
405s  randg ("seed", 234);
405s  x2 = nbinrnd (5, 0.8, 100, 1);
405s  pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
405s  [phat, pci] = nbinfit (x1);
405s  assert ([pd{1}.R, pd{1}.P], phat);
405s  assert (paramci (pd{1}), pci);
405s  [phat, pci] = nbinfit (x2);
405s  assert ([pd{2}.R, pd{2}.P], phat);
405s  assert (paramci (pd{2}), pci);
405s ***** warning <fitdist: no data in group '2' to fit a 'nbin' distribution.> ...
405s  fitdist ([nbinrnd(2, 0.5, 100, 1); nan(100, 1)], "nbin", ...
405s           "By", [ones(100, 1); 2*ones(100, 1)]);
405s ***** test
405s  x = normrnd (1, 1, 100, 1);
405s  pd = fitdist (x, "normal");
405s  [muhat, sigmahat, muci, sigmaci] = normfit (x);
405s  assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
405s  assert (paramci (pd), [muci, sigmaci]);
405s ***** test
405s  x1 = normrnd (1, 1, 100, 1);
405s  x2 = normrnd (5, 2, 100, 1);
405s  pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
405s  [muhat, sigmahat, muci, sigmaci] = normfit (x1);
405s  assert ([pd{1}.mu, pd{1}.sigma], [muhat, sigmahat]);
405s  assert (paramci (pd{1}), [muci, sigmaci]);
405s  [muhat, sigmahat, muci, sigmaci] = normfit (x2);
405s  assert ([pd{2}.mu, pd{2}.sigma], [muhat, sigmahat]);
405s  assert (paramci (pd{2}), [muci, sigmaci]);
406s ***** warning <fitdist: no data in group '2' to fit a 'normal' distribution.> ...
406s  fitdist ([normrnd(1, 1, 100, 1); nan(100, 1)], "normal", ...
406s           "By", [ones(100, 1); 2*ones(100, 1)]);
406s ***** test
406s  x = poissrnd (1, 100, 1);
406s  pd = fitdist (x, "poisson");
406s  [phat, pci] = poissfit (x);
406s  assert (pd.lambda, phat);
406s  assert (paramci (pd), pci);
406s ***** test
406s  x1 = poissrnd (1, 100, 1);
406s  x2 = poissrnd (5, 100, 1);
406s  pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
406s  [phat, pci] = poissfit (x1);
406s  assert (pd{1}.lambda, phat);
406s  assert (paramci (pd{1}), pci);
406s  [phat, pci] = poissfit (x2);
406s  assert (pd{2}.lambda, phat);
406s  assert (paramci (pd{2}), pci);
406s ***** warning <fitdist: no data in group '2' to fit a 'poisson' distribution.> ...
406s  fitdist ([poissrnd(1, 100, 1); nan(100, 1)], "poisson", ...
406s           "By", [ones(100, 1); 2*ones(100, 1)]);
406s ***** test
406s  x = raylrnd (1, 100, 1);
406s  pd = fitdist (x, "rayleigh");
406s  [phat, pci] = raylfit (x);
406s  assert (pd.sigma, phat);
406s  assert (paramci (pd), pci);
406s ***** test
406s  x1 = raylrnd (1, 100, 1);
406s  x2 = raylrnd (5, 100, 1);
406s  pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
406s  [phat, pci] = raylfit (x1);
406s  assert (pd{1}.sigma, phat);
406s  assert (paramci (pd{1}), pci);
406s  [phat, pci] = raylfit (x2);
406s  assert (pd{2}.sigma, phat);
406s  assert (paramci (pd{2}), pci);
406s ***** warning <fitdist: no data in group '2' to fit a 'rayleigh' distribution.> ...
406s  fitdist ([raylrnd(1, 100, 1); nan(100, 1)], "rayleigh", ...
406s           "By", [ones(100, 1); 2*ones(100, 1)]);
406s ***** test
406s  x = ricernd (1, 1, 100, 1);
406s  pd = fitdist (x, "rician");
406s  [phat, pci] = ricefit (x);
406s  assert ([pd.s, pd.sigma], phat);
406s  assert (paramci (pd), pci);
406s ***** test
406s  x1 = ricernd (1, 1, 100, 1);
406s  x2 = ricernd (5, 2, 100, 1);
406s  pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
406s  [phat, pci] = ricefit (x1);
406s  assert ([pd{1}.s, pd{1}.sigma], phat);
406s  assert (paramci (pd{1}), pci);
406s  [phat, pci] = ricefit (x2);
406s  assert ([pd{2}.s, pd{2}.sigma], phat);
406s  assert (paramci (pd{2}), pci);
407s ***** warning <fitdist: no data in group '2' to fit a 'rician' distribution.> ...
407s  fitdist ([ricernd(1, 1, 100, 1); nan(100, 1)], "rician", ...
407s           "By", [ones(100, 1); 2*ones(100, 1)]);
407s ***** warning <fitdist: 'Stable' distribution not supported yet.> ...
407s  fitdist ([1 2 3 4 5], "Stable");
407s ***** test
407s  x = tlsrnd (0, 1, 1, 100, 1);
407s  pd = fitdist (x, "tlocationscale");
407s  [phat, pci] = tlsfit (x);
407s  assert ([pd.mu, pd.sigma, pd.nu], phat);
407s  assert (paramci (pd), pci);
407s ***** test
407s  x1 = tlsrnd (0, 1, 1, 100, 1);
407s  x2 = tlsrnd (5, 2, 1, 100, 1);
407s  pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
407s  [phat, pci] = tlsfit (x1);
407s  assert ([pd{1}.mu, pd{1}.sigma, pd{1}.nu], phat);
407s  assert (paramci (pd{1}), pci);
407s  [phat, pci] = tlsfit (x2);
407s  assert ([pd{2}.mu, pd{2}.sigma, pd{2}.nu], phat);
407s  assert (paramci (pd{2}), pci);
407s ***** warning <fitdist: no data in group '2' to fit a 'tlocationscale' distribution.> ...
408s  fitdist ([tlsrnd(0, 1, 1, 100, 1); nan(100, 1)], "tlocationscale", ...
408s           "By", [ones(100, 1); 2*ones(100, 1)]);
408s ***** test
408s  x = [1 2 3 4 5];
408s  pd = fitdist (x, "weibull");
408s  [phat, pci] = wblfit (x);
408s  assert ([pd.lambda, pd.k], phat);
408s  assert (paramci (pd), pci);
408s ***** test
408s  x = [1 2 3 4 5 6 7 8 9 10];
408s  pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
408s  [phat, pci] = wblfit (x(1:5));
408s  assert ([pd{1}.lambda, pd{1}.k], phat);
408s  assert (paramci (pd{1}), pci);
408s  [phat, pci] = wblfit (x(6:10));
408s  assert ([pd{2}.lambda, pd{2}.k], phat);
408s  assert (paramci (pd{2}), pci);
408s ***** warning <fitdist: no data in group '2' to fit a 'weibull' distribution.> ...
408s  fitdist ([1 2 3 4 5 NaN NaN NaN NaN NaN], "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
408s ***** error <fitdist: DISTNAME is required.> fitdist (1)
408s ***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
408s ***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
408s ***** error <fitdist: X must be a numeric vector of real values.> ...
408s  fitdist (ones (2), "normal")
408s ***** error <fitdist: X must be a numeric vector of real values.> ...
408s  fitdist ([i, 2, 3], "normal")
408s ***** error <fitdist: X must be a numeric vector of real values.> ...
408s  fitdist (["a", "s", "d"], "normal")
408s ***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
408s  fitdist ([1, 2, 3], "normal", "By")
408s ***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
408s  fitdist ([1, 2, 3], "normal", "By", [1, 2])
408s ***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
408s  fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
408s ***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
408s  fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
408s ***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
408s  fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
408s ***** error <fitdist: invalid value for 'alpha' argument.> ...
408s  fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
408s ***** error <fitdist: invalid value for 'alpha' argument.> ...
408s  fitdist ([1, 2, 3], "normal", "alpha", i)
408s ***** error <fitdist: invalid value for 'alpha' argument.> ...
408s  fitdist ([1, 2, 3], "normal", "alpha", -0.5)
408s ***** error <fitdist: invalid value for 'alpha' argument.> ...
408s  fitdist ([1, 2, 3], "normal", "alpha", 1.5)
408s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
408s  fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
408s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
408s  fitdist ([1, 2, 3], "normal", "ntrials", 0)
408s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
408s  fitdist ([1, 2, 3], "normal", "options", 0)
408s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
408s  fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
408s ***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
408s ***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
408s ***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
408s ***** error <fitdist: unknown parameter name.> ...
408s  fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
408s ***** error <fitdist: no data in X to fit a 'normal' distribution.> ...
408s  fitdist (nan (100,1), "normal");
408s ***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
408s  [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
408s ***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
408s  fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
408s ***** error <fitdist: invalid MU value for half-normal distribution.> ...
408s  fitdist ([1, 2, 3], "halfnormal", "mu", 2);
408s 103 tests, 103 passed, 0 known failure, 0 skipped
408s [inst/dist_wrap/makedist.m]
408s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/makedist.m
408s ***** test
408s  pd = makedist ("beta");
408s  assert (class (pd), "BetaDistribution");
408s  assert (pd.a, 1);
408s  assert (pd.b, 1);
408s ***** test
408s  pd = makedist ("beta", "a", 5);
408s  assert (pd.a, 5);
408s  assert (pd.b, 1);
408s ***** test
408s  pd = makedist ("beta", "b", 5);
408s  assert (pd.a, 1);
408s  assert (pd.b, 5);
408s ***** test
408s  pd = makedist ("beta", "a", 3, "b", 5);
408s  assert (pd.a, 3);
408s  assert (pd.b, 5);
408s ***** test
408s  pd = makedist ("binomial");
408s  assert (class (pd), "BinomialDistribution");
408s  assert (pd.N, 1);
408s  assert (pd.p, 0.5);
408s ***** test
408s  pd = makedist ("binomial", "N", 5);
408s  assert (pd.N, 5);
408s  assert (pd.p, 0.5);
408s ***** test
408s  pd = makedist ("binomial", "p", 0.2);
408s  assert (pd.N, 1);
408s  assert (pd.p, 0.2);
408s ***** test
408s  pd = makedist ("binomial", "N", 3, "p", 0.3);
408s  assert (pd.N, 3);
408s  assert (pd.p, 0.3);
408s ***** test
408s  pd = makedist ("birnbaumsaunders");
408s  assert (class (pd), "BirnbaumSaundersDistribution");
408s  assert (pd.beta, 1);
408s  assert (pd.gamma, 1);
408s ***** test
408s  pd = makedist ("birnbaumsaunders", "beta", 5);
408s  assert (pd.beta, 5);
408s  assert (pd.gamma, 1);
408s ***** test
408s  pd = makedist ("birnbaumsaunders", "gamma", 5);
408s  assert (pd.beta, 1);
408s  assert (pd.gamma, 5);
408s ***** test
408s  pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
408s  assert (pd.beta, 3);
408s  assert (pd.gamma, 5);
408s ***** test
408s  pd = makedist ("burr");
408s  assert (class (pd), "BurrDistribution");
408s  assert (pd.alpha, 1);
408s  assert (pd.c, 1);
408s  assert (pd.k, 1);
408s ***** test
408s  pd = makedist ("burr", "k", 5);
408s  assert (pd.alpha, 1);
408s  assert (pd.c, 1);
408s  assert (pd.k, 5);
408s ***** test
408s  pd = makedist ("burr", "c", 5);
408s  assert (pd.alpha, 1);
408s  assert (pd.c, 5);
408s  assert (pd.k, 1);
408s ***** test
408s  pd = makedist ("burr", "alpha", 3, "c", 5);
408s  assert (pd.alpha, 3);
408s  assert (pd.c, 5);
408s  assert (pd.k, 1);
408s ***** test
408s  pd = makedist ("burr", "k", 3, "c", 5);
408s  assert (pd.alpha, 1);
408s  assert (pd.c, 5);
408s  assert (pd.k, 3);
408s ***** test
408s  pd = makedist ("exponential");
408s  assert (class (pd), "ExponentialDistribution");
408s  assert (pd.mu, 1);
408s ***** test
408s  pd = makedist ("exponential", "mu", 5);
408s  assert (pd.mu, 5);
408s ***** test
408s  pd = makedist ("extremevalue");
408s  assert (class (pd), "ExtremeValueDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("extremevalue", "mu", 5);
408s  assert (class (pd), "ExtremeValueDistribution");
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("ev", "sigma", 5);
408s  assert (class (pd), "ExtremeValueDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("ev", "mu", -3, "sigma", 5);
408s  assert (class (pd), "ExtremeValueDistribution");
408s  assert (pd.mu, -3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("gamma");
408s  assert (class (pd), "GammaDistribution");
408s  assert (pd.a, 1);
408s  assert (pd.b, 1);
408s ***** test
408s  pd = makedist ("gamma", "a", 5);
408s  assert (pd.a, 5);
408s  assert (pd.b, 1);
408s ***** test
408s  pd = makedist ("gamma", "b", 5);
408s  assert (pd.a, 1);
408s  assert (pd.b, 5);
408s ***** test
408s  pd = makedist ("gamma", "a", 3, "b", 5);
408s  assert (pd.a, 3);
408s  assert (pd.b, 5);
408s ***** test
408s  pd = makedist ("GeneralizedExtremeValue");
408s  assert (class (pd), "GeneralizedExtremeValueDistribution");
408s  assert (pd.k, 0);
408s  assert (pd.sigma, 1);
408s  assert (pd.mu, 0);
408s ***** test
408s  pd = makedist ("GeneralizedExtremeValue", "k", 5);
408s  assert (pd.k, 5);
408s  assert (pd.sigma, 1);
408s  assert (pd.mu, 0);
408s ***** test
408s  pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
408s  assert (pd.k, 0);
408s  assert (pd.sigma, 5);
408s  assert (pd.mu, 0);
408s ***** test
408s  pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
408s  assert (pd.k, 3);
408s  assert (pd.sigma, 5);
408s  assert (pd.mu, 0);
408s ***** test
408s  pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
408s  assert (pd.k, 0);
408s  assert (pd.sigma, 5);
408s  assert (pd.mu, 3);
408s ***** test
408s  pd = makedist ("GeneralizedPareto");
408s  assert (class (pd), "GeneralizedParetoDistribution");
408s  assert (pd.k, 1);
408s  assert (pd.sigma, 1);
408s  assert (pd.theta, 1);
408s ***** test
408s  pd = makedist ("GeneralizedPareto", "k", 5);
408s  assert (pd.k, 5);
408s  assert (pd.sigma, 1);
408s  assert (pd.theta, 1);
408s ***** test
408s  pd = makedist ("GeneralizedPareto", "sigma", 5);
408s  assert (pd.k, 1);
408s  assert (pd.sigma, 5);
408s  assert (pd.theta, 1);
408s ***** test
408s  pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
408s  assert (pd.k, 3);
408s  assert (pd.sigma, 5);
408s  assert (pd.theta, 1);
408s ***** test
408s  pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
408s  assert (pd.k, 1);
408s  assert (pd.sigma, 5);
408s  assert (pd.theta, 3);
408s ***** test
408s  pd = makedist ("HalfNormal");
408s  assert (class (pd), "HalfNormalDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("HalfNormal", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("HalfNormal", "sigma", 5);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
408s  assert (pd.mu, 3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("InverseGaussian");
408s  assert (class (pd), "InverseGaussianDistribution");
408s  assert (pd.mu, 1);
408s  assert (pd.lambda, 1);
408s ***** test
408s  pd = makedist ("InverseGaussian", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.lambda, 1);
408s ***** test
408s  pd = makedist ("InverseGaussian", "lambda", 5);
408s  assert (pd.mu, 1);
408s  assert (pd.lambda, 5);
408s ***** test
408s  pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
408s  assert (pd.mu, 3);
408s  assert (pd.lambda, 5);
408s ***** test
408s  pd = makedist ("logistic");
408s  assert (class (pd), "LogisticDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("logistic", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("logistic", "sigma", 5);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("logistic", "mu", 3, "sigma", 5);
408s  assert (pd.mu, 3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("loglogistic");
408s  assert (class (pd), "LoglogisticDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("loglogistic", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("loglogistic", "sigma", 5);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
408s  assert (pd.mu, 3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("Lognormal");
408s  assert (class (pd), "LognormalDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Lognormal", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Lognormal", "sigma", 5);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
408s  assert (pd.mu, -3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("Loguniform");
408s  assert (class (pd), "LoguniformDistribution");
408s  assert (pd.Lower, 1);
408s  assert (pd.Upper, 4);
408s ***** test
408s  pd = makedist ("Loguniform", "Lower", 2);
408s  assert (pd.Lower, 2);
408s  assert (pd.Upper, 4);
408s ***** test
408s  pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
408s  assert (pd.Lower, 1);
408s  assert (pd.Upper, 3);
408s ***** test
408s  pd = makedist ("Multinomial");
408s  assert (class (pd), "MultinomialDistribution");
408s  assert (pd.Probabilities, [0.5, 0.5]);
408s ***** test
408s  pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
408s  assert (class (pd), "MultinomialDistribution");
408s  assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
408s ***** test
408s  pd = makedist ("Nakagami");
408s  assert (class (pd), "NakagamiDistribution");
408s  assert (pd.mu, 1);
408s  assert (pd.omega, 1);
408s ***** test
408s  pd = makedist ("Nakagami", "mu", 5);
408s  assert (class (pd), "NakagamiDistribution");
408s  assert (pd.mu, 5);
408s  assert (pd.omega, 1);
408s ***** test
408s  pd = makedist ("Nakagami", "omega", 0.3);
408s  assert (class (pd), "NakagamiDistribution");
408s  assert (pd.mu, 1);
408s  assert (pd.omega, 0.3);
408s ***** test
408s  pd = makedist ("NegativeBinomial");
408s  assert (class (pd), "NegativeBinomialDistribution");
408s  assert (pd.R, 1);
408s  assert (pd.P, 0.5);
408s ***** test
408s  pd = makedist ("NegativeBinomial", "R", 5);
408s  assert (class (pd), "NegativeBinomialDistribution");
408s  assert (pd.R, 5);
408s  assert (pd.P, 0.5);
408s ***** test
408s  pd = makedist ("NegativeBinomial", "p", 0.3);
408s  assert (class (pd), "NegativeBinomialDistribution");
408s  assert (pd.R, 1);
408s  assert (pd.P, 0.3);
408s ***** test
408s  pd = makedist ("Normal");
408s  assert (class (pd), "NormalDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Normal", "mu", 5);
408s  assert (class (pd), "NormalDistribution");
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Normal", "sigma", 5);
408s  assert (class (pd), "NormalDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("Normal", "mu", -3, "sigma", 5);
408s  assert (class (pd), "NormalDistribution");
408s  assert (pd.mu, -3);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("PiecewiseLinear");
408s  assert (class (pd), "PiecewiseLinearDistribution");
408s  assert (pd.x, [0; 1]);
408s  assert (pd.Fx, [0; 1]);
408s ***** test
408s  pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
408s  assert (pd.x, [0; 1; 2]);
408s  assert (pd.Fx, [0; 0.5; 1]);
408s ***** test
408s  pd = makedist ("Poisson");
408s  assert (class (pd), "PoissonDistribution");
408s  assert (pd.lambda, 1);
408s ***** test
408s  pd = makedist ("Poisson", "lambda", 5);
408s  assert (pd.lambda, 5);
408s ***** test
408s  pd = makedist ("Rayleigh");
408s  assert (class (pd), "RayleighDistribution");
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Rayleigh", "sigma", 5);
408s  assert (pd.sigma, 5);
408s ***** test
408s  pd = makedist ("Rician");
408s  assert (class (pd), "RicianDistribution");
408s  assert (pd.s, 1);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Rician", "s", 3);
408s  assert (pd.s, 3);
408s  assert (pd.sigma, 1);
408s ***** test
408s  pd = makedist ("Rician", "sigma", 3);
408s  assert (pd.s, 1);
408s  assert (pd.sigma, 3);
408s ***** test
408s  pd = makedist ("Rician", "s", 2, "sigma", 3);
408s  assert (pd.s, 2);
408s  assert (pd.sigma, 3);
408s ***** warning
408s  pd = makedist ("stable");
408s  assert (class (pd), "double");
408s  assert (isempty (pd), true);
408s ***** test
408s  pd = makedist ("tlocationscale");
408s  assert (class (pd), "tLocationScaleDistribution");
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 1);
408s  assert (pd.nu, 5);
408s ***** test
408s  pd = makedist ("tlocationscale", "mu", 5);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 1);
408s  assert (pd.nu, 5);
408s ***** test
408s  pd = makedist ("tlocationscale", "sigma", 2);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 2);
408s  assert (pd.nu, 5);
408s ***** test
408s  pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
408s  assert (pd.mu, 5);
408s  assert (pd.sigma, 2);
408s  assert (pd.nu, 5);
408s ***** test
408s  pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
408s  assert (pd.mu, 0);
408s  assert (pd.sigma, 2);
408s  assert (pd.nu, 1);
408s ***** test
408s  pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
408s  assert (pd.mu, -2);
408s  assert (pd.sigma, 3);
408s  assert (pd.nu, 1);
408s ***** test
408s  pd = makedist ("Triangular");
408s  assert (class (pd), "TriangularDistribution");
408s  assert (pd.A, 0);
408s  assert (pd.B, 0.5);
408s  assert (pd.C, 1);
408s ***** test
408s  pd = makedist ("Triangular", "A", -2);
408s  assert (pd.A, -2);
408s  assert (pd.B, 0.5);
408s  assert (pd.C, 1);
408s ***** test
408s  pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
408s  assert (pd.A, 0.5);
408s  assert (pd.B, 0.9);
408s  assert (pd.C, 1);
408s ***** test
408s  pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
408s  assert (pd.A, 1);
408s  assert (pd.B, 2);
408s  assert (pd.C, 5);
408s ***** test
408s  pd = makedist ("Uniform");
408s  assert (class (pd), "UniformDistribution");
408s  assert (pd.Lower, 0);
408s  assert (pd.Upper, 1);
408s ***** test
408s  pd = makedist ("Uniform", "Lower", -2);
408s  assert (pd.Lower, -2);
408s  assert (pd.Upper, 1);
408s ***** test
408s  pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
408s  assert (pd.Lower, 1);
408s  assert (pd.Upper, 3);
408s ***** test
408s  pd = makedist ("Weibull");
408s  assert (class (pd), "WeibullDistribution");
408s  assert (pd.lambda, 1);
408s  assert (pd.k, 1);
408s ***** test
408s  pd = makedist ("Weibull", "lambda", 3);
408s  assert (pd.lambda, 3);
408s  assert (pd.k, 1);
408s ***** test
408s  pd = makedist ("Weibull", "lambda", 3, "k", 2);
408s  assert (pd.lambda, 3);
408s  assert (pd.k, 2);
408s ***** error <makedist: DISTNAME must be a character vector.> makedist (1)
408s ***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
408s ***** error <makedist: unrecognized distribution name.> makedist ("some")
408s ***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
408s  makedist ("Beta", "a")
408s ***** error <makedist: unknown parameter for 'Beta' distribution.> ...
408s  makedist ("Beta", "a", 1, "Q", 23)
408s ***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
408s  makedist ("Binomial", "N", 1, "Q", 23)
408s ***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
408s  makedist ("BirnbaumSaunders", "N", 1)
408s ***** error <makedist: unknown parameter for 'Burr' distribution.> ...
408s  makedist ("Burr", "lambda", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
408s  makedist ("extremevalue", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
408s  makedist ("exponential", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
408s  makedist ("Gamma", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
408s  makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
408s  makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
408s  makedist ("HalfNormal", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
408s  makedist ("InverseGaussian", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
408s  makedist ("Logistic", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
408s  makedist ("Loglogistic", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
408s  makedist ("Lognormal", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
408s  makedist ("Loguniform", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
408s  makedist ("Multinomial", "k", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
408s  makedist ("Nakagami", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
408s  makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Normal' distribution.> ...
408s  makedist ("Normal", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
408s  makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
408s  makedist ("Poisson", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
408s  makedist ("Rayleigh", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Rician' distribution.> ...
408s  makedist ("Rician", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Stable' distribution.> ...
408s  makedist ("Stable", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
408s  makedist ("tLocationScale", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
408s  makedist ("Triangular", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
408s  makedist ("Uniform", "mu", 1, "sdfs", 34)
408s ***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
408s  makedist ("Weibull", "mu", 1, "sdfs", 34)
408s 131 tests, 131 passed, 0 known failure, 0 skipped
408s [inst/dist_wrap/cdf.m]
408s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/cdf.m
408s ***** shared x
408s  x = [1:5];
408s ***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
408s ***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
408s ***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
408s ***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
408s ***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
408s ***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
408s ***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
408s ***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
408s ***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
408s ***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
408s ***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
408s ***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
408s ***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
408s ***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
408s ***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
408s ***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
408s ***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
408s ***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
409s ***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
409s ***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
409s ***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
409s ***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
409s ***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
409s ***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
409s ***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
409s ***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
409s ***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
409s ***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
409s ***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
409s ***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
409s ***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
409s ***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
409s ***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
409s ***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
409s ***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
409s ***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
409s ***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
409s ***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
409s ***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
409s ***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
409s ***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
409s ***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
409s ***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
409s ***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
409s ***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
409s ***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
409s ***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
409s ***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
409s ***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
409s ***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
409s ***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
409s ***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
409s ***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
409s ***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
409s ***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
409s ***** error<cdf: distribution NAME must be a char string.> cdf (1)
409s ***** error<cdf: distribution NAME must be a char string.> cdf ({"beta"})
409s ***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
409s ***** error<cdf: X must be numeric.> cdf ("beta", "text")
409s ***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
409s ***** error<cdf: distribution parameters must be numeric.> ...
409s  cdf ("Beta", x, "a", 2)
409s ***** error<cdf: distribution parameters must be numeric.> ...
409s  cdf ("Beta", x, 5, "")
409s ***** error<cdf: distribution parameters must be numeric.> ...
409s  cdf ("Beta", x, 5, {2})
409s ***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
409s ***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
409s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
409s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
409s 86 tests, 86 passed, 0 known failure, 0 skipped
409s [inst/dist_wrap/pdf.m]
409s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/pdf.m
409s ***** shared x
409s  x = [1:5];
409s ***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
409s ***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
409s ***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
409s ***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
409s ***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
409s ***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
409s ***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
409s ***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
409s ***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
409s ***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
409s ***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
409s ***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
409s ***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
409s ***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
409s ***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
409s ***** assert (pdf ("exp", x, 5), exppdf (x, 5))
409s ***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
409s ***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
409s ***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
409s ***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
409s ***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
409s ***** assert (pdf ("geo", x, 5), geopdf (x, 5))
409s ***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
409s ***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
409s ***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
409s ***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
409s ***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
409s ***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
409s ***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
409s ***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
409s ***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
409s ***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
409s ***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
409s ***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
410s ***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
410s ***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
410s ***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
410s ***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
410s ***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
410s ***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
410s ***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
410s ***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
410s ***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
410s ***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
410s ***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
410s ***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
410s ***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
410s ***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
410s ***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
410s ***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
410s ***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
410s ***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
410s ***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
410s ***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
410s ***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
410s ***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
410s ***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
410s ***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
410s ***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
410s ***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
410s ***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
410s ***** assert (pdf ("t", x, 5), tpdf (x, 5))
410s ***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
410s ***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
410s ***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
410s ***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
410s ***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
410s ***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
410s ***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
410s ***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
410s ***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
410s ***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
410s ***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
410s ***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
410s ***** error<pdf: distribution NAME must a char string.> pdf (1)
410s ***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
410s ***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
410s ***** error<pdf: X must be numeric.> pdf ("beta", "text")
410s ***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
410s ***** error<pdf: distribution parameters must be numeric.> ...
410s  pdf ("Beta", x, "a", 2)
410s ***** error<pdf: distribution parameters must be numeric.> ...
410s  pdf ("Beta", x, 5, "")
410s ***** error<pdf: distribution parameters must be numeric.> ...
410s  pdf ("Beta", x, 5, {2})
410s ***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
410s ***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
410s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
410s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
410s 86 tests, 86 passed, 0 known failure, 0 skipped
410s [inst/dist_wrap/mle.m]
410s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_wrap/mle.m
410s ***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
410s ***** error <mle: X must be a numeric vector of real values.> mle ("text")
410s ***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
410s ***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
410s  mle ([1:50], "distribution")
410s ***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
410s  mle ([1:50], "censoring", logical ([1,0,1,0]))
410s ***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
410s  mle ([1:50], "frequency", [1,0,1,0])
410s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
410s  mle ([1 0 1 0], "frequency", [-1 1 0 0])
410s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
410s  mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
410s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
410s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
410s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
410s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
410s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
410s  mle ([1:50], "ntrials", -1)
410s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
410s  mle ([1:50], "ntrials", [20, 50])
410s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
410s  mle ([1:50], "ntrials", [20.3])
410s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
410s  mle ([1:50], "ntrials", 3i)
410s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
410s  mle ([1:50], "options", 4)
410s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
410s  mle ([1:50], "options", struct ("x", 3))
410s ***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
410s ***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
410s  mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
410s ***** error <mle: invalid data for the Bernoulli distribution.> ...
410s  mle ([1 2 1 0], "distribution", "bernoulli")
410s ***** error <mle: censoring is not supported for the Beta distribution.> ...
410s  mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Binomial distribution.> ...
410s  mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
410s ***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
410s  mle ([1 0 1 0], "distribution", "bino")
410s ***** error <mle: censoring is not supported for the Geometric distribution.> ...
410s  mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
410s  mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
410s  mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
410s ***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
410s  mle ([1 0 -1 0], "distribution", "gp")
410s ***** error <mle: censoring is not supported for the Half Normal distribution.> ...
410s  mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
410s ***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
410s  mle ([1 0 -1 0], "distribution", "hn")
410s ***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
410s  mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Poisson distribution.> ...
410s  mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
410s  mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
410s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
410s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
410s ***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
410s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
410s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
410s 36 tests, 36 passed, 0 known failure, 0 skipped
410s [inst/Classification/CompactClassificationGAM.m]
410s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/CompactClassificationGAM.m
410s ***** demo
410s  ## Create a generalized additive model classifier and its compact version
410s  # and compare their size
410s 
410s  load fisheriris
410s  X = meas;
410s  Y = species;
410s 
410s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
410s  CMdl = crossval (Mdl)
410s ***** test
410s  Mdl = CompactClassificationGAM ();
410s  assert (class (Mdl), "CompactClassificationGAM")
410s ***** test
410s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
410s  y = [0; 0; 1; 1];
410s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
410s  Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
410s  CMdl = compact (Mdl);
410s  assert (class (CMdl), "CompactClassificationGAM");
410s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
410s  assert (CMdl.ClassNames, {'0'; '1'})
410s  assert (CMdl.PredictorNames, PredictorNames)
410s  assert (CMdl.BaseModel.Intercept, 0)
411s ***** test
411s  load fisheriris
411s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
411s  X = meas(inds, :);
411s  Y = species(inds, :)';
411s  Y = strcmp (Y, 'virginica')';
411s  Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
411s  CMdl = compact (Mdl);
411s  assert (class (CMdl), "CompactClassificationGAM");
411s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
411s  assert (CMdl.ClassNames, {'0'; '1'})
411s  assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
411s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
411s  assert (CMdl.ModelwInt.Intercept, 0)
416s ***** test
416s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
416s  Y = [0; 1; 0; 1; 1];
416s  Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
416s  CMdl = compact (Mdl);
416s  assert (class (CMdl), "CompactClassificationGAM");
416s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
416s  assert (CMdl.ClassNames, {'0'; '1'})
416s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
416s  assert (CMdl.Knots, [4, 4, 4])
416s  assert (CMdl.Order, [3, 3, 3])
416s  assert (CMdl.DoF, [7, 7, 7])
416s  assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
417s ***** error<CompactClassificationGAM: invalid classification object.> ...
417s  CompactClassificationGAM (1)
417s ***** test
417s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
417s  y = [1; 0; 1; 0; 1];
417s  Mdl = fitcgam (x, y, "interactions", "all");
417s  CMdl = compact (Mdl);
417s  l = {'0'; '0'; '0'; '0'; '0'};
417s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
417s       0.4259, 0.5741; 0.3760, 0.6240];
417s  [labels, scores] = predict (CMdl, x);
417s  assert (class (CMdl), "CompactClassificationGAM");
417s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
417s  assert (CMdl.ClassNames, {'1'; '0'})
417s  assert (CMdl.PredictorNames, {'x1', 'x2'})
417s  assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
417s  assert (labels, l)
417s  assert (scores, s, 1e-1)
420s ***** test
420s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
420s  y = [0; 0; 1; 1];
420s  interactions = [false, true, false; true, false, true; false, true, false];
420s  Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
420s  CMdl = compact (Mdl);
420s  [label, score] = predict (CMdl, x, "includeinteractions", true);
420s  l = {'0'; '0'; '1'; '1'};
420s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
420s  assert (class (CMdl), "CompactClassificationGAM");
420s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
420s  assert (CMdl.ClassNames, {'0'; '1'})
420s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
420s  assert (CMdl.ModelwInt.Intercept, 0)
420s  assert (label, l)
420s  assert (score, s, 1e-1)
424s ***** shared CMdl
424s  Mdl = fitcgam (ones (4,2), ones (4,1));
424s  CMdl = compact (Mdl);
425s ***** error<CompactClassificationGAM.predict: too few input arguments.> ...
425s  predict (CMdl)
425s ***** error<CompactClassificationGAM.predict: XC is empty.> ...
425s  predict (CMdl, [])
425s ***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
425s  predict (CMdl, 1)
425s 10 tests, 10 passed, 0 known failure, 0 skipped
425s [inst/Classification/ConfusionMatrixChart.m]
425s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ConfusionMatrixChart.m
425s ***** demo
425s  ## Create a simple ConfusionMatrixChart Object
425s 
425s  cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
425s  NormalizedValues = cm.NormalizedValues
425s  ClassLabels = cm.ClassLabels
425s ***** test
425s  hf = figure ("visible", "off");
425s  unwind_protect
425s    cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
425s    assert (isa (cm, "ConfusionMatrixChart"), true);
425s  unwind_protect_cleanup
425s    close (hf);
425s  end_unwind_protect
425s 1 test, 1 passed, 0 known failure, 0 skipped
425s [inst/Classification/ClassificationDiscriminant.m]
425s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationDiscriminant.m
425s ***** demo
425s  ## Create discriminant classifier
425s  ## Evaluate some model predictions on new data.
425s 
425s  load fisheriris
425s  x = meas;
425s  y = species;
425s  xc = [min(x); mean(x); max(x)];
425s  obj = fitcdiscr (x, y);
425s  [label, score, cost] = predict (obj, xc);
425s ***** demo
425s  load fisheriris
425s  model = fitcdiscr (meas, species);
425s  X = mean (meas);
425s  Y = {'versicolor'};
425s  ## Compute loss for discriminant model
425s  L = loss (model, X, Y)
425s ***** demo
425s  load fisheriris
425s  mdl = fitcdiscr (meas, species);
425s  X = mean (meas);
425s  Y = {'versicolor'};
425s  ## Margin for discriminant model
425s  m = margin (mdl, X, Y)
425s ***** demo
425s  load fisheriris
425s  x = meas;
425s  y = species;
425s  obj = fitcdiscr (x, y, "gamma", 0.4);
425s  ## Cross-validation for discriminant model
425s  CVMdl = crossval (obj)
425s ***** test
425s  load fisheriris
425s  x = meas;
425s  y = species;
425s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
425s  Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
425s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
425s           0.092721, 0.115388, 0.055244, 0.032710; ...
425s           0.167514, 0.055244, 0.185188, 0.042665; ...
425s           0.038401, 0.032710, 0.042665, 0.041882];
425s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
425s        5.9360, 2.7700, 4.2600, 1.3260; ...
425s        6.5880, 2.9740, 5.5520, 2.0260];
425s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
425s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
425s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
425s  assert (class (Mdl), "ClassificationDiscriminant");
425s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
425s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
425s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
425s  assert (Mdl.ClassNames, unique (species))
425s  assert (Mdl.Sigma, sigma, 1e-6)
425s  assert (Mdl.Mu, mu, 1e-14)
425s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
425s  assert (Mdl.LogDetSigma, -9.9585, 1e-4)
425s  assert (Mdl.PredictorNames, PredictorNames)
425s ***** test
425s  load fisheriris
425s  x = meas;
425s  y = species;
425s  Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
425s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
425s           0.046361, 0.115388, 0.027622, 0.016355; ...
425s           0.083757, 0.027622, 0.185188, 0.021333; ...
425s           0.019201, 0.016355, 0.021333, 0.041882];
425s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
425s        5.9360, 2.7700, 4.2600, 1.3260; ...
425s        6.5880, 2.9740, 5.5520, 2.0260];
425s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
425s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
425s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
425s  assert (class (Mdl), "ClassificationDiscriminant");
425s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
425s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
425s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
425s  assert (Mdl.ClassNames, unique (species))
425s  assert (Mdl.Sigma, sigma, 1e-6)
425s  assert (Mdl.Mu, mu, 1e-14)
425s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
425s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
425s ***** shared X, Y, MODEL
425s  X = rand (10,2);
425s  Y = [ones(5,1);2*ones(5,1)];
425s  MODEL = ClassificationDiscriminant (X, Y);
425s ***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
425s ***** error<ClassificationDiscriminant: too few input arguments.> ...
425s  ClassificationDiscriminant (ones(4, 1))
425s ***** error<ClassificationDiscriminant: Name-Value arguments must be in pairs.> ...
425s  ClassificationDiscriminant (X, Y, "prior")
425s ***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
425s  ClassificationDiscriminant (ones (4,2), ones (1,4))
425s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
425s  ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
425s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
425s  ClassificationDiscriminant (X, Y, "PredictorNames", "A")
425s ***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
425s  ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
425s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
425s  ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
425s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
425s  ClassificationDiscriminant (X, Y, "ResponseName", 1)
426s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
426s  ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
426s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
426s  ClassificationDiscriminant (X, Y, "ClassNames", {1})
426s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
426s  ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
426s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
426s  ClassificationDiscriminant ([1;2;3;4;5], ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
426s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
426s  ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
426s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
426s  ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
426s ***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
426s  ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
426s ***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
426s  ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
426s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
426s  ClassificationDiscriminant (X, Y, "Cost", [1, 2])
426s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
426s  ClassificationDiscriminant (X, Y, "Cost", "string")
426s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
426s  ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
426s ***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
426s  ClassificationDiscriminant (X, Y, "Cost", ones (3))
426s ***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
426s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
426s ***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
426s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
426s ***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
426s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
426s ***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
426s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
426s ***** test
426s  load fisheriris
426s  x = meas;
426s  y = species;
426s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
426s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
426s  assert (label, {'versicolor'})
426s  assert (score, [0, 0.9999, 0.0001], 1e-4)
426s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
426s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
426s  assert (label, {'versicolor'})
426s  assert (score, [0, 0.6368, 0.3632], 1e-4)
426s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
426s ***** test
426s  load fisheriris
426s  x = meas;
426s  y = species;
426s  xc = [min(x); mean(x); max(x)];
426s  Mdl = fitcdiscr (x, y);
426s  [label, score, cost] = predict (Mdl, xc);
426s  l = {'setosa'; 'versicolor'; 'virginica'};
426s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
426s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
426s  assert (label, l)
426s  assert (score, s, 1e-4)
426s  assert (cost, c, 1e-4)
426s ***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
426s  predict (MODEL)
426s ***** error<ClassificationDiscriminant.predict: XC is empty.> ...
426s  predict (MODEL, [])
426s ***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
426s  predict (MODEL, 1)
426s ***** test
426s  load fisheriris
426s  model = fitcdiscr (meas, species);
426s  x = mean (meas);
426s  y = {'versicolor'};
426s  L = loss (model, x, y);
426s  assert (L, 0)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6];
426s  y = {'A'; 'B'; 'A'};
426s  model = fitcdiscr (x, y, "Gamma", 0.4);
426s  x_test = [1, 6; 3, 3];
426s  y_test = {'A'; 'B'};
426s  L = loss (model, x_test, y_test);
426s  assert (L, 0.3333, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6; 7, 8];
426s  y = ['1'; '2'; '3'; '1'];
426s  model = fitcdiscr (x, y, "gamma" , 0.5);
426s  x_test = [3, 3];
426s  y_test = ['1'];
426s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
426s  assert (L, 0.2423, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6; 7, 8];
426s  y = ['1'; '2'; '3'; '1'];
426s  model = fitcdiscr (x, y, "gamma" , 0.5);
426s  x_test = [3, 3; 5, 7];
426s  y_test = ['1'; '2'];
426s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
426s  assert (L, 0.3333, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6; 7, 8];
426s  y = ['1'; '2'; '3'; '1'];
426s  model = fitcdiscr (x, y, "gamma" , 0.5);
426s  x_test = [3, 3; 5, 7];
426s  y_test = ['1'; '2'];
426s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
426s  assert (L, 0.5886, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6; 7, 8];
426s  y = ['1'; '2'; '3'; '1'];
426s  model = fitcdiscr (x, y, "gamma" , 0.5);
426s  x_test = [3, 3; 5, 7];
426s  y_test = ['1'; '2'];
426s  W = [1; 2];
426s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
426s  assert (L, 0.5107, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6];
426s  y = {'A'; 'B'; 'A'};
426s  model = fitcdiscr (x, y, "gamma" , 0.5);
426s  x_with_nan = [1, 2; NaN, 4];
426s  y_test = {'A'; 'B'};
426s  L = loss (model, x_with_nan, y_test);
426s  assert (L, 0.3333, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6];
426s  y = {'A'; 'B'; 'A'};
426s  model = fitcdiscr (x, y);
426s  x_with_nan = [1, 2; NaN, 4];
426s  y_test = {'A'; 'B'};
426s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
426s  assert (isnan (L))
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6];
426s  y = {'A'; 'B'; 'A'};
426s  model = fitcdiscr (x, y);
426s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
426s  L = loss (model, x, y, 'LossFun', customLossFun);
426s  assert (L, 0.8889, 1e-4)
426s ***** test
426s  x = [1, 2; 3, 4; 5, 6];
426s  y = [1; 2; 1];
426s  model = fitcdiscr (x, y);
426s  L = loss (model, x, y, 'LossFun', 'classiferror');
426s  assert (L, 0.3333, 1e-4)
426s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
426s  loss (MODEL)
426s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
426s  loss (MODEL, ones (4,2))
426s ***** error<ClassificationDiscriminant.loss: X is empty.> ...
426s  loss (MODEL, [], zeros (2))
426s ***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
426s  loss (MODEL, 1, zeros (2))
426s ***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
426s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
426s ***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
426s  loss (MODEL, ones (4,2), ones (3,1))
426s ***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
426s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
426s ***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
426s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
426s  load fisheriris
426s  mdl = fitcdiscr (meas, species);
426s  X = mean (meas);
426s  Y = {'versicolor'};
426s  m = margin (mdl, X, Y);
426s  assert (m, 1, 1e-6)
426s ***** test
426s  X = [1, 2; 3, 4; 5, 6];
426s  Y = [1; 2; 1];
426s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
426s  m = margin (mdl, X, Y);
426s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
426s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
426s  margin (MODEL)
426s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
426s  margin (MODEL, ones (4,2))
426s ***** error<ClassificationDiscriminant.margin: X is empty.> ...
426s  margin (MODEL, [], zeros (2))
426s ***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
426s  margin (MODEL, 1, zeros (2))
426s ***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
426s  margin (MODEL, ones (4,2), ones (3,1))
426s ***** shared x, y, obj
426s  load fisheriris
426s  x = meas;
426s  y = species;
426s  obj = fitcdiscr (x, y, "gamma", 0.4);
426s ***** test
426s  CVMdl = crossval (obj);
426s  assert (class (CVMdl), "ClassificationPartitionedModel")
426s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
426s  assert (CVMdl.KFold == 10)
426s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
426s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
426s ***** test
426s  CVMdl = crossval (obj, "KFold", 3);
426s  assert (class (CVMdl), "ClassificationPartitionedModel")
426s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
426s  assert (CVMdl.KFold == 3)
426s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
426s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
426s ***** test
426s  CVMdl = crossval (obj, "HoldOut", 0.2);
426s  assert (class (CVMdl), "ClassificationPartitionedModel")
426s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
426s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
426s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
426s ***** test
426s  CVMdl = crossval (obj, "LeaveOut", 'on');
426s  assert (class (CVMdl), "ClassificationPartitionedModel")
426s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
426s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
426s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
428s ***** test
428s  partition = cvpartition (y, 'KFold', 3);
428s  CVMdl = crossval (obj, 'cvPartition', partition);
428s  assert (class (CVMdl), "ClassificationPartitionedModel")
428s  assert (CVMdl.KFold == 3)
428s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
428s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
428s ***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
428s  crossval (obj, "kfold")
428s ***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
428s  crossval (obj, "kfold", 12, "holdout", 0.2)
428s ***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
428s  crossval (obj, "kfold", 'a')
428s ***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
428s  crossval (obj, "holdout", 2)
428s ***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
428s  crossval (obj, "leaveout", 1)
428s ***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
428s  crossval (obj, "cvpartition", 1)
428s 67 tests, 67 passed, 0 known failure, 0 skipped
428s [inst/Classification/ClassificationPartitionedModel.m]
428s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationPartitionedModel.m
428s ***** demo
428s 
428s  load fisheriris
428s  x = meas;
428s  y = species;
428s 
428s  ## Create a KNN classifier model
428s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
428s 
428s  ## Create a partition for 5-fold cross-validation
428s  partition = cvpartition (y, "KFold", 5);
428s 
428s  ## Create the ClassificationPartitionedModel object
428s  cvModel = crossval (obj, 'cvPartition', partition)
428s ***** demo
428s 
428s  load fisheriris
428s  x = meas;
428s  y = species;
428s 
428s  ## Create a KNN classifier model
428s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
428s 
428s  ## Create the ClassificationPartitionedModel object
428s  cvModel = crossval (obj);
428s 
428s  ## Predict the class labels for the observations not used for training
428s  [label, score, cost] = kfoldPredict (cvModel);
428s  fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
428s           sum (strcmp (label, y)) / numel (y) *100, ...
428s           sum (strcmp (label, y)), numel (y))
428s ***** test
428s  load fisheriris
428s  a = fitcdiscr (meas, species, "gamma", 0.3);
428s  cvModel = crossval (a, "KFold", 5);
428s  assert (class (cvModel), "ClassificationPartitionedModel");
428s  assert (cvModel.NumObservations, 150);
428s  assert (numel (cvModel.Trained), 5);
428s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
428s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
428s  assert (cvModel.KFold, 5);
428s ***** test
428s  load fisheriris
428s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
428s  cvModel = crossval (a, "HoldOut", 0.3);
428s  assert (class (cvModel), "ClassificationPartitionedModel");
428s  assert ({cvModel.X, cvModel.Y}, {meas, species});
428s  assert (cvModel.NumObservations, 150);
428s  assert (numel (cvModel.Trained), 1);
428s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
428s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
428s ***** test
428s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
428s  y = ["a"; "a"; "b"; "b"];
428s  a = fitcgam (x, y, "Interactions", "all");
428s  cvModel = crossval (a, "KFold", 2);
428s  assert (class (cvModel), "ClassificationPartitionedModel");
428s  assert (cvModel.NumObservations, 4);
428s  assert (numel (cvModel.Trained), 2);
428s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
428s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
428s  assert (cvModel.KFold, 2);
440s ***** test
440s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
440s  y = ["a"; "a"; "b"; "b"];
440s  a = fitcgam (x, y);
440s  cvModel = crossval (a, "LeaveOut", "on");
440s  assert (class (cvModel), "ClassificationPartitionedModel");
440s  assert ({cvModel.X, cvModel.Y}, {x, y});
440s  assert (cvModel.NumObservations, 4);
440s  assert (numel (cvModel.Trained), 4);
440s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
440s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
447s ***** test
447s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
447s  y = ["a"; "a"; "b"; "b"];
447s  a = fitcknn (x, y);
447s  partition = cvpartition (y, "KFold", 2);
447s  cvModel = ClassificationPartitionedModel (a, partition);
447s  assert (class (cvModel), "ClassificationPartitionedModel");
447s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
447s  assert (cvModel.NumObservations, 4);
447s  assert (cvModel.ModelParameters.NumNeighbors, 1);
447s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
447s  assert (cvModel.ModelParameters.Distance, "euclidean");
447s  assert (! cvModel.ModelParameters.Standardize);
447s ***** test
447s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
447s  y = ["a"; "a"; "b"; "b"];
447s  a = fitcknn (x, y, "NSMethod", "exhaustive");
447s  partition = cvpartition (y, "HoldOut", 0.2);
447s  cvModel = ClassificationPartitionedModel (a, partition);
447s  assert (class (cvModel), "ClassificationPartitionedModel");
447s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
447s  assert ({cvModel.X, cvModel.Y}, {x, y});
447s  assert (cvModel.NumObservations, 4);
447s  assert (cvModel.ModelParameters.NumNeighbors, 1);
447s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
447s  assert (cvModel.ModelParameters.Distance, "euclidean");
447s  assert (! cvModel.ModelParameters.Standardize);
447s ***** test
447s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
447s  y = ["a"; "a"; "b"; "b"];
447s  k = 2;
447s  a = fitcknn (x, y, "NumNeighbors" ,k);
447s  partition = cvpartition (numel (y), "LeaveOut");
447s  cvModel = ClassificationPartitionedModel (a, partition);
447s  assert (class (cvModel), "ClassificationPartitionedModel");
447s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
447s  assert ({cvModel.X, cvModel.Y}, {x, y});
447s  assert (cvModel.NumObservations, 4);
447s  assert (cvModel.ModelParameters.NumNeighbors, k);
447s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
447s  assert (cvModel.ModelParameters.Distance, "euclidean");
447s  assert (! cvModel.ModelParameters.Standardize);
447s ***** test
447s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
447s  y = {"a"; "a"; "b"; "b"};
447s  a = fitcnet (x, y, "IterationLimit", 50);
447s  cvModel = crossval (a, "KFold", 2);
447s  assert (class (cvModel), "ClassificationPartitionedModel");
447s  assert (cvModel.NumObservations, 4);
447s  assert (numel (cvModel.Trained), 2);
447s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
447s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
447s  assert (cvModel.KFold, 2);
447s ***** test
447s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
447s  y = {"a"; "a"; "b"; "b"};
447s  a = fitcnet (x, y, "LayerSizes", [5, 3]);
447s  cvModel = crossval (a, "LeaveOut", "on");
447s  assert (class (cvModel), "ClassificationPartitionedModel");
447s  assert ({cvModel.X, cvModel.Y}, {x, y});
447s  assert (cvModel.NumObservations, 4);
447s  assert (numel (cvModel.Trained), 4);
447s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
447s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
447s ***** test
447s  load fisheriris
447s  inds = ! strcmp (species, 'setosa');
447s  x = meas(inds, 3:4);
447s  y = grp2idx (species(inds));
447s  SVMModel = fitcsvm (x,y);
447s  CVMdl = crossval (SVMModel, "KFold", 5);
447s  assert (class (CVMdl), "ClassificationPartitionedModel")
447s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
447s  assert (CVMdl.KFold == 5)
447s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
447s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
447s ***** test
447s  load fisheriris
447s  inds = ! strcmp (species, 'setosa');
447s  x = meas(inds, 3:4);
447s  y = grp2idx (species(inds));
447s  obj = fitcsvm (x, y);
447s  CVMdl = crossval (obj, "HoldOut", 0.2);
447s  assert (class (CVMdl), "ClassificationPartitionedModel")
447s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
447s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
447s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
447s ***** test
447s  load fisheriris
447s  inds = ! strcmp (species, 'setosa');
447s  x = meas(inds, 3:4);
447s  y = grp2idx (species(inds));
447s  obj = fitcsvm (x, y);
447s  CVMdl = crossval (obj, "LeaveOut", 'on');
447s  assert (class (CVMdl), "ClassificationPartitionedModel")
447s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
447s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
447s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
448s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
448s  ClassificationPartitionedModel ()
448s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
448s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
448s ***** error<ClassificationPartitionedModel: unsupported model type.> ...
448s  ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
448s  randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
448s ***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
448s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
448s  ones (4,1)), 'Holdout')
448s ***** test
448s  load fisheriris
448s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
448s  cvModel = crossval (a, "Kfold", 4);
448s  [label, score, cost] = kfoldPredict (cvModel);
448s  assert (class(cvModel), "ClassificationPartitionedModel");
448s  assert ({cvModel.X, cvModel.Y}, {meas, species});
448s  assert (cvModel.NumObservations, 150);
448s ***** # assert (label, {"b"; "b"; "a"; "a"});
448s ***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
448s ***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
448s ***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
448s ***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
448s ***** test
448s  x = ones(4, 11);
448s  y = {"a"; "a"; "b"; "b"};
448s  k = 3;
448s  a = fitcknn (x, y, "NumNeighbors", k);
448s  partition = cvpartition (numel (y), "LeaveOut");
448s  cvModel = ClassificationPartitionedModel (a, partition);
448s  [label, score, cost] = kfoldPredict (cvModel);
448s  assert (class(cvModel), "ClassificationPartitionedModel");
448s  assert ({cvModel.X, cvModel.Y}, {x, y});
448s  assert (cvModel.NumObservations, 4);
448s  assert (cvModel.ModelParameters.NumNeighbors, k);
448s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
448s  assert (cvModel.ModelParameters.Distance, "euclidean");
448s  assert (! cvModel.ModelParameters.Standardize);
448s  assert (label, {"b"; "b"; "a"; "a"});
448s  assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
448s           0.6667, 0.3333], 1e-4);
448s  assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
448s           0.3333, 0.6667], 1e-4);
448s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
448s  [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
448s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
448s  [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
450s 20 tests, 20 passed, 0 known failure, 0 skipped
450s [inst/Classification/ClassificationNeuralNetwork.m]
450s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationNeuralNetwork.m
450s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
450s  ClassificationNeuralNetwork ()
450s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
450s  ClassificationNeuralNetwork (ones(10,2))
450s ***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones (5,1))
450s ***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
450s  ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
450s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
450s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
450s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
450s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
450s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
450s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
450s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
450s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
450s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
450s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
450s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
450s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", {1})
450s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
450s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
450s  ClassificationNeuralNetwork (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
450s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
450s  ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
450s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
450s  ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
450s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
450s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
450s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
450s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
450s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
450s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
450s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
450s ***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
450s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
450s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
450s  "Activations", {"sigmoid", "unsupported_type"})
450s ***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
450s ***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
450s ***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
450s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
450s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
451s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
451s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
451s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
451s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
451s ***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
451s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
451s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
451s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
451s ***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
451s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
451s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
451s  ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
451s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
451s  ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
451s ***** shared x, y, objST, Mdl
451s  load fisheriris
451s  x = meas;
451s  y = grp2idx (species);
451s  Mdl = fitcnet (x, y, "IterationLimit", 100);
451s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
451s  Mdl.ScoreTransform = "a";
451s ***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
451s  predict (Mdl)
451s ***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
451s  predict (Mdl, [])
451s ***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
451s  predict (Mdl, 1)
451s ***** test
451s  CVMdl = crossval (Mdl, "KFold", 5);
451s  assert (class (CVMdl), "ClassificationPartitionedModel")
451s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
451s  assert (CVMdl.KFold == 5)
451s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
451s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
451s ***** test
451s  CVMdl = crossval (Mdl, "HoldOut", 0.2);
451s  assert (class (CVMdl), "ClassificationPartitionedModel")
451s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
451s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
451s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
451s ***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
451s  crossval (Mdl, "KFold")
451s ***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
451s  crossval (Mdl, "KFold", 5, "leaveout", 'on')
451s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
451s  crossval (Mdl, "KFold", 'a')
451s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
451s  crossval (Mdl, "KFold", 1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
451s  crossval (Mdl, "KFold", -1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
451s  crossval (Mdl, "KFold", 11.5)
451s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
451s  crossval (Mdl, "KFold", [1,2])
451s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
451s  crossval (Mdl, "Holdout", 'a')
451s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
451s  crossval (Mdl, "Holdout", 11.5)
451s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
451s  crossval (Mdl, "Holdout", -1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
451s  crossval (Mdl, "Holdout", 0)
451s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
451s  crossval (Mdl, "Holdout", 1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
451s  crossval (Mdl, "Leaveout", 1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
451s  crossval (Mdl, "CVPartition", 1)
451s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
451s  crossval (Mdl, "CVPartition", 'a')
451s ***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
451s  crossval (Mdl, "some", "some")
451s 58 tests, 58 passed, 0 known failure, 0 skipped
451s [inst/Classification/CompactClassificationSVM.m]
451s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/CompactClassificationSVM.m
451s ***** demo
451s  ## Create a support vectors machine classifier and its compact version
451s  # and compare their size
451s 
451s  load fisheriris
451s  X = meas;
451s  Y = species;
451s 
451s  Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
451s  CMdl = crossval (Mdl)
451s ***** error<CompactClassificationSVM: invalid classification object.> ...
451s  CompactClassificationSVM (1)
451s ***** shared x, y, CMdl
451s  load fisheriris
451s  inds = ! strcmp (species, 'setosa');
451s  x = meas(inds, 3:4);
451s  y = grp2idx (species(inds));
451s ***** test
451s  xc = [min(x); mean(x); max(x)];
451s  Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
451s  CMdl = compact (Mdl);
451s  assert (isempty (CMdl.Alpha), true)
451s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
451s  [label, score] = predict (CMdl, xc);
451s  assert (label, [1; 2; 2]);
451s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
451s  assert (score(:,1), -score(:,2), eps)
451s ***** test
451s  Mdl = fitcsvm (x, y);
451s  CMdl = compact (Mdl);
451s  assert (isempty (CMdl.Beta), true)
451s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
451s  assert (numel (CMdl.Alpha), 24)
451s  assert (CMdl.Bias, -14.415, 1e-3)
451s  xc = [min(x); mean(x); max(x)];
451s  label = predict (CMdl, xc);
451s  assert (label, [1; 2; 2]);
451s ***** error<CompactClassificationSVM.predict: too few input arguments.> ...
451s  predict (CMdl)
451s ***** error<CompactClassificationSVM.predict: XC is empty.> ...
451s  predict (CMdl, [])
451s ***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
451s  predict (CMdl, 1)
451s ***** test
451s  CMdl.ScoreTransform = "a";
451s ***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
451s  [labels, scores] = predict (CMdl, x);
451s ***** test
451s  rand ("seed", 1);
451s  C = cvpartition (y, 'HoldOut', 0.15);
451s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
451s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
451s  CMdl = compact (Mdl);
451s  testInds = test (C);
451s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
451s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
451s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
451s  computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
451s  assert (computed_margin, expected_margin, 1e-4);
451s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
451s  margin (CMdl)
451s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
451s  margin (CMdl, zeros (2))
451s ***** error<CompactClassificationSVM.margin: X is empty.> ...
451s  margin (CMdl, [], 1)
451s ***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
451s  margin (CMdl, 1, 1)
451s ***** error<CompactClassificationSVM.margin: Y is empty.> ...
451s  margin (CMdl, [1, 2], [])
451s ***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
451s  margin (CMdl, [1, 2], [1; 2])
451s ***** test
451s  rand ("seed", 1);
451s  C = cvpartition (y, 'HoldOut', 0.15);
451s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
451s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
451s  CMdl = compact (Mdl);
451s  testInds = test (C);
451s  L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
451s  L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
451s  L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
451s  L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
451s  L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
451s  L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
451s  assert (L1, 2.8711, 1e-4);
451s  assert (L2, 0.5333, 1e-4);
451s  assert (L3, 10.9685, 1e-4);
451s  assert (L4, 1.9827, 1e-4);
451s  assert (L5, 1.5849, 1e-4);
451s  assert (L6, 7.6739, 1e-4);
451s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
451s  loss (CMdl)
451s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
451s  loss (CMdl, zeros (2))
451s ***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
451s  loss (CMdl, [1, 2], 1, "LossFun")
451s ***** error<CompactClassificationSVM.loss: X is empty.> ...
451s  loss (CMdl, [], zeros (2))
451s ***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
451s  loss (CMdl, 1, zeros (2))
451s ***** error<CompactClassificationSVM.loss: Y is empty.> ...
451s  loss (CMdl, [1, 2], [])
451s ***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
451s  loss (CMdl, [1, 2], [1; 2])
451s ***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
451s  loss (CMdl, [1, 2], 1, "LossFun", 1)
451s ***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
451s  loss (CMdl, [1, 2], 1, "LossFun", "some")
451s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
451s  loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
451s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
451s  loss (CMdl, [1, 2], 1, "Weights", 'a')
451s ***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
451s  loss (CMdl, [1, 2], 1, "Weights", [1, 2])
451s ***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
451s  loss (CMdl, [1, 2], 1, "some", "some")
451s 29 tests, 29 passed, 0 known failure, 0 skipped
451s [inst/Classification/CompactClassificationNeuralNetwork.m]
451s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/CompactClassificationNeuralNetwork.m
451s ***** demo
451s  ## Create a neural network classifier and its compact version
451s  # and compare their size
451s 
451s  load fisheriris
451s  X = meas;
451s  Y = species;
451s 
451s  Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
451s  CMdl = crossval (Mdl)
451s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
451s  CompactClassificationDiscriminant (1)
451s ***** shared x, y, CMdl
451s  load fisheriris
451s  x = meas;
451s  y = grp2idx (species);
451s  Mdl = fitcnet (x, y, "IterationLimit", 100);
451s  CMdl = compact (Mdl);
451s ***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
451s  predict (CMdl)
451s ***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
451s  predict (CMdl, [])
451s ***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
451s  predict (CMdl, 1)
451s ***** error<CompactClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
451s  CMdl.ScoreTransform = "a";
451s 5 tests, 5 passed, 0 known failure, 0 skipped
451s [inst/Classification/CompactClassificationDiscriminant.m]
451s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/CompactClassificationDiscriminant.m
451s ***** demo
451s  ## Create a discriminant analysis classifier and its compact version
451s  # and compare their size
451s 
451s  load fisheriris
451s  X = meas;
451s  Y = species;
451s 
451s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
451s  CMdl = crossval (Mdl)
451s ***** test
451s  load fisheriris
451s  x = meas;
451s  y = species;
451s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
451s  Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
451s  CMdl = compact (Mdl);
451s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
451s           0.092721, 0.115388, 0.055244, 0.032710; ...
451s           0.167514, 0.055244, 0.185188, 0.042665; ...
451s           0.038401, 0.032710, 0.042665, 0.041882];
451s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
451s        5.9360, 2.7700, 4.2600, 1.3260; ...
451s        6.5880, 2.9740, 5.5520, 2.0260];
451s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
451s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
451s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
451s  assert (class (CMdl), "CompactClassificationDiscriminant");
451s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
451s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
451s  assert (CMdl.ClassNames, unique (species))
451s  assert (CMdl.Sigma, sigma, 1e-6)
451s  assert (CMdl.Mu, mu, 1e-14)
451s  assert (CMdl.LogDetSigma, -9.9585, 1e-4)
451s  assert (CMdl.PredictorNames, PredictorNames)
451s ***** test
451s  load fisheriris
451s  x = meas;
451s  y = species;
451s  Mdl = fitcdiscr (x, y, "Gamma", 0.5);
451s  CMdl = compact (Mdl);
451s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
451s           0.046361, 0.115388, 0.027622, 0.016355; ...
451s           0.083757, 0.027622, 0.185188, 0.021333; ...
451s           0.019201, 0.016355, 0.021333, 0.041882];
451s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
451s        5.9360, 2.7700, 4.2600, 1.3260; ...
451s        6.5880, 2.9740, 5.5520, 2.0260];
451s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
451s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
451s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
451s  assert (class (CMdl), "CompactClassificationDiscriminant");
451s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
451s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
451s  assert (CMdl.ClassNames, unique (species))
451s  assert (CMdl.Sigma, sigma, 1e-6)
451s  assert (CMdl.Mu, mu, 1e-14)
451s  assert (CMdl.LogDetSigma, -8.6884, 1e-4)
451s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
451s  CompactClassificationDiscriminant (1)
451s ***** test
451s  load fisheriris
451s  x = meas;
451s  y = species;
451s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
451s  CMdl = compact (Mdl);
451s  [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
451s  assert (label, {'versicolor'})
451s  assert (score, [0, 0.9999, 0.0001], 1e-4)
451s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
451s  [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
451s  assert (label, {'versicolor'})
451s  assert (score, [0, 0.6368, 0.3632], 1e-4)
451s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
452s ***** test
452s  load fisheriris
452s  x = meas;
452s  y = species;
452s  xc = [min(x); mean(x); max(x)];
452s  Mdl = fitcdiscr (x, y);
452s  CMdl = compact (Mdl);
452s  [label, score, cost] = predict (CMdl, xc);
452s  l = {'setosa'; 'versicolor'; 'virginica'};
452s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
452s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
452s  assert (label, l)
452s  assert (score, s, 1e-4)
452s  assert (cost, c, 1e-4)
452s ***** shared MODEL
452s  X = rand (10,2);
452s  Y = [ones(5,1);2*ones(5,1)];
452s  MODEL = compact (ClassificationDiscriminant (X, Y));
452s ***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
452s  predict (MODEL)
452s ***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
452s  predict (MODEL, [])
452s ***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
452s  predict (MODEL, 1)
452s ***** test
452s  load fisheriris
452s  model = fitcdiscr (meas, species);
452s  x = mean (meas);
452s  y = {'versicolor'};
452s  L = loss (model, x, y);
452s  assert (L, 0)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6];
452s  y = {'A'; 'B'; 'A'};
452s  model = fitcdiscr (x, y, "Gamma", 0.4);
452s  x_test = [1, 6; 3, 3];
452s  y_test = {'A'; 'B'};
452s  L = loss (model, x_test, y_test);
452s  assert (L, 0.3333, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6; 7, 8];
452s  y = ['1'; '2'; '3'; '1'];
452s  model = fitcdiscr (x, y, "gamma" , 0.5);
452s  x_test = [3, 3];
452s  y_test = ['1'];
452s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
452s  assert (L, 0.2423, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6; 7, 8];
452s  y = ['1'; '2'; '3'; '1'];
452s  model = fitcdiscr (x, y, "gamma" , 0.5);
452s  x_test = [3, 3; 5, 7];
452s  y_test = ['1'; '2'];
452s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
452s  assert (L, 0.3333, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6; 7, 8];
452s  y = ['1'; '2'; '3'; '1'];
452s  model = fitcdiscr (x, y, "gamma" , 0.5);
452s  x_test = [3, 3; 5, 7];
452s  y_test = ['1'; '2'];
452s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
452s  assert (L, 0.5886, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6; 7, 8];
452s  y = ['1'; '2'; '3'; '1'];
452s  model = fitcdiscr (x, y, "gamma" , 0.5);
452s  x_test = [3, 3; 5, 7];
452s  y_test = ['1'; '2'];
452s  W = [1; 2];
452s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
452s  assert (L, 0.5107, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6];
452s  y = {'A'; 'B'; 'A'};
452s  model = fitcdiscr (x, y, "gamma" , 0.5);
452s  x_with_nan = [1, 2; NaN, 4];
452s  y_test = {'A'; 'B'};
452s  L = loss (model, x_with_nan, y_test);
452s  assert (L, 0.3333, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6];
452s  y = {'A'; 'B'; 'A'};
452s  model = fitcdiscr (x, y);
452s  x_with_nan = [1, 2; NaN, 4];
452s  y_test = {'A'; 'B'};
452s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
452s  assert (isnan (L))
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6];
452s  y = {'A'; 'B'; 'A'};
452s  model = fitcdiscr (x, y);
452s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
452s  L = loss (model, x, y, 'LossFun', customLossFun);
452s  assert (L, 0.8889, 1e-4)
452s ***** test
452s  x = [1, 2; 3, 4; 5, 6];
452s  y = [1; 2; 1];
452s  model = fitcdiscr (x, y);
452s  L = loss (model, x, y, 'LossFun', 'classiferror');
452s  assert (L, 0.3333, 1e-4)
452s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
452s  loss (MODEL)
452s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
452s  loss (MODEL, ones (4,2))
452s ***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
452s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
452s ***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
452s  loss (MODEL, ones (4,2), ones (3,1))
452s ***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
452s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
452s ***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
452s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
452s  load fisheriris
452s  mdl = fitcdiscr (meas, species);
452s  X = mean (meas);
452s  Y = {'versicolor'};
452s  m = margin (mdl, X, Y);
452s  assert (m, 1, 1e-6)
452s ***** test
452s  X = [1, 2; 3, 4; 5, 6];
452s  Y = [1; 2; 1];
452s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
452s  m = margin (mdl, X, Y);
452s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
452s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
452s  margin (MODEL)
452s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
452s  margin (MODEL, ones (4,2))
452s ***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
452s  margin (MODEL, ones (4,2), ones (3,1))
452s 28 tests, 28 passed, 0 known failure, 0 skipped
452s [inst/Classification/ClassificationSVM.m]
452s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationSVM.m
452s ***** demo
452s  ## Create a Support Vector Machine classifier and determine margin for test
452s  ## data.
452s  load fisheriris
452s  rng(1);  ## For reproducibility
452s 
452s  ## Select indices of the non-setosa species
452s  inds = !strcmp(species, 'setosa');
452s 
452s   ## Select features and labels for non-setosa species
452s  X = meas(inds, 3:4);
452s  Y = grp2idx(species(inds));
452s 
452s  ##  Convert labels to +1 and -1
452s  unique_classes = unique(Y);
452s  Y(Y == unique_classes(1)) = -1;
452s  Y(Y == unique_classes(2)) = 1;
452s 
452s  ## Partition data for training and testing
452s  cv = cvpartition(Y, 'HoldOut', 0.15);
452s  X_train = X(training(cv), :);
452s  Y_train = Y(training(cv));
452s  X_test = X(test(cv), :);
452s  Y_test = Y(test(cv));
452s 
452s  ## Train the SVM model
452s  CVSVMModel = fitcsvm(X_train, Y_train);
452s 
452s  ## Calculate margins
452s  m = margin(CVSVMModel, X_test, Y_test);
452s  disp(m);
452s ***** demo
452s  ## Create a Support Vector Machine classifier and determine loss for test
452s  ## data.
452s  load fisheriris
452s  rng(1);  ## For reproducibility
452s 
452s   ## Select indices of the non-setosa species
452s  inds = !strcmp(species, 'setosa');
452s 
452s   ## Select features and labels for non-setosa species
452s  X = meas(inds, 3:4);
452s  Y = grp2idx(species(inds));
452s 
452s  ##  Convert labels to +1 and -1
452s  unique_classes = unique(Y);
452s  Y(Y == unique_classes(1)) = -1;
452s  Y(Y == unique_classes(2)) = 1;
452s 
452s  ## Randomly partition the data into training and testing sets
452s  cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training
452s 
452s  X_train = X(training(cv), :);
452s  Y_train = Y(training(cv));
452s 
452s  X_test = X(test(cv), :);
452s  Y_test = Y(test(cv));
452s 
452s  ## Train the SVM model
452s  SVMModel = fitcsvm(X_train, Y_train);
452s 
452s  ## Calculate loss
452s 
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
452s  L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
452s ***** test
452s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
452s  3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
452s  y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
452s  a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
452s  assert (class (a), "ClassificationSVM");
452s  assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
452s  assert ({a.X, a.Y}, {x, y})
452s  assert (a.NumObservations, 5)
452s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
452s  assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
452s ***** test
452s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
452s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
452s  a = ClassificationSVM (x, y);
452s  assert (class (a), "ClassificationSVM");
452s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
452s  assert (a.ModelParameters.BoxConstraint, 1)
452s  assert (a.ClassNames, [1; -1])
452s  assert (a.ModelParameters.KernelOffset, 0)
452s ***** test
452s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
452s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
452s  a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
452s  "KernelOffset", 2);
452s  assert (class (a), "ClassificationSVM");
452s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
452s  assert (a.ModelParameters.BoxConstraint, 2)
452s  assert (a.ModelParameters.KernelOffset, 2)
452s ***** test
452s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
452s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
452s  a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
452s  "PolynomialOrder", 3);
452s  assert (class (a), "ClassificationSVM");
452s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
452s  assert (a.ModelParameters.PolynomialOrder, 3)
452s ***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
452s ***** error<ClassificationSVM: too few input arguments.> ...
452s  ClassificationSVM (ones(10,2))
452s ***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
452s  ClassificationSVM (ones(10,2), ones (5,1))
452s ***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
452s ***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
452s ***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
452s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
452s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
452s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
452s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", {1})
452s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
452s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
452s  ClassificationSVM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
452s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
452s  ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
452s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
452s  ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
452s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
452s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
452s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
452s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
452s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
452s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
452s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
452s ***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
452s ***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
452s ***** error<ClassificationSVM: unsupported Kernel function.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
452s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
452s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
452s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
452s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
452s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
452s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
452s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
452s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
452s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
452s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
452s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
452s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
452s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
452s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
452s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
452s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
452s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
452s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
452s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
452s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
452s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
452s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
452s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
452s ***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
452s ***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
452s  ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
452s ***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
452s  ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
452s ***** error<ClassificationSVM: invalid values in X.> ...
452s  ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
452s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
452s  ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
452s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
452s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
452s ***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
452s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
452s ***** shared x, y, x_train, x_test, y_train, y_test, objST
452s  load fisheriris
452s  inds = ! strcmp (species, 'setosa');
452s  x = meas(inds, 3:4);
452s  y = grp2idx (species(inds));
452s ***** test
452s  xc = [min(x); mean(x); max(x)];
452s  obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
452s  assert (isempty (obj.Alpha), true)
452s  assert (sum (obj.IsSupportVector), numel (obj.Beta))
452s  [label, score] = predict (obj, xc);
452s  assert (label, [1; 2; 2]);
452s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
452s  assert (score(:,1), -score(:,2), eps)
452s  obj = fitPosterior (obj);
452s  [label, probs] = predict (obj, xc);
452s  assert (probs(:,2), [0.97555; 0.428164; 0.030385], 3e-2);
452s  assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
452s ***** test
452s  obj = fitcsvm (x, y);
452s  assert (isempty (obj.Beta), true)
452s  assert (sum (obj.IsSupportVector), numel (obj.Alpha))
452s  assert (numel (obj.Alpha), 24)
452s  assert (obj.Bias, -14.415, 1e-3)
452s  xc = [min(x); mean(x); max(x)];
452s  label = predict (obj, xc);
452s  assert (label, [1; 2; 2]);
452s ***** error<ClassificationSVM.predict: too few input arguments.> ...
452s  predict (ClassificationSVM (ones (40,2), ones (40,1)))
452s ***** error<ClassificationSVM.predict: XC is empty.> ...
452s  predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
452s ***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
452s  predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
452s ***** test
452s  objST = fitcsvm (x, y);
452s  objST.ScoreTransform = "a";
452s ***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
452s  [labels, scores] = predict (objST, x);
452s ***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
452s  [labels, scores] = resubPredict (objST);
452s ***** test
452s  rand ("seed", 1);
452s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
452s                        'Tolerance', 1e-7);
452s  obj = CVSVMModel.Trained{1};
452s  testInds = test (CVSVMModel.Partition);
452s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
452s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
452s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
452s  computed_margin = margin (obj, x(testInds,:), y(testInds,:));
452s  assert (computed_margin, expected_margin, 1e-4);
452s ***** error<ClassificationSVM.margin: too few input arguments.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
452s ***** error<ClassificationSVM.margin: too few input arguments.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
452s ***** error<ClassificationSVM.margin: X is empty.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
452s ***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
452s ***** error<ClassificationSVM.margin: Y is empty.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
452s ***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
452s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
452s ***** test
452s  rand ("seed", 1);
452s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
452s  obj = CVSVMModel.Trained{1};
452s  testInds = test (CVSVMModel.Partition);
452s  L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
452s  L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
452s  L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
452s  L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
452s  L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
452s  L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
452s  assert (L1, 2.8711, 1e-4);
452s  assert (L2, 0.5333, 1e-4);
452s  assert (L3, 10.9685, 1e-4);
452s  assert (L4, 1.9827, 1e-4);
452s  assert (L5, 1.5849, 1e-4);
452s  assert (L6, 7.6739, 1e-4);
452s ***** error<ClassificationSVM.loss: too few input arguments.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
452s ***** error<ClassificationSVM.loss: too few input arguments.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
452s ***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones(2,1), "LossFun")
452s ***** error<ClassificationSVM.loss: X is empty.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
452s ***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
452s ***** error<ClassificationSVM.loss: Y is empty.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
452s ***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
452s ***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "LossFun", 1)
452s ***** error<ClassificationSVM.loss: unsupported Loss function.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "LossFun", "some")
452s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "Weights", ['a','b'])
452s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "Weights", 'a')
452s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "Weights", [1,2,3])
452s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "Weights", 3)
452s ***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
452s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
452s  ones (2,1), "some", "some")
452s ***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
452s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
453s ***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
453s ***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
453s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
453s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
453s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
453s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
453s ***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
453s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
453s ***** test
453s  SVMModel = fitcsvm (x, y);
453s  CVMdl = crossval (SVMModel, "KFold", 5);
453s  assert (class (CVMdl), "ClassificationPartitionedModel")
453s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
453s  assert (CVMdl.KFold == 5)
453s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
453s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
453s ***** test
453s  obj = fitcsvm (x, y);
453s  CVMdl = crossval (obj, "HoldOut", 0.2);
453s  assert (class (CVMdl), "ClassificationPartitionedModel")
453s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
453s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
453s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
453s ***** test
453s  obj = fitcsvm (x, y);
453s  CVMdl = crossval (obj, "LeaveOut", 'on');
453s  assert (class (CVMdl), "ClassificationPartitionedModel")
453s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
453s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
453s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
453s ***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
453s ***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
453s  "KFold", 5, "leaveout", 'on')
453s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
453s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
453s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
453s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
453s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
453s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
453s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
453s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
454s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
454s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
454s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
454s ***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
454s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
454s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
454s ***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
454s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
454s 115 tests, 115 passed, 0 known failure, 0 skipped
454s [inst/Classification/ClassificationKNN.m]
454s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationKNN.m
454s ***** demo
454s  ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
454s  ## Evaluate some model predictions on new data.
454s 
454s  load fisheriris
454s  x = meas;
454s  y = species;
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
454s  [label, score, cost] = predict (obj, xc)
454s ***** demo
454s  load fisheriris
454s  x = meas;
454s  y = species;
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
454s 
454s  ## Create a cross-validated model
454s  CVMdl = crossval (obj)
454s ***** demo
454s  load fisheriris
454s  x = meas;
454s  y = species;
454s  covMatrix = cov (x);
454s 
454s  ## Fit the k-NN model using the 'mahalanobis' distance
454s  ## and the custom covariance matrix
454s  obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
454s  'Cov', covMatrix);
454s 
454s  ## Create a partition model using cvpartition
454s  Partition = cvpartition (size (x, 1), 'kfold', 12);
454s 
454s  ## Create cross-validated model using 'cvPartition' name-value argument
454s  CVMdl = crossval (obj, 'cvPartition', Partition)
454s 
454s  ## Access the trained model from first fold of cross-validation
454s  CVMdl.Trained{1}
454s ***** demo
454s  X = [1, 2; 3, 4; 5, 6];
454s  Y = {'A'; 'B'; 'A'};
454s  model = fitcknn (X, Y);
454s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
454s  ## Calculate loss using custom loss function
454s  L = loss (model, X, Y, 'LossFun', customLossFun)
454s ***** demo
454s  X = [1, 2; 3, 4; 5, 6];
454s  Y = {'A'; 'B'; 'A'};
454s  model = fitcknn (X, Y);
454s  ## Calculate loss using 'mincost' loss function
454s  L = loss (model, X, Y, 'LossFun', 'mincost')
454s ***** demo
454s  X = [1, 2; 3, 4; 5, 6];
454s  Y = ['1'; '2'; '3'];
454s  model = fitcknn (X, Y);
454s  X_test = [3, 3; 5, 7];
454s  Y_test = ['1'; '2'];
454s  ## Specify custom Weights
454s  W = [1; 2];
454s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
454s ***** demo
454s  load fisheriris
454s  mdl = fitcknn (meas, species);
454s  X = mean (meas);
454s  Y = {'versicolor'};
454s  m = margin (mdl, X, Y)
454s ***** demo
454s  X = [1, 2; 4, 5; 7, 8; 3, 2];
454s  Y = [2; 1; 3; 2];
454s  ## Train the model
454s  mdl = fitcknn (X, Y);
454s  ## Specify Vars and Labels
454s  Vars = 1;
454s  Labels = 2;
454s  ## Calculate partialDependence
454s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
454s ***** demo
454s  X = [1, 2; 4, 5; 7, 8; 3, 2];
454s  Y = [2; 1; 3; 2];
454s  ## Train the model
454s  mdl = fitcknn (X, Y);
454s  ## Specify Vars and Labels
454s  Vars = 1;
454s  Labels = 1;
454s  queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
454s  ## Calculate partialDependence using queryPoints
454s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
454s  queryPoints)
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y);
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  k = 10;
454s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = ones (4, 11);
454s  y = ["a"; "a"; "b"; "b"];
454s  k = 10;
454s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  k = 10;
454s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  k = 10;
454s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  weights = ones (4,1);
454s  a = ClassificationKNN (x, y, "Standardize", 1);
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.Standardize}, {true})
454s  assert ({a.Sigma}, {std(x, [], 1)})
454s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  weights = ones (4,1);
454s  a = ClassificationKNN (x, y, "Standardize", false);
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.Standardize}, {false})
454s  assert ({a.Sigma}, {[]})
454s  assert ({a.Mu}, {[]})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  s = ones (1, 3);
454s  a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.DistParameter}, {s})
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
454s  assert (class (a), "ClassificationKNN");
454s  assert (a.DistParameter, 5)
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
454s                         "NSMethod", "exhaustive");
454s  assert (class (a), "ClassificationKNN");
454s  assert (a.DistParameter, 5)
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
454s  assert (class (a), "ClassificationKNN");
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
454s  assert ({a.BucketSize}, {20})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y, "IncludeTies", true);
454s  assert (class (a), "ClassificationKNN");
454s  assert (a.IncludeTies, true);
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y);
454s  assert (class (a), "ClassificationKNN");
454s  assert (a.IncludeTies, false);
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  a = ClassificationKNN (x, y);
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Prior, [0.5; 0.5])
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  prior = [0.5; 0.5];
454s  a = ClassificationKNN (x, y, "Prior", "empirical");
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Prior, prior)
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "a"; "b"];
454s  prior = [0.75; 0.25];
454s  a = ClassificationKNN (x, y, "Prior", "empirical");
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Prior, prior)
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "a"; "b"];
454s  prior = [0.5; 0.5];
454s  a = ClassificationKNN (x, y, "Prior", "uniform");
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Prior, prior)
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  cost = eye (2);
454s  a = ClassificationKNN (x, y, "Cost", cost);
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Cost, [1, 0; 0, 1])
454s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
454s  y = ["a"; "a"; "b"; "b"];
454s  cost = eye (2);
454s  a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
454s  assert (class (a), "ClassificationKNN")
454s  assert (a.Cost, [1, 0; 0, 1])
454s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
454s  assert ({a.BucketSize}, {50})
454s ***** test
454s  x = [1, 2; 3, 4; 5,6; 5, 8];
454s  y = {'9'; '9'; '6'; '7'};
454s  a = ClassificationKNN (x, y);
454s  assert (a.Prior, [0.25; 0.25; 0.5])
454s ***** test
454s  load fisheriris
454s  x = meas;
454s  y = species;
454s  ClassNames = {'setosa', 'versicolor', 'virginica'};
454s  a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
454s  assert (a.ClassNames, ClassNames')
454s ***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
454s ***** error<ClassificationKNN: too few input arguments.> ...
454s  ClassificationKNN (ones(4, 1))
454s ***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
454s  ClassificationKNN (ones (4,2), ones (1,4))
454s ***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
454s  ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
454s ***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
454s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
454s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
454s ***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
454s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
454s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
454s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
454s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
454s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
454s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", {1})
454s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
454s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
454s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
454s  ClassificationKNN (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
454s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
454s  ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
454s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
454s  ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
454s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
454s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
454s ***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
454s ***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
454s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
454s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
454s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
454s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
454s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
454s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
454s ***** error<ClassificationKNN: unsupported distance metric.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
454s ***** error<ClassificationKNN: invalid function handle for distance metric.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
454s                     @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
454s ***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
454s                     @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
454s ***** error<ClassificationKNN: invalid distance metric.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
454s ***** error<ClassificationKNN: invalid distance metric.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
454s ***** error<ClassificationKNN: invalid distance metric.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
454s ***** error<ClassificationKNN: function handle for distance weight must return the> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
454s ***** error<ClassificationKNN: invalid distance weight.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
454s ***** error<ClassificationKNN: invalid distance weight.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
454s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
454s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
454s ***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
454s ***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
454s ***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
454s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
454s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
454s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
454s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
454s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
454s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
454s ***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
454s ***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
454s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
454s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
454s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
454s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
454s ***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
454s ***** error<ClassificationKNN: invalid values in X.> ...
454s  ClassificationKNN ([1;2;3;'a';4], ones (5,1))
454s ***** error<ClassificationKNN: invalid values in X.> ...
454s  ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
454s ***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
454s ***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
454s ***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
454s ***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
454s ***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
454s ***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
454s ***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
454s ***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
454s ***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
454s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
454s ***** shared x, y
454s  load fisheriris
454s  x = meas;
454s  y = species;
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 5);
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"; "versicolor"; "virginica"})
454s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
454s  assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"; "versicolor"; "virginica"})
454s  assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
454s  assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
454s  [l, s, c] = predict (obj, xc);
454s  assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
454s  assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"; "versicolor"; "virginica"})
454s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
454s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
454s ***** test
454s  xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
454s          5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
454s  obj = fitcknn (x, y, "NumNeighbors", 5);
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
454s  assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
454s  assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 5);
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 0.6, 0.4], 1e-4)
454s  assert (c, [1, 0.4, 0.6], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 0.5, 0.5], 1e-4)
454s  assert (c, [1, 0.5, 0.5], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"})
454s  assert (s, [0.9, 0.1, 0], 1e-4)
454s  assert (c, [0.1, 0.9, 1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
454s  assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"})
454s  assert (s, [0.8, 0.2, 0], 1e-4)
454s  assert (c, [0.2, 0.8, 1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 1, 0], 1e-4)
454s  assert (c, [1, 0, 1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 0.7, 0.3], 1e-4)
454s  assert (c, [1, 0.3, 0.7], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 0.6, 0.4], 1e-4)
454s  assert (c, [1, 0.4, 0.6], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"virginica"})
454s  assert (s, [0, 0.1, 0.9], 1e-4)
454s  assert (c, [1, 0.9, 0.1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"virginica"})
454s  assert (s, [0, 0.1, 0.9], 1e-4)
454s  assert (c, [1, 0.9, 0.1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"versicolor"})
454s  assert (s, [0, 1, 0], 1e-4)
454s  assert (c, [1, 0, 1], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"})
454s  assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
454s  assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
454s ***** test
454s  xc = [5, 3, 5, 1.45];
454s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"})
454s  assert (s, [0.8, 0.2, 0], 1e-4)
454s  assert (c, [0.2, 0.8, 1], 1e-4)
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa"; "versicolor"; "virginica"})
454s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
454s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
454s ***** test
454s  xc = [min(x); mean(x); max(x)];
454s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
454s  [l, s, c] = predict (obj, xc);
454s  assert (l, {"setosa";"setosa";"setosa"})
454s  assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
454s  assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
455s ***** error<ClassificationKNN.predict: too few input arguments.> ...
455s  predict (ClassificationKNN (ones (4,2), ones (4,1)))
455s ***** error<ClassificationKNN.predict: XC is empty.> ...
455s  predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
455s ***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
455s  predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
455s ***** test
455s  load fisheriris
455s  model = fitcknn (meas, species, 'NumNeighbors', 5);
455s  X = mean (meas);
455s  Y = {'versicolor'};
455s  L = loss (model, X, Y);
455s  assert (L, 0)
455s ***** test
455s  load fisheriris
455s  model = fitcknn (meas, species, 'NumNeighbors', 5);
455s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
455s  assert (L, 0.1413, 1e-4)
455s ***** test
455s  load fisheriris
455s  model = fitcknn (meas, species);
455s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
455s  assert (L, 0.1269, 1e-4)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = {'A'; 'B'; 'A'};
455s  model = fitcknn (X, Y);
455s  X_test = [1, 6; 3, 3];
455s  Y_test = {'A'; 'B'};
455s  L = loss (model, X_test, Y_test);
455s  assert (abs (L - 0.6667) > 1e-5)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = {'A'; 'B'; 'A'};
455s  model = fitcknn (X, Y);
455s  X_with_nan = [1, 2; NaN, 4];
455s  Y_test = {'A'; 'B'};
455s  L = loss (model, X_with_nan, Y_test);
455s  assert (abs (L - 0.3333) < 1e-4)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = {'A'; 'B'; 'A'};
455s  model = fitcknn (X, Y);
455s  X_with_nan = [1, 2; NaN, 4];
455s  Y_test = {'A'; 'B'};
455s  L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
455s  assert (isnan (L))
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = {'A'; 'B'; 'A'};
455s  model = fitcknn (X, Y);
455s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
455s  L = loss (model, X, Y, 'LossFun', customLossFun);
455s  assert (L, 0)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = [1; 2; 1];
455s  model = fitcknn (X, Y);
455s  L = loss (model, X, Y, 'LossFun', 'classiferror');
455s  assert (L, 0)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = [true; false; true];
455s  model = fitcknn (X, Y);
455s  L = loss (model, X, Y, 'LossFun', 'binodeviance');
455s  assert (abs (L - 0.1269) < 1e-4)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = ['1'; '2'; '1'];
455s  model = fitcknn (X, Y);
455s  L = loss (model, X, Y, 'LossFun', 'classiferror');
455s  assert (L, 0)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = ['1'; '2'; '3'];
455s  model = fitcknn (X, Y);
455s  X_test = [3, 3];
455s  Y_test = ['1'];
455s  L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
455s  assert (L, 1)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = ['1'; '2'; '3'];
455s  model = fitcknn (X, Y);
455s  X_test = [3, 3; 5, 7];
455s  Y_test = ['1'; '2'];
455s  L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
455s  assert (L, 1)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = ['1'; '2'; '3'];
455s  model = fitcknn (X, Y);
455s  X_test = [3, 3; 5, 7];
455s  Y_test = ['1'; '2'];
455s  L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
455s  assert (L, 1)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = ['1'; '2'; '3'];
455s  model = fitcknn (X, Y);
455s  X_test = [3, 3; 5, 7];
455s  Y_test = ['1'; '2'];
455s  W = [1; 2];
455s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
455s  assert (abs (L - 0.6931) < 1e-4)
455s ***** error<ClassificationKNN.loss: too few input arguments.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)))
455s ***** error<ClassificationKNN.loss: too few input arguments.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
455s ***** error<ClassificationKNN.loss: X is empty.> ...
455s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
455s ***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
455s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
455s ***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
455s         ones (4,1), 'LossFun')
455s ***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
455s ***** error<ClassificationKNN.loss: invalid loss function.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
455s         ones (4,1), 'LossFun', 'a')
455s ***** error<ClassificationKNN.loss: invalid Weights.> ...
455s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
455s         ones (4,1), 'Weights', 'w')
455s ***** test
455s  load fisheriris
455s  mdl = fitcknn (meas, species, 'NumNeighbors', 5);
455s  X = mean (meas);
455s  Y = {'versicolor'};
455s  m = margin (mdl, X, Y);
455s  assert (m, 1)
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = [1; 2; 3];
455s  mdl = fitcknn (X, Y);
455s  m = margin (mdl, X, Y);
455s  assert (m, [1; 1; 1])
455s ***** test
455s  X = [7, 8; 9, 10];
455s  Y = ['1'; '2'];
455s  mdl = fitcknn (X, Y);
455s  m = margin (mdl, X, Y);
455s  assert (m, [1; 1])
455s ***** test
455s  X = [11, 12];
455s  Y = {'1'};
455s  mdl = fitcknn (X, Y);
455s  m = margin (mdl, X, Y);
455s  assert (isnan (m))
455s ***** test
455s  X = [1, 2; 3, 4; 5, 6];
455s  Y = [1; 2; 3];
455s  mdl = fitcknn (X, Y);
455s  X1 = [15, 16];
455s  Y1 = [1];
455s  m = margin (mdl, X1, Y1);
455s  assert (m, -1)
455s ***** error<ClassificationKNN.margin: too few input arguments.> ...
455s  margin (ClassificationKNN (ones (4,2), ones (4,1)))
455s ***** error<ClassificationKNN.margin: too few input arguments.> ...
455s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
455s ***** error<ClassificationKNN.margin: X is empty.> ...
455s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
455s ***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
455s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
455s ***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
455s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
455s ***** shared X, Y, mdl
455s  X = [1, 2; 4, 5; 7, 8; 3, 2];
455s  Y = [2; 1; 3; 2];
455s  mdl = fitcknn (X, Y);
455s ***** test
455s  Vars = 1;
455s  Labels = 2;
455s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
455s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
455s  0.5000, 0.5000];
455s  assert (pd, pdm)
456s ***** test
456s  Vars = 1;
456s  Labels = 2;
456s  [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
456s  'NumObservationsToSample', 5);
456s  pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
456s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
456s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
456s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
456s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
456s  assert (abs (pdm - pd) < 1)
456s ***** test
456s  Vars = 1;
456s  Labels = 2;
456s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
456s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000];
456s  assert (pd, pdm)
456s ***** test
456s  Vars = [1, 2];
456s  Labels = 1;
456s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
456s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
456s                             queryPoints, 'UseParallel', true);
456s  pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
456s  assert (pd, pdm)
456s ***** test
456s  Vars = 1;
456s  Labels = [1; 2];
456s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
456s  pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
456s  0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
456s  0.5000, 0.5000, 0.5000];
456s  assert (pd, pdm)
457s ***** test
457s  Vars = [1, 2];
457s  Labels = [1; 2];
457s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
457s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
457s  pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
457s  pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
457s  pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
457s  assert (pd, pdm)
457s ***** test
457s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
457s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
457s  X = [X1, double(X2)];
457s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
457s  mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
457s  Vars = 1;
457s  Labels = 1;
457s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
457s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
457s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
457s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
457s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
457s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
457s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
457s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
457s  0.7500, 0.7500, 0.7500];
457s  assert (pd, pdm)
458s ***** test
458s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
458s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
458s  X = [X1, double(X2)];
458s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
458s  predictorNames = {'Feature1', 'Feature2'};
458s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
458s  Vars = 'Feature1';
458s  Labels = 1;
458s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
458s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
458s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
458s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
458s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
458s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
458s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
458s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
458s  0.7500, 0.7500, 0.7500];
458s  assert (pd, pdm)
458s ***** test
458s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
458s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
458s  X = [X1, double(X2)];
458s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
458s  predictorNames = {'Feature1', 'Feature2'};
458s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
458s  new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
458s  new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
458s  new_X = [new_X1, double(new_X2)];
458s  Vars = 'Feature1';
458s  Labels = 1;
458s  [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
458s  pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
458s  0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
458s  1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
458s  1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
458s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
458s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
458s  assert (pd, pdm)
459s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
459s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
459s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
459s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
459s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
459s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
459s  ones (4,1), 'NumObservationsToSample')
459s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
459s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
459s  ones (4,1), 2)
459s ***** shared x, y, obj
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  covMatrix = cov (x);
459s  obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
459s       'mahalanobis', 'Cov', covMatrix);
459s ***** test
459s  CVMdl = crossval (obj);
459s  assert (class (CVMdl), "ClassificationPartitionedModel")
459s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
459s  assert (CVMdl.KFold == 10)
459s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
459s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
459s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
459s  assert (!CVMdl.ModelParameters.Standardize)
459s ***** test
459s  CVMdl = crossval (obj, "KFold", 5);
459s  assert (class (CVMdl), "ClassificationPartitionedModel")
459s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
459s  assert (CVMdl.KFold == 5)
459s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
459s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
459s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
459s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
459s ***** test
459s  obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
459s  CVMdl = crossval (obj, "HoldOut", 0.2);
459s  assert (class (CVMdl), "ClassificationPartitionedModel")
459s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
459s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
459s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
459s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
459s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
459s ***** test
459s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
459s  CVMdl = crossval (obj, "LeaveOut", 'on');
459s  assert (class (CVMdl), "ClassificationPartitionedModel")
459s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
459s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
459s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
459s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
459s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
460s ***** test
460s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
460s  partition = cvpartition (y, 'KFold', 3);
460s  CVMdl = crossval (obj, 'cvPartition', partition);
460s  assert (class (CVMdl), "ClassificationPartitionedModel")
460s  assert (CVMdl.KFold == 3)
460s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
460s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
460s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
460s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
460s ***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
460s ***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
460s ***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
460s ***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
460s ***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
460s ***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
460s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
460s 165 tests, 165 passed, 0 known failure, 0 skipped
460s [inst/Classification/ClassificationGAM.m]
460s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/Classification/ClassificationGAM.m
460s ***** demo
460s  ## Train a GAM classifier for binary classification
460s  ## using specific data and plot the decision boundaries.
460s 
460s  ## Define specific data
460s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
460s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
460s  Y = [0; 0; 0; 0; 0; ...
460s      1; 1; 1; 1; 1];
460s 
460s  ## Train the GAM model
460s  obj = fitcgam (X, Y, "Interactions", "all")
460s 
460s  ## Create a grid of values for prediction
460s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
460s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
460s  [x1G, x2G] = meshgrid (x1, x2);
460s  XGrid = [x1G(:), x2G(:)];
460s  [labels, score] = predict (obj, XGrid);
460s ***** test
460s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
460s  y = [0; 0; 1; 1];
460s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
460s  a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
460s  assert (class (a), "ClassificationGAM");
460s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
460s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
460s  assert (a.ClassNames, {'0'; '1'})
460s  assert (a.PredictorNames, PredictorNames)
460s  assert (a.BaseModel.Intercept, 0)
461s ***** test
461s  load fisheriris
461s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
461s  X = meas(inds, :);
461s  Y = species(inds, :)';
461s  Y = strcmp (Y, 'virginica')';
461s  a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
461s  assert (class (a), "ClassificationGAM");
461s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
461s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
461s  assert (a.ClassNames, {'0'; '1'})
461s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
461s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
461s  assert (a.ModelwInt.Intercept, 0)
466s ***** test
466s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
466s  Y = [0; 1; 0; 1; 1];
466s  a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
466s  assert (class (a), "ClassificationGAM");
466s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
466s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
466s  assert (a.ClassNames, {'0'; '1'})
466s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
466s  assert (a.Knots, [4, 4, 4])
466s  assert (a.Order, [3, 3, 3])
466s  assert (a.DoF, [7, 7, 7])
466s  assert (a.BaseModel.Intercept, 0.4055, 1e-1)
468s ***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
468s ***** error<ClassificationGAM: too few input arguments.> ...
468s  ClassificationGAM (ones(4, 1))
468s ***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
468s  ClassificationGAM (ones (4,2), ones (1,4))
468s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
468s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
468s ***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
468s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
468s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
468s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
468s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
468s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
468s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", {1})
468s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
468s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
468s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
468s  ClassificationGAM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
468s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
468s  ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
468s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
468s  ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
468s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
468s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
468s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
468s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
468s ***** test
468s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
468s  y = [1; 0; 1; 0; 1];
468s  a = ClassificationGAM (x, y, "interactions", "all");
468s  l = {'0'; '0'; '0'; '0'; '0'};
468s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
468s       0.4259, 0.5741; 0.3760, 0.6240];
468s  [labels, scores] = predict (a, x);
468s  assert (class (a), "ClassificationGAM");
468s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
468s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
468s  assert (a.ClassNames, {'1'; '0'})
468s  assert (a.PredictorNames, {'x1', 'x2'})
468s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
468s  assert (labels, l)
468s  assert (scores, s, 1e-1)
470s ***** test
470s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
470s  y = [0; 0; 1; 1];
470s  interactions = [false, true, false; true, false, true; false, true, false];
470s  a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
470s  [label, score] = predict (a, x, "includeinteractions", true);
470s  l = {'0'; '0'; '1'; '1'};
470s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
470s  assert (class (a), "ClassificationGAM");
470s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
470s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
470s  assert (a.ClassNames, {'0'; '1'})
470s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
470s  assert (a.ModelwInt.Intercept, 0)
470s  assert (label, l)
470s  assert (score, s, 1e-1)
475s ***** error<ClassificationGAM.predict: too few input arguments.> ...
475s  predict (ClassificationGAM (ones (4,2), ones (4,1)))
475s ***** error<ClassificationGAM.predict: XC is empty.> ...
475s  predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
476s ***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
476s  predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
478s ***** shared x, y, obj
478s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6];
478s  y = [0; 0; 1; 1; 0];
478s  obj = fitcgam (x, y);
479s ***** test
479s  CVMdl = crossval (obj);
479s  assert (class (CVMdl), "ClassificationPartitionedModel")
479s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
479s  assert (CVMdl.KFold == 4)
479s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
479s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
479s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
479s warning: called from
479s     cvpartition at line 764 column 19
479s     crossval at line 792 column 9
479s     __test__ at line 3 column 2
479s     test at line 685 column 11
479s     /tmp/tmp.YdhB1UcfDH at line 3142 column 2
479s 
485s ***** test
485s  CVMdl = crossval (obj, "KFold", 2);
485s  assert (class (CVMdl), "ClassificationPartitionedModel")
485s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
485s  assert (CVMdl.KFold == 2)
485s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
485s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
488s ***** test
488s  CVMdl = crossval (obj, "HoldOut", 0.2);
488s  assert (class (CVMdl), "ClassificationPartitionedModel")
488s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
488s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
488s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
489s ***** test
489s  partition = cvpartition (y, 'KFold', 3);
489s  CVMdl = crossval (obj, 'cvPartition', partition);
489s  assert (class (CVMdl), "ClassificationPartitionedModel")
489s  assert (CVMdl.KFold == 3)
489s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
489s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
489s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
489s warning: called from
489s     cvpartition at line 764 column 19
489s     __test__ at line 3 column 2
489s     test at line 685 column 11
489s     /tmp/tmp.YdhB1UcfDH at line 3142 column 2
489s 
494s ***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
494s  crossval (obj, "kfold")
494s ***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
494s  crossval (obj, "kfold", 12, "holdout", 0.2)
494s ***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
494s  crossval (obj, "kfold", 'a')
494s ***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
494s  crossval (obj, "holdout", 2)
494s ***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
494s  crossval (obj, "leaveout", 1)
494s ***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
494s  crossval (obj, "cvpartition", 1)
494s 35 tests, 35 passed, 0 known failure, 0 skipped
494s [inst/createns.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/createns.m
494s ***** test
494s  ## Default ExhaustiveSearcher
494s  X = [1, 2; 3, 4; 5, 6];
494s  obj = createns (X);
494s  assert (isa (obj, "ExhaustiveSearcher"));
494s  assert (obj.X, X);
494s  assert (obj.Distance, "euclidean");
494s ***** test
494s  ## KDTreeSearcher with default parameters
494s  X = [1, 2; 3, 4; 5, 6];
494s  obj = createns (X, "NSMethod", "kdtree");
494s  assert (isa (obj, "KDTreeSearcher"));
494s  assert (obj.X, X);
494s  assert (obj.Distance, "euclidean");
494s ***** test
494s  ## hnswSearcher with custom parameters
494s  X = [1, 2; 3, 4; 5, 6];
494s  obj = createns (X, "NSMethod", "hnsw", "MaxNumLinksPerNode", 2, "TrainSetSize", 3);
494s  assert (isa (obj, "hnswSearcher"));
494s  assert (obj.X, X);
494s  assert (obj.MaxNumLinksPerNode, 2);
494s  assert (obj.TrainSetSize, 3);
494s ***** test
494s  ## ExhaustiveSearcher with custom distance
494s  X = [1, 2; 3, 4];
494s  obj = createns (X, "NSMethod", "exhaustive", "Distance", "cityblock");
494s  assert (isa (obj, "ExhaustiveSearcher"));
494s  assert (obj.Distance, "cityblock");
494s ***** error<createns: too few input arguments.>
494s  createns ()
494s ***** error<createns: Name-Value arguments must be in pairs.>
494s  X = [1, 2; 3, 4]; createns (X, "NSMethod")
494s ***** error<createns: X must be a finite numeric matrix.>
494s  createns ([1; Inf; 3])
494s ***** error<createns: 'NSMethod' must be a string.>
494s  X = [1, 2; 3, 4]; createns (X, "NSMethod", 1)
494s ***** error<createns: invalid 'NSMethod' value: 'invalid'.>
494s  X = [1, 2; 3, 4]; createns (X, "NSMethod", "invalid")
494s 9 tests, 9 passed, 0 known failure, 0 skipped
494s [inst/rmmissing.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/rmmissing.m
494s ***** assert (rmmissing ([1,NaN,3]), [1,3])
494s ***** assert (rmmissing ('abcd f'), 'abcdf')
494s ***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
494s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
494s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
494s ***** assert (rmmissing ([1,2;NaN,2]), [1,2])
494s ***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
494s ***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
494s ***** test
494s  x = [1:6];
494s  x([2,4]) = NaN;
494s  [~, idx] = rmmissing (x);
494s  assert (idx, logical ([0, 1, 0, 1, 0, 0]));
494s  assert (class(idx), 'logical');
494s  x = reshape (x, [2, 3]);
494s  [~, idx] = rmmissing (x);
494s  assert (idx, logical ([0; 1]));
494s  assert (class(idx), 'logical');
494s  [~, idx] = rmmissing (x, 2);
494s  assert (idx, logical ([1, 1, 0]));
494s  assert (class(idx), 'logical');
494s  [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
494s  assert (idx, logical ([0; 1]));
494s  assert (class(idx), 'logical');
494s  [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
494s  assert (idx, logical ([0, 0, 0]));
494s  assert (class(idx), 'logical');
494s ***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
494s ***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
494s ***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
494s ***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
494s ***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
494s ***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
494s ***** assert (rmmissing ([]), [])
494s ***** assert (rmmissing (ones (1,0)), ones (1,0))
494s ***** assert (rmmissing (ones (1,0), 1), ones (1,0))
494s ***** assert (rmmissing (ones (1,0), 2), ones (1,0))
494s ***** assert (rmmissing (ones (0,1)), ones (0,1))
494s ***** assert (rmmissing (ones (0,1), 1), ones (0,1))
494s ***** assert (rmmissing (ones (0,1), 2), ones (0,1))
494s ***** error <input dimension> rmmissing (ones (0,1,2))
494s ***** error rmmissing ()
494s ***** error <input dimension> rmmissing (ones(2,2,2))
494s ***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
494s ***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
494s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
494s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
494s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
494s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
494s 31 tests, 31 passed, 0 known failure, 0 skipped
494s [inst/cdfcalc.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/cdfcalc.m
494s ***** test
494s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
494s  [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
494s  assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
494s  assert (xCDF, [2, 3, 4, 5, 6]');
494s  assert (n, 10);
494s ***** shared x
494s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
494s ***** error yCDF = cdfcalc (x);
494s ***** error [yCDF, xCDF] = cdfcalc ();
494s ***** error [yCDF, xCDF] = cdfcalc (x, x);
494s ***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
494s 5 tests, 5 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/burrlike.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/burrlike.m
494s ***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
494s ***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
494s ***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
494s ***** error<burrlike: PARAMS must be a three-element vector.> ...
494s  burrlike ([1, 2], [1, 3, 5, 7])
494s ***** error<burrlike: PARAMS must be a three-element vector.> ...
494s  burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
494s ***** error<burrlike: X and CENSOR vector mismatch.> ...
494s  burrlike ([1, 2, 3], [1:5], [0, 0, 0])
494s ***** error<burrlike: X and FREQ vector mismatch.> ...
494s  burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
494s ***** error<burrlike: X and FREQ vector mismatch.> ...
494s  burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
494s 8 tests, 8 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/hnfit.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/hnfit.m
494s ***** demo
494s  ## Sample 2 populations from different half-normal distributions
494s  rand ("seed", 1);   # for reproducibility
494s  r1 = hnrnd (0, 5, 5000, 1);
494s  rand ("seed", 2);   # for reproducibility
494s  r2 = hnrnd (0, 2, 5000, 1);
494s  r = [r1, r2];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, [0.5:20], 1);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  hold on
494s 
494s  ## Estimate their shape parameters
494s  mu_sigmaA = hnfit (r(:,1), 0);
494s  mu_sigmaB = hnfit (r(:,2), 0);
494s 
494s  ## Plot their estimated PDFs
494s  x = [0:0.2:10];
494s  y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
494s  plot (x, y, "-pr");
494s  y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
494s  plot (x, y, "-sg");
494s  xlim ([0, 10])
494s  ylim ([0, 0.5])
494s  legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
494s           "Normalized HIST of sample 2 with μ=0 and σ=2", ...
494s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
494s                   mu_sigmaA(1), mu_sigmaA(2)), ...
494s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
494s                   mu_sigmaB(1), mu_sigmaB(2))})
494s  title ("Two population samples from different half-normal distributions")
494s  hold off
494s ***** test
494s  x = 1:20;
494s  [paramhat, paramci] = hnfit (x, 0);
494s  assert (paramhat, [0, 11.9791], 1e-4);
494s  assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
494s ***** test
494s  x = 1:20;
494s  [paramhat, paramci] = hnfit (x, 0, 0.01);
494s  assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
494s ***** error<hnfit: function called with too few input arguments.> hnfit ()
494s ***** error<hnfit: function called with too few input arguments.> hnfit (1)
494s ***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
494s ***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
494s ***** error<hnfit: MU must be a real scalar value.> ...
494s  hnfit ([0.5, 1.2], [0, 1]);
494s ***** error<hnfit: MU must be a real scalar value.> ...
494s  hnfit ([0.5, 1.2], 5+i);
494s ***** error<hnfit: X cannot contain values less than MU.> ...
494s  hnfit ([1:5], 2);
494s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
494s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
494s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
494s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
494s ***** error<hnfit: X and FREQ vectors mismatch.>
494s  hnfit ([1 2 3], 0, [], [1 5])
494s ***** error<hnfit: FREQ must not contain negative values.>
494s  hnfit ([1 2 3], 0, [], [1 5 -1])
494s 15 tests, 15 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/evlike.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/evlike.m
494s ***** test
494s  x = 1:50;
494s  [nlogL, acov] = evlike ([2.3, 1.2], x);
494s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
494s  assert (nlogL, 3.242264755689906e+17, 1e-14);
494s  assert (acov, avar_out, 1e-3);
494s ***** test
494s  x = 1:50;
494s  [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
494s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
494s  assert (nlogL, 481898704.0472211, 1e-6);
494s  assert (acov, avar_out, 1e-3);
494s ***** test
494s  x = 1:50;
494s  [nlogL, acov] = evlike ([21, 15], x);
494s  avar_out = [11.73913876598908, -5.9546128523121216; ...
494s              -5.954612852312121, 3.708060045170236];
494s  assert (nlogL, 223.7612479380652, 1e-13);
494s  assert (acov, avar_out, 1e-14);
494s ***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
494s ***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
494s ***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
494s ***** error<evlike: X and CENSOR vectors mismatch.> ...
494s  evlike ([12, 15], [1:50], [1, 2, 3])
494s ***** error<evlike: X and FREQ vectors mismatch.> ...
494s  evlike ([12, 15], [1:50], [], [1, 2, 3])
494s 8 tests, 8 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/gevfit.m]
494s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gevfit.m
494s ***** demo
494s  ## Sample 2 populations from 2 different exponential distributions
494s  rand ("seed", 1);   # for reproducibility
494s  r1 = gevrnd (-0.5, 1, 2, 5000, 1);
494s  rand ("seed", 2);   # for reproducibility
494s  r2 = gevrnd (0, 1, -4, 5000, 1);
494s  r = [r1, r2];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, 50, 5);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  hold on
494s 
494s  ## Estimate their k, sigma, and mu parameters
494s  k_sigma_muA = gevfit (r(:,1));
494s  k_sigma_muB = gevfit (r(:,2));
494s 
494s  ## Plot their estimated PDFs
494s  x = [-10:0.5:20];
494s  y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
494s  plot (x, y, "-pr");
494s  y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
494s  plot (x, y, "-sg");
494s  ylim ([0, 0.7])
494s  xlim ([-7, 5])
494s  legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
494s           "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
494s      sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
494s                  k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
494s      sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
494s                  k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
494s  title ("Two population samples from different exponential distributions")
494s  hold off
494s ***** test
494s  x = 1:50;
494s  [pfit, pci] = gevfit (x);
494s  pfit_out = [-0.4407, 15.1923, 21.5309];
494s  pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
494s  assert (pfit, pfit_out, 1e-3);
494s  assert (pci, pci_out, 1e-3);
495s ***** test
495s  x = 1:2:50;
495s  [pfit, pci] = gevfit (x);
495s  pfit_out = [-0.4434, 15.2024, 21.0532];
495s  pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
495s  assert (pfit, pfit_out, 1e-3);
495s  assert (pci, pci_out, 1e-3);
495s ***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
495s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
495s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
495s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
495s ***** error<gevfit: X and FREQ vectors mismatch.> ...
495s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
495s ***** error<gevfit: FREQ must not contain negative values.> ...
495s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
495s ***** error<gevfit: FREQ must contain integer values.> ...
495s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
495s ***** error<gevfit: 'options' argument must be a structure> ...
495s  gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
495s ***** error<gevfit: 'options' argument must be a structure> ...
495s  gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
495s 11 tests, 11 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/bisalike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/bisalike.m
495s ***** test
495s  nlogL = bisalike ([16.2649, 1.0156], [1:50]);
495s  assert (nlogL, 215.5905, 1e-4);
495s ***** test
495s  nlogL = bisalike ([2.5585, 0.5839], [1:5]);
495s  assert (nlogL, 8.9950, 1e-4);
495s ***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
495s ***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
495s ***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
495s ***** error<bisalike: PARAMS must be a two-element vector.> ...
495s  bisalike ([1, 0.2, 3], [1, 3, 5, 7])
495s ***** error<bisalike: X and CENSOR vector mismatch.> ...
495s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
495s ***** error<bisalike: X and FREQ vector mismatch.> ...
495s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
495s ***** error<bisalike: X and FREQ vector mismatch.> ...
495s  bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
495s 9 tests, 9 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/hnlike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/hnlike.m
495s ***** test
495s  x = 1:20;
495s  paramhat = hnfit (x, 0);
495s  [nlogL, acov] = hnlike (paramhat, x);
495s  assert (nlogL, 64.179177404891300, 1e-14);
495s ***** test
495s  x = 1:20;
495s  paramhat = hnfit (x, 0);
495s  [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
495s  assert (nlogL, 64.179177404891300, 1e-14);
495s ***** error<hnlike: function called with too few input arguments.> ...
495s  hnlike ([12, 15]);
495s ***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
495s ***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
495s ***** error<hnlike: X must be a vector of real values.> ...
495s  hnlike ([0, 3], ones (2));
495s ***** error<hnlike: X must be a vector of real values.> ...
495s  hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
495s ***** error<hnlike: X and FREQ vectors mismatch.> ...
495s  hnlike ([1, 2], ones (10, 1), ones (8,1))
495s ***** error<hnlike: FREQ must not contain negative values.> ...
495s  hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
495s 9 tests, 9 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/evfit.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/evfit.m
495s ***** demo
495s  ## Sample 3 populations from different extreme value distributions
495s  rand ("seed", 1);    # for reproducibility
495s  r1 = evrnd (2, 5, 400, 1);
495s  rand ("seed", 12);    # for reproducibility
495s  r2 = evrnd (-5, 3, 400, 1);
495s  rand ("seed", 13);    # for reproducibility
495s  r3 = evrnd (14, 8, 400, 1);
495s  r = [r1, r2, r3];
495s 
495s  ## Plot them normalized and fix their colors
495s  hist (r, 25, 0.4);
495s  h = findobj (gca, "Type", "patch");
495s  set (h(1), "facecolor", "c");
495s  set (h(2), "facecolor", "g");
495s  set (h(3), "facecolor", "r");
495s  ylim ([0, 0.28])
495s  xlim ([-30, 30]);
495s  hold on
495s 
495s  ## Estimate their MU and SIGMA parameters
495s  mu_sigmaA = evfit (r(:,1));
495s  mu_sigmaB = evfit (r(:,2));
495s  mu_sigmaC = evfit (r(:,3));
495s 
495s  ## Plot their estimated PDFs
495s  x = [min(r(:)):max(r(:))];
495s  y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
495s  plot (x, y, "-pr");
495s  y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
495s  plot (x, y, "-sg");
495s  y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
495s  plot (x, y, "-^c");
495s  legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
495s           "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
495s           "Normalized HIST of sample 3 with μ=14 and σ=8", ...
495s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
495s                   mu_sigmaA(1), mu_sigmaA(2)), ...
495s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
495s                   mu_sigmaB(1), mu_sigmaB(2)), ...
495s           sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
495s                   mu_sigmaC(1), mu_sigmaC(2))})
495s  title ("Three population samples from different extreme value distributions")
495s  hold off
495s ***** test
495s  x = 1:50;
495s  [paramhat, paramci] = evfit (x);
495s  paramhat_out = [32.6811, 13.0509];
495s  paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
495s  assert (paramhat, paramhat_out, 1e-4);
495s  assert (paramci, paramci_out, 1e-4);
495s ***** test
495s  x = 1:50;
495s  [paramhat, paramci] = evfit (x, 0.01);
495s  paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
495s  assert (paramci, paramci_out, 1e-4);
495s ***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
495s ***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
495s ***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
495s ***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
495s ***** error<evfit: X and FREQ vectors mismatch.>
495s  evfit ([1 2 3], 0.05, [], [1 5])
495s ***** error<evfit: FREQ must not contain negative values.>
495s  evfit ([1 2 3], 0.05, [], [1 5 -1])
495s ***** error<evfit: 'options' 5th argument must be a structure> ...
495s  evfit ([1:10], 0.05, [], [], 5)
495s 9 tests, 9 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/logilike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/logilike.m
495s ***** test
495s  nlogL = logilike ([25.5, 8.7725], [1:50]);
495s  assert (nlogL, 206.6769, 1e-4);
495s ***** test
495s  nlogL = logilike ([3, 0.8645], [1:5]);
495s  assert (nlogL, 9.0699, 1e-4);
495s ***** error<logilike: function called with too few input arguments.> logilike (3.25)
495s ***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
495s ***** error<logilike: PARAMS must be a two-element vector.> ...
495s  logilike ([1, 0.2, 3], [1, 3, 5, 7])
495s ***** error<logilike: X and CENSOR vector mismatch.> ...
495s  logilike ([1.5, 0.2], [1:5], [0, 0, 0])
495s ***** error<logilike: X and FREQ vector mismatch.> ...
495s  logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
495s ***** error<logilike: X and FREQ vector mismatch.> ...
495s  logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
495s 8 tests, 8 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/betalike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/betalike.m
495s ***** test
495s  x = 0.01:0.02:0.99;
495s  [nlogL, avar] = betalike ([2.3, 1.2], x);
495s  avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
495s  assert (nlogL, 17.873477715879040, 3e-14);
495s  assert (avar, avar_out, 1e-7);
495s ***** test
495s  x = 0.01:0.02:0.99;
495s  [nlogL, avar] = betalike ([1, 4], x);
495s  avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
495s  assert (nlogL, 79.648061114839550, 1e-13);
495s  assert (avar, avar_out, 1e-7);
495s ***** test
495s  x = 0.00:0.02:1;
495s  [nlogL, avar] = betalike ([1, 4], x);
495s  avar_out = [0.00000801564765, 0.00000131397245; ...
495s              0.00000131397245, 0.00070827639442];
495s  assert (nlogL, 573.2008434477486, 1e-10);
495s  assert (avar, avar_out, 1e-14);
495s ***** error<betalike: function called with too few input arguments.> ...
495s  betalike ([12, 15]);
495s ***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
495s ***** error<betalike: X and FREQ vectors mismatch.> ...
495s  betalike ([12, 15], ones (10, 1), ones (8,1))
495s ***** error<betalike: FREQ must not contain negative values.> ...
495s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
495s ***** error<betalike: FREQ must contain integer values.> ...
495s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
495s 8 tests, 8 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/betafit.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/betafit.m
495s ***** demo
495s  ## Sample 2 populations from different Beta distributions
495s  randg ("seed", 1);   # for reproducibility
495s  r1 = betarnd (2, 5, 500, 1);
495s  randg ("seed", 2);   # for reproducibility
495s  r2 = betarnd (2, 2, 500, 1);
495s  r = [r1, r2];
495s 
495s  ## Plot them normalized and fix their colors
495s  hist (r, 12, 15);
495s  h = findobj (gca, "Type", "patch");
495s  set (h(1), "facecolor", "c");
495s  set (h(2), "facecolor", "g");
495s  hold on
495s 
495s  ## Estimate their shape parameters
495s  a_b_A = betafit (r(:,1));
495s  a_b_B = betafit (r(:,2));
495s 
495s  ## Plot their estimated PDFs
495s  x = [min(r(:)):0.01:max(r(:))];
495s  y = betapdf (x, a_b_A(1), a_b_A(2));
495s  plot (x, y, "-pr");
495s  y = betapdf (x, a_b_B(1), a_b_B(2));
495s  plot (x, y, "-sg");
495s  ylim ([0, 4])
495s  legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
495s           "Normalized HIST of sample 2 with α=2 and β=2", ...
495s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
495s                   a_b_A(1), a_b_A(2)), ...
495s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
495s                   a_b_B(1), a_b_B(2))})
495s  title ("Two population samples from different Beta distributions")
495s  hold off
495s ***** test
495s  x = 0.01:0.02:0.99;
495s  [paramhat, paramci] = betafit (x);
495s  paramhat_out = [1.0199, 1.0199];
495s  paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
495s  assert (paramhat, paramhat_out, 1e-4);
495s  assert (paramci, paramci_out, 1e-4);
495s ***** test
495s  x = 0.01:0.02:0.99;
495s  [paramhat, paramci] = betafit (x, 0.01);
495s  paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
495s  assert (paramci, paramci_out, 1e-4);
495s ***** test
495s  x = 0.00:0.02:1;
495s  [paramhat, paramci] = betafit (x);
495s  paramhat_out = [0.0875, 0.1913];
495s  paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
495s  assert (paramhat, paramhat_out, 1e-4);
495s  assert (paramci, paramci_out, 1e-4);
495s ***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
495s ***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
495s ***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
495s ***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
495s ***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
495s ***** error<betafit: X and FREQ vectors mismatch.> ...
495s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
495s ***** error<betafit: FREQ must not contain negative values.> ...
495s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
495s ***** error<betafit: FREQ must contain integer values.> ...
495s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
495s ***** error<betafit: 'options' argument must be a structure> ...
495s  betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
495s ***** error<betafit: 'options' argument must be a structure> ...
495s  betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
495s 13 tests, 13 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/invglike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/invglike.m
495s ***** test
495s  nlogL = invglike ([25.5, 19.6973], [1:50]);
495s  assert (nlogL, 219.1516, 1e-4);
495s ***** test
495s  nlogL = invglike ([3, 8.1081], [1:5]);
495s  assert (nlogL, 9.0438, 1e-4);
495s ***** error<invglike: function called with too few input arguments.> invglike (3.25)
495s ***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
495s ***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
495s ***** error<invglike: PARAMS must be a two-element vector.> ...
495s  invglike ([1, 0.2, 3], [1, 3, 5, 7])
495s ***** error<invglike: X and CENSOR vector mismatch.> ...
495s  invglike ([1.5, 0.2], [1:5], [0, 0, 0])
495s ***** error<invglike: X and FREQ vector mismatch.> ...
495s  invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
495s ***** error<invglike: X and FREQ vector mismatch.> ...
495s  invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
495s 9 tests, 9 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/gumbellike.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gumbellike.m
495s ***** test
495s  x = 1:50;
495s  [nlogL, avar] = gumbellike ([2.3, 1.2], x);
495s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
495s  assert (nlogL, 3.242264755689906e+17, 1e-14);
495s  assert (avar, avar_out, 1e-3);
495s ***** test
495s  x = 1:50;
495s  [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
495s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
495s  assert (nlogL, 481898704.0472211, 1e-6);
495s  assert (avar, avar_out, 1e-3);
495s ***** test
495s  x = 1:50;
495s  [nlogL, avar] = gumbellike ([21, 15], x);
495s  avar_out = [11.73913876598908, -5.9546128523121216; ...
495s              -5.954612852312121, 3.708060045170236];
495s  assert (nlogL, 223.7612479380652, 1e-13);
495s  assert (avar, avar_out, 1e-14);
495s ***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
495s ***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
495s ***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
495s ***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
495s ***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
495s 8 tests, 8 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/ricefit.m]
495s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/ricefit.m
495s ***** demo
495s  ## Sample 3 populations from different Gamma distributions
495s  randg ("seed", 5);    # for reproducibility
495s  randp ("seed", 6);
495s  r1 = ricernd (1, 2, 3000, 1);
495s  randg ("seed", 2);    # for reproducibility
495s  randp ("seed", 8);
495s  r2 = ricernd (2, 4, 3000, 1);
495s  randg ("seed", 7);    # for reproducibility
495s  randp ("seed", 9);
495s  r3 = ricernd (7.5, 1, 3000, 1);
495s  r = [r1, r2, r3];
495s 
495s  ## Plot them normalized and fix their colors
495s  hist (r, 75, 4);
495s  h = findobj (gca, "Type", "patch");
495s  set (h(1), "facecolor", "c");
495s  set (h(2), "facecolor", "g");
495s  set (h(3), "facecolor", "r");
495s  ylim ([0, 0.7]);
495s  xlim ([0, 12]);
495s  hold on
495s 
495s  ## Estimate their α and β parameters
495s  s_sigmaA = ricefit (r(:,1));
495s  s_sigmaB = ricefit (r(:,2));
495s  s_sigmaC = ricefit (r(:,3));
495s 
495s  ## Plot their estimated PDFs
495s  x = [0.01,0.1:0.2:18];
495s  y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
495s  plot (x, y, "-pr");
495s  y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
495s  plot (x, y, "-sg");
495s  y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
495s  plot (x, y, "-^c");
495s  hold off
495s  legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
495s           "Normalized HIST of sample 2 with s=2 and σ=4", ...
495s           "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
495s           sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
495s                   s_sigmaA(1), s_sigmaA(2)), ...
495s           sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
495s                   s_sigmaB(1), s_sigmaB(2)), ...
495s           sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
495s                   s_sigmaC(1), s_sigmaC(2))})
495s  title ("Three population samples from different Rician distributions")
495s  hold off
495s ***** test
495s  [paramhat, paramci] = ricefit ([1:50]);
495s  assert (paramhat, [15.3057, 17.6668], 1e-4);
495s  assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
495s ***** test
495s  [paramhat, paramci] = ricefit ([1:50], 0.01);
495s  assert (paramhat, [15.3057, 17.6668], 1e-4);
495s  assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
495s ***** test
495s  [paramhat, paramci] = ricefit ([1:5]);
495s  assert (paramhat, [2.3123, 1.6812], 1e-4);
495s  assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
495s ***** test
495s  [paramhat, paramci] = ricefit ([1:5], 0.01);
495s  assert (paramhat, [2.3123, 1.6812], 1e-4);
495s  assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
495s ***** test
495s  freq = [1 1 1 1 5];
495s  [paramhat, paramci] = ricefit ([1:5], [], [], freq);
495s  assert (paramhat, [3.5181, 1.5565], 1e-4);
495s  assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
495s ***** test
495s  censor = [1 0 0 0 0];
495s  [paramhat, paramci] = ricefit ([1:5], [], censor);
495s  assert (paramhat, [3.2978, 1.1527], 1e-4);
495s  assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
496s ***** assert (class (ricefit (single ([1:50]))), "single")
496s ***** error<ricefit: X must be a vector.> ricefit (ones (2))
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
496s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
496s ***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
496s ***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
496s ***** error<ricefit: FREQ cannot have negative values.> ...
496s  ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
496s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
496s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
496s 19 tests, 19 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/invgfit.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/invgfit.m
496s ***** demo
496s  ## Sample 3 populations from different inverse Gaussian distributions
496s  rand ("seed", 5); randn ("seed", 5);   # for reproducibility
496s  r1 = invgrnd (1, 0.2, 2000, 1);
496s  rand ("seed", 2); randn ("seed", 2);   # for reproducibility
496s  r2 = invgrnd (1, 3, 2000, 1);
496s  rand ("seed", 7); randn ("seed", 7);   # for reproducibility
496s  r3 = invgrnd (3, 1, 2000, 1);
496s  r = [r1, r2, r3];
496s 
496s  ## Plot them normalized and fix their colors
496s  hist (r, [0.1:0.1:3.2], 9);
496s  h = findobj (gca, "Type", "patch");
496s  set (h(1), "facecolor", "c");
496s  set (h(2), "facecolor", "g");
496s  set (h(3), "facecolor", "r");
496s  ylim ([0, 3]);
496s  xlim ([0, 3]);
496s  hold on
496s 
496s  ## Estimate their MU and LAMBDA parameters
496s  mu_lambdaA = invgfit (r(:,1));
496s  mu_lambdaB = invgfit (r(:,2));
496s  mu_lambdaC = invgfit (r(:,3));
496s 
496s  ## Plot their estimated PDFs
496s  x = [0:0.1:3];
496s  y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
496s  plot (x, y, "-pr");
496s  y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
496s  plot (x, y, "-sg");
496s  y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
496s  plot (x, y, "-^c");
496s  hold off
496s  legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
496s           "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
496s           "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
496s           sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
496s                   mu_lambdaA(1), mu_lambdaA(2)), ...
496s           sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
496s                   mu_lambdaB(1), mu_lambdaB(2)), ...
496s           sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
496s                   mu_lambdaC(1), mu_lambdaC(2))})
496s  title ("Three population samples from different inverse Gaussian distributions")
496s  hold off
496s ***** test
496s  paramhat = invgfit ([1:50]);
496s  paramhat_out = [25.5, 19.6973];
496s  assert (paramhat, paramhat_out, 1e-4);
496s ***** test
496s  paramhat = invgfit ([1:5]);
496s  paramhat_out = [3, 8.1081];
496s  assert (paramhat, paramhat_out, 1e-4);
496s ***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
496s ***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
496s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
496s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
496s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
496s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
496s  invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
496s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
496s  invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
496s ***** error<invgfit: X and FREQ vectors mismatch.> ...
496s  invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
496s ***** error<invgfit: X and FREQ vectors mismatch.> ...
496s  invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
496s ***** error<invgfit: 'options' 5th argument must be a structure> ...
496s  invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
496s 12 tests, 12 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/gamlike.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gamlike.m
496s ***** test
496s  [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
496s  assert (nlogL, 19.4426, 1e-4);
496s  assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
496s ***** test
496s  [nlogL, acov] = gamlike([2, 3], [5:45]);
496s  assert (nlogL, 305.8070, 1e-4);
496s  assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
496s ***** test
496s  [nlogL, acov] = gamlike([2, 13], [5:45]);
496s  assert (nlogL, 163.2261, 1e-4);
496s  assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
496s ***** error<gamlike: function called with too few input arguments.> ...
496s  gamlike ([12, 15])
496s ***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
496s ***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
496s ***** error<gamlike: X and CENSOR vectors mismatch.> ...
496s  gamlike ([12, 15], [1:50], [1, 2, 3])
496s ***** error<gamlike: X and FREQ vectors mismatch.> ...
496s  gamlike ([12, 15], [1:50], [], [1, 2, 3])
496s 8 tests, 8 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/poissfit.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/poissfit.m
496s ***** demo
496s  ## Sample 3 populations from 3 different Poisson distributions
496s  randp ("seed", 2);    # for reproducibility
496s  r1 = poissrnd (1, 1000, 1);
496s  randp ("seed", 2);    # for reproducibility
496s  r2 = poissrnd (4, 1000, 1);
496s  randp ("seed", 3);    # for reproducibility
496s  r3 = poissrnd (10, 1000, 1);
496s  r = [r1, r2, r3];
496s 
496s  ## Plot them normalized and fix their colors
496s  hist (r, [0:20], 1);
496s  h = findobj (gca, "Type", "patch");
496s  set (h(1), "facecolor", "c");
496s  set (h(2), "facecolor", "g");
496s  set (h(3), "facecolor", "r");
496s  hold on
496s 
496s  ## Estimate their lambda parameter
496s  lambdahat = poissfit (r);
496s 
496s  ## Plot their estimated PDFs
496s  x = [0:20];
496s  y = poisspdf (x, lambdahat(1));
496s  plot (x, y, "-pr");
496s  y = poisspdf (x, lambdahat(2));
496s  plot (x, y, "-sg");
496s  y = poisspdf (x, lambdahat(3));
496s  plot (x, y, "-^c");
496s  xlim ([0, 20])
496s  ylim ([0, 0.4])
496s  legend ({"Normalized HIST of sample 1 with λ=1", ...
496s           "Normalized HIST of sample 2 with λ=4", ...
496s           "Normalized HIST of sample 3 with λ=10", ...
496s           sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
496s                   lambdahat(1)), ...
496s           sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
496s                   lambdahat(2)), ...
496s           sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
496s                   lambdahat(3))})
496s  title ("Three population samples from different Poisson distributions")
496s  hold off
496s ***** test
496s  x = [1 3 2 4 5 4 3 4];
496s  [lhat, lci] = poissfit (x);
496s  assert (lhat, 3.25)
496s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
496s ***** test
496s  x = [1 3 2 4 5 4 3 4];
496s  [lhat, lci] = poissfit (x, 0.01);
496s  assert (lhat, 3.25)
496s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
496s ***** test
496s  x = [1 2 3 4 5];
496s  f = [1 1 2 3 1];
496s  [lhat, lci] = poissfit (x, [], f);
496s  assert (lhat, 3.25)
496s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
496s ***** test
496s  x = [1 2 3 4 5];
496s  f = [1 1 2 3 1];
496s  [lhat, lci] = poissfit (x, 0.01, f);
496s  assert (lhat, 3.25)
496s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
496s ***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
496s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
496s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
496s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
496s ***** error<poissfit: X and FREQ vectors mismatch.>
496s  poissfit ([1 2 3], [], [1 5])
496s ***** error<poissfit: FREQ must not contain negative values.>
496s  poissfit ([1 2 3], [], [1 5 -1])
496s 10 tests, 10 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/wbllike.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/wbllike.m
496s ***** test
496s  x = 1:50;
496s  [nlogL, acov] = wbllike ([2.3, 1.2], x);
496s  avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
496s  assert (nlogL, 945.9589180651594, 1e-12);
496s  assert (acov, avar_out, 1e-4);
496s ***** test
496s  x = 1:50;
496s  [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
496s  avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
496s  assert (nlogL, 424.9879809704742, 6e-14);
496s  assert (acov, avar_out, 1e-4);
496s ***** test
496s  x = 1:50;
496s  [nlogL, acov] = wbllike ([21, 15], x);
496s  avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
496s  assert (nlogL, 1635190.328991511, 1e-8);
496s  assert (acov, avar_out, 1e-8);
496s ***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
496s ***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
496s ***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
496s ***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
496s ***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
496s ***** error<wbllike: FREQ cannot have negative values.> ...
496s  wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
496s 9 tests, 9 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/expfit.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/expfit.m
496s ***** demo
496s  ## Sample 3 populations from 3 different exponential distributions
496s  rande ("seed", 1);   # for reproducibility
496s  r1 = exprnd (2, 4000, 1);
496s  rande ("seed", 2);   # for reproducibility
496s  r2 = exprnd (5, 4000, 1);
496s  rande ("seed", 3);   # for reproducibility
496s  r3 = exprnd (12, 4000, 1);
496s  r = [r1, r2, r3];
496s 
496s  ## Plot them normalized and fix their colors
496s  hist (r, 48, 0.52);
496s  h = findobj (gca, "Type", "patch");
496s  set (h(1), "facecolor", "c");
496s  set (h(2), "facecolor", "g");
496s  set (h(3), "facecolor", "r");
496s  hold on
496s 
496s  ## Estimate their mu parameter
496s  muhat = expfit (r);
496s 
496s  ## Plot their estimated PDFs
496s  x = [0:max(r(:))];
496s  y = exppdf (x, muhat(1));
496s  plot (x, y, "-pr");
496s  y = exppdf (x, muhat(2));
496s  plot (x, y, "-sg");
496s  y = exppdf (x, muhat(3));
496s  plot (x, y, "-^c");
496s  ylim ([0, 0.6])
496s  xlim ([0, 40])
496s  legend ({"Normalized HIST of sample 1 with μ=2", ...
496s           "Normalized HIST of sample 2 with μ=5", ...
496s           "Normalized HIST of sample 3 with μ=12", ...
496s           sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
496s           sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
496s           sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
496s  title ("Three population samples from different exponential distributions")
496s  hold off
496s ***** assert (expfit (1), 1)
496s ***** assert (expfit (1:3), 2)
496s ***** assert (expfit ([1:3]'), 2)
496s ***** assert (expfit (1:3, []), 2)
496s ***** assert (expfit (1:3, [], [], []), 2)
496s ***** assert (expfit (magic (3)), [5 5 5])
496s ***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
496s ***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
496s ***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
496s ***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
496s ***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
496s ***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
496s ***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
496s ***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
496s ***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
496s ***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
496s ***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
496s ***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
496s ***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
496s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
496s ***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
496s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
496s  [1.75 4.75 7.75])
496s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
496s  [1.75 4.75 7.75])
496s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
496s  [3.5 19/3 31/3])
496s ***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
496s ***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
496s ***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
496s ***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
496s ***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
496s ***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
496s ***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
496s              1000*eps)
496s ***** assert ([~,muci] = expfit (1:3, 0.1), ...
496s                           [0.953017262058213; 7.337731146400207], 1000*eps)
496s ***** assert ([~,muci] = expfit ([1:3;2:4]), ...
496s              [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
496s              12.385982973214016, 20.643304955356694, 28.900626937499371], ...
496s              1000*eps)
496s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
496s              100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
496s              1.184936706156216, 1.974894510260360, 2.764852314364504], ...
496s              1000*eps)
496s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
496s              [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
496s              4.587722594914109, 8.257900670845396, 11.928078746776684], ...
496s              1000*eps)
496s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
496s              [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
496s              8.081825275395081, 14.547285495711145, 21.012745716027212], ...
496s              1000*eps)
496s ***** test
496s  x = reshape (1:8, [4 2]);
496s  x(4) = NaN;
496s  [muhat,muci] = expfit (x);
496s  assert ({muhat, muci}, {[NaN, 6.5], ...
496s          [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
496s ***** test
496s  x = magic (3);
496s  censor = [0 1 0; 0 1 0; 0 1 0];
496s  freq = [1 1 0; 1 1 0; 1 1 0];
496s  [muhat,muci] = expfit (x, [], censor, freq);
496s  assert ({muhat, muci}, {[5 NaN NaN], ...
496s                  [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
496s ***** error expfit ()
496s ***** error expfit (1,2,3,4,5)
496s ***** error [a b censor] = expfit (1)
496s ***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
496s ***** error <X cannot be negative> expfit ([-1 2 3 4 5])
496s ***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
496s ***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
496s ***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
496s ***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
496s 47 tests, 47 passed, 0 known failure, 0 skipped
496s [inst/dist_fit/gamfit.m]
496s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gamfit.m
496s ***** demo
496s  ## Sample 3 populations from different Gamma distributions
496s  randg ("seed", 5);    # for reproducibility
496s  r1 = gamrnd (1, 2, 2000, 1);
496s  randg ("seed", 2);    # for reproducibility
496s  r2 = gamrnd (2, 2, 2000, 1);
496s  randg ("seed", 7);    # for reproducibility
496s  r3 = gamrnd (7.5, 1, 2000, 1);
496s  r = [r1, r2, r3];
496s 
496s  ## Plot them normalized and fix their colors
496s  hist (r, 75, 4);
496s  h = findobj (gca, "Type", "patch");
496s  set (h(1), "facecolor", "c");
496s  set (h(2), "facecolor", "g");
496s  set (h(3), "facecolor", "r");
496s  ylim ([0, 0.62]);
496s  xlim ([0, 12]);
496s  hold on
496s 
496s  ## Estimate their α and β parameters
496s  a_bA = gamfit (r(:,1));
496s  a_bB = gamfit (r(:,2));
496s  a_bC = gamfit (r(:,3));
496s 
496s  ## Plot their estimated PDFs
496s  x = [0.01,0.1:0.2:18];
496s  y = gampdf (x, a_bA(1), a_bA(2));
496s  plot (x, y, "-pr");
496s  y = gampdf (x, a_bB(1), a_bB(2));
496s  plot (x, y, "-sg");
496s  y = gampdf (x, a_bC(1), a_bC(2));
496s  plot (x, y, "-^c");
496s  hold off
496s  legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
496s           "Normalized HIST of sample 2 with α=2 and β=2", ...
496s           "Normalized HIST of sample 3 with α=7.5 and β=1", ...
496s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
496s                   a_bA(1), a_bA(2)), ...
496s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
496s                   a_bB(1), a_bB(2)), ...
496s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
496s                   a_bC(1), a_bC(2))})
496s  title ("Three population samples from different Gamma distributions")
496s  hold off
496s ***** shared x
496s  x = [1.2 1.6 1.7 1.8 1.9 2.0 2.2 2.6 3.0 3.5 4.0 4.8 5.6 6.6 7.6];
496s ***** test
496s  [paramhat, paramci] = gamfit (x);
496s  assert (paramhat, [3.4248, 0.9752], 1e-4);
496s  assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
496s ***** test
496s  [paramhat, paramci] = gamfit (x, 0.01);
496s  assert (paramhat, [3.4248, 0.9752], 1e-4);
496s  assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
496s ***** test
496s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
496s  [paramhat, paramci] = gamfit (x, [], [], freq);
496s  assert (paramhat, [3.3025, 1.0615], 1e-4);
496s  assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
496s ***** test
496s  [paramhat, paramci] = gamfit (x, [], [], [1:15]);
496s  assert (paramhat, [4.4484, 0.9689], 1e-4);
496s  assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
496s ***** test
496s  [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
496s  assert (paramhat, [4.4484, 0.9689], 1e-4);
496s  assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
496s ***** test
496s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
496s  [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
496s  assert (paramhat, [4.7537, 0.9308], 1e-4);
496s  assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
497s ***** test
497s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
497s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
497s  [paramhat, paramci] = gamfit (x, [], cens, freq);
497s  assert (paramhat, [3.4736, 1.0847], 1e-4);
497s  assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
497s ***** test
497s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
497s  assert (paramhat, [Inf, 0]);
497s  assert (paramci, [Inf, 0; Inf, 0]);
497s ***** test
497s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
497s  assert (paramhat, [NaN, NaN]);
497s  assert (paramci, [NaN, NaN; NaN, NaN]);
497s ***** test
497s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
497s  assert (paramhat, [Inf, 0]);
497s  assert (paramci, [Inf, 0; Inf, 0]);
497s ***** assert (class (gamfit (single (x))), "single")
497s ***** error<gamfit: X must be a vector.> gamfit (ones (2))
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
497s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
497s ***** error<gamfit: X and FREQ vectors mismatch.>
497s  gamfit ([1 2 3], 0.05, [], [1 5])
497s ***** error<gamfit: FREQ must not contain negative values.>
497s  gamfit ([1 2 3], 0.05, [], [1 5 -1])
497s ***** error<gamfit: 'options' 5th argument must be a structure> ...
497s  gamfit ([1:10], 0.05, [], [], 5)
497s ***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
497s ***** error<gamfit: X must contain positive values when censored.> ...
497s  gamfit ([1 2 0], [], [1 0 0])
497s 23 tests, 23 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/ricelike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/ricelike.m
497s ***** test
497s  nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
497s  assert (nlogL, 204.5230311010569, 1e-12);
497s ***** test
497s  nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
497s  assert (nlogL, 8.65562164930058, 1e-12);
497s ***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
497s ***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
497s ***** error<ricelike: PARAMS must be a two-element vector.> ...
497s  ricelike ([1, 0.2, 3], [1, 3, 5, 7])
497s ***** error<ricelike: X and CENSOR vector mismatch.> ...
497s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
497s ***** error<ricelike: X and FREQ vector mismatch.> ...
497s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
497s ***** error<ricelike: X and FREQ vector mismatch.> ...
497s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
497s ***** error<ricelike: FREQ must not contain negative values.> ...
497s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
497s 9 tests, 9 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/gplike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gplike.m
497s ***** test
497s  k = 0.8937; sigma = 1.3230; theta = 1;
497s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
497s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
497s  [nlogL, acov] = gplike ([k, sigma, theta], x);
497s  assert (nlogL, 21.736, 1e-3);
497s  assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
497s ***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
497s ***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
497s ***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
497s ***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
497s ***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
497s ***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
497s ***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
497s ***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
497s ***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
497s ***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
497s ***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
497s ***** error<gplike: function called with too few input arguments.> gplike ()
497s ***** error<gplike: function called with too few input arguments.> gplike (1)
497s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
497s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
497s ***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
497s ***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
497s ***** error<gplike: X and FREQ vectors mismatch.> ...
497s  gplike ([1, 2, 0], ones (10, 1), ones (8,1))
497s ***** error<gplike: FREQ must not contain negative values.> ...
497s  gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
497s 20 tests, 20 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/rayllike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/rayllike.m
497s ***** test
497s  x = [1 3 2 4 5 4 3 4];
497s  [nlogL, acov] = rayllike (3.25, x);
497s  assert (nlogL, 14.7442, 1e-4)
497s ***** test
497s  x = [1 2 3 4 5];
497s  f = [1 1 2 3 1];
497s  [nlogL, acov] = rayllike (3.25, x, [], f);
497s  assert (nlogL, 14.7442, 1e-4)
497s ***** test
497s  x = [1 2 3 4 5 6];
497s  f = [1 1 2 3 1 0];
497s  [nlogL, acov] = rayllike (3.25, x, [], f);
497s  assert (nlogL, 14.7442, 1e-4)
497s ***** test
497s  x = [1 2 3 4 5 6];
497s  c = [0 0 0 0 0 1];
497s  f = [1 1 2 3 1 0];
497s  [nlogL, acov] = rayllike (3.25, x, c, f);
497s  assert (nlogL, 14.7442, 1e-4)
497s ***** error<rayllike: function called with too few input arguments.> rayllike (1)
497s ***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
497s ***** error<rayllike: X must be a vector of non-negative values.> ...
497s  rayllike (3.25, ones (10, 2))
497s ***** error<rayllike: X must be a vector of non-negative values.> ...
497s  rayllike (3.25, [1 2 3 -4 5])
497s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
497s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
497s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
497s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
497s ***** error<rayllike: X and FREQ vectors mismatch.> ...
497s  rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
497s ***** error<rayllike: X and FREQ vectors mismatch.> ...
497s  rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
497s ***** error<rayllike: FREQ must not contain negative values.> ...
497s  rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
497s 13 tests, 13 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/lognlike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/lognlike.m
497s ***** test
497s  x = 1:50;
497s  [nlogL, avar] = lognlike ([0, 0.25], x);
497s  avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
497s  assert (nlogL, 3962.330333301793, 1e-10);
497s  assert (avar, avar_out, 1e-7);
497s ***** test
497s  x = 1:50;
497s  [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
497s  avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
497s  assert (nlogL, 2473.183051225747, 1e-10);
497s  assert (avar, avar_out, 1e-7);
497s ***** test
497s  x = 1:50;
497s  [nlogL, avar] = lognlike ([0, 0.5], x);
497s  avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
497s  assert (nlogL, 1119.072424020455, 1e-12);
497s  assert (avar, avar_out, 1e-6);
497s ***** test
497s  x = 1:50;
497s  censor = ones (1, 50);
497s  censor([2, 4, 6, 8, 12, 14]) = 0;
497s  [nlogL, avar] = lognlike ([0, 0.5], x, censor);
497s  avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
497s  assert (nlogL, 1091.746371145497, 1e-12);
497s  assert (avar, avar_out, 1e-6);
497s ***** test
497s  x = 1:50;
497s  censor = ones (1, 50);
497s  censor([2, 4, 6, 8, 12, 14]) = 0;
497s  [nlogL, avar] = lognlike ([0, 1], x, censor);
497s  avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
497s  assert (nlogL, 349.3969104144271, 1e-12);
497s  assert (avar, avar_out, 1e-6);
497s ***** error<lognlike: function called with too few input arguments.> ...
497s  lognlike ([12, 15]);
497s ***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
497s ***** error<lognlike: PARAMS must be a two-element vector.> ...
497s  lognlike ([12, 15, 3], [1:50]);
497s ***** error<lognlike: X and CENSOR vectors mismatch.> ...
497s  lognlike ([12, 15], [1:50], [1, 2, 3]);
497s ***** error<lognlike: X and FREQ vectors mismatch.> ...
497s  lognlike ([12, 15], [1:50], [], [1, 2, 3]);
497s 10 tests, 10 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/binolike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/binolike.m
497s ***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
497s ***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
497s ***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
497s ***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
497s ***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
497s ***** test
497s  [nlogL, acov] = binolike ([3, 0.333], 3);
497s  assert (acov(4), 0.0740, 1e-4)
497s ***** error<binolike: function called with too few input arguments.> binolike (3.25)
497s ***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
497s ***** error<binolike: PARAMS must be a two-element vector.> ...
497s  binolike ([1, 0.2, 3], [1, 3, 5, 7])
497s ***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
497s ***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
497s ***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
497s ***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
497s ***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
497s ***** error<binolike: X and FREQ vectors mismatch.> ...
497s  binolike ([5, 0.5], ones (10, 1), ones (8,1))
497s ***** error<binolike: FREQ must not contain negative values.> ...
497s  binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
497s ***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
497s ***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
497s 18 tests, 18 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/gumbelfit.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gumbelfit.m
497s ***** demo
497s  ## Sample 3 populations from different Gumbel distributions
497s  rand ("seed", 1);    # for reproducibility
497s  r1 = gumbelrnd (2, 5, 400, 1);
497s  rand ("seed", 11);    # for reproducibility
497s  r2 = gumbelrnd (-5, 3, 400, 1);
497s  rand ("seed", 16);    # for reproducibility
497s  r3 = gumbelrnd (14, 8, 400, 1);
497s  r = [r1, r2, r3];
497s 
497s  ## Plot them normalized and fix their colors
497s  hist (r, 25, 0.32);
497s  h = findobj (gca, "Type", "patch");
497s  set (h(1), "facecolor", "c");
497s  set (h(2), "facecolor", "g");
497s  set (h(3), "facecolor", "r");
497s  ylim ([0, 0.28])
497s  xlim ([-11, 50]);
497s  hold on
497s 
497s  ## Estimate their MU and BETA parameters
497s  mu_betaA = gumbelfit (r(:,1));
497s  mu_betaB = gumbelfit (r(:,2));
497s  mu_betaC = gumbelfit (r(:,3));
497s 
497s  ## Plot their estimated PDFs
497s  x = [min(r(:)):max(r(:))];
497s  y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
497s  plot (x, y, "-pr");
497s  y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
497s  plot (x, y, "-sg");
497s  y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
497s  plot (x, y, "-^c");
497s  legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
497s           "Normalized HIST of sample 2 with μ=-5 and β=3", ...
497s           "Normalized HIST of sample 3 with μ=14 and β=8", ...
497s           sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
497s                   mu_betaA(1), mu_betaA(2)), ...
497s           sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
497s                   mu_betaB(1), mu_betaB(2)), ...
497s           sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
497s                   mu_betaC(1), mu_betaC(2))})
497s  title ("Three population samples from different Gumbel distributions")
497s  hold off
497s ***** test
497s  x = 1:50;
497s  [paramhat, paramci] = gumbelfit (x);
497s  paramhat_out = [18.3188, 13.0509];
497s  paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
497s  assert (paramhat, paramhat_out, 1e-4);
497s  assert (paramci, paramci_out, 1e-4);
497s ***** test
497s  x = 1:50;
497s  [paramhat, paramci] = gumbelfit (x, 0.01);
497s  paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
497s  assert (paramci, paramci_out, 1e-4);
497s ***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
497s ***** error<gumbelfit: X must be a double-precision vector.> ...
497s  gumbelfit (single (ones (1,5)));
497s ***** error<gumbelfit: X must NOT contain missing values> ...
497s  gumbelfit ([1, 2, 3, 4, NaN]);
497s ***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
497s ***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
497s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
497s ***** error<gumbelfit: X and FREQ vectors mismatch.> ...
497s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
497s ***** error<gamfit: FREQ must not contain negative values.>
497s  gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
497s ***** error<gumbelfit: 'options' 5th argument> ...
497s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
497s 10 tests, 10 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/nakalike.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/nakalike.m
497s ***** test
497s  nlogL = nakalike ([0.735504, 858.5], [1:50]);
497s  assert (nlogL, 202.8689, 1e-4);
497s ***** test
497s  nlogL = nakalike ([1.17404, 11], [1:5]);
497s  assert (nlogL, 8.6976, 1e-4);
497s ***** test
497s  nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
497s  assert (nlogL, 8.6976, 1e-4);
497s ***** test
497s  nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
497s  assert (nlogL, 8.6976, 1e-4);
497s ***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
497s ***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
497s ***** error<nakalike: PARAMS must be a two-element vector.> ...
497s  nakalike ([1, 0.2, 3], [1, 3, 5, 7])
497s ***** error<nakalike: X and CENSOR vector mismatch.> ...
497s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
497s ***** error<nakalike: X and FREQ vector mismatch.> ...
497s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
497s ***** error<nakalike: X and FREQ vector mismatch.> ...
497s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
497s ***** error<nakalike: FREQ must not contain negative values.> ...
497s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
497s 11 tests, 11 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/tlsfit.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/tlsfit.m
497s ***** demo
497s  ## Sample 3 populations from 3 different location-scale T distributions
497s  randn ("seed", 1);    # for reproducibility
497s  randg ("seed", 2);    # for reproducibility
497s  r1 = tlsrnd (-4, 3, 1, 2000, 1);
497s  randn ("seed", 3);    # for reproducibility
497s  randg ("seed", 4);    # for reproducibility
497s  r2 = tlsrnd (0, 3, 1, 2000, 1);
497s  randn ("seed", 5);    # for reproducibility
497s  randg ("seed", 6);    # for reproducibility
497s  r3 = tlsrnd (5, 5, 4, 2000, 1);
497s  r = [r1, r2, r3];
497s 
497s  ## Plot them normalized and fix their colors
497s  hist (r, [-21:21], [1, 1, 1]);
497s  h = findobj (gca, "Type", "patch");
497s  set (h(1), "facecolor", "c");
497s  set (h(2), "facecolor", "g");
497s  set (h(3), "facecolor", "r");
497s  ylim ([0, 0.25]);
497s  xlim ([-20, 20]);
497s  hold on
497s 
497s  ## Estimate their lambda parameter
497s  mu_sigma_nuA = tlsfit (r(:,1));
497s  mu_sigma_nuB = tlsfit (r(:,2));
497s  mu_sigma_nuC = tlsfit (r(:,3));
497s 
497s  ## Plot their estimated PDFs
497s  x = [-20:0.1:20];
497s  y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
497s  plot (x, y, "-pr");
497s  y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
497s  plot (x, y, "-sg");
497s  y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
497s  plot (x, y, "-^c");
497s  hold off
497s  legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
497s           "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
497s           "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
497s           sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
497s                   mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
497s           sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
497s                   mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
497s           sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
497s                   mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
497s  title ("Three population samples from different location-scale T distributions")
497s  hold off
497s ***** test
497s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
497s  [paramhat, paramci] = tlsfit (x);
497s  paramhat_out = [0.035893, 0.862711, 0.649261];
497s  paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
497s  assert (paramhat, paramhat_out, 1e-6);
497s  assert (paramci, paramci_out, 1e-5);
497s ***** test
497s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
497s  [paramhat, paramci] = tlsfit (x, 0.01);
497s  paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
497s  assert (paramci, paramci_out, 1e-4);
497s ***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
497s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
497s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
497s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
497s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
497s  tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
497s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
497s  tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
497s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
497s  tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
497s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
497s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
497s ***** error<tlsfit: FREQ cannot have negative values.> ...
497s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
497s ***** error<tlsfit: 'options' 5th argument must be a structure> ...
497s  tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
497s 12 tests, 12 passed, 0 known failure, 0 skipped
497s [inst/dist_fit/raylfit.m]
497s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/raylfit.m
497s ***** demo
497s  ## Sample 3 populations from 3 different Rayleigh distributions
497s  rand ("seed", 2);    # for reproducibility
497s  r1 = raylrnd (1, 1000, 1);
497s  rand ("seed", 2);    # for reproducibility
497s  r2 = raylrnd (2, 1000, 1);
497s  rand ("seed", 3);    # for reproducibility
497s  r3 = raylrnd (4, 1000, 1);
497s  r = [r1, r2, r3];
497s 
497s  ## Plot them normalized and fix their colors
497s  hist (r, [0.5:0.5:10.5], 2);
497s  h = findobj (gca, "Type", "patch");
497s  set (h(1), "facecolor", "c");
497s  set (h(2), "facecolor", "g");
497s  set (h(3), "facecolor", "r");
497s  hold on
497s 
497s  ## Estimate their lambda parameter
497s  sigmaA = raylfit (r(:,1));
497s  sigmaB = raylfit (r(:,2));
497s  sigmaC = raylfit (r(:,3));
497s 
497s  ## Plot their estimated PDFs
497s  x = [0:0.1:10];
497s  y = raylpdf (x, sigmaA);
497s  plot (x, y, "-pr");
497s  y = raylpdf (x, sigmaB);
497s  plot (x, y, "-sg");
497s  y = raylpdf (x, sigmaC);
497s  plot (x, y, "-^c");
497s  xlim ([0, 10])
497s  ylim ([0, 0.7])
497s  legend ({"Normalized HIST of sample 1 with σ=1", ...
497s           "Normalized HIST of sample 2 with σ=2", ...
497s           "Normalized HIST of sample 3 with σ=4", ...
497s           sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
497s                   sigmaA), ...
497s           sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
497s                   sigmaB), ...
497s           sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
497s                   sigmaC)})
497s  title ("Three population samples from different Rayleigh distributions")
497s  hold off
497s ***** test
497s  x = [1 3 2 4 5 4 3 4];
497s  [shat, sci] = raylfit (x);
497s  assert (shat, 2.4495, 1e-4)
497s  assert (sci, [1.8243; 3.7279], 1e-4)
497s ***** test
497s  x = [1 3 2 4 5 4 3 4];
497s  [shat, sci] = raylfit (x, 0.01);
497s  assert (shat, 2.4495, 1e-4)
497s  assert (sci, [1.6738; 4.3208], 1e-4)
498s ***** test
498s  x = [1 2 3 4 5];
498s  f = [1 1 2 3 1];
498s  [shat, sci] = raylfit (x, [], [], f);
498s  assert (shat, 2.4495, 1e-4)
498s  assert (sci, [1.8243; 3.7279], 1e-4)
498s ***** test
498s  x = [1 2 3 4 5];
498s  f = [1 1 2 3 1];
498s  [shat, sci] = raylfit (x, 0.01, [], f);
498s  assert (shat, 2.4495, 1e-4)
498s  assert (sci, [1.6738; 4.3208], 1e-4)
498s ***** test
498s  x = [1 2 3 4 5 6];
498s  c = [0 0 0 0 0 1];
498s  f = [1 1 2 3 1 1];
498s  [shat, sci] = raylfit (x, 0.01, c, f);
498s  assert (shat, 2.4495, 1e-4)
498s  assert (sci, [1.6738; 4.3208], 1e-4)
498s ***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
498s ***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
498s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
498s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
498s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
498s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
498s  raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
498s  raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<raylfit: X and FREQ vectors mismatch.> ...
498s  raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<raylfit: X and FREQ vectors mismatch.> ...
498s  raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<raylfit: X and FREQ vectors mismatch.>
498s  raylfit ([1 2 3], [], [], [1 5])
498s ***** error<raylfit: FREQ must not contain negative values.>
498s  raylfit ([1 2 3], [], [], [1 5 -1])
498s 16 tests, 16 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/binofit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/binofit.m
498s ***** demo
498s  ## Sample 2 populations from different binomial distributions
498s  rand ("seed", 1);    # for reproducibility
498s  r1 = binornd (50, 0.15, 1000, 1);
498s  rand ("seed", 2);    # for reproducibility
498s  r2 = binornd (100, 0.5, 1000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 23, 0.35);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  hold on
498s 
498s  ## Estimate their probability of success
498s  pshatA = binofit (r(:,1), 50);
498s  pshatB = binofit (r(:,2), 100);
498s 
498s  ## Plot their estimated PDFs
498s  x = [min(r(:,1)):max(r(:,1))];
498s  y = binopdf (x, 50, mean (pshatA));
498s  plot (x, y, "-pg");
498s  x = [min(r(:,2)):max(r(:,2))];
498s  y = binopdf (x, 100, mean (pshatB));
498s  plot (x, y, "-sc");
498s  ylim ([0, 0.2])
498s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
498s           "Normalized HIST of sample 2 with ps=0.50", ...
498s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
498s                   mean (pshatA)), ...
498s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
498s                   mean (pshatB))})
498s  title ("Two population samples from different binomial distributions")
498s  hold off
498s ***** test
498s  x = 0:3;
498s  [pshat, psci] = binofit (x, 3);
498s  assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
498s  assert (psci(1,:), [0, 0.7076], 1e-4);
498s  assert (psci(2,:), [0.0084, 0.9057], 1e-4);
498s  assert (psci(3,:), [0.0943, 0.9916], 1e-4);
498s  assert (psci(4,:), [0.2924, 1.0000], 1e-4);
498s ***** error<binofit: function called with too few input arguments.> ...
498s  binofit ([1 2 3 4])
498s ***** error<binofit: X cannot have negative values.> ...
498s  binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
498s ***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
498s ***** error<binofit: N must be a non-negative integer.> ...
498s  binofit ([1, 4, 3, 2], [1, 2, -1, 3])
498s ***** error<binofit: N must be a scalar or the same size as X.> ...
498s  binofit ([1, 4, 3, 2], [5, 5, 5])
498s ***** error<binofit: N must be at least as large as X.> ...
498s  binofit ([1, 4, 3, 2], [5, 3, 5, 5])
498s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
498s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
498s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
498s 10 tests, 10 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/geofit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/geofit.m
498s ***** demo
498s  ## Sample 2 populations from different geometric distributions
498s  rande ("seed", 1);    # for reproducibility
498s  r1 = geornd (0.15, 1000, 1);
498s  rande ("seed", 2);    # for reproducibility
498s  r2 = geornd (0.5, 1000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 0:0.5:20.5, 1);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  hold on
498s 
498s  ## Estimate their probability of success
498s  pshatA = geofit (r(:,1));
498s  pshatB = geofit (r(:,2));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0:15];
498s  y = geopdf (x, pshatA);
498s  plot (x, y, "-pg");
498s  y = geopdf (x, pshatB);
498s  plot (x, y, "-sc");
498s  xlim ([0, 15])
498s  ylim ([0, 0.6])
498s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
498s           "Normalized HIST of sample 2 with ps=0.50", ...
498s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
498s                   mean (pshatA)), ...
498s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
498s                   mean (pshatB))})
498s  title ("Two population samples from different geometric distributions")
498s  hold off
498s ***** test
498s  x = 0:5;
498s  [pshat, psci] = geofit (x);
498s  assert (pshat, 0.2857, 1e-4);
498s  assert (psci, [0.092499; 0.478929], 1e-5);
498s ***** test
498s  x = 0:5;
498s  [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
498s  assert (pshat, 0.2857, 1e-4);
498s  assert (psci, [0.092499; 0.478929], 1e-5);
498s ***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
498s ***** error<geofit: function called with too few input arguments.> geofit ()
498s ***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
498s ***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
498s ***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
498s ***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
498s ***** error<geofit: X and FREQ vector mismatch.> ...
498s  geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
498s ***** error<geofit: X and FREQ vector mismatch.> ...
498s  geofit ([1.5, 0.2], [], [1, 1, 1])
498s 10 tests, 10 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/logifit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/logifit.m
498s ***** demo
498s  ## Sample 3 populations from different logistic distributions
498s  rand ("seed", 5)  # for reproducibility
498s  r1 = logirnd (2, 1, 2000, 1);
498s  rand ("seed", 2)   # for reproducibility
498s  r2 = logirnd (5, 2, 2000, 1);
498s  rand ("seed", 7)   # for reproducibility
498s  r3 = logirnd (9, 4, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [-6:20], 1);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  set (h(3), "facecolor", "r");
498s  ylim ([0, 0.3]);
498s  xlim ([-5, 20]);
498s  hold on
498s 
498s  ## Estimate their MU and LAMBDA parameters
498s  mu_sA = logifit (r(:,1));
498s  mu_sB = logifit (r(:,2));
498s  mu_sC = logifit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [-5:0.5:20];
498s  y = logipdf (x, mu_sA(1), mu_sA(2));
498s  plot (x, y, "-pr");
498s  y = logipdf (x, mu_sB(1), mu_sB(2));
498s  plot (x, y, "-sg");
498s  y = logipdf (x, mu_sC(1), mu_sC(2));
498s  plot (x, y, "-^c");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
498s           "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
498s           "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
498s           sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
498s                   mu_sA(1), mu_sA(2)), ...
498s           sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
498s                   mu_sB(1), mu_sB(2)), ...
498s           sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
498s                   mu_sC(1), mu_sC(2))})
498s  title ("Three population samples from different logistic distributions")
498s  hold off
498s ***** test
498s  paramhat = logifit ([1:50]);
498s  paramhat_out = [25.5, 8.7724];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = logifit ([1:5]);
498s  paramhat_out = [3, 0.8645];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
498s  paramhat_out = [3, 0.8645];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
498s  paramhat_out = logifit ([1:5, 5]);
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** error<logifit: X must be a vector.> logifit (ones (2,5));
498s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
498s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<logifit: X and CENSOR vectors mismatch.> ...
498s  logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<logifit: X and CENSOR vectors mismatch.> ...
498s  logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<logifit: X and FREQ vectors mismatch.> ...
498s  logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<logifit: X and FREQ vectors mismatch.> ...
498s  logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<logifit: 'options' 5th argument must be a structure> ...
498s  logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 13 tests, 13 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/poisslike.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/poisslike.m
498s ***** test
498s  x = [1 3 2 4 5 4 3 4];
498s  [nlogL, avar] = poisslike (3.25, x);
498s  assert (nlogL, 13.9533, 1e-4)
498s ***** test
498s  x = [1 2 3 4 5];
498s  f = [1 1 2 3 1];
498s  [nlogL, avar] = poisslike (3.25, x, f);
498s  assert (nlogL, 13.9533, 1e-4)
498s ***** error<poisslike: function called with too few input arguments.> poisslike (1)
498s ***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
498s ***** error<poisslike: X must be a vector of non-negative values.> ...
498s  poisslike (3.25, ones (10, 2))
498s ***** error<poisslike: X must be a vector of non-negative values.> ...
498s  poisslike (3.25, [1 2 3 -4 5])
498s ***** error<poisslike: X and FREQ vectors mismatch.> ...
498s  poisslike (3.25, ones (10, 1), ones (8,1))
498s ***** error<poisslike: FREQ must not contain negative values.> ...
498s  poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
498s 8 tests, 8 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gpfit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gpfit.m
498s ***** demo
498s  ## Sample 2 populations from different generalized Pareto distributions
498s  ## Assume location parameter θ is known
498s  theta = 0;
498s  rand ("seed", 5);    # for reproducibility
498s  r1 = gprnd (1, 2, theta, 20000, 1);
498s  rand ("seed", 2);    # for reproducibility
498s  r2 = gprnd (3, 1, theta, 20000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [0.1:0.2:100], 5);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "r");
498s  set (h(2), "facecolor", "c");
498s  ylim ([0, 1]);
498s  xlim ([0, 5]);
498s  hold on
498s 
498s  ## Estimate their α and β parameters
498s  k_sigmaA = gpfit (r(:,1), theta);
498s  k_sigmaB = gpfit (r(:,2), theta);
498s 
498s  ## Plot their estimated PDFs
498s  x = [0.01, 0.1:0.2:18];
498s  y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
498s  plot (x, y, "-pc");
498s  y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
498s  plot (x, y, "-sr");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
498s           "Normalized HIST of sample 2 with k=2 and σ=2", ...
498s           sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
498s                   k_sigmaA(1), k_sigmaA(2)), ...
498s           sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
498s                   k_sigmaB(1), k_sigmaB(2))})
498s  title ("Two population samples from different generalized Pareto distributions")
498s  text (2, 0.7, "Known location parameter θ = 0")
498s  hold off
498s ***** test
498s  k = 0.8937; sigma = 1.3230; theta = 1;
498s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
498s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
498s  [hat, ci] = gpfit (x, theta);
498s  assert (hat, [k, sigma, theta], 1e-4);
498s  assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
498s ***** error<gpfit: function called with too few input arguments.> gpfit ()
498s ***** error<gpfit: function called with too few input arguments.> gpfit (1)
498s ***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
498s ***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
498s ***** error<gpfit: THETA must be a real scalar value.> ...
498s  gpfit ([0.5, 1.2], [0, 1]);
498s ***** error<gpfit: THETA must be a real scalar value.> ...
498s  gpfit ([0.5, 1.2], 5+i);
498s ***** error<gpfit: X cannot contain values less than THETA.> ...
498s  gpfit ([1:5], 2);
498s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
498s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
498s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
498s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
498s ***** error<gpfit: X and FREQ vectors mismatch.>
498s  gpfit ([1 2 3], 0, [], [1 5])
498s ***** error<gpfit: FREQ must not contain negative values.>
498s  gpfit ([1 2 3], 0, [], [1 5 -1])
498s ***** error<gpfit: 'options' 5th argument must be a structure> ...
498s  gpfit ([1:10], 1, 0.05, [], 5)
498s 15 tests, 15 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/bisafit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/bisafit.m
498s ***** demo
498s  ## Sample 3 populations from different Birnbaum-Saunders distributions
498s  rand ("seed", 5);    # for reproducibility
498s  r1 = bisarnd (1, 0.5, 2000, 1);
498s  rand ("seed", 2);    # for reproducibility
498s  r2 = bisarnd (2, 0.3, 2000, 1);
498s  rand ("seed", 7);    # for reproducibility
498s  r3 = bisarnd (4, 0.5, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 80, 4.2);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  set (h(3), "facecolor", "r");
498s  ylim ([0, 1.1]);
498s  xlim ([0, 8]);
498s  hold on
498s 
498s  ## Estimate their α and β parameters
498s  beta_gammaA = bisafit (r(:,1));
498s  beta_gammaB = bisafit (r(:,2));
498s  beta_gammaC = bisafit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0:0.1:8];
498s  y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
498s  plot (x, y, "-pr");
498s  y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
498s  plot (x, y, "-sg");
498s  y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
498s  plot (x, y, "-^c");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
498s           "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
498s           "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
498s           sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
498s                   beta_gammaA(1), beta_gammaA(2)), ...
498s           sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
498s                   beta_gammaB(1), beta_gammaB(2)), ...
498s           sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
498s                   beta_gammaC(1), beta_gammaC(2))})
498s  title ("Three population samples from different Birnbaum-Saunders distributions")
498s  hold off
498s ***** test
498s  paramhat = bisafit ([1:50]);
498s  paramhat_out = [16.2649, 1.0156];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = bisafit ([1:5]);
498s  paramhat_out = [2.5585, 0.5839];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
498s ***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
498s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
498s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
498s  bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
498s  bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<bisafit: X and FREQ vectors mismatch.> ...
498s  bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<bisafit: X and FREQ vectors mismatch.> ...
498s  bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<bisafit: 'options' 5th argument must be a structure> ...
498s  bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 12 tests, 12 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/normlike.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/normlike.m
498s ***** error<normlike: too few input arguments.> normlike ([12, 15]);
498s ***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
498s ***** error<normlike: PARAMS must be a two-element vector.> ...
498s  normlike ([12, 15, 3], [1:50]);
498s ***** error<normlike: X and CENSOR vectors mismatch.> ...
498s  normlike ([12, 15], [1:50], [1, 2, 3]);
498s ***** error<normlike: X and FREQ vectors mismatch.> ...
498s  normlike ([12, 15], [1:50], [], [1, 2, 3]);
498s ***** error<normlike: FREQ must not contain negative values.> ...
498s  normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
498s ***** test
498s  x = 1:50;
498s  [nlogL, avar] = normlike ([2.3, 1.2], x);
498s  avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
498s  assert (nlogL, 13014.95883783327, 1e-10);
498s  assert (avar, avar_out, 1e-4);
498s ***** test
498s  x = 1:50;
498s  [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
498s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
498s  assert (nlogL, 2854.802587833265, 1e-10);
498s  assert (avar, avar_out, 1e-4);
498s ***** test
498s  x = 1:50;
498s  [nlogL, avar] = normlike ([21, 15], x);
498s  avar_out = [5.460474308300396, -1.600790513833993; ...
498s              -1.600790513833993, 2.667984189723321];
498s  assert (nlogL, 206.738325604233, 1e-12);
498s  assert (avar, avar_out, 1e-14);
498s ***** test
498s  x = 1:50;
498s  censor = ones (1, 50);
498s  censor([2, 4, 6, 8, 12, 14]) = 0;
498s  [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
498s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
498s  assert (nlogL, Inf);
498s  assert (avar, [NaN, NaN; NaN, NaN]);
498s ***** test
498s  x = 1:50;
498s  censor = ones (1, 50);
498s  censor([2, 4, 6, 8, 12, 14]) = 0;
498s  [nlogL, avar] = normlike ([21, 15], x, censor);
498s  avar_out = [24.4824488866131, -10.6649544179636; ...
498s              -10.6649544179636, 6.22827849965737];
498s  assert (nlogL, 86.9254371829733, 1e-12);
498s  assert (avar, avar_out, 8e-14);
498s 11 tests, 11 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/unidfit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/unidfit.m
498s ***** demo
498s  ## Sample 2 populations from different discrete uniform distributions
498s  rand ("seed", 1);    # for reproducibility
498s  r1 = unidrnd (5, 1000, 1);
498s  rand ("seed", 2);    # for reproducibility
498s  r2 = unidrnd (9, 1000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 0:0.5:20.5, 1);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  hold on
498s 
498s  ## Estimate their probability of success
498s  NhatA = unidfit (r(:,1));
498s  NhatB = unidfit (r(:,2));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0:10];
498s  y = unidpdf (x, NhatA);
498s  plot (x, y, "-pg");
498s  y = unidpdf (x, NhatB);
498s  plot (x, y, "-sc");
498s  xlim ([0, 10])
498s  ylim ([0, 0.4])
498s  legend ({"Normalized HIST of sample 1 with N=5", ...
498s           "Normalized HIST of sample 2 with N=9", ...
498s           sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
498s           sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
498s  title ("Two population samples from different discrete uniform distributions")
498s  hold off
498s ***** test
498s  x = 0:5;
498s  [Nhat, Nci] = unidfit (x);
498s  assert (Nhat, 5);
498s  assert (Nci, [5; 9]);
498s ***** test
498s  x = 0:5;
498s  [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
498s  assert (Nhat, 5);
498s  assert (Nci, [5; 9]);
498s ***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
498s ***** error<unidfit: function called with too few input arguments.> unidfit ()
498s ***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
498s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
498s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
498s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
498s ***** error<unidfit: X and FREQ vector mismatch.> ...
498s  unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
498s ***** error<unidfit: X and FREQ vector mismatch.> ...
498s  unidfit ([1.5, 0.2], [], [1, 1, 1])
498s ***** error<unidfit: FREQ cannot have negative values.> ...
498s  unidfit ([1.5, 0.2], [], [1, -1])
498s 11 tests, 11 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/logllike.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/logllike.m
498s ***** test
498s  [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
498s  assert (nlogL, 211.2965, 1e-4);
498s  assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
498s ***** test
498s  [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
498s  assert (nlogL, 9.2206, 1e-4);
498s  assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
498s ***** error<logllike: function called with too few input arguments.> logllike (3.25)
498s ***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
498s ***** error<logllike: PARAMS must be a two-element vector.> ...
498s  logllike ([1, 0.2, 3], [1, 3, 5, 7])
498s ***** error<logllike: X and CENSOR vector mismatch.> ...
498s  logllike ([1.5, 0.2], [1:5], [0, 0, 0])
498s ***** error<logllike: X and FREQ vector mismatch.> ...
498s  logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
498s ***** error<logllike: X and FREQ vector mismatch.> ...
498s  logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
498s 8 tests, 8 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gevlike.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gevlike.m
498s ***** test
498s  x = 1;
498s  k = 0.2;
498s  sigma = 0.3;
498s  mu = 0.5;
498s  [L, C] = gevlike ([k sigma mu], x);
498s  expected_L = 0.75942;
498s  expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
498s  assert (L, expected_L, 0.001);
498s  assert (C, inv (expected_C), 0.001);
498s ***** test
498s  x = 1;
498s  k = 0;
498s  sigma = 0.3;
498s  mu = 0.5;
498s  [L, C] = gevlike ([k sigma mu], x);
498s  expected_L = 0.65157;
498s  expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
498s  assert (L, expected_L, 0.001);
498s  assert (C, inv (expected_C), 0.001);
498s ***** test
498s  x = -5:-1;
498s  k = -0.2;
498s  sigma = 0.3;
498s  mu = 0.5;
498s  [L, C] = gevlike ([k sigma mu], x);
498s  expected_L = 3786.4;
498s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
498s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
498s                8.7297e-05, 1.2034e-05, -0.0019125];
498s  assert (L, expected_L, -0.001);
498s  assert (C, expected_C, -0.001);
498s ***** test
498s  x = -5:0;
498s  k = -0.2;
498s  sigma = 0.3;
498s  mu = 0.5;
498s  [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
498s  expected_L = 3786.4;
498s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
498s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
498s                8.7297e-05, 1.2034e-05, -0.0019125];
498s  assert (L, expected_L, -0.001);
498s  assert (C, expected_C, -0.001);
498s ***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
498s ***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
498s ***** error<gevlike: PARAMS must be a three-element vector.> ...
498s  gevlike ([1, 2], [1, 3, 5, 7])
498s ***** error<gevlike: PARAMS must be a three-element vector.> ...
498s  gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
498s ***** error<gevlike: X and FREQ vectors mismatch.> ...
498s  gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
498s ***** error<gevlike: FREQ must not contain negative values.> ...
498s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
498s ***** error<gevlike: FREQ must contain integer values.> ...
498s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
498s 11 tests, 11 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/loglfit.m]
498s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/loglfit.m
498s ***** demo
498s  ## Sample 3 populations from different log-logistic distributions
498s  rand ("seed", 5)  # for reproducibility
498s  r1 = loglrnd (0, 1, 2000, 1);
498s  rand ("seed", 2)   # for reproducibility
498s  r2 = loglrnd (0, 0.5, 2000, 1);
498s  rand ("seed", 7)   # for reproducibility
498s  r3 = loglrnd (0, 0.125, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [0.05:0.1:2.5], 10);
498s  h = findobj (gca, "Type", "patch");
498s  set (h(1), "facecolor", "c");
498s  set (h(2), "facecolor", "g");
498s  set (h(3), "facecolor", "r");
498s  ylim ([0, 3.5]);
498s  xlim ([0, 2.0]);
498s  hold on
498s 
498s  ## Estimate their MU and LAMBDA parameters
498s  a_bA = loglfit (r(:,1));
498s  a_bB = loglfit (r(:,2));
498s  a_bC = loglfit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0.01:0.1:2.01];
498s  y = loglpdf (x, a_bA(1), a_bA(2));
498s  plot (x, y, "-pr");
498s  y = loglpdf (x, a_bB(1), a_bB(2));
498s  plot (x, y, "-sg");
498s  y = loglpdf (x, a_bC(1), a_bC(2));
498s  plot (x, y, "-^c");
498s  legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
498s           "Normalized HIST of sample 2 with α=1 and β=2", ...
498s           "Normalized HIST of sample 3 with α=1 and β=8", ...
498s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
498s                   a_bA(1), a_bA(2)), ...
498s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
498s                   a_bB(1), a_bB(2)), ...
498s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
498s                   a_bC(1), a_bC(2))})
498s  title ("Three population samples from different log-logistic distributions")
498s  hold off
498s ***** test
498s  [paramhat, paramci] = loglfit ([1:50]);
498s  paramhat_out = [3.09717, 0.468525];
498s  paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
498s  assert (paramhat, paramhat_out, 1e-5);
498s  assert (paramci, paramci_out, 1e-5);
498s ***** test
498s  paramhat = loglfit ([1:5]);
498s  paramhat_out = [1.01124, 0.336449];
498s  assert (paramhat, paramhat_out, 1e-5);
498s ***** test
498s  paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
498s  paramhat_out = [1.01124, 0.336449];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
498s  paramhat_out = loglfit ([1:5, 5]);
498s  assert (paramhat, paramhat_out, 1e-4);
499s ***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
499s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
499s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
499s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
499s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
499s  loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
499s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
499s  loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
499s ***** error<loglfit: X and FREQ vectors mismatch.> ...
499s  loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
499s ***** error<loglfit: X and FREQ vectors mismatch.> ...
499s  loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
499s ***** error<loglfit: 'options' 5th argument must be a structure> ...
499s  loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
499s 13 tests, 13 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/normfit.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/normfit.m
499s ***** demo
499s  ## Sample 3 populations from 3 different normal distributions
499s  randn ("seed", 1);    # for reproducibility
499s  r1 = normrnd (2, 5, 5000, 1);
499s  randn ("seed", 2);    # for reproducibility
499s  r2 = normrnd (5, 2, 5000, 1);
499s  randn ("seed", 3);    # for reproducibility
499s  r3 = normrnd (9, 4, 5000, 1);
499s  r = [r1, r2, r3];
499s 
499s  ## Plot them normalized and fix their colors
499s  hist (r, 15, 0.4);
499s  h = findobj (gca, "Type", "patch");
499s  set (h(1), "facecolor", "c");
499s  set (h(2), "facecolor", "g");
499s  set (h(3), "facecolor", "r");
499s  hold on
499s 
499s  ## Estimate their mu and sigma parameters
499s  [muhat, sigmahat] = normfit (r);
499s 
499s  ## Plot their estimated PDFs
499s  x = [min(r(:)):max(r(:))];
499s  y = normpdf (x, muhat(1), sigmahat(1));
499s  plot (x, y, "-pr");
499s  y = normpdf (x, muhat(2), sigmahat(2));
499s  plot (x, y, "-sg");
499s  y = normpdf (x, muhat(3), sigmahat(3));
499s  plot (x, y, "-^c");
499s  ylim ([0, 0.5])
499s  xlim ([-20, 20])
499s  hold off
499s  legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
499s           "Normalized HIST of sample 2 with mu=5, σ=2", ...
499s           "Normalized HIST of sample 3 with mu=9, σ=4", ...
499s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   muhat(1), sigmahat(1)), ...
499s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   muhat(2), sigmahat(2)), ...
499s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   muhat(3), sigmahat(3))}, "location", "northwest")
499s  title ("Three population samples from different normal distributions")
499s  hold off
499s ***** test
499s  load lightbulb
499s  idx = find (lightbulb(:,2) == 0);
499s  censoring = lightbulb(idx,3) == 1;
499s  [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
499s  assert (muHat, 9496.59586737857, 1e-11);
499s  assert (sigmaHat, 3064.021012796456, 2e-12);
499s ***** test
499s  randn ("seed", 234);
499s  x = normrnd (3, 5, [1000, 1]);
499s  [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
499s  assert (muCI(1) < 3);
499s  assert (muCI(2) > 3);
499s  assert (sigmaCI(1) < 5);
499s  assert (sigmaCI(2) > 5);
499s ***** error<normfit: X must not be a multi-dimensional array.> ...
499s  normfit (ones (3,3,3))
499s ***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
499s  normfit (ones (20,3), [], zeros (20,1))
499s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
499s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
499s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
499s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
499s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
499s ***** error<normfit: X and CENSOR vectors mismatch.> ...
499s  normfit (ones (20,1), [], zeros(15,1))
499s ***** error<normfit: X and FREQ vectors mismatch.> ...
499s  normfit (ones (20,1), [], zeros(20,1), ones(25,1))
499s ***** error<normfit: FREQ must not contain negative values.> ...
499s  normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
499s ***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
499s 13 tests, 13 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/wblfit.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/wblfit.m
499s ***** demo
499s  ## Sample 3 populations from 3 different Weibull distributions
499s  rande ("seed", 1);    # for reproducibility
499s  r1 = wblrnd(2, 4, 2000, 1);
499s  rande ("seed", 2);    # for reproducibility
499s  r2 = wblrnd(5, 2, 2000, 1);
499s  rande ("seed", 5);    # for reproducibility
499s  r3 = wblrnd(1, 5, 2000, 1);
499s  r = [r1, r2, r3];
499s 
499s  ## Plot them normalized and fix their colors
499s  hist (r, 30, [2.5 2.1 3.2]);
499s  h = findobj (gca, "Type", "patch");
499s  set (h(1), "facecolor", "c");
499s  set (h(2), "facecolor", "g");
499s  set (h(3), "facecolor", "r");
499s  ylim ([0, 2]);
499s  xlim ([0, 10]);
499s  hold on
499s 
499s  ## Estimate their lambda parameter
499s  lambda_kA = wblfit (r(:,1));
499s  lambda_kB = wblfit (r(:,2));
499s  lambda_kC = wblfit (r(:,3));
499s 
499s  ## Plot their estimated PDFs
499s  x = [0:0.1:15];
499s  y = wblpdf (x, lambda_kA(1), lambda_kA(2));
499s  plot (x, y, "-pr");
499s  y = wblpdf (x, lambda_kB(1), lambda_kB(2));
499s  plot (x, y, "-sg");
499s  y = wblpdf (x, lambda_kC(1), lambda_kC(2));
499s  plot (x, y, "-^c");
499s  hold off
499s  legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
499s           "Normalized HIST of sample 2 with λ=5 and k=2", ...
499s           "Normalized HIST of sample 3 with λ=1 and k=5", ...
499s           sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
499s                   lambda_kA(1), lambda_kA(2)), ...
499s           sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
499s                   lambda_kB(1), lambda_kB(2)), ...
499s           sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
499s                   lambda_kC(1), lambda_kC(2))})
499s  title ("Three population samples from different Weibull distributions")
499s  hold off
499s ***** test
499s  x = 1:50;
499s  [paramhat, paramci] = wblfit (x);
499s  paramhat_out = [28.3636, 1.7130];
499s  paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
499s  assert (paramhat, paramhat_out, 1e-4);
499s  assert (paramci, paramci_out, 1e-4);
499s ***** test
499s  x = 1:50;
499s  [paramhat, paramci] = wblfit (x, 0.01);
499s  paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
499s  assert (paramci, paramci_out, 1e-4);
499s ***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
499s ***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
499s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
499s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
499s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
499s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
499s  wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
499s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
499s  wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
499s ***** error<wblfit: X and FREQ vectors mismatch.> ...
499s  wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
499s ***** error<wblfit: FREQ cannot have negative values.> ...
499s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
499s ***** error<wblfit: X and FREQ vectors mismatch.> ...
499s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
499s ***** error<wblfit: 'options' 5th argument must be a structure> ...
499s  wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
499s 13 tests, 13 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/lognfit.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/lognfit.m
499s ***** demo
499s  ## Sample 3 populations from 3 different log-normal distributions
499s  randn ("seed", 1);    # for reproducibility
499s  r1 = lognrnd (0, 0.25, 1000, 1);
499s  randn ("seed", 2);    # for reproducibility
499s  r2 = lognrnd (0, 0.5, 1000, 1);
499s  randn ("seed", 3);    # for reproducibility
499s  r3 = lognrnd (0, 1, 1000, 1);
499s  r = [r1, r2, r3];
499s 
499s  ## Plot them normalized and fix their colors
499s  hist (r, 30, 2);
499s  h = findobj (gca, "Type", "patch");
499s  set (h(1), "facecolor", "c");
499s  set (h(2), "facecolor", "g");
499s  set (h(3), "facecolor", "r");
499s  hold on
499s 
499s  ## Estimate their mu and sigma parameters
499s  mu_sigmaA = lognfit (r(:,1));
499s  mu_sigmaB = lognfit (r(:,2));
499s  mu_sigmaC = lognfit (r(:,3));
499s 
499s  ## Plot their estimated PDFs
499s  x = [0:0.1:6];
499s  y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
499s  plot (x, y, "-pr");
499s  y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
499s  plot (x, y, "-sg");
499s  y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
499s  plot (x, y, "-^c");
499s  ylim ([0, 2])
499s  xlim ([0, 6])
499s  hold off
499s  legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
499s           "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
499s           "Normalized HIST of sample 3 with mu=0, σ=1", ...
499s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   mu_sigmaA(1), mu_sigmaA(2)), ...
499s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   mu_sigmaB(1), mu_sigmaB(2)), ...
499s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
499s                   mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
499s  title ("Three population samples from different log-normal distributions")
499s  hold off
499s ***** test
499s  randn ("seed", 1);
499s  x = lognrnd (3, 5, [1000, 1]);
499s  [paramhat, paramci] = lognfit (x, 0.01);
499s  assert (paramci(1,1) < 3);
499s  assert (paramci(1,2) > 3);
499s  assert (paramci(2,1) < 5);
499s  assert (paramci(2,2) > 5);
499s ***** error<lognfit: X must be a numeric vector of positive values.> ...
499s  lognfit (ones (20,3))
499s ***** error<lognfit: X must be a numeric vector of positive values.> ...
499s  lognfit ({1, 2, 3, 4, 5})
499s ***** error<lognfit: X must be a numeric vector of positive values.> ...
499s  lognfit ([-1, 2, 3, 4, 5])
499s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
499s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
499s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
499s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
499s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
499s ***** error<lognfit: X and CENSOR vectors mismatch.> ...
499s  lognfit (ones (20,1), [], zeros(15,1))
499s ***** error<lognfit: X and FREQ vectors mismatch.> ...
499s  lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
499s ***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
499s 12 tests, 12 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/nbinlike.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/nbinlike.m
499s ***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
499s ***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
499s ***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
499s ***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
499s ***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
499s ***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
499s ***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
499s ***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
499s ***** error<nbinlike: PARAMS must be a two-element vector.> ...
499s  nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
499s ***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
499s ***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
499s ***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
499s ***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
499s ***** error<nbinlike: X and FREQ vectors mismatch.> ...
499s  nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
499s ***** error<nbinlike: FREQ must not contain negative values.> ...
499s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
499s ***** error<nbinlike: FREQ must contain integer values.> ...
499s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
499s 16 tests, 16 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/gevfit_lmom.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/gevfit_lmom.m
499s ***** xtest <31070>
499s  data = 1:50;
499s  [pfit, pci] = gevfit_lmom (data);
499s  expected_p = [-0.28 15.01 20.22]';
499s  assert (pfit, expected_p, 0.1);
499s 1 test, 1 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/tlslike.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/tlslike.m
499s ***** test
499s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
499s  [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
499s  acov_out = [0.2525, 0.0670, 0.0288; ...
499s              0.0670, 0.5724, 0.1786; ...
499s              0.0288, 0.1786, 0.1789];
499s  assert (nlogL, 17.9979636579, 1e-10);
499s  assert (acov, acov_out, 1e-4);
499s ***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
499s ***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
499s ***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
499s ***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
499s ***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
499s ***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
499s 7 tests, 7 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/nbinfit.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/nbinfit.m
499s ***** demo
499s  ## Sample 2 populations from different negative binomial distributions
499s  randp ("seed", 5); randg ("seed", 5);    # for reproducibility
499s  r1 = nbinrnd (2, 0.15, 5000, 1);
499s  randp ("seed", 8); randg ("seed", 8);    # for reproducibility
499s  r2 = nbinrnd (5, 0.2, 5000, 1);
499s  r = [r1, r2];
499s 
499s  ## Plot them normalized and fix their colors
499s  hist (r, [0:51], 1);
499s  h = findobj (gca, "Type", "patch");
499s  set (h(1), "facecolor", "c");
499s  set (h(2), "facecolor", "g");
499s  hold on
499s 
499s  ## Estimate their probability of success
499s  r_psA = nbinfit (r(:,1));
499s  r_psB = nbinfit (r(:,2));
499s 
499s  ## Plot their estimated PDFs
499s  x = [0:40];
499s  y = nbinpdf (x, r_psA(1), r_psA(2));
499s  plot (x, y, "-pg");
499s  x = [min(r(:,2)):max(r(:,2))];
499s  y = nbinpdf (x, r_psB(1), r_psB(2));
499s  plot (x, y, "-sc");
499s  ylim ([0, 0.1])
499s  xlim ([0, 50])
499s  legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
499s           "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
499s           sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
499s                   r_psA(1), r_psA(2)), ...
499s           sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
499s                   r_psB(1), r_psB(2))})
499s  title ("Two population samples from negative different binomial distributions")
499s  hold off
499s ***** test
499s  [paramhat, paramci] = nbinfit ([1:50]);
499s  assert (paramhat, [2.420857, 0.086704], 1e-6);
499s  assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
499s  assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
499s ***** test
499s  [paramhat, paramci] = nbinfit ([1:20]);
499s  assert (paramhat, [3.588233, 0.254697], 1e-6);
499s  assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
499s  assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
499s ***** test
499s  [paramhat, paramci] = nbinfit ([1:10]);
499s  assert (paramhat, [8.8067, 0.6156], 1e-4);
499s  assert (paramci(:,1), [0; 30.7068], 1e-4);
499s  assert (paramci(:,2), [0.0217; 1], 1e-4);
499s ***** test
499s  [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
499s  assert (paramhat, [8.8067, 0.6156], 1e-4);
499s  assert (paramci(:,1), [0; 30.7068], 1e-4);
499s  assert (paramci(:,2), [0.0217; 1], 1e-4);
499s ***** test
499s  [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
499s  assert (paramhat, [8.8067, 0.6156], 1e-4);
499s  assert (paramci(:,1), [0; 30.7068], 1e-4);
499s  assert (paramci(:,2), [0.0217; 1], 1e-4);
499s ***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
499s ***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
499s ***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
499s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
499s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
499s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
499s ***** error<nbinfit: X and FREQ vectors mismatch.> ...
499s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
499s ***** error<nbinfit: FREQ must not contain negative values.> ...
499s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
499s ***** error<nbinfit: FREQ must contain integer values.> ...
499s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
499s ***** error<nbinfit: 'options' argument must be a structure> ...
499s  nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
499s ***** error<nbinfit: 'options' argument must be a structure> ...
499s  nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
499s 16 tests, 16 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/explike.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/explike.m
499s ***** test
499s  x = 12;
499s  beta = 5;
499s  [L, V] = explike (beta, x);
499s  expected_L = 4.0094;
499s  expected_V = 6.5789;
499s  assert (L, expected_L, 0.001);
499s  assert (V, expected_V, 0.001);
499s ***** test
499s  x = 1:5;
499s  beta = 2;
499s  [L, V] = explike (beta, x);
499s  expected_L = 10.9657;
499s  expected_V = 0.4;
499s  assert (L, expected_L, 0.001);
499s  assert (V, expected_V, 0.001);
499s ***** error<explike: function called with too few input arguments.> explike ()
499s ***** error<explike: function called with too few input arguments.> explike (2)
499s ***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
499s ***** error<explike: X must be a vector.> explike (3, ones (10, 2))
499s ***** error<explike: X and CENSOR vectors mismatch.> ...
499s  explike (3, [1:50], [1, 2, 3])
499s ***** error<explike: X and FREQ vectors mismatch.> ...
499s  explike (3, [1:50], [], [1, 2, 3])
499s 8 tests, 8 passed, 0 known failure, 0 skipped
499s [inst/dist_fit/burrfit.m]
499s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/burrfit.m
499s ***** demo
499s  ## Sample 3 populations from different Burr type XII distributions
499s  rand ("seed", 4);    # for reproducibility
499s  r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
499s  rand ("seed", 2);    # for reproducibility
499s  r2 = burrrnd (1, 3, 1, 10000, 1);
499s  rand ("seed", 9);    # for reproducibility
499s  r3 = burrrnd (0.5, 2, 3, 10000, 1);
499s  r = [r1, r2, r3];
499s 
499s  ## Plot them normalized and fix their colors
499s  hist (r, [0.1:0.2:20], [18, 5, 3]);
499s  h = findobj (gca, "Type", "patch");
499s  set (h(1), "facecolor", "c");
499s  set (h(2), "facecolor", "g");
499s  set (h(3), "facecolor", "r");
499s  ylim ([0, 3]);
499s  xlim ([0, 5]);
499s  hold on
499s 
499s  ## Estimate their α and β parameters
499s  lambda_c_kA = burrfit (r(:,1));
499s  lambda_c_kB = burrfit (r(:,2));
499s  lambda_c_kC = burrfit (r(:,3));
499s 
499s  ## Plot their estimated PDFs
499s  x = [0.01:0.15:15];
499s  y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
499s  plot (x, y, "-pr");
499s  y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
499s  plot (x, y, "-sg");
499s  y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
499s  plot (x, y, "-^c");
499s  hold off
499s  legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
499s           "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
499s           "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
499s   sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
499s           lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
499s   sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
499s           lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
499s   sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
499s           lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
499s  title ("Three population samples from different Burr type XII distributions")
499s  hold off
499s ***** test
499s  l = 1; c = 2; k = 3;
499s  r = burrrnd (l, c, k, 100000, 1);
499s  lambda_c_kA = burrfit (r);
499s  assert (lambda_c_kA(1), l, 0.2);
499s  assert (lambda_c_kA(2), c, 0.2);
499s  assert (lambda_c_kA(3), k, 0.3);
501s ***** test
501s  l = 0.5; c = 1; k = 3;
501s  r = burrrnd (l, c, k, 100000, 1);
501s  lambda_c_kA = burrfit (r);
501s  assert (lambda_c_kA(1), l, 0.2);
501s  assert (lambda_c_kA(2), c, 0.2);
501s  assert (lambda_c_kA(3), k, 0.3);
502s ***** test
502s  l = 1; c = 3; k = 1;
502s  r = burrrnd (l, c, k, 100000, 1);
502s  lambda_c_kA = burrfit (r);
502s  assert (lambda_c_kA(1), l, 0.2);
502s  assert (lambda_c_kA(2), c, 0.2);
502s  assert (lambda_c_kA(3), k, 0.3);
504s ***** test
504s  l = 3; c = 2; k = 1;
504s  r = burrrnd (l, c, k, 100000, 1);
504s  lambda_c_kA = burrfit (r);
504s  assert (lambda_c_kA(1), l, 0.2);
504s  assert (lambda_c_kA(2), c, 0.2);
504s  assert (lambda_c_kA(3), k, 0.3);
506s ***** test
506s  l = 4; c = 2; k = 4;
506s  r = burrrnd (l, c, k, 100000, 1);
506s  lambda_c_kA = burrfit (r);
506s  assert (lambda_c_kA(1), l, 0.2);
506s  assert (lambda_c_kA(2), c, 0.2);
506s  assert (lambda_c_kA(3), k, 0.3);
507s ***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
507s ***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
507s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
507s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
507s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
507s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
507s  burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
508s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
508s  burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
508s ***** error<burrfit: X and FREQ vectors mismatch.>
508s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
508s ***** error<burrfit: FREQ must not contain negative values.>
508s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
508s ***** error<burrfit: 'options' 5th argument must be a structure> ...
508s  burrfit ([1:10], 0.05, [], [], 5)
508s 15 tests, 15 passed, 0 known failure, 0 skipped
508s [inst/dist_fit/nakafit.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/nakafit.m
508s ***** demo
508s  ## Sample 3 populations from different Nakagami distributions
508s  randg ("seed", 5)  # for reproducibility
508s  r1 = nakarnd (0.5, 1, 2000, 1);
508s  randg ("seed", 2)   # for reproducibility
508s  r2 = nakarnd (5, 1, 2000, 1);
508s  randg ("seed", 7)   # for reproducibility
508s  r3 = nakarnd (2, 2, 2000, 1);
508s  r = [r1, r2, r3];
508s 
508s  ## Plot them normalized and fix their colors
508s  hist (r, [0.05:0.1:3.5], 10);
508s  h = findobj (gca, "Type", "patch");
508s  set (h(1), "facecolor", "c");
508s  set (h(2), "facecolor", "g");
508s  set (h(3), "facecolor", "r");
508s  ylim ([0, 2.5]);
508s  xlim ([0, 3.0]);
508s  hold on
508s 
508s  ## Estimate their MU and LAMBDA parameters
508s  mu_omegaA = nakafit (r(:,1));
508s  mu_omegaB = nakafit (r(:,2));
508s  mu_omegaC = nakafit (r(:,3));
508s 
508s  ## Plot their estimated PDFs
508s  x = [0.01:0.1:3.01];
508s  y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
508s  plot (x, y, "-pr");
508s  y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
508s  plot (x, y, "-sg");
508s  y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
508s  plot (x, y, "-^c");
508s  legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
508s           "Normalized HIST of sample 2 with μ=5 and ω=1", ...
508s           "Normalized HIST of sample 3 with μ=2 and ω=2", ...
508s           sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
508s                   mu_omegaA(1), mu_omegaA(2)), ...
508s           sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
508s                   mu_omegaB(1), mu_omegaB(2)), ...
508s           sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
508s                   mu_omegaC(1), mu_omegaC(2))})
508s  title ("Three population samples from different Nakagami distributions")
508s  hold off
508s ***** test
508s  paramhat = nakafit ([1:50]);
508s  paramhat_out = [0.7355, 858.5];
508s  assert (paramhat, paramhat_out, 1e-4);
508s ***** test
508s  paramhat = nakafit ([1:5]);
508s  paramhat_out = [1.1740, 11];
508s  assert (paramhat, paramhat_out, 1e-4);
508s ***** test
508s  paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
508s  paramhat_out = [1.1740, 11];
508s  assert (paramhat, paramhat_out, 1e-4);
508s ***** test
508s  paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
508s  paramhat_out = nakafit ([1:5, 5]);
508s  assert (paramhat, paramhat_out, 1e-4);
508s ***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
508s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
508s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
508s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
508s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
508s  nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
508s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
508s  nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
508s ***** error<nakafit: X and FREQ vectors mismatch.> ...
508s  nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
508s ***** error<nakafit: X and FREQ vectors mismatch.> ...
508s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
508s ***** error<nakafit: FREQ must not contain negative values.> ...
508s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
508s ***** error<nakafit: FREQ must contain integer values.> ...
508s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
508s ***** error<nakafit: 'options' 5th argument must be a structure> ...
508s  nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
508s 15 tests, 15 passed, 0 known failure, 0 skipped
508s [inst/dist_fit/unifit.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/dist_fit/unifit.m
508s ***** demo
508s  ## Sample 2 populations from different continuous uniform distributions
508s  rand ("seed", 5);    # for reproducibility
508s  r1 = unifrnd (2, 5, 2000, 1);
508s  rand ("seed", 6);    # for reproducibility
508s  r2 = unifrnd (3, 9, 2000, 1);
508s  r = [r1, r2];
508s 
508s  ## Plot them normalized and fix their colors
508s  hist (r, 0:0.5:10, 2);
508s  h = findobj (gca, "Type", "patch");
508s  set (h(1), "facecolor", "c");
508s  set (h(2), "facecolor", "g");
508s  hold on
508s 
508s  ## Estimate their probability of success
508s  a_bA = unifit (r(:,1));
508s  a_bB = unifit (r(:,2));
508s 
508s  ## Plot their estimated PDFs
508s  x = [0:10];
508s  y = unifpdf (x, a_bA(1), a_bA(2));
508s  plot (x, y, "-pg");
508s  y = unifpdf (x, a_bB(1), a_bB(2));
508s  plot (x, y, "-sc");
508s  xlim ([1, 10])
508s  ylim ([0, 0.5])
508s  legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
508s           "Normalized HIST of sample 2 with a=3 and b=9", ...
508s           sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
508s                   a_bA(1), a_bA(2)), ...
508s           sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
508s                   a_bB(1), a_bB(2))})
508s  title ("Two population samples from different continuous uniform distributions")
508s  hold off
508s ***** test
508s  x = 0:5;
508s  [paramhat, paramci] = unifit (x);
508s  assert (paramhat, [0, 5]);
508s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
508s ***** test
508s  x = 0:5;
508s  [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
508s  assert (paramhat, [0, 5]);
508s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
508s ***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
508s ***** error<unifit: function called with too few input arguments.> unifit ()
508s ***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
508s ***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
508s ***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
508s ***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
508s ***** error<unifit: X and FREQ vector mismatch.> ...
508s  unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
508s ***** error<unifit: FREQ cannot have negative values.> ...
508s  unifit ([1.5, 0.2], [], [1, -1])
508s ***** error<unifit: X and FREQ vector mismatch.> ...
508s  unifit ([1.5, 0.2], [], [1, 1, 1])
508s 11 tests, 11 passed, 0 known failure, 0 skipped
508s [inst/randsample.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/randsample.m
508s ***** test
508s  n = 20;
508s  k = 5;
508s  x = randsample(n, k);
508s  assert (size(x), [1 k]);
508s  x = randsample(n, k, true);
508s  assert (size(x), [1 k]);
508s  x = randsample(n, k, false);
508s  assert (size(x), [1 k]);
508s  x = randsample(n, k, true, ones(n, 1));
508s  assert (size(x), [1 k]);
508s  x = randsample(1:n, k);
508s  assert (size(x), [1 k]);
508s  x = randsample(1:n, k, true);
508s  assert (size(x), [1 k]);
508s  x = randsample(1:n, k, false);
508s  assert (size(x), [1 k]);
508s  x = randsample(1:n, k, true, ones(n, 1));
508s  assert (size(x), [1 k]);
508s  x = randsample((1:n)', k);
508s  assert (size(x), [k 1]);
508s  x = randsample((1:n)', k, true);
508s  assert (size(x), [k 1]);
508s  x = randsample((1:n)', k, false);
508s  assert (size(x), [k 1]);
508s  x = randsample((1:n)', k, true, ones(n, 1));
508s  assert (size(x), [k 1]);
508s  n = 10;
508s  k = 100;
508s  x = randsample(n, k, true, 1:n);
508s  assert (size(x), [1 k]);
508s  x = randsample((1:n)', k, true);
508s  assert (size(x), [k 1]);
508s  x = randsample(k, k, false, 1:k);
508s  assert (size(x), [1 k]);
508s 1 test, 1 passed, 0 known failure, 0 skipped
508s [inst/chi2test.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/chi2test.m
508s ***** error chi2test ();
508s ***** error chi2test ([1, 2, 3, 4, 5]);
508s ***** error chi2test ([1, 2; 2, 1+3i]);
508s ***** error chi2test ([NaN, 6; 34, 12]);
508s ***** error<chi2test: optional arguments are not supported for 2-way> ...
508s  p = chi2test (ones (3, 3), "mutual", []);
508s ***** error<chi2test: invalid model name for testing a 3-way table.> ...
508s  p = chi2test (ones (3, 3, 3), "testtype", 2);
508s ***** error<chi2test: optional arguments must be in pairs.> ...
508s  p = chi2test (ones (3, 3, 3), "mutual");
508s ***** error<chi2test: value must be numeric in optional argument> ...
508s  p = chi2test (ones (3, 3, 3), "joint", ["a"]);
508s ***** error<chi2test: value must be empty or scalar in optional argument> ...
508s  p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
508s ***** error<chi2test: optional arguments are not supported for k> ...
508s  p = chi2test (ones (3, 3, 3, 4), "mutual", [])
508s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
508s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
508s ***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
508s ***** test
508s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
508s  p = chi2test (x);
508s  assert (p, 0.017787, 1e-6);
508s ***** test
508s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
508s  [p, chisq] = chi2test (x);
508s  assert (chisq, 11.9421, 1e-4);
508s ***** test
508s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
508s  [p, chisq, df] = chi2test (x);
508s  assert (df, 4);
508s ***** test
508s ***** shared x
508s  x(:,:,1) = [59, 32; 9,16];
508s  x(:,:,2) = [55, 24;12,33];
508s  x(:,:,3) = [107,80;17,56];%!
508s ***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
508s ***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
508s ***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
508s ***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
508s ***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
508s ***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
508s ***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
508s ***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
508s ***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
508s ***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
508s ***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
508s ***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
508s ***** test
508s  [pval, chisq, df, E] = chi2test (x);
508s  assert (chisq, 64.0982, 1e-4);
508s  assert (df, 7);
508s  assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
508s ***** test
508s  [pval, chisq, df, E] = chi2test (x, "joint", 2);
508s  assert (chisq, 56.0943, 1e-4);
508s  assert (df, 5);
508s  assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
508s ***** test
508s  [pval, chisq, df, E] = chi2test (x, "marginal", 3);
508s  assert (chisq, 146.6058, 1e-4);
508s  assert (df, 9);
508s  assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
508s ***** test
508s  [pval, chisq, df, E] = chi2test (x, "conditional", 3);
508s  assert (chisq, 52.2509, 1e-4);
508s  assert (df, 3);
508s  assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
508s ***** test
508s  [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
508s  assert (chisq, 1.6034, 1e-4);
508s  assert (df, 2);
508s  assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
508s 34 tests, 34 passed, 0 known failure, warning: matrix singular to machine precision, rcond = 3.50566e-20
508s warning: called from
508s     regress at line 131 column 5
508s     __test__ at line 33 column 3
508s     test at line 685 column 11
508s     /tmp/tmp.YdhB1UcfDH at line 3590 column 2
508s 
508s 0 skipped
508s [inst/regress.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/regress.m
508s ***** test
508s  % Longley data from the NIST Statistical Reference Dataset
508s  Z = [  60323    83.0   234289   2356     1590    107608  1947
508s         61122    88.5   259426   2325     1456    108632  1948
508s         60171    88.2   258054   3682     1616    109773  1949
508s         61187    89.5   284599   3351     1650    110929  1950
508s         63221    96.2   328975   2099     3099    112075  1951
508s         63639    98.1   346999   1932     3594    113270  1952
508s         64989    99.0   365385   1870     3547    115094  1953
508s         63761   100.0   363112   3578     3350    116219  1954
508s         66019   101.2   397469   2904     3048    117388  1955
508s         67857   104.6   419180   2822     2857    118734  1956
508s         68169   108.4   442769   2936     2798    120445  1957
508s         66513   110.8   444546   4681     2637    121950  1958
508s         68655   112.6   482704   3813     2552    123366  1959
508s         69564   114.2   502601   3931     2514    125368  1960
508s         69331   115.7   518173   4806     2572    127852  1961
508s         70551   116.9   554894   4007     2827    130081  1962 ];
508s  % Results certified by NIST using 500 digit arithmetic
508s  % b and standard error in b
508s  V = [  -3482258.63459582         890420.383607373
508s          15.0618722713733         84.9149257747669
508s         -0.358191792925910E-01    0.334910077722432E-01
508s         -2.02022980381683         0.488399681651699
508s         -1.03322686717359         0.214274163161675
508s         -0.511041056535807E-01    0.226073200069370
508s          1829.15146461355         455.478499142212 ];
508s  Rsq = 0.995479004577296;
508s  F = 330.285339234588;
508s  y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
508s  alpha = 0.05;
508s  [b, bint, r, rint, stats] = regress (y, X, alpha);
508s  assert(b,V(:,1),4e-6);
508s  assert(stats(1),Rsq,1e-12);
508s  assert(stats(2),F,3e-8);
508s  assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
508s 1 test, 1 passed, 0 known failure, 0 skipped
508s [inst/anovan.m]
508s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/inst/anovan.m
508s ***** demo
508s 
508s  # Two-sample unpaired test on independent samples (equivalent to Student's
508s  # t-test). Note that the absolute value of t-statistic can be obtained by
508s  # taking the square root of the reported F statistic. In this example,
508s  # t = sqrt (1.44) = 1.20.
508s 
508s  score = [54 23 45 54 45 43 34 65 77 46 65]';
508s  gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
508s            "female" "female" "female"}';
508s 
508s  [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
508s ***** demo
508s 
508s  # Two-sample paired test on dependent or matched samples equivalent to a
508s  # paired t-test. As for the first example, the t-statistic can be obtained by
508s  # taking the square root of the reported F statistic. Note that the interaction
508s  # between treatment x subject was dropped from the full model by assigning
508s  # subject as a random factor (').
508s 
508s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
508s  treatment = {"before" "after"; "before" "after"; "before" "after";
508s               "before" "after"; "before" "after"}';
508s  subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
508s 
508s  [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
508s                             "model", "full", "random", 2, "sstype", 2, ...
508s                             "varnames", {"treatment", "subject"}, ...
508s                             "display", "on");
508s ***** demo
508s 
508s  # One-way ANOVA on the data from a study on the strength of structural beams,
508s  # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
508s 
508s  strength = [82 86 79 83 84 85 86 87 74 82 ...
508s             78 75 76 77 79 79 77 78 82 79]';
508s  alloy = {"st","st","st","st","st","st","st","st", ...
508s           "al1","al1","al1","al1","al1","al1", ...
508s           "al2","al2","al2","al2","al2","al2"}';
508s 
508s  [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
508s                             "varnames", "alloy");
508s ***** demo
508s 
508s  # One-way repeated measures ANOVA on the data from a study on the number of
508s  # words recalled by 10 subjects for three time conditions, in Loftus & Masson
508s  # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
508s  # between seconds x subject was dropped from the full model by assigning
508s  # subject as a random factor (').
508s 
508s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
508s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
508s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
508s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
508s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
508s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
508s 
508s  [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
508s                             "model", "full", "random", 2, "sstype", 2, ...
508s                             "display", "on", "varnames", {"seconds", "subject"});
508s ***** demo
508s 
508s  # Balanced two-way ANOVA with interaction on the data from a study of popcorn
508s  # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
508s  # New York: MacMillan
508s 
508s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
508s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
508s  brands = {"Gourmet", "National", "Generic"; ...
508s            "Gourmet", "National", "Generic"; ...
508s            "Gourmet", "National", "Generic"; ...
508s            "Gourmet", "National", "Generic"; ...
508s            "Gourmet", "National", "Generic"; ...
508s            "Gourmet", "National", "Generic"};
508s  popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
508s            "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
508s 
508s  [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
508s                             "display", "on", "model", "full", ...
508s                             "varnames", {"brands", "popper"});
508s ***** demo
508s 
508s  # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
508s  # gender and having a college degree on salaries of company employees,
508s  # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
508s 
508s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
508s            25 29 27 19 18 21 20 21 22 19]';
508s  gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
508s            "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
508s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
508s 
508s  [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
508s                             "sstype", 3, "display", "on", "varnames", ...
508s                             {"gender", "degree"});
508s ***** demo
508s 
508s  # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
508s  # adding sugar and/or milk on the tendency of coffee to make people babble,
508s  # in from Navarro (2019): 16.10
508s 
508s  sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
508s           "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
508s  milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
508s          "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
508s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
508s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
508s 
508s  [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
508s                             "sstype", 3, "display", "on", ...
508s                             "varnames", {"sugar", "milk"});
508s ***** demo
508s 
508s  # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
508s  # of three different drugs, biofeedback and diet on patient blood pressure,
508s  # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
508s  # * Missing values introduced to make the sample sizes unequal to test the
508s  #   calculation of different types of sums-of-squares
508s 
508s  drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
508s          "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
508s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
508s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
508s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
508s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
508s  feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
508s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
508s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
508s  diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
508s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
508s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
508s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
508s        173 194 197 190 176 198 164 190 169 164 176 175;
508s        186 194 201 215 219 209 164 166 159 182 187 174 ...
508s        189 194 217 206 199 195 171 173 196 199 180 NaN;
508s        180 187 199 170 204 194 162 184 183 156 180 173 ...
508s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
508s 
508s  [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
508s                                     "model", "full", "sstype", 3, ...
508s                                     "display", "on", ...
508s                                     "varnames", {"drug", "feedback", "diet"});
508s ***** demo
508s 
508s  # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
508s  # factor. The data is from a randomized block design study on the effects
508s  # of antioxidant treatment on glutathione-S-transferase (GST) levels in
508s  # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
508s  # Note that all interactions involving block were dropped from the full model
508s  # by assigning block as a random factor (').
508s 
508s  measurement = [444 614 423 625 408  856 447 719 ...
508s                 764 831 586 782 609 1002 606 766]';
508s  strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
508s           "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
508s  treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
508s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
508s 
508s  [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
508s                             "sstype", 2, "model", "full", "random", 3, ...
508s                             "display", "on", ...
508s                             "varnames", {"strain", "treatment", "block"});
508s ***** demo
508s 
508s  # One-way ANCOVA on data from a study of the additive effects of species
508s  # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
508s  # Text eveR
508s 
508s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
508s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
508s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
508s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
508s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
508s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
508s  species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
508s             "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
508s             "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
508s 
508s  [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
508s                            "continuous", 2, "sstype", "h", "display", "on", ...
508s                            "varnames", {"species", "temp"});
508s ***** demo
508s 
508s  # Factorial ANCOVA on data from a study of the effects of treatment and
508s  # exercise on stress reduction score after adjusting for age. Data from R
508s  # datarium package).
508s 
508s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
508s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
508s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
508s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
508s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
508s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
508s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
508s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
508s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
508s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
508s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
508s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
508s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
508s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
508s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
508s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
508s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
508s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
508s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
508s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
508s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
508s 
508s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
508s                             "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
508s                             "continuous", 3, "sstype", "h", "display", "on", ...
508s                             "varnames", {"treatment", "exercise", "age"});
508s ***** demo
508s 
508s  # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
508s  # relating to the contrasts are shown in the table of model parameters, and
508s  # can be retrieved from the STATS.coeffs output.
508s 
508s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
508s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
508s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
508s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
508s         25.694 ]';
508s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
508s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
508s  C = [ 0.4001601  0.3333333  0.5  0.0
508s        0.4001601  0.3333333 -0.5  0.0
508s        0.4001601 -0.6666667  0.0  0.0
508s       -0.6002401  0.0000000  0.0  0.5
508s       -0.6002401  0.0000000  0.0 -0.5];
508s 
508s  [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
508s                           "alpha", 0.05, "display", "on");
508s ***** demo
508s 
508s  # One-way ANOVA with the linear model fit by weighted least squares to
508s  # account for heteroskedasticity. In this example, the variance appears
508s  # proportional to the outcome, so weights have been estimated by initially
508s  # fitting the model without weights and regressing the absolute residuals on
508s  # the fitted values. Although this data could have been analysed by Welch's
508s  # ANOVA test, the approach here can generalize to ANOVA models with more than
508s  # one factor.
508s 
508s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
508s       2, 2, 2, 2, 2, 2, 2, 2, ...
508s       3, 3, 3, 3, 3, 3, 3, 3]';
508s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
508s       10, 25, 66, 43, 47, 56,  6, 39, ...
508s       11, 39, 26, 35, 25, 14, 24, 17]';
508s 
508s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
508s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
508s  b = polyfit (fitted, abs (STATS.resid), 1);
508s  v = polyval (b, fitted);  # Variance as a function of the fitted values
508s  figure("Name", "Regression of the absolute residuals on the fitted values");
508s  plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
508s  xlabel("Fitted values"); ylabel("Absolute residuals");
508s 
508s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
508s ***** test
508s  score = [54 23 45 54 45 43 34 65 77 46 65]';
508s  gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
508s            'female' 'female' 'female'}';
508s 
508s  [P, T, STATS] = anovan (score,gender,'display','off');
508s  assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
508s  assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
508s  assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
508s  assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
508s ***** test
508s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
508s  treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
508s               'before' 'after'; 'before' 'after'}';
508s  subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
508s 
508s  [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
508s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
508s  assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
508s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
508s  assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
508s ***** test
508s  strength = [82 86 79 83 84 85 86 87 74 82 ...
508s             78 75 76 77 79 79 77 78 82 79]';
508s  alloy = {'st','st','st','st','st','st','st','st', ...
508s           'al1','al1','al1','al1','al1','al1', ...
508s           'al2','al2','al2','al2','al2','al2'}';
508s 
508s  [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
508s  assert (P(1), 0.000152643638830491, 1e-09);
508s  assert (ATAB{2,6}, 15.4, 1e-09);
508s ***** test
508s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
508s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
508s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
508s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
508s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
508s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
508s 
508s  [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
508s  assert (P(1), 1.51865926758752e-07, 1e-09);
508s  assert (ATAB{2,2}, 52.2666666666667, 1e-09);
508s  assert (ATAB{3,2}, 942.533333333333, 1e-09);
508s  assert (ATAB{4,2}, 11.0666666666667, 1e-09);
508s ***** test
508s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
508s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
508s  brands = {'Gourmet', 'National', 'Generic'; ...
508s            'Gourmet', 'National', 'Generic'; ...
508s            'Gourmet', 'National', 'Generic'; ...
508s            'Gourmet', 'National', 'Generic'; ...
508s            'Gourmet', 'National', 'Generic'; ...
508s            'Gourmet', 'National', 'Generic'};
508s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
508s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
508s 
508s  [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
508s  assert (P(1), 7.67895738278171e-07, 1e-09);
508s  assert (P(2), 0.000100373896304998, 1e-09);
508s  assert (P(3), 0.746215396636649, 1e-09);
508s  assert (ATAB{2,6}, 56.7, 1e-09);
508s  assert (ATAB{3,6}, 32.4, 1e-09);
508s  assert (ATAB{4,6}, 0.29999999999997, 1e-09);
508s ***** test
508s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
508s            25 29 27 19 18 21 20 21 22 19]';
508s  gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
508s            'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
508s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
508s 
508s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
508s  assert (P(1), 0.747462549227232, 1e-09);
508s  assert (P(2), 1.03809316857694e-08, 1e-09);
508s  assert (P(3), 0.523689833702691, 1e-09);
508s  assert (ATAB{2,2}, 0.296969696969699, 1e-09);
508s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
508s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
508s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
508s  [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
508s  assert (P(1), 2.53445097305047e-08, 1e-09);
508s  assert (P(2), 0.00388133678528749, 1e-09);
508s  assert (P(3), 0.523689833702671, 1e-09);
508s  assert (ATAB{2,2}, 242.227272727273, 1e-09);
508s  assert (ATAB{3,2}, 30.4615384615384, 1e-09);
508s  assert (ATAB{4,2}, 1.17482517482523, 1e-09);
508s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
508s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
508s  assert (P(1), 0.00388133678528743, 1e-09);
508s  assert (P(2), 1.03809316857694e-08, 1e-09);
508s  assert (P(3), 0.523689833702691, 1e-09);
508s  assert (ATAB{2,2}, 30.4615384615385, 1e-09);
508s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
508s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
508s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
508s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
508s  assert (P(1), 0.00442898146583742, 1e-09);
508s  assert (P(2), 1.30634252053587e-08, 1e-09);
508s  assert (P(3), 0.523689833702691, 1e-09);
508s  assert (ATAB{2,2}, 29.3706293706294, 1e-09);
508s  assert (ATAB{3,2}, 264.335664335664, 1e-09);
508s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
508s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
508s ***** test
508s  sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
508s           'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
508s  milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
508s          'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
508s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
508s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
508s 
508s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
508s  assert (P(1), 0.0108632139833963, 1e-09);
508s  assert (P(2), 0.0810606976703546, 1e-09);
508s  assert (P(3), 0.00175433329935627, 1e-09);
508s  assert (ATAB{2,2}, 3.55752380952381, 1e-09);
508s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
508s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
508s  assert (ATAB{5,2}, 3.1625, 1e-09);
508s  [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
508s  assert (P(1), 0.0373333189297505, 1e-09);
508s  assert (P(2), 0.017075098787169, 1e-09);
508s  assert (P(3), 0.00175433329935627, 1e-09);
508s  assert (ATAB{2,2}, 1.444, 1e-09);
508s  assert (ATAB{3,2}, 3.06963228699552, 1e-09);
508s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
508s  assert (ATAB{5,2}, 3.1625, 1e-09);
508s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
508s  assert (P(1), 0.017075098787169, 1e-09);
508s  assert (P(2), 0.0810606976703546, 1e-09);
508s  assert (P(3), 0.00175433329935627, 1e-09);
508s  assert (ATAB{2,2}, 3.06963228699552, 1e-09);
508s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
508s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
508s  assert (ATAB{5,2}, 3.1625,  1e-09);
508s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
508s  assert (P(1), 0.0454263063473954, 1e-09);
508s  assert (P(2), 0.0746719907091438, 1e-09);
508s  assert (P(3), 0.00175433329935627, 1e-09);
508s  assert (ATAB{2,2}, 2.13184977578476, 1e-09);
508s  assert (ATAB{3,2}, 1.00413461538462, 1e-09);
508s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
508s  assert (ATAB{5,2}, 3.1625, 1e-09);
508s ***** test
508s  drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
508s          'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
508s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
508s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
508s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
508s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
508s  feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
508s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
508s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
508s  diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
508s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
508s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
508s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
508s        173 194 197 190 176 198 164 190 169 164 176 175;
508s        186 194 201 215 219 209 164 166 159 182 187 174 ...
508s        189 194 217 206 199 195 171 173 196 199 180 NaN;
508s        180 187 199 170 204 194 162 184 183 156 180 173 ...
508s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
508s 
508s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
508s  assert (P(1), 7.02561843825325e-05, 1e-09);
508s  assert (P(2), 0.000425806013389362, 1e-09);
508s  assert (P(3), 6.16780773446401e-07, 1e-09);
508s  assert (P(4), 0.261347622678438, 1e-09);
508s  assert (P(5), 0.0542278432357043, 1e-09);
508s  assert (P(6), 0.590353225626655, 1e-09);
508s  assert (P(7), 0.0861628249564267, 1e-09);
508s  assert (ATAB{2,2}, 3614.70355731226, 1e-09);
508s  assert (ATAB{3,2}, 2227.46639771024, 1e-09);
508s  assert (ATAB{4,2}, 5008.25614451819, 1e-09);
508s  assert (ATAB{5,2}, 437.066007908781, 1e-09);
508s  assert (ATAB{6,2}, 976.180770397332, 1e-09);
508s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
508s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
508s  assert (ATAB{9,2}, 9065.8,  1e-09);
508s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
508s  assert (P(1), 9.4879638470754e-05, 1e-09);
508s  assert (P(2), 0.00124177666315809, 1e-09);
508s  assert (P(3), 6.86162012732911e-07, 1e-09);
508s  assert (P(4), 0.260856132341256, 1e-09);
508s  assert (P(5), 0.0523758623892078, 1e-09);
508s  assert (P(6), 0.590353225626655, 1e-09);
508s  assert (P(7), 0.0861628249564267, 1e-09);
508s  assert (ATAB{2,2}, 3481.72176560122, 1e-09);
508s  assert (ATAB{3,2}, 1837.08812970469, 1e-09);
508s  assert (ATAB{4,2}, 4957.20277938622, 1e-09);
508s  assert (ATAB{5,2}, 437.693674777847, 1e-09);
508s  assert (ATAB{6,2}, 988.431929811402, 1e-09);
508s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
508s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
508s  assert (ATAB{9,2}, 9065.8,  1e-09);
508s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
508s  assert (P(1), 0.000106518678028207, 1e-09);
508s  assert (P(2), 0.00125371366571508, 1e-09);
508s  assert (P(3), 5.30813260778464e-07, 1e-09);
508s  assert (P(4), 0.308353667232981, 1e-09);
508s  assert (P(5), 0.0562901327343161, 1e-09);
508s  assert (P(6), 0.599091042141092, 1e-09);
508s  assert (P(7), 0.0861628249564267, 1e-09);
508s  assert (ATAB{2,2}, 3430.88156424581, 1e-09);
508s  assert (ATAB{3,2}, 1833.68031496063, 1e-09);
508s  assert (ATAB{4,2}, 5080.48346456693, 1e-09);
508s  assert (ATAB{5,2}, 382.07709497207, 1e-09);
508s  assert (ATAB{6,2}, 963.037988826813, 1e-09);
508s  assert (ATAB{7,2}, 44.4519685039322, 1e-09);
508s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
508s  assert (ATAB{9,2}, 9065.8, 1e-09);
508s ***** test
508s  measurement = [444 614 423 625 408  856 447 719 ...
508s                 764 831 586 782 609 1002 606 766]';
508s  strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
508s           'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
508s  treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
508s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
508s 
508s  [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
508s  assert (P(1), 0.0914352969909372, 1e-09);
508s  assert (P(2), 5.04077373924908e-05, 1e-09);
508s  assert (P(4), 0.0283196918836667, 1e-09);
508s  assert (ATAB{2,2}, 286.132500000002, 1e-09);
508s  assert (ATAB{3,2}, 2275.29, 1e-09);
508s  assert (ATAB{4,2}, 1242.5625, 1e-09);
508s  assert (ATAB{5,2}, 495.905000000001, 1e-09);
508s  assert (ATAB{6,2}, 207.007499999999, 1e-09);
509s ***** test
509s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
509s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
509s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
509s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
509s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
509s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
509s  species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
509s             'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
509s             'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
509s 
509s  [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
509s  assert (P(1), 6.27153318786007e-14, 1e-09);
509s  assert (P(2), 2.48773241196644e-25, 1e-09);
509s  assert (ATAB{2,2}, 598.003953318404, 1e-09);
509s  assert (ATAB{3,2}, 4376.08256843712, 1e-09);
509s  assert (ATAB{4,2}, 89.3498685376726, 1e-09);
509s  assert (ATAB{2,6}, 187.399388123951, 1e-09);
509s  assert (ATAB{3,6}, 1371.35413763454, 1e-09);
509s ***** test
509s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
509s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
509s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
509s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
509s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
509s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
509s  treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
509s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
509s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
509s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
509s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
509s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
509s  exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
509s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
509s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
509s              'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
509s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
509s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
509s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
509s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
509s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
509s 
509s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
509s  assert (P(5), 0.9245630968248468, 1e-09);
509s  assert (P(6), 0.791115159521822, 1e-09);
509s  assert (P(7), 0.9296668751457956, 1e-09);
509s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
509s  assert (P(1), 0.00158132928938933, 1e-09);
509s  assert (P(2), 2.12537505039986e-07, 1e-09);
509s  assert (P(3), 0.00390292555160047, 1e-09);
509s  assert (P(4), 0.0164086580775543, 1e-09);
509s  assert (ATAB{2,6}, 11.0956027650549, 1e-09);
509s  assert (ATAB{3,6}, 20.8195665467178, 1e-09);
509s  assert (ATAB{4,6}, 9.10966630720186, 1e-09);
509s  assert (ATAB{5,6}, 4.4457923698584, 1e-09);
509s ***** test
509s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
509s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
509s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
509s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
509s         25.694 ]';
509s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
509s  C = [ 0.4001601  0.3333333  0.5  0.0
509s        0.4001601  0.3333333 -0.5  0.0
509s        0.4001601 -0.6666667  0.0  0.0
509s       -0.6002401  0.0000000  0.0  0.5
509s       -0.6002401  0.0000000  0.0 -0.5];
509s 
509s  [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
509s  assert (STATS.coeffs(1,1), 19.4001, 1e-04);
509s  assert (STATS.coeffs(2,1), -9.3297, 1e-04);
509s  assert (STATS.coeffs(3,1), -5.0000, 1e-04);
509s  assert (STATS.coeffs(4,1), -8.0000, 1e-04);
509s  assert (STATS.coeffs(5,1), -8.0000, 1e-04);
509s  assert (STATS.coeffs(1,2), 0.4831, 1e-04);
509s  assert (STATS.coeffs(2,2), 0.9694, 1e-04);
509s  assert (STATS.coeffs(3,2), 1.3073, 1e-04);
509s  assert (STATS.coeffs(4,2), 1.6411, 1e-04);
509s  assert (STATS.coeffs(5,2), 1.4507, 1e-04);
509s  assert (STATS.coeffs(1,5), 40.161, 1e-03);
509s  assert (STATS.coeffs(2,5), -9.624, 1e-03);
509s  assert (STATS.coeffs(3,5), -3.825, 1e-03);
509s  assert (STATS.coeffs(4,5), -4.875, 1e-03);
509s  assert (STATS.coeffs(5,5), -5.515, 1e-03);
509s  assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
509s  assert (STATS.coeffs(3,6), 0.000572, 1e-06);
509s  assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
509s  assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
509s 12 tests, 12 passed, 0 known failure, 0 skipped
509s Checking C++ files ...
509s [src/fcnnpredict.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/fcnnpredict.cc
509s ***** shared X, Y, MODEL
509s  load fisheriris
509s  X = meas;
509s  Y = grp2idx (species);
509s  MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
509s ***** test
509s  [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
509s  assert (numel (Y_pred), numel (Y));
509s  assert (isequal (size (Y_pred), size (Y)), true);
509s  assert (columns (Y_scores), numel (unique (Y)));
509s  assert (rows (Y_scores), numel (Y));
509s ***** error <fcnnpredict: too few input arguments.> ...
509s  fcnnpredict (MODEL);
509s ***** error <fcnnpredict: too many output arguments.> ...
509s  [Q, W, E] = fcnnpredict (MODEL, X);
509s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
509s  fcnnpredict (1, X);
509s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
509s  fcnnpredict (struct ("L", {1, 2, 3}), X);
509s ***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
509s  fcnnpredict (struct ("L", 1), X);
509s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", 1), X);
509s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {1}), X);
509s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
509s ***** error <fcnnpredict: model does not have an 'Activations' field.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
509s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
509s                       "Activations", [2]), X);
509s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
509s                       "Activations", [2; 2]), X);
509s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
509s                       "Activations", {{2, 2}}), X);
509s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
509s                       "Activations", {{"sigmoid", "softmax"}}), X);
509s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
509s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
509s                       "Activations", "sigmoid"), X);
509s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
509s  fcnnpredict (MODEL, complex (X));
509s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
509s  fcnnpredict (MODEL, {1, 2, 3, 4});
509s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
509s  fcnnpredict (MODEL, "asd");
509s ***** error <fcnnpredict: XC cannot be empty.> ...
509s  fcnnpredict (MODEL, []);
509s ***** error <fcnnpredict: the features in XC do not match the trained model.> ...
509s  fcnnpredict (MODEL, X(:,[1:3]));
509s 20 tests, 20 passed, 0 known failure, 0 skipped
509s [src/svmpredict.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/svmpredict.cc
509s ***** test
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s  model = svmtrain (L, D, '-c 1 -g 0.07');
509s  [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
509s  assert (size (predict_label), size (dec_values));
509s  assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
509s  assert (dec_values(1), 1.225836001973273, 1e-14);
509s  assert (dec_values(2), -0.3212992933043805, 1e-14);
509s  assert (predict_label(1), 1);
509s ***** shared L, D, model
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s  model = svmtrain (L, D, '-c 1 -g 0.07');
509s ***** error <svmpredict: wrong number of output arguments.> ...
509s  [p, a] = svmpredict (L, D, model);
509s ***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
509s ***** error <svmpredict: label vector and instance matrix must be double.> ...
509s  p = svmpredict (single (L), D, model);
509s ***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
509s 5 tests, 5 passed, 0 known failure, 0 skipped
509s [src/libsvmwrite.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/libsvmwrite.cc
509s ***** shared L, D
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s ***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
509s ***** error <libsvmwrite: length of label vector does not match instances.> ...
509s  libsvmwrite (tempname (), [L;L], D);
509s ***** error <libsvmwrite: wrong number of output arguments.> ...
509s  OUT = libsvmwrite (tempname (), L, D);
509s ***** error <libsvmwrite: label vector and instance matrix must be double.> ...
509s  libsvmwrite (tempname (), single (L), D);
509s ***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
509s ***** error <libsvmwrite: instance_matrix must be sparse.> ...
509s  libsvmwrite (tempname (), L, full (D));
509s ***** error <libsvmwrite: wrong number of input arguments.> ...
509s  libsvmwrite (tempname (), L, D, D);
509s 7 tests, 7 passed, 0 known failure, 0 skipped
509s [src/fcnntrain.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/fcnntrain.cc
509s ***** shared X, Y, MODEL
509s  load fisheriris
509s  X = meas;
509s  Y = grp2idx (species);
509s ***** error <fcnntrain: too few input arguments.> ...
509s  model = fcnntrain (X, Y);
509s ***** error <fcnntrain: too many output arguments.> ...
509s  [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: X must be a real numeric matrix.> ...
509s  fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: X must be a real numeric matrix.> ...
509s  fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: X cannot be empty.> ...
509s  fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
509s  fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
509s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
509s  fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: Y cannot be empty.> ...
509s  fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: X and Y must have the same number of rows.> ...
509s  fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: labels in Y must be positive integers.> ...
509s  fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
509s  fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
509s  fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: cannot have a layer of zero size.> ...
509s  fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: invalid 'Activations' code.> ...
509s  fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: invalid 'Activations' code.> ...
509s  fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
509s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
509s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
509s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
509s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
509s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
509s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
509s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
509s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
509s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
509s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
509s 33 tests, 33 passed, 0 known failure, 0 skipped
509s [src/editDistance.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/editDistance.cc
509s ***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
509s ***** error <editDistance: too many output arguments.> ...
509s  [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
509s ***** error <editDistance: too many output arguments.> ...
509s  [C, IA] = editDistance ({"AS","SD","AD"});
509s ***** error <editDistance: minDist must be a scalar value.> ...
509s  d = editDistance ({"AS","SD","AD"}, [1, 2]);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ({"AS","SD","AD"}, -2);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ({"AS","SD","AD"}, 1.25);
509s ***** error <editDistance: minDist must be a scalar value.> ...
509s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
509s ***** error <editDistance: minDist must be a scalar value.> ...
509s  d = editDistance ("string1", "string2", [1, 2]);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ("string1", "string2", -2);
509s ***** error <editDistance: minDist must be a nonnegative integer.> ...
509s  d = editDistance ("string1", "string2", 1.25);
509s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
509s  d = editDistance ({{"string1", "string2"}, 2});
509s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
509s  d = editDistance ({{"string1", "string2"}, 2}, 2);
509s ***** error <editDistance: STR1 must be a cellstr.> ...
509s  d = editDistance ([1, 2, 3]);
509s ***** error <editDistance: STR1 must be a cellstr.> ...
509s  d = editDistance (["AS","SD","AD","AS"]);
509s ***** error <editDistance: STR1 must be a cellstr.> ...
509s  d = editDistance (["AS","SD","AD"], 2);
509s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
509s  d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
509s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
509s  d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
509s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
509s  d = editDistance ([1,2,3], {"AS","AS","AD"});
509s ***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
509s  d = editDistance ({1,2,3}, {"AS","SD","AD"});
509s ***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
509s  d = editDistance ({"AS","SD","AD"}, {1,2,3});
509s ***** error <editDistance: cellstr input arguments size mismatch.> ...
509s  d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
509s ***** test
509s  d = editDistance ({"AS","SD","AD"});
509s  assert (d, [2; 1; 1]);
509s  assert (class (d), "double");
509s ***** test
509s  C = editDistance ({"AS","SD","AD"}, 1);
509s  assert (iscellstr (C), true);
509s  assert (C, {"AS";"SD"});
509s ***** test
509s  [C, IA] = editDistance ({"AS","SD","AD"}, 1);
509s  assert (class (IA), "double");
509s  assert (IA, [1;2]);
509s ***** test
509s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
509s  [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
509s  assert (class (IA), "double");
509s  assert (A(IA), C);
509s ***** test
509s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
509s  [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
509s  assert (class (IA), "cell");
509s  assert (C, {"ASS"; "FDE"; "OPA"});
509s  assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
509s  assert (A(IA{2}), {"FDE"; "EDS"});
509s  assert (A(IA{3}), {"OPA"});
509s ***** test
509s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
509s  [C, IA, IC] = editDistance (A, 2);
509s  assert (class (IA), "double");
509s  assert (A(IA), C);
509s  assert (IC, [1; 1; 3; 1; 5]);
509s ***** test
509s  d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
509s  assert (d, [0; 1; 2]);
509s  assert (class (d), "double");
509s ***** test
509s  d = editDistance ({"AS","SD","AD"}, {"AS"});
509s  assert (d, [0; 2; 1]);
509s  assert (class (d), "double");
509s ***** test
509s  d = editDistance ({"AS"}, {"AS","SD","AD"});
509s  assert (d, [0; 2; 1]);
509s  assert (class (d), "double");
509s ***** test
509s  b = editDistance ("Octave", "octave");
509s  assert (b, 1);
509s  assert (class (b), "double");
509s 33 tests, 33 passed, 0 known failure, 0 skipped
509s [src/svmtrain.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/svmtrain.cc
509s ***** test
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s  model = svmtrain(L, D, '-c 1 -g 0.07');
509s  [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
509s  assert (isstruct (model), true);
509s  assert (isfield (model, "Parameters"), true);
509s  assert (model.totalSV, 130);
509s  assert (model.nr_class, 2);
509s  assert (size (model.Label), [2, 1]);
509s ***** shared L, D
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s ***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
509s ***** error <svmtrain: label vector and instance matrix must be double.> ...
509s  model = svmtrain (single (L), D);
509s ***** error <svmtrain: wrong number of input arguments.> ...
509s  model = svmtrain (L, D, "", "");
509s 4 tests, 4 passed, 0 known failure, 0 skipped
509s [src/libsvmread.cc]
509s >>>>> /tmp/autopkgtest.NoTi52/build.da7/src/src/libsvmread.cc
509s ***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
509s ***** error <libsvmread: wrong number of input or output arguments.> ...
509s  D = libsvmread ("filename");
509s ***** test
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s  assert (size (L), [270, 1]);
509s  assert (size (D), [270, 13]);
509s ***** test
509s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
509s  assert (issparse (L), false);
509s  assert (issparse (D), true);
509s 4 tests, 4 passed, 0 known failure, 0 skipped
510s Done running the unit tests.
510s Summary: 11023 tests, 11021 passed, 1 known failures, 1 skipped
510s autopkgtest [04:50:58]: test command1: -----------------------]
512s autopkgtest [04:51:00]: test command1:  - - - - - - - - - - results - - - - - - - - - -
512s command1             PASS
513s autopkgtest [04:51:01]: @@@@@@@@@@@@@@@@@@@@ summary
513s command1             PASS