0s autopkgtest [08:48:47]: starting date and time: 2026-02-05 08:48:47+0000
  0s autopkgtest [08:48:47]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op
  0s autopkgtest [08:48:47]: host juju-7f2275-prod-proposed-migration-environment-2; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.yfg6aan5/out --timeout-copy=6000 --needs-internet=try --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:glibc --apt-upgrade octave-statistics --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=glibc/2.42-2ubuntu5 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-cpu2-ram4-disk20-ppc64el --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-2@sto01-ppc64el-5.secgroup --name adt-resolute-ppc64el-octave-statistics-20260205-084846-juju-7f2275-prod-proposed-migration-environment-2-1998f123-f730-45cb-911e-45af89193c05 --image adt/ubuntu-resolute-ppc64el-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-2 --net-id=net_prod-autopkgtest-workers-ppc64el -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/
  7s Creating nova instance adt-resolute-ppc64el-octave-statistics-20260205-084846-juju-7f2275-prod-proposed-migration-environment-2-1998f123-f730-45cb-911e-45af89193c05 from image adt/ubuntu-resolute-ppc64el-server-20260205.img (UUID f866c950-0b62-4023-bac6-0f13279e15ed)...
 70s autopkgtest [08:49:57]: testbed dpkg architecture: ppc64el
 71s autopkgtest [08:49:58]: testbed apt version: 3.1.14
 71s autopkgtest [08:49:58]: @@@@@@@@@@@@@@@@@@@@ test bed setup
 71s autopkgtest [08:49:58]: testbed release detected to be: None
 72s autopkgtest [08:49:59]: updating testbed package index (apt update)
 72s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB]
 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
 73s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [35.4 kB]
 73s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [5260 B]
 73s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [228 kB]
 73s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1719 kB]
 73s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el Packages [261 kB]
 73s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/universe ppc64el Packages [1514 kB]
 73s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse ppc64el Packages [21.9 kB]
 73s Fetched 3910 kB in 1s (3599 kB/s)
 74s Reading package lists...
 75s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease
 75s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease
 75s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease
 75s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease
 76s Reading package lists...
 76s Reading package lists...
 76s Building dependency tree...
 76s Reading state information...
 76s Calculating upgrade...
 76s The following packages will be upgraded:
 76s   libc-bin libc-gconv-modules-extra libc6 locales pollinate
 76s   python3-referencing sed
 76s 7 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
 76s Need to get 8612 kB of archives.
 76s After this operation, 0 B of additional disk space will be used.
 76s Get:1 http://ftpmaster.internal/ubuntu resolute/main ppc64el sed ppc64el 4.9-2build3 [211 kB]
 77s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el libc-gconv-modules-extra ppc64el 2.42-2ubuntu5 [1448 kB]
 78s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el libc6 ppc64el 2.42-2ubuntu5 [1913 kB]
 80s Get:4 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el libc-bin ppc64el 2.42-2ubuntu5 [748 kB]
 81s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el locales all 2.42-2ubuntu5 [4255 kB]
 86s Get:6 http://ftpmaster.internal/ubuntu resolute/main ppc64el pollinate all 4.33-4ubuntu5 [14.0 kB]
 86s Get:7 http://ftpmaster.internal/ubuntu resolute/main ppc64el python3-referencing all 0.36.2-1ubuntu2 [22.2 kB]
 86s dpkg-preconfigure: unable to re-open stdin: No such file or directory
 86s Fetched 8612 kB in 9s (945 kB/s)
 86s (Reading database ... 
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(Reading database ... 82008 files and directories currently installed.)
 86s Preparing to unpack .../sed_4.9-2build3_ppc64el.deb ...
 86s Unpacking sed (4.9-2build3) over (4.9-2build2) ...
 86s Setting up sed (4.9-2build3) ...
 87s (Reading database ... 
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(Reading database ... 82008 files and directories currently installed.)
 87s Preparing to unpack .../libc-gconv-modules-extra_2.42-2ubuntu5_ppc64el.deb ...
 87s Unpacking libc-gconv-modules-extra:ppc64el (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 87s Setting up libc-gconv-modules-extra:ppc64el (2.42-2ubuntu5) ...
 87s (Reading database ... 
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(Reading database ... 82008 files and directories currently installed.)
 87s Preparing to unpack .../libc6_2.42-2ubuntu5_ppc64el.deb ...
 87s Unpacking libc6:ppc64el (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 88s Setting up libc6:ppc64el (2.42-2ubuntu5) ...
 88s (Reading database ... 
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(Reading database ... 82008 files and directories currently installed.)
 88s Preparing to unpack .../libc-bin_2.42-2ubuntu5_ppc64el.deb ...
 88s Unpacking libc-bin (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 88s Setting up libc-bin (2.42-2ubuntu5) ...
 88s (Reading database ... 
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(Reading database ... 82008 files and directories currently installed.)
 88s Preparing to unpack .../locales_2.42-2ubuntu5_all.deb ...
 89s Unpacking locales (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 89s Preparing to unpack .../pollinate_4.33-4ubuntu5_all.deb ...
 89s Unpacking pollinate (4.33-4ubuntu5) over (4.33-4ubuntu4) ...
 89s Preparing to unpack .../python3-referencing_0.36.2-1ubuntu2_all.deb ...
 90s Unpacking python3-referencing (0.36.2-1ubuntu2) over (0.36.2-1ubuntu1) ...
 90s Setting up locales (2.42-2ubuntu5) ...
 90s Generating locales (this might take a while)...
 91s   en_US.UTF-8... done
 91s Generation complete.
 91s Setting up pollinate (4.33-4ubuntu5) ...
102s Setting up python3-referencing (0.36.2-1ubuntu2) ...
102s Processing triggers for man-db (2.13.1-1) ...
104s Processing triggers for install-info (7.2-5) ...
104s Processing triggers for systemd (259-1ubuntu3) ...
105s autopkgtest [08:50:32]: upgrading testbed (apt dist-upgrade and autopurge)
105s Reading package lists...
105s Building dependency tree...
105s Reading state information...
106s Calculating upgrade...
106s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
106s Reading package lists...
106s Building dependency tree...
106s Reading state information...
106s Solving dependencies...
106s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
107s autopkgtest [08:50:34]: rebooting testbed after setup commands that affected boot
153s autopkgtest [08:51:20]: testbed running kernel: Linux 6.18.0-9-generic #9-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 12 16:45:54 UTC 2026
156s autopkgtest [08:51:23]: @@@@@@@@@@@@@@@@@@@@ apt-source octave-statistics
159s Get:1 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (dsc) [2289 B]
159s Get:2 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (tar) [1424 kB]
159s Get:3 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (diff) [10.3 kB]
160s gpgv: Signature made Mon Oct 27 09:10:08 2025 UTC
160s gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
160s gpgv: Can't check signature: No public key
160s dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.6-2.dsc: no acceptable signature found
160s autopkgtest [08:51:27]: testing package octave-statistics version 1.7.6-2
161s autopkgtest [08:51:28]: build not needed
170s autopkgtest [08:51:37]: test command1: preparing testbed
170s Reading package lists...
170s Building dependency tree...
170s Reading state information...
170s Solving dependencies...
171s The following NEW packages will be installed:
171s   aglfn appstream autoconf automake autopoint autotools-dev cme comerr-dev cpp
171s   cpp-15 cpp-15-powerpc64le-linux-gnu cpp-powerpc64le-linux-gnu debhelper
171s   debugedit dh-autoreconf dh-octave dh-octave-autopkgtest
171s   dh-strip-nondeterminism diffstat dwz fontconfig fontconfig-config
171s   fonts-freefont-otf g++ g++-15 g++-15-powerpc64le-linux-gnu
171s   g++-powerpc64le-linux-gnu gcc gcc-15 gcc-15-powerpc64le-linux-gnu
171s   gcc-powerpc64le-linux-gnu gettext gfortran gfortran-15
171s   gfortran-15-powerpc64le-linux-gnu gfortran-powerpc64le-linux-gnu
171s   gnuplot-data gnuplot-nox hdf5-helpers intltool-debian krb5-multidev
171s   libaec-dev libaec0 libalgorithm-c3-perl libaliased-perl libamd3 libaom3
171s   libapp-cmd-perl libappstream5 libapt-pkg-perl libarchive-zip-perl
171s   libarpack2t64 libarray-intspan-perl libasan8 libasound2-data libasound2t64
171s   libasyncns0 libavahi-client3 libavahi-common-data libavahi-common3
171s   libb-hooks-endofscope-perl libb-hooks-op-check-perl libb-keywords-perl
171s   libb2-1 libberkeleydb-perl libblas-dev libblas3 libboolean-perl
171s   libbrotli-dev libc-dev-bin libc6-dev libcairo2 libcamd3 libcapture-tiny-perl
171s   libcarp-assert-more-perl libcc1-0 libccolamd3 libcgi-pm-perl libcholmod5
171s   libclass-c3-perl libclass-data-inheritable-perl libclass-inspector-perl
171s   libclass-load-perl libclass-method-modifiers-perl libclass-tiny-perl
171s   libclass-xsaccessor-perl libclone-choose-perl libclone-perl libcolamd3
171s   libconfig-inifiles-perl libconfig-model-backend-yaml-perl
171s   libconfig-model-dpkg-perl libconfig-model-perl libconfig-tiny-perl
171s   libconst-fast-perl libconvert-binhex-perl libcpanel-json-xs-perl
171s   libcrypt-dev libcups2t64 libcurl4-openssl-dev libcxsparse4
171s   libdata-dpath-perl libdata-messagepack-perl libdata-optlist-perl
171s   libdata-section-perl libdata-validate-domain-perl libdata-validate-ip-perl
171s   libdata-validate-uri-perl libdatrie1 libde265-0 libdebhelper-perl
171s   libdeflate0 libdevel-callchecker-perl libdevel-size-perl
171s   libdevel-stacktrace-perl libdouble-conversion3 libduktape207
171s   libdynaloader-functions-perl libegl-mesa0 libegl1 libemail-address-xs-perl
171s   libencode-locale-perl liberror-perl libevent-2.1-7t64
171s   libexception-class-perl libexporter-lite-perl libexporter-tiny-perl
171s   libfftw3-bin libfftw3-dev libfftw3-double3 libfftw3-long3 libfftw3-single3
171s   libfile-basedir-perl libfile-find-rule-perl libfile-homedir-perl
171s   libfile-listing-perl libfile-sharedir-perl libfile-stripnondeterminism-perl
171s   libfile-which-perl libflac14 libfltk-gl1.3t64 libfltk1.3t64 libfont-ttf-perl
171s   libfontconfig1 libfontenc1 libfyaml0 libgbm1 libgcc-15-dev libgd3
171s   libgetopt-long-descriptive-perl libgfortran-15-dev libgfortran5 libgl-dev
171s   libgl1 libgl1-mesa-dri libgl2ps1.4 libglpk40 libglu1-mesa libglvnd0
171s   libglx-dev libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0t64
171s   libgnutls-openssl27t64 libgnutls28-dev libgomp1
171s   libgraphicsmagick++-q16-12t64 libgraphicsmagick-q16-3t64 libgraphite2-3
171s   libgssrpc4t64 libharfbuzz0b libhash-merge-perl libhdf5-310 libhdf5-cpp-310
171s   libhdf5-dev libhdf5-fortran-310 libhdf5-hl-310 libhdf5-hl-cpp-310
171s   libhdf5-hl-fortran-310 libheif-plugin-aomdec libheif-plugin-libde265
171s   libheif1 libhtml-form-perl libhtml-html5-entities-perl libhtml-parser-perl
171s   libhtml-tagset-perl libhtml-tokeparser-simple-perl libhtml-tree-perl
171s   libhttp-cookies-perl libhttp-date-perl libhttp-message-perl
171s   libhttp-negotiate-perl libhwy1t64 libice6 libidn2-dev libimagequant0
171s   libimport-into-perl libindirect-perl libinput-bin libinput10 libintl-perl
171s   libio-html-perl libio-interactive-perl libio-socket-ssl-perl
171s   libio-string-perl libio-stringy-perl libio-tiecombine-perl libipc-run3-perl
171s   libipc-system-simple-perl libisl23 libiterator-perl libiterator-util-perl
171s   libitm1 libjack-jackd2-0 libjbig0 libjpeg-dev libjpeg-turbo8
171s   libjpeg-turbo8-dev libjpeg8 libjpeg8-dev libjson-maybexs-perl libjson-perl
171s   libjxl0.11 libkadm5clnt-mit12 libkadm5srv-mit12 libkdb5-10t64 libkrb5-dev
171s   liblapack-dev liblapack3 liblcms2-2 libldap-dev liblerc4
171s   liblingua-en-inflect-perl liblist-compare-perl liblist-moreutils-perl
171s   liblist-moreutils-xs-perl liblist-someutils-perl liblist-utilsby-perl
171s   liblog-any-adapter-screen-perl liblog-any-perl liblog-log4perl-perl liblsan0
171s   libltdl7 liblua5.4-0 liblwp-mediatypes-perl liblwp-protocol-https-perl
171s   libmailtools-perl libmarkdown2 libmd4c0 libmime-tools-perl libmldbm-perl
171s   libmodule-implementation-perl libmodule-pluggable-perl
171s   libmodule-runtime-perl libmoo-perl libmoox-aliases-perl libmouse-perl
171s   libmousex-nativetraits-perl libmousex-strictconstructor-perl libmp3lame0
171s   libmpc3 libmpg123-0t64 libmro-compat-perl libmtdev1t64
171s   libnamespace-clean-perl libncurses-dev libnet-domain-tld-perl
171s   libnet-http-perl libnet-ipv6addr-perl libnet-netmask-perl
171s   libnet-smtp-ssl-perl libnet-ssleay-perl libnetaddr-ip-perl libnghttp2-dev
171s   libnumber-compare-perl libobject-pad-perl libogg0 libopengl0 libopus0
171s   libp11-kit-dev libpackage-stash-perl libpango-1.0-0 libpangocairo-1.0-0
171s   libpangoft2-1.0-0 libparams-classify-perl libparams-util-perl
171s   libparams-validate-perl libparse-debcontrol-perl libparse-recdescent-perl
171s   libpath-iterator-rule-perl libpath-tiny-perl libpcre2-16-0
171s   libperl-critic-perl libperlio-gzip-perl libperlio-utf8-strict-perl
171s   libpixman-1-0 libpkgconf3 libpod-constants-perl libpod-parser-perl
171s   libpod-pom-perl libpod-spell-perl libportaudio2 libppi-perl
171s   libppix-quotelike-perl libppix-regexp-perl libppix-utils-perl
171s   libproc-processtable-perl libproxy1v5 libpsl-dev libpulse0 libqhull-r8.0
171s   libqrupdate1 libqscintilla2-qt6-15 libqscintilla2-qt6-l10n
171s   libqt6core5compat6 libqt6core6t64 libqt6dbus6 libqt6gui6 libqt6help6
171s   libqt6network6 libqt6opengl6 libqt6openglwidgets6 libqt6printsupport6
171s   libqt6sql6 libqt6widgets6 libqt6xml6 libquadmath0 libreadline-dev
171s   libreadonly-perl libregexp-common-perl libregexp-pattern-license-perl
171s   libregexp-pattern-perl libregexp-wildcards-perl librole-tiny-perl
171s   librtmp-dev libsafe-isa-perl libsamplerate0 libsereal-decoder-perl
171s   libsereal-encoder-perl libset-intspan-perl libsharpyuv0 libsm6 libsndfile1
171s   libsoftware-copyright-perl libsoftware-license-perl
171s   libsoftware-licensemoreutils-perl libsort-versions-perl libspqr4
171s   libssh2-1-dev libssl-dev libstdc++-15-dev libstemmer0d libstrictures-perl
171s   libstring-copyright-perl libstring-escape-perl libstring-format-perl
171s   libstring-license-perl libstring-rewriteprefix-perl libsub-exporter-perl
171s   libsub-exporter-progressive-perl libsub-identify-perl libsub-install-perl
171s   libsub-name-perl libsub-quote-perl libsub-uplevel-perl libsuitesparseconfig7
171s   libsyntax-keyword-try-perl libsz2 libtask-weaken-perl libtasn1-6-dev
171s   libterm-readkey-perl libtest-exception-perl libtext-autoformat-perl
171s   libtext-glob-perl libtext-levenshtein-damerau-perl
171s   libtext-levenshteinxs-perl libtext-markdown-discount-perl
171s   libtext-reform-perl libtext-template-perl libtext-unidecode-perl
171s   libtext-wrapper-perl libtext-xslate-perl libthai-data libthai0 libtiff6
171s   libtime-duration-perl libtime-moment-perl libtimedate-perl libtoml-tiny-perl
171s   libtool libtry-tiny-perl libts0t64 libtsan2 libubsan1 libumfpack6
171s   libunbound8 libunicode-utf8-perl libunwind8 liburi-perl
171s   libvariable-magic-perl libvorbis0a libvorbisenc2 libvulkan1 libwacom-common
171s   libwacom9 libwayland-client0 libwebp7 libwebpmux3 libwmflite-0.2-7
171s   libwww-mechanize-perl libwww-perl libwww-robotrules-perl libx11-dev
171s   libx11-xcb1 libxau-dev libxaw7 libxcb-cursor0 libxcb-dri3-0 libxcb-glx0
171s   libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present0 libxcb-randr0
171s   libxcb-render-util0 libxcb-render0 libxcb-shape0 libxcb-shm0 libxcb-sync1
171s   libxcb-util1 libxcb-xfixes0 libxcb-xinput0 libxcb-xkb1 libxcb1-dev
171s   libxcursor1 libxdmcp-dev libxfixes3 libxfont2 libxft2 libxinerama1
171s   libxkbcommon-x11-0 libxkbfile1 libxml-libxml-perl
171s   libxml-namespacesupport-perl libxml-sax-base-perl libxml-sax-perl libxmu6
171s   libxpm4 libxrandr2 libxrender1 libxs-parse-keyword-perl
171s   libxs-parse-sublike-perl libxshmfence1 libxt6t64 libxxf86vm1
171s   libyaml-libyaml-perl libyaml-pp-perl libyaml-tiny-perl libzstd-dev
171s   licensecheck lintian linux-libc-dev lzip lzop m4 mesa-libgallium nettle-dev
171s   octave octave-common octave-dev octave-io octave-statistics
171s   octave-statistics-common patchutils perl-openssl-defaults perltidy pkgconf
171s   pkgconf-bin po-debconf rpcsvc-proto t1utils tex-common texinfo texinfo-lib
171s   unzip x11-common x11-xkb-utils x11proto-dev xorg-sgml-doctools
171s   xserver-common xtrans-dev xvfb zlib1g-dev
171s 0 upgraded, 495 newly installed, 0 to remove and 0 not upgraded.
171s Need to get 212 MB of archives.
171s After this operation, 796 MB of additional disk space will be used.
171s Get:1 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfyaml0 ppc64el 0.9.3-1 [282 kB]
171s Get:2 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstemmer0d ppc64el 3.0.1-1 [179 kB]
171s Get:3 http://ftpmaster.internal/ubuntu resolute/main ppc64el libappstream5 ppc64el 1.1.1-1 [275 kB]
171s Get:4 http://ftpmaster.internal/ubuntu resolute/main ppc64el appstream ppc64el 1.1.1-1 [77.9 kB]
171s Get:5 http://ftpmaster.internal/ubuntu resolute/main ppc64el m4 ppc64el 1.4.20-2 [236 kB]
171s Get:6 http://ftpmaster.internal/ubuntu resolute/main ppc64el autoconf all 2.72-3.1ubuntu1 [384 kB]
171s Get:7 http://ftpmaster.internal/ubuntu resolute/main ppc64el autotools-dev all 20240727.1 [43.4 kB]
171s Get:8 http://ftpmaster.internal/ubuntu resolute/main ppc64el automake all 1:1.18.1-3build1 [582 kB]
171s Get:9 http://ftpmaster.internal/ubuntu resolute/main ppc64el autopoint all 0.23.2-1 [620 kB]
171s Get:10 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcapture-tiny-perl all 0.50-1 [20.7 kB]
171s Get:11 http://ftpmaster.internal/ubuntu resolute/main ppc64el libparams-util-perl ppc64el 1.102-3build1 [21.3 kB]
171s Get:12 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-install-perl all 0.929-1 [9764 B]
171s Get:13 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-optlist-perl all 0.114-1 [9708 B]
171s Get:14 http://ftpmaster.internal/ubuntu resolute/main ppc64el libb-hooks-op-check-perl ppc64el 0.22-3build2 [9884 B]
171s Get:15 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdynaloader-functions-perl all 0.004-2 [11.5 kB]
171s Get:16 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdevel-callchecker-perl ppc64el 0.009-2 [14.1 kB]
171s Get:17 http://ftpmaster.internal/ubuntu resolute/main ppc64el libparams-classify-perl ppc64el 0.015-2build6 [22.2 kB]
171s Get:18 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmodule-runtime-perl all 0.018-1 [15.2 kB]
171s Get:19 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtry-tiny-perl all 0.32-1 [21.2 kB]
171s Get:20 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmodule-implementation-perl all 0.09-2 [12.0 kB]
171s Get:21 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpackage-stash-perl all 0.40-1 [19.5 kB]
171s Get:22 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libclass-load-perl all 0.25-2 [12.7 kB]
171s Get:23 http://ftpmaster.internal/ubuntu resolute/main ppc64el libio-stringy-perl all 2.113-2 [45.3 kB]
171s Get:24 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libparams-validate-perl ppc64el 1.31-2build4 [55.4 kB]
171s Get:25 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-exporter-perl all 0.990-1 [49.0 kB]
171s Get:26 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libgetopt-long-descriptive-perl all 0.116-2 [25.0 kB]
171s Get:27 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libio-tiecombine-perl all 1.005-3 [9464 B]
171s Get:28 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libmodule-pluggable-perl all 5.2-5 [19.5 kB]
171s Get:29 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libstring-rewriteprefix-perl all 0.009-1 [6310 B]
171s Get:30 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libapp-cmd-perl all 0.338-1 [58.5 kB]
172s Get:31 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libboolean-perl all 0.46-3 [8430 B]
172s Get:32 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsub-uplevel-perl all 0.2800-3 [11.6 kB]
172s Get:33 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtest-exception-perl all 0.43-3 [13.4 kB]
172s Get:34 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libcarp-assert-more-perl all 2.9.0-1 [19.4 kB]
172s Get:35 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-which-perl all 1.27-2 [12.5 kB]
172s Get:36 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-homedir-perl all 1.006-2 [37.0 kB]
172s Get:37 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libclone-choose-perl all 0.010-2 [7738 B]
172s Get:38 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhash-merge-perl all 0.302-1 [13.0 kB]
172s Get:39 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjson-perl all 4.10000-1 [81.9 kB]
172s Get:40 http://ftpmaster.internal/ubuntu resolute/main ppc64el libexporter-tiny-perl all 1.006003-1 [35.5 kB]
172s Get:41 http://ftpmaster.internal/ubuntu resolute/universe ppc64el liblist-moreutils-xs-perl ppc64el 0.430-4build1 [43.2 kB]
172s Get:42 http://ftpmaster.internal/ubuntu resolute/universe ppc64el liblist-moreutils-perl all 0.430-2 [38.2 kB]
172s Get:43 http://ftpmaster.internal/ubuntu resolute/universe ppc64el liblog-log4perl-perl all 1.57-1 [345 kB]
172s Get:44 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmouse-perl ppc64el 2.6.1-1 [138 kB]
172s Get:45 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libmousex-nativetraits-perl all 1.09-3 [53.2 kB]
172s Get:46 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libmousex-strictconstructor-perl all 0.02-3 [4582 B]
172s Get:47 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libparse-recdescent-perl all 1.967015+dfsg-4 [139 kB]
172s Get:48 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpath-tiny-perl all 0.148-1 [47.9 kB]
172s Get:49 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libpod-pom-perl all 2.01-4 [61.3 kB]
172s Get:50 http://ftpmaster.internal/ubuntu resolute/main ppc64el libregexp-common-perl all 2024080801-1 [162 kB]
172s Get:51 http://ftpmaster.internal/ubuntu resolute/main ppc64el libyaml-tiny-perl all 1.76-1 [24.2 kB]
172s Get:52 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libconfig-model-perl all 2.155-1 [356 kB]
172s Get:53 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libyaml-pp-perl all 0.39.0-1 [107 kB]
172s Get:54 http://ftpmaster.internal/ubuntu resolute/universe ppc64el cme all 1.044-2 [68.0 kB]
172s Get:55 http://ftpmaster.internal/ubuntu resolute/main ppc64el libisl23 ppc64el 0.27-1build1 [893 kB]
172s Get:56 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmpc3 ppc64el 1.3.1-2 [62.5 kB]
172s Get:57 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [11.4 MB]
179s Get:58 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-15 ppc64el 15.2.0-12ubuntu1 [1038 B]
179s Get:59 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [5746 B]
179s Get:60 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp ppc64el 4:15.2.0-4ubuntu1 [22.4 kB]
179s Get:61 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdebhelper-perl all 13.28ubuntu1 [97.4 kB]
179s Get:62 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcc1-0 ppc64el 15.2.0-12ubuntu1 [49.0 kB]
179s Get:63 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgomp1 ppc64el 15.2.0-12ubuntu1 [169 kB]
179s Get:64 http://ftpmaster.internal/ubuntu resolute/main ppc64el libitm1 ppc64el 15.2.0-12ubuntu1 [32.2 kB]
179s Get:65 http://ftpmaster.internal/ubuntu resolute/main ppc64el libasan8 ppc64el 15.2.0-12ubuntu1 [3006 kB]
180s Get:66 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblsan0 ppc64el 15.2.0-12ubuntu1 [1374 kB]
181s Get:67 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtsan2 ppc64el 15.2.0-12ubuntu1 [2729 kB]
182s Get:68 http://ftpmaster.internal/ubuntu resolute/main ppc64el libubsan1 ppc64el 15.2.0-12ubuntu1 [1231 kB]
182s Get:69 http://ftpmaster.internal/ubuntu resolute/main ppc64el libquadmath0 ppc64el 15.2.0-12ubuntu1 [160 kB]
182s Get:70 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgcc-15-dev ppc64el 15.2.0-12ubuntu1 [1670 kB]
183s Get:71 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [22.4 MB]
193s Get:72 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-15 ppc64el 15.2.0-12ubuntu1 [530 kB]
194s Get:73 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [1220 B]
194s Get:74 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc ppc64el 4:15.2.0-4ubuntu1 [5032 B]
194s Get:75 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el libc-dev-bin ppc64el 2.42-2ubuntu5 [23.9 kB]
194s Get:76 http://ftpmaster.internal/ubuntu resolute/main ppc64el linux-libc-dev ppc64el 6.18.0-9.9 [1847 kB]
194s Get:77 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcrypt-dev ppc64el 1:4.5.1-1 [162 kB]
194s Get:78 http://ftpmaster.internal/ubuntu resolute/main ppc64el rpcsvc-proto ppc64el 1.4.3-1build1 [84.2 kB]
195s Get:79 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el libc6-dev ppc64el 2.42-2ubuntu5 [2079 kB]
195s Get:80 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtool all 2.5.4-9 [169 kB]
195s Get:81 http://ftpmaster.internal/ubuntu resolute/main ppc64el dh-autoreconf all 21 [12.5 kB]
195s Get:82 http://ftpmaster.internal/ubuntu resolute/main ppc64el libarchive-zip-perl all 1.68-1 [90.2 kB]
195s Get:83 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-stripnondeterminism-perl all 1.15.0-1build1 [20.6 kB]
195s Get:84 http://ftpmaster.internal/ubuntu resolute/main ppc64el dh-strip-nondeterminism all 1.15.0-1build1 [5110 B]
195s Get:85 http://ftpmaster.internal/ubuntu resolute/main ppc64el debugedit ppc64el 1:5.2-3build1 [57.3 kB]
195s Get:86 http://ftpmaster.internal/ubuntu resolute/main ppc64el dwz ppc64el 0.16-2 [142 kB]
195s Get:87 http://ftpmaster.internal/ubuntu resolute/main ppc64el gettext ppc64el 0.23.2-1 [1177 kB]
196s Get:88 http://ftpmaster.internal/ubuntu resolute/main ppc64el intltool-debian all 0.35.0+20060710.6build1 [24.1 kB]
196s Get:89 http://ftpmaster.internal/ubuntu resolute/main ppc64el po-debconf all 1.0.22 [215 kB]
196s Get:90 http://ftpmaster.internal/ubuntu resolute/main ppc64el debhelper all 13.28ubuntu1 [916 kB]
196s Get:91 http://ftpmaster.internal/ubuntu resolute/universe ppc64el aglfn all 1.7+git20191031.4036a9c-2build1 [33.2 kB]
196s Get:92 http://ftpmaster.internal/ubuntu resolute/universe ppc64el gnuplot-data all 6.0.2+dfsg1-2ubuntu1 [76.0 kB]
196s Get:93 http://ftpmaster.internal/ubuntu resolute/universe ppc64el fonts-freefont-otf all 20211204+svn4273-4build1 [4594 kB]
198s Get:94 http://ftpmaster.internal/ubuntu resolute/main ppc64el fontconfig-config ppc64el 2.17.1-3ubuntu1 [38.5 kB]
198s Get:95 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfontconfig1 ppc64el 2.17.1-3ubuntu1 [193 kB]
198s Get:96 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpixman-1-0 ppc64el 0.46.4-1 [347 kB]
198s Get:97 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-render0 ppc64el 1.17.0-2ubuntu1 [17.4 kB]
198s Get:98 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-shm0 ppc64el 1.17.0-2ubuntu1 [6072 B]
198s Get:99 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxrender1 ppc64el 1:0.9.12-1 [23.0 kB]
198s Get:100 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcairo2 ppc64el 1.18.4-3 [759 kB]
199s Get:101 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsharpyuv0 ppc64el 1.5.0-0.1build1 [24.7 kB]
199s Get:102 http://ftpmaster.internal/ubuntu resolute/main ppc64el libaom3 ppc64el 3.13.1-2 [2908 kB]
200s Get:103 http://ftpmaster.internal/ubuntu resolute/main ppc64el libheif-plugin-aomdec ppc64el 1.21.2-1 [14.4 kB]
200s Get:104 http://ftpmaster.internal/ubuntu resolute/main ppc64el libde265-0 ppc64el 1.0.16-1build1 [300 kB]
200s Get:105 http://ftpmaster.internal/ubuntu resolute/main ppc64el libheif-plugin-libde265 ppc64el 1.21.2-1 [10.2 kB]
200s Get:106 http://ftpmaster.internal/ubuntu resolute/main ppc64el libheif1 ppc64el 1.21.2-1 [657 kB]
201s Get:107 http://ftpmaster.internal/ubuntu resolute/main ppc64el libimagequant0 ppc64el 2.18.0-1build1 [43.2 kB]
201s Get:108 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-turbo8 ppc64el 2.1.5-4ubuntu2 [215 kB]
201s Get:109 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg8 ppc64el 8c-2ubuntu11 [2148 B]
201s Get:110 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdeflate0 ppc64el 1.23-2 [63.3 kB]
201s Get:111 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjbig0 ppc64el 2.1-6.1ubuntu3 [37.1 kB]
201s Get:112 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblerc4 ppc64el 4.0.0+ds-5ubuntu2 [315 kB]
201s Get:113 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwebp7 ppc64el 1.5.0-0.1build1 [330 kB]
201s Get:114 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtiff6 ppc64el 4.7.0-3ubuntu3 [307 kB]
201s Get:115 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxpm4 ppc64el 1:3.5.17-1build3 [50.5 kB]
201s Get:116 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgd3 ppc64el 2.3.3-13ubuntu1 [165 kB]
201s Get:117 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblua5.4-0 ppc64el 5.4.8-1 [213 kB]
201s Get:118 http://ftpmaster.internal/ubuntu resolute/main ppc64el fontconfig ppc64el 2.17.1-3ubuntu1 [182 kB]
201s Get:119 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgraphite2-3 ppc64el 1.3.14-11ubuntu1 [85.3 kB]
201s Get:120 http://ftpmaster.internal/ubuntu resolute/main ppc64el libharfbuzz0b ppc64el 12.3.2-1 [663 kB]
202s Get:121 http://ftpmaster.internal/ubuntu resolute/main ppc64el libthai-data all 0.1.30-1 [155 kB]
202s Get:122 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdatrie1 ppc64el 0.2.14-1 [22.7 kB]
202s Get:123 http://ftpmaster.internal/ubuntu resolute/main ppc64el libthai0 ppc64el 0.1.30-1 [22.5 kB]
202s Get:124 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpango-1.0-0 ppc64el 1.57.0-1 [283 kB]
202s Get:125 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpangoft2-1.0-0 ppc64el 1.57.0-1 [61.2 kB]
202s Get:126 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpangocairo-1.0-0 ppc64el 1.57.0-1 [31.0 kB]
202s Get:127 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwebpmux3 ppc64el 1.5.0-0.1build1 [31.3 kB]
202s Get:128 http://ftpmaster.internal/ubuntu resolute/universe ppc64el gnuplot-nox ppc64el 6.0.2+dfsg1-2ubuntu1 [1390 kB]
203s Get:129 http://ftpmaster.internal/ubuntu resolute/universe ppc64el dh-octave-autopkgtest all 1.14.0 [11.9 kB]
203s Get:130 http://ftpmaster.internal/ubuntu resolute/main ppc64el libapt-pkg-perl ppc64el 0.1.43 [71.1 kB]
203s Get:131 http://ftpmaster.internal/ubuntu resolute/main ppc64el libarray-intspan-perl all 2.004-2 [25.0 kB]
203s Get:132 http://ftpmaster.internal/ubuntu resolute/main ppc64el libconfig-inifiles-perl all 3.000003-4 [38.5 kB]
203s Get:133 http://ftpmaster.internal/ubuntu resolute/main ppc64el libyaml-libyaml-perl ppc64el 0.904.0+ds-1 [46.5 kB]
203s Get:134 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libconfig-model-backend-yaml-perl all 2.134-2 [10.5 kB]
203s Get:135 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libexporter-lite-perl all 0.09-2 [9748 B]
203s Get:136 http://ftpmaster.internal/ubuntu resolute/main ppc64el libencode-locale-perl all 1.05-3 [11.6 kB]
203s Get:137 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtimedate-perl all 2.3300-2 [34.0 kB]
203s Get:138 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhttp-date-perl all 6.06-1 [10.2 kB]
203s Get:139 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-listing-perl all 6.16-1 [11.3 kB]
203s Get:140 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-tagset-perl all 3.24-1 [14.1 kB]
203s Get:141 http://ftpmaster.internal/ubuntu resolute/main ppc64el liburi-perl all 5.34-2build1 [100 kB]
203s Get:142 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-parser-perl ppc64el 3.83-1build1 [91.8 kB]
203s Get:143 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-tree-perl all 5.07-3 [200 kB]
203s Get:144 http://ftpmaster.internal/ubuntu resolute/main ppc64el libclone-perl ppc64el 0.47-1 [11.1 kB]
203s Get:145 http://ftpmaster.internal/ubuntu resolute/main ppc64el libio-html-perl all 1.004-3 [15.9 kB]
203s Get:146 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblwp-mediatypes-perl all 6.04-2 [20.1 kB]
203s Get:147 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhttp-message-perl all 7.01-1ubuntu1 [76.1 kB]
203s Get:148 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhttp-cookies-perl all 6.11-1 [18.2 kB]
203s Get:149 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhttp-negotiate-perl all 6.01-2 [12.4 kB]
203s Get:150 http://ftpmaster.internal/ubuntu resolute/main ppc64el perl-openssl-defaults ppc64el 7build4 [6710 B]
203s Get:151 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-ssleay-perl ppc64el 1.94-3 [323 kB]
203s Get:152 http://ftpmaster.internal/ubuntu resolute/main ppc64el libio-socket-ssl-perl all 2.098-1 [205 kB]
203s Get:153 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-http-perl all 6.24-1build1 [21.7 kB]
203s Get:154 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblwp-protocol-https-perl all 6.14-1 [9040 B]
203s Get:155 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwww-robotrules-perl all 6.02-1build1 [12.4 kB]
203s Get:156 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwww-perl all 6.81-1build1 [141 kB]
203s Get:157 http://ftpmaster.internal/ubuntu resolute/main ppc64el liberror-perl all 0.17030-1 [23.5 kB]
203s Get:158 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libparse-debcontrol-perl all 2.005-6 [20.4 kB]
203s Get:159 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsoftware-copyright-perl all 0.015-1 [14.4 kB]
203s Get:160 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libalgorithm-c3-perl all 0.11-2 [10.2 kB]
203s Get:161 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libclass-c3-perl all 0.35-2 [18.4 kB]
203s Get:162 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libmro-compat-perl all 0.15-2 [10.1 kB]
203s Get:163 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libdata-section-perl all 0.200008-1 [11.6 kB]
203s Get:164 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-template-perl all 1.61-1 [48.5 kB]
203s Get:165 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsoftware-license-perl all 0.104007-1 [123 kB]
203s Get:166 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsoftware-licensemoreutils-perl all 1.009-1 [21.5 kB]
203s Get:167 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsort-versions-perl all 1.62-3 [7378 B]
203s Get:168 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-reform-perl all 1.20-5 [35.4 kB]
203s Get:169 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-autoformat-perl all 1.750000-2 [29.8 kB]
203s Get:170 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-levenshtein-damerau-perl all 0.41-3 [10.8 kB]
203s Get:171 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtoml-tiny-perl all 0.20-1 [21.8 kB]
203s Get:172 http://ftpmaster.internal/ubuntu resolute/main ppc64el libclass-inspector-perl all 1.36-3 [15.4 kB]
203s Get:173 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-sharedir-perl all 1.118-3 [14.0 kB]
203s Get:174 http://ftpmaster.internal/ubuntu resolute/main ppc64el libindirect-perl ppc64el 0.39-2build5 [22.9 kB]
203s Get:175 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxs-parse-keyword-perl ppc64el 0.49-1 [65.2 kB]
203s Get:176 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxs-parse-sublike-perl ppc64el 0.41-1 [51.2 kB]
203s Get:177 http://ftpmaster.internal/ubuntu resolute/main ppc64el libobject-pad-perl ppc64el 0.823-2 [143 kB]
203s Get:178 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsyntax-keyword-try-perl ppc64el 0.31-1 [26.5 kB]
203s Get:179 http://ftpmaster.internal/ubuntu resolute/main ppc64el libio-interactive-perl all 1.027-1 [10.8 kB]
203s Get:180 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblog-any-perl all 1.718-1build1 [69.6 kB]
203s Get:181 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblog-any-adapter-screen-perl all 0.141-1 [12.9 kB]
203s Get:182 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-exporter-progressive-perl all 0.001013-3 [6718 B]
203s Get:183 http://ftpmaster.internal/ubuntu resolute/main ppc64el libvariable-magic-perl ppc64el 0.64-1build1 [37.2 kB]
203s Get:184 http://ftpmaster.internal/ubuntu resolute/main ppc64el libb-hooks-endofscope-perl all 0.28-2 [15.8 kB]
203s Get:185 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-identify-perl ppc64el 0.14-4 [10.4 kB]
203s Get:186 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-name-perl ppc64el 0.28-1 [10.9 kB]
203s Get:187 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnamespace-clean-perl all 0.27-2 [14.0 kB]
203s Get:188 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnumber-compare-perl all 0.03-3 [5974 B]
203s Get:189 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtext-glob-perl all 0.11-3 [6780 B]
203s Get:190 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpath-iterator-rule-perl all 1.015-2 [39.9 kB]
203s Get:191 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpod-parser-perl all 1.67-1 [80.6 kB]
203s Get:192 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpod-constants-perl all 0.19-2 [16.3 kB]
203s Get:193 http://ftpmaster.internal/ubuntu resolute/main ppc64el libset-intspan-perl all 1.19-3 [24.8 kB]
203s Get:194 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstring-copyright-perl all 0.003014-1 [20.5 kB]
203s Get:195 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstring-escape-perl all 2010.002-3 [16.1 kB]
203s Get:196 http://ftpmaster.internal/ubuntu resolute/main ppc64el libregexp-pattern-license-perl all 3.11.2-1 [85.3 kB]
203s Get:197 http://ftpmaster.internal/ubuntu resolute/main ppc64el libregexp-pattern-perl all 0.2.14-2 [17.6 kB]
203s Get:198 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstring-license-perl all 0.0.11-1ubuntu1 [34.3 kB]
203s Get:199 http://ftpmaster.internal/ubuntu resolute/main ppc64el licensecheck all 3.3.9-1ubuntu2 [37.2 kB]
203s Get:200 http://ftpmaster.internal/ubuntu resolute/main ppc64el diffstat ppc64el 1.68-1 [34.9 kB]
203s Get:201 http://ftpmaster.internal/ubuntu resolute/main ppc64el libberkeleydb-perl ppc64el 0.66-2 [121 kB]
203s Get:202 http://ftpmaster.internal/ubuntu resolute/main ppc64el libclass-xsaccessor-perl ppc64el 1.19-4build6 [35.2 kB]
203s Get:203 http://ftpmaster.internal/ubuntu resolute/main ppc64el libconfig-tiny-perl all 2.30-1 [14.7 kB]
203s Get:204 http://ftpmaster.internal/ubuntu resolute/main ppc64el libconst-fast-perl all 0.014-2 [8034 B]
203s Get:205 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcpanel-json-xs-perl ppc64el 4.40-1 [123 kB]
203s Get:206 http://ftpmaster.internal/ubuntu resolute/main ppc64el libaliased-perl all 0.34-3 [12.8 kB]
203s Get:207 http://ftpmaster.internal/ubuntu resolute/main ppc64el libclass-data-inheritable-perl all 0.10-1 [8038 B]
203s Get:208 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdevel-stacktrace-perl all 2.0500-1 [22.1 kB]
203s Get:209 http://ftpmaster.internal/ubuntu resolute/main ppc64el libexception-class-perl all 1.45-1 [28.6 kB]
203s Get:210 http://ftpmaster.internal/ubuntu resolute/main ppc64el libiterator-perl all 0.03+ds1-2 [18.8 kB]
203s Get:211 http://ftpmaster.internal/ubuntu resolute/main ppc64el libiterator-util-perl all 0.02+ds1-2 [14.1 kB]
203s Get:212 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-dpath-perl all 0.60-1 [37.3 kB]
203s Get:213 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-domain-tld-perl all 1.75-4 [29.0 kB]
203s Get:214 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-validate-domain-perl all 0.15-1 [10.4 kB]
203s Get:215 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-ipv6addr-perl all 1.02-1 [21.0 kB]
203s Get:216 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-netmask-perl all 2.0003-1build1 [24.8 kB]
203s Get:217 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnetaddr-ip-perl ppc64el 4.079+dfsg-2build5 [81.4 kB]
203s Get:218 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-validate-ip-perl all 0.31-1 [17.2 kB]
203s Get:219 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-validate-uri-perl all 0.07-3 [10.8 kB]
203s Get:220 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdevel-size-perl ppc64el 0.85-1 [20.1 kB]
203s Get:221 http://ftpmaster.internal/ubuntu resolute/main ppc64el libemail-address-xs-perl ppc64el 1.05-1build5 [33.6 kB]
203s Get:222 http://ftpmaster.internal/ubuntu resolute/main ppc64el libipc-system-simple-perl all 1.30-2 [22.3 kB]
203s Get:223 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-basedir-perl all 0.09-2 [14.4 kB]
203s Get:224 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfile-find-rule-perl all 0.35-1build1 [24.0 kB]
203s Get:225 http://ftpmaster.internal/ubuntu resolute/main ppc64el libio-string-perl all 1.08-4 [11.1 kB]
203s Get:226 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfont-ttf-perl all 1.06-2 [323 kB]
203s Get:227 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-html5-entities-perl all 0.004-3 [21.6 kB]
203s Get:228 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-tokeparser-simple-perl all 3.16-4 [38.0 kB]
203s Get:229 http://ftpmaster.internal/ubuntu resolute/main ppc64el libipc-run3-perl all 0.049-1 [28.8 kB]
203s Get:230 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjson-maybexs-perl all 1.004008-1 [11.1 kB]
203s Get:231 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblist-compare-perl all 0.55-2 [62.9 kB]
203s Get:232 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblist-someutils-perl all 0.59-1 [30.4 kB]
203s Get:233 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblist-utilsby-perl all 0.12-2 [14.9 kB]
203s Get:234 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmldbm-perl all 2.05-4 [16.0 kB]
203s Get:235 http://ftpmaster.internal/ubuntu resolute/main ppc64el libclass-method-modifiers-perl all 2.15-1 [16.1 kB]
203s Get:236 http://ftpmaster.internal/ubuntu resolute/main ppc64el libimport-into-perl all 1.002005-2 [10.7 kB]
203s Get:237 http://ftpmaster.internal/ubuntu resolute/main ppc64el librole-tiny-perl all 2.002004-1 [16.3 kB]
203s Get:238 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsub-quote-perl all 2.006009-1ubuntu1 [20.3 kB]
203s Get:239 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmoo-perl all 2.005005-1 [47.4 kB]
203s Get:240 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstrictures-perl all 2.000006-1build1 [15.2 kB]
203s Get:241 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmoox-aliases-perl all 0.001006-2 [6796 B]
203s Get:242 http://ftpmaster.internal/ubuntu resolute/main ppc64el libperlio-gzip-perl ppc64el 0.20-1build5 [15.7 kB]
203s Get:243 http://ftpmaster.internal/ubuntu resolute/main ppc64el libperlio-utf8-strict-perl ppc64el 0.010-1build4 [12.0 kB]
203s Get:244 http://ftpmaster.internal/ubuntu resolute/main ppc64el libproc-processtable-perl ppc64el 0.637-1 [36.8 kB]
203s Get:245 http://ftpmaster.internal/ubuntu resolute/main ppc64el libregexp-wildcards-perl all 1.05-3 [12.9 kB]
203s Get:246 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsereal-decoder-perl ppc64el 5.004+ds-1build5 [131 kB]
203s Get:247 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsereal-encoder-perl ppc64el 5.004+ds-1build4 [120 kB]
203s Get:248 http://ftpmaster.internal/ubuntu resolute/main ppc64el libterm-readkey-perl ppc64el 2.38-2build5 [24.7 kB]
203s Get:249 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtext-levenshteinxs-perl ppc64el 0.03-5build5 [8634 B]
203s Get:250 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmarkdown2 ppc64el 2.2.7-2.1build1 [49.1 kB]
203s Get:251 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtext-markdown-discount-perl ppc64el 0.18-1 [12.5 kB]
203s Get:252 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdata-messagepack-perl ppc64el 1.02-3 [33.4 kB]
203s Get:253 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtext-xslate-perl ppc64el 3.5.9-2build1 [165 kB]
203s Get:254 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtime-duration-perl all 1.21-2 [12.3 kB]
203s Get:255 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtime-moment-perl ppc64el 0.46-1 [86.6 kB]
203s Get:256 http://ftpmaster.internal/ubuntu resolute/main ppc64el libunicode-utf8-perl ppc64el 0.63-1 [22.1 kB]
203s Get:257 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcgi-pm-perl all 4.71-1build1 [185 kB]
203s Get:258 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhtml-form-perl all 6.13-1build1 [31.3 kB]
203s Get:259 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwww-mechanize-perl all 2.20-1ubuntu1 [95.5 kB]
203s Get:260 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxml-namespacesupport-perl all 1.12-2 [13.5 kB]
203s Get:261 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxml-sax-base-perl all 1.09-3 [18.9 kB]
203s Get:262 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxml-sax-perl all 1.02+dfsg-4 [52.4 kB]
203s Get:263 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxml-libxml-perl ppc64el 2.0207+dfsg+really+2.0207-0ubuntu7 [315 kB]
203s Get:264 http://ftpmaster.internal/ubuntu resolute/main ppc64el lzip ppc64el 1.25-4 [95.8 kB]
203s Get:265 http://ftpmaster.internal/ubuntu resolute/main ppc64el lzop ppc64el 1.04-2build4 [88.8 kB]
203s Get:266 http://ftpmaster.internal/ubuntu resolute/main ppc64el patchutils ppc64el 0.4.3-1 [91.4 kB]
203s Get:267 http://ftpmaster.internal/ubuntu resolute/main ppc64el t1utils ppc64el 1.41-4build4 [76.9 kB]
203s Get:268 http://ftpmaster.internal/ubuntu resolute/main ppc64el unzip ppc64el 6.0-29ubuntu1 [200 kB]
203s Get:269 http://ftpmaster.internal/ubuntu resolute/main ppc64el lintian all 2.127.0ubuntu1 [1079 kB]
204s Get:270 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libconfig-model-dpkg-perl all 3.016 [194 kB]
204s Get:271 http://ftpmaster.internal/ubuntu resolute/main ppc64el libconvert-binhex-perl all 1.125-3 [27.1 kB]
204s Get:272 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnet-smtp-ssl-perl all 1.04-2 [6218 B]
204s Get:273 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmailtools-perl all 2.22-1 [77.1 kB]
204s Get:274 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmime-tools-perl all 5.515-1 [187 kB]
204s Get:275 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libb-keywords-perl all 1.29-1 [10.4 kB]
204s Get:276 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libclass-tiny-perl all 1.008-2 [16.4 kB]
204s Get:277 http://ftpmaster.internal/ubuntu resolute/universe ppc64el liblingua-en-inflect-perl all 1.905-2 [50.8 kB]
204s Get:278 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libpod-spell-perl all 1.27-1 [30.4 kB]
204s Get:279 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsafe-isa-perl all 1.000010-1build1 [7334 B]
204s Get:280 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtask-weaken-perl all 1.06-2 [7924 B]
204s Get:281 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libppi-perl all 1.284-1 [281 kB]
204s Get:282 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libreadonly-perl all 2.050-3 [19.9 kB]
204s Get:283 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libppix-quotelike-perl all 0.023-1 [71.6 kB]
204s Get:284 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libppix-regexp-perl all 0.091-1 [234 kB]
204s Get:285 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libppix-utils-perl all 0.003-2 [28.4 kB]
204s Get:286 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libstring-format-perl all 1.18-1build1 [8824 B]
204s Get:287 http://ftpmaster.internal/ubuntu resolute/universe ppc64el perltidy all 20250105-1build1 [645 kB]
205s Get:288 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libperl-critic-perl all 1.156-1 [654 kB]
205s Get:289 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-wrapper-perl all 1.05-4 [9820 B]
205s Get:290 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsuitesparseconfig7 ppc64el 1:7.12.1+dfsg-1 [22.1 kB]
205s Get:291 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libamd3 ppc64el 1:7.12.1+dfsg-1 [44.6 kB]
205s Get:292 http://ftpmaster.internal/ubuntu resolute/main ppc64el libblas3 ppc64el 3.12.1-7ubuntu1 [291 kB]
206s Get:293 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgfortran5 ppc64el 15.2.0-12ubuntu1 [620 kB]
206s Get:294 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblapack3 ppc64el 3.12.1-7ubuntu1 [2960 kB]
208s Get:295 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libarpack2t64 ppc64el 3.9.1-6 [107 kB]
208s Get:296 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libccolamd3 ppc64el 1:7.12.1+dfsg-1 [41.2 kB]
208s Get:297 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libcamd3 ppc64el 1:7.12.1+dfsg-1 [38.5 kB]
208s Get:298 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcolamd3 ppc64el 1:7.12.1+dfsg-1 [31.0 kB]
208s Get:299 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libcholmod5 ppc64el 1:7.12.1+dfsg-1 [985 kB]
209s Get:300 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libcxsparse4 ppc64el 1:7.12.1+dfsg-1 [115 kB]
209s Get:301 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfftw3-double3 ppc64el 3.3.10-2fakesync1build2 [445 kB]
209s Get:302 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfftw3-single3 ppc64el 3.3.10-2fakesync1build2 [458 kB]
209s Get:303 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxfixes3 ppc64el 1:6.0.0-2build2 [11.8 kB]
209s Get:304 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcursor1 ppc64el 1:1.2.3-1build1 [28.3 kB]
209s Get:305 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxft2 ppc64el 2.3.6-1build2 [61.6 kB]
209s Get:306 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxinerama1 ppc64el 2:1.1.4-3build2 [6844 B]
209s Get:307 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libfltk1.3t64 ppc64el 1.3.11-3 [714 kB]
209s Get:308 http://ftpmaster.internal/ubuntu resolute/main ppc64el libglvnd0 ppc64el 1.7.0-3 [67.2 kB]
209s Get:309 http://ftpmaster.internal/ubuntu resolute/main ppc64el libx11-xcb1 ppc64el 2:1.8.12-1build1 [8022 B]
209s Get:310 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-dri3-0 ppc64el 1.17.0-2ubuntu1 [7964 B]
209s Get:311 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-present0 ppc64el 1.17.0-2ubuntu1 [6340 B]
209s Get:312 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-randr0 ppc64el 1.17.0-2ubuntu1 [19.1 kB]
209s Get:313 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-sync1 ppc64el 1.17.0-2ubuntu1 [9864 B]
209s Get:314 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-xfixes0 ppc64el 1.17.0-2ubuntu1 [10.8 kB]
209s Get:315 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxshmfence1 ppc64el 1.3.3-1build1 [5420 B]
209s Get:316 http://ftpmaster.internal/ubuntu resolute/main ppc64el mesa-libgallium ppc64el 25.3.3-1ubuntu1 [12.5 MB]
218s Get:317 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgbm1 ppc64el 25.3.3-1ubuntu1 [41.7 kB]
218s Get:318 http://ftpmaster.internal/ubuntu resolute/main ppc64el libvulkan1 ppc64el 1.4.335.0-1 [175 kB]
218s Get:319 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgl1-mesa-dri ppc64el 25.3.3-1ubuntu1 [36.4 kB]
218s Get:320 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-glx0 ppc64el 1.17.0-2ubuntu1 [26.4 kB]
218s Get:321 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxxf86vm1 ppc64el 1:1.1.4-2 [12.0 kB]
218s Get:322 http://ftpmaster.internal/ubuntu resolute/main ppc64el libglx-mesa0 ppc64el 25.3.3-1ubuntu1 [132 kB]
218s Get:323 http://ftpmaster.internal/ubuntu resolute/main ppc64el libglx0 ppc64el 1.7.0-3 [42.3 kB]
218s Get:324 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgl1 ppc64el 1.7.0-3 [103 kB]
218s Get:325 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libfltk-gl1.3t64 ppc64el 1.3.11-3 [46.0 kB]
218s Get:326 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libgl2ps1.4 ppc64el 1.4.2+dfsg1-4 [58.1 kB]
218s Get:327 http://ftpmaster.internal/ubuntu resolute/main ppc64el libltdl7 ppc64el 2.5.4-9 [51.5 kB]
218s Get:328 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libglpk40 ppc64el 5.0-2 [488 kB]
218s Get:329 http://ftpmaster.internal/ubuntu resolute/main ppc64el libopengl0 ppc64el 1.7.0-3 [35.6 kB]
218s Get:330 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libglu1-mesa ppc64el 9.0.2-1.1build2 [274 kB]
218s Get:331 http://ftpmaster.internal/ubuntu resolute/main ppc64el libhwy1t64 ppc64el 1.3.0-2 [251 kB]
218s Get:332 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblcms2-2 ppc64el 2.17-1 [246 kB]
218s Get:333 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjxl0.11 ppc64el 0.11.1-6ubuntu1 [1411 kB]
218s Get:334 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwmflite-0.2-7 ppc64el 0.2.13-2 [81.5 kB]
218s Get:335 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libgraphicsmagick-q16-3t64 ppc64el 1.4+really1.3.45+hg17696-1build1 [1610 kB]
219s Get:336 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libgraphicsmagick++-q16-12t64 ppc64el 1.4+really1.3.45+hg17696-1build1 [132 kB]
219s Get:337 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libsz2 ppc64el 1.1.5-1 [25.3 kB]
219s Get:338 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-310 ppc64el 1.14.6+repack-2 [1545 kB]
220s Get:339 http://ftpmaster.internal/ubuntu resolute/main ppc64el libasound2-data all 1.2.15.3-1ubuntu1 [21.4 kB]
220s Get:340 http://ftpmaster.internal/ubuntu resolute/main ppc64el libasound2t64 ppc64el 1.2.15.3-1ubuntu1 [523 kB]
220s Get:341 http://ftpmaster.internal/ubuntu resolute/main ppc64el libopus0 ppc64el 1.6.1-1 [3720 kB]
222s Get:342 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsamplerate0 ppc64el 0.2.2-4build2 [1346 kB]
222s Get:343 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjack-jackd2-0 ppc64el 1.9.22~dfsg-5 [333 kB]
222s Get:344 http://ftpmaster.internal/ubuntu resolute/main ppc64el libasyncns0 ppc64el 0.8-7 [13.3 kB]
222s Get:345 http://ftpmaster.internal/ubuntu resolute/main ppc64el libogg0 ppc64el 1.3.6-2 [25.7 kB]
222s Get:346 http://ftpmaster.internal/ubuntu resolute/main ppc64el libflac14 ppc64el 1.5.0+ds-5 [213 kB]
222s Get:347 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmp3lame0 ppc64el 3.100-6build2 [217 kB]
222s Get:348 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmpg123-0t64 ppc64el 1.33.3-2 [214 kB]
222s Get:349 http://ftpmaster.internal/ubuntu resolute/main ppc64el libvorbis0a ppc64el 1.3.7-3build1 [148 kB]
222s Get:350 http://ftpmaster.internal/ubuntu resolute/main ppc64el libvorbisenc2 ppc64el 1.3.7-3build1 [85.4 kB]
222s Get:351 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsndfile1 ppc64el 1.2.2-4 [322 kB]
222s Get:352 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpulse0 ppc64el 1:17.0+dfsg1-2ubuntu4 [316 kB]
222s Get:353 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libportaudio2 ppc64el 19.7.0+git20251227.3270c9ae-0ubuntu1 [96.6 kB]
222s Get:354 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqhull-r8.0 ppc64el 2020.2-8 [227 kB]
222s Get:355 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqrupdate1 ppc64el 1.1.5-3 [48.7 kB]
222s Get:356 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqscintilla2-qt6-l10n all 2.14.1+dfsg-2 [55.5 kB]
222s Get:357 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libb2-1 ppc64el 0.98.1-1.1build2 [17.6 kB]
222s Get:358 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libdouble-conversion3 ppc64el 3.4.0-1 [46.0 kB]
222s Get:359 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpcre2-16-0 ppc64el 10.46-1 [292 kB]
222s Get:360 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6core6t64 ppc64el 6.9.2+dfsg-3ubuntu2 [2314 kB]
224s Get:361 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwayland-client0 ppc64el 1.24.0-2 [32.8 kB]
224s Get:362 http://ftpmaster.internal/ubuntu resolute/main ppc64el libegl-mesa0 ppc64el 25.3.3-1ubuntu1 [147 kB]
224s Get:363 http://ftpmaster.internal/ubuntu resolute/main ppc64el libegl1 ppc64el 1.7.0-3 [34.9 kB]
224s Get:364 http://ftpmaster.internal/ubuntu resolute/main ppc64el x11-common all 1:7.7+24ubuntu1 [22.4 kB]
224s Get:365 http://ftpmaster.internal/ubuntu resolute/main ppc64el libice6 ppc64el 2:1.1.1-1build1 [51.9 kB]
224s Get:366 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmtdev1t64 ppc64el 1.1.7-1build1 [18.7 kB]
224s Get:367 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwacom-common all 2.16.1-1 [113 kB]
224s Get:368 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwacom9 ppc64el 2.16.1-1 [34.8 kB]
224s Get:369 http://ftpmaster.internal/ubuntu resolute/main ppc64el libinput-bin ppc64el 1.30.1-1 [25.6 kB]
224s Get:370 http://ftpmaster.internal/ubuntu resolute/main ppc64el libinput10 ppc64el 1.30.1-1 [196 kB]
224s Get:371 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libmd4c0 ppc64el 0.5.2-2build1 [60.0 kB]
224s Get:372 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6dbus6 ppc64el 6.9.2+dfsg-3ubuntu2 [322 kB]
224s Get:373 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsm6 ppc64el 2:1.2.6-1 [18.6 kB]
224s Get:374 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libts0t64 ppc64el 1.22-1.1build2 [70.1 kB]
224s Get:375 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-util1 ppc64el 0.4.1-1build1 [11.2 kB]
224s Get:376 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libxcb-image0 ppc64el 0.4.0-2build2 [13.1 kB]
224s Get:377 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libxcb-render-util0 ppc64el 0.3.10-1build1 [11.3 kB]
224s Get:378 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libxcb-cursor0 ppc64el 0.1.5-1build1 [11.9 kB]
224s Get:379 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libxcb-icccm4 ppc64el 0.4.2-1build1 [11.2 kB]
224s Get:380 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libxcb-keysyms1 ppc64el 0.4.1-1build1 [9078 B]
224s Get:381 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-shape0 ppc64el 1.17.0-2ubuntu1 [6420 B]
224s Get:382 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-xinput0 ppc64el 1.17.0-2ubuntu1 [38.3 kB]
224s Get:383 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-xkb1 ppc64el 1.17.0-2ubuntu1 [37.6 kB]
224s Get:384 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxkbcommon-x11-0 ppc64el 1.12.3-1 [15.6 kB]
224s Get:385 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6gui6 ppc64el 6.9.2+dfsg-3ubuntu2 [4279 kB]
225s Get:386 http://ftpmaster.internal/ubuntu resolute/main ppc64el libavahi-common-data ppc64el 0.8-17ubuntu2 [31.5 kB]
225s Get:387 http://ftpmaster.internal/ubuntu resolute/main ppc64el libavahi-common3 ppc64el 0.8-17ubuntu2 [26.3 kB]
225s Get:388 http://ftpmaster.internal/ubuntu resolute/main ppc64el libavahi-client3 ppc64el 0.8-17ubuntu2 [31.1 kB]
225s Get:389 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcups2t64 ppc64el 2.4.16-1ubuntu1 [353 kB]
225s Get:390 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6widgets6 ppc64el 6.9.2+dfsg-3ubuntu2 [3270 kB]
226s Get:391 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6printsupport6 ppc64el 6.9.2+dfsg-3ubuntu2 [247 kB]
226s Get:392 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqscintilla2-qt6-15 ppc64el 2.14.1+dfsg-2 [1375 kB]
227s Get:393 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6core5compat6 ppc64el 6.9.2-3build1 [173 kB]
227s Get:394 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6sql6 ppc64el 6.9.2+dfsg-3ubuntu2 [167 kB]
227s Get:395 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6help6 ppc64el 6.9.2-5 [225 kB]
227s Get:396 http://ftpmaster.internal/ubuntu resolute/main ppc64el libduktape207 ppc64el 2.7.0+tests-0ubuntu4 [155 kB]
227s Get:397 http://ftpmaster.internal/ubuntu resolute/main ppc64el libproxy1v5 ppc64el 0.5.12-1 [30.3 kB]
227s Get:398 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6network6 ppc64el 6.9.2+dfsg-3ubuntu2 [973 kB]
227s Get:399 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6opengl6 ppc64el 6.9.2+dfsg-3ubuntu2 [499 kB]
227s Get:400 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6openglwidgets6 ppc64el 6.9.2+dfsg-3ubuntu2 [45.5 kB]
227s Get:401 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libqt6xml6 ppc64el 6.9.2+dfsg-3ubuntu2 [93.2 kB]
227s Get:402 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libspqr4 ppc64el 1:7.12.1+dfsg-1 [205 kB]
227s Get:403 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libumfpack6 ppc64el 1:7.12.1+dfsg-1 [367 kB]
227s Get:404 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libtext-unidecode-perl all 1.30-3 [105 kB]
227s Get:405 http://ftpmaster.internal/ubuntu resolute/main ppc64el libintl-perl all 1.35-1 [701 kB]
227s Get:406 http://ftpmaster.internal/ubuntu resolute/universe ppc64el texinfo-lib ppc64el 7.2-5 [540 kB]
228s Get:407 http://ftpmaster.internal/ubuntu resolute/universe ppc64el tex-common all 6.20 [30.0 kB]
228s Get:408 http://ftpmaster.internal/ubuntu resolute/universe ppc64el texinfo all 7.2-5 [1217 kB]
228s Get:409 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave-common all 10.3.0-3 [6088 kB]
230s Get:410 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave ppc64el 10.3.0-3 [10.5 MB]
232s Get:411 http://ftpmaster.internal/ubuntu resolute/main ppc64el libncurses-dev ppc64el 6.6+20251231-1 [505 kB]
232s Get:412 http://ftpmaster.internal/ubuntu resolute/main ppc64el libreadline-dev ppc64el 8.3-3 [252 kB]
232s Get:413 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-fortran-310 ppc64el 1.14.6+repack-2 [117 kB]
232s Get:414 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-hl-310 ppc64el 1.14.6+repack-2 [76.3 kB]
232s Get:415 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-hl-fortran-310 ppc64el 1.14.6+repack-2 [35.1 kB]
232s Get:416 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-cpp-310 ppc64el 1.14.6+repack-2 [128 kB]
232s Get:417 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-hl-cpp-310 ppc64el 1.14.6+repack-2 [11.7 kB]
232s Get:418 http://ftpmaster.internal/ubuntu resolute/main ppc64el zlib1g-dev ppc64el 1:1.3.dfsg+really1.3.1-1ubuntu2 [903 kB]
233s Get:419 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-turbo8-dev ppc64el 2.1.5-4ubuntu2 [358 kB]
233s Get:420 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg8-dev ppc64el 8c-2ubuntu11 [1484 B]
233s Get:421 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-dev ppc64el 8c-2ubuntu11 [1486 B]
233s Get:422 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libaec0 ppc64el 1.1.5-1 [29.7 kB]
233s Get:423 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libaec-dev ppc64el 1.1.5-1 [31.9 kB]
233s Get:424 http://ftpmaster.internal/ubuntu resolute/main ppc64el libbrotli-dev ppc64el 1.1.0-2build6 [428 kB]
233s Get:425 http://ftpmaster.internal/ubuntu resolute/main ppc64el libidn2-dev ppc64el 2.3.8-4 [125 kB]
233s Get:426 http://ftpmaster.internal/ubuntu resolute/main ppc64el comerr-dev ppc64el 2.1-1.47.2-3ubuntu2 [45.4 kB]
233s Get:427 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgssrpc4t64 ppc64el 1.22.1-2 [64.9 kB]
233s Get:428 http://ftpmaster.internal/ubuntu resolute/main ppc64el libkadm5clnt-mit12 ppc64el 1.22.1-2 [43.9 kB]
233s Get:429 http://ftpmaster.internal/ubuntu resolute/main ppc64el libkdb5-10t64 ppc64el 1.22.1-2 [48.6 kB]
233s Get:430 http://ftpmaster.internal/ubuntu resolute/main ppc64el libkadm5srv-mit12 ppc64el 1.22.1-2 [64.5 kB]
233s Get:431 http://ftpmaster.internal/ubuntu resolute/main ppc64el krb5-multidev ppc64el 1.22.1-2 [126 kB]
233s Get:432 http://ftpmaster.internal/ubuntu resolute/main ppc64el libkrb5-dev ppc64el 1.22.1-2 [11.9 kB]
233s Get:433 http://ftpmaster.internal/ubuntu resolute/main ppc64el libldap-dev ppc64el 2.6.10+dfsg-1ubuntu5 [381 kB]
233s Get:434 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpkgconf3 ppc64el 1.8.1-4build1 [37.9 kB]
233s Get:435 http://ftpmaster.internal/ubuntu resolute/main ppc64el pkgconf-bin ppc64el 1.8.1-4build1 [22.7 kB]
233s Get:436 http://ftpmaster.internal/ubuntu resolute/main ppc64el pkgconf ppc64el 1.8.1-4build1 [16.8 kB]
233s Get:437 http://ftpmaster.internal/ubuntu resolute/main ppc64el libnghttp2-dev ppc64el 1.64.0-1.1ubuntu1 [138 kB]
233s Get:438 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpsl-dev ppc64el 0.21.2-1.1build2 [80.9 kB]
233s Get:439 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgmpxx4ldbl ppc64el 2:6.3.0+dfsg-5ubuntu1 [10.2 kB]
233s Get:440 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgmp-dev ppc64el 2:6.3.0+dfsg-5ubuntu1 [382 kB]
233s Get:441 http://ftpmaster.internal/ubuntu resolute/main ppc64el libevent-2.1-7t64 ppc64el 2.1.12-stable-10build1 [172 kB]
233s Get:442 http://ftpmaster.internal/ubuntu resolute/main ppc64el libunbound8 ppc64el 1.24.2-1ubuntu1 [576 kB]
233s Get:443 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgnutls-dane0t64 ppc64el 3.8.10-3ubuntu1 [25.5 kB]
233s Get:444 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgnutls-openssl27t64 ppc64el 3.8.10-3ubuntu1 [25.0 kB]
233s Get:445 http://ftpmaster.internal/ubuntu resolute/main ppc64el libp11-kit-dev ppc64el 0.25.10-1 [29.3 kB]
233s Get:446 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtasn1-6-dev ppc64el 4.21.0-2 [106 kB]
233s Get:447 http://ftpmaster.internal/ubuntu resolute/main ppc64el nettle-dev ppc64el 3.10.2-1 [1235 kB]
234s Get:448 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgnutls28-dev ppc64el 3.8.10-3ubuntu1 [1252 kB]
234s Get:449 http://ftpmaster.internal/ubuntu resolute/main ppc64el librtmp-dev ppc64el 2.4+20151223.gitfa8646d.1-3 [77.1 kB]
234s Get:450 http://ftpmaster.internal/ubuntu resolute/main ppc64el libssl-dev ppc64el 3.5.3-1ubuntu2 [3418 kB]
236s Get:451 http://ftpmaster.internal/ubuntu resolute/main ppc64el libssh2-1-dev ppc64el 1.11.1-1build1 [318 kB]
236s Get:452 http://ftpmaster.internal/ubuntu resolute/main ppc64el libzstd-dev ppc64el 1.5.7+dfsg-3 [528 kB]
236s Get:453 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcurl4-openssl-dev ppc64el 8.18.0-1ubuntu1 [660 kB]
237s Get:454 http://ftpmaster.internal/ubuntu resolute/universe ppc64el hdf5-helpers ppc64el 1.14.6+repack-2 [17.3 kB]
237s Get:455 http://ftpmaster.internal/ubuntu resolute/universe ppc64el libhdf5-dev ppc64el 1.14.6+repack-2 [3941 kB]
238s Get:456 http://ftpmaster.internal/ubuntu resolute/main ppc64el xorg-sgml-doctools all 1:1.11-1.1build1 [10.3 kB]
238s Get:457 http://ftpmaster.internal/ubuntu resolute/main ppc64el x11proto-dev all 2025.1-1 [608 kB]
239s Get:458 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxau-dev ppc64el 1:1.0.11-1build1 [10.7 kB]
239s Get:459 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxdmcp-dev ppc64el 1:1.1.5-2 [27.4 kB]
239s Get:460 http://ftpmaster.internal/ubuntu resolute/main ppc64el xtrans-dev all 1.6.0-1build1 [66.2 kB]
239s Get:461 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb1-dev ppc64el 1.17.0-2ubuntu1 [97.3 kB]
239s Get:462 http://ftpmaster.internal/ubuntu resolute/main ppc64el libx11-dev ppc64el 2:1.8.12-1build1 [856 kB]
239s Get:463 http://ftpmaster.internal/ubuntu resolute/main ppc64el libglx-dev ppc64el 1.7.0-3 [14.1 kB]
239s Get:464 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgl-dev ppc64el 1.7.0-3 [103 kB]
239s Get:465 http://ftpmaster.internal/ubuntu resolute/main ppc64el libblas-dev ppc64el 3.12.1-7ubuntu1 [306 kB]
239s Get:466 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblapack-dev ppc64el 3.12.1-7ubuntu1 [6357 kB]
241s Get:467 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfftw3-long3 ppc64el 3.3.10-2fakesync1build2 [458 kB]
241s Get:468 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfftw3-bin ppc64el 3.3.10-2fakesync1build2 [35.5 kB]
241s Get:469 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfftw3-dev ppc64el 3.3.10-2fakesync1build2 [1100 kB]
242s Get:470 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgfortran-15-dev ppc64el 15.2.0-12ubuntu1 [651 kB]
242s Get:471 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [12.3 MB]
244s Get:472 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-15 ppc64el 15.2.0-12ubuntu1 [18.1 kB]
244s Get:473 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [1020 B]
244s Get:474 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran ppc64el 4:15.2.0-4ubuntu1 [1166 B]
244s Get:475 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstdc++-15-dev ppc64el 15.2.0-12ubuntu1 [2747 kB]
244s Get:476 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [13.0 MB]
245s Get:477 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-15 ppc64el 15.2.0-12ubuntu1 [25.3 kB]
245s Get:478 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [970 B]
245s Get:479 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++ ppc64el 4:15.2.0-4ubuntu1 [1092 B]
245s Get:480 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave-dev ppc64el 10.3.0-3 [469 kB]
245s Get:481 http://ftpmaster.internal/ubuntu resolute/universe ppc64el dh-octave all 1.14.0 [21.1 kB]
245s Get:482 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfontenc1 ppc64el 1:1.1.8-1build2 [16.1 kB]
245s Get:483 http://ftpmaster.internal/ubuntu resolute/main ppc64el libunwind8 ppc64el 1.8.3-0ubuntu1 [68.2 kB]
245s Get:484 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxt6t64 ppc64el 1:1.2.1-1.3 [203 kB]
245s Get:485 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxmu6 ppc64el 2:1.1.3-4 [56.9 kB]
245s Get:486 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxaw7 ppc64el 2:1.0.16-1build1 [234 kB]
245s Get:487 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxfont2 ppc64el 1:2.0.6-2 [119 kB]
245s Get:488 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxkbfile1 ppc64el 1:1.1.0-1build5 [90.2 kB]
245s Get:489 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxrandr2 ppc64el 2:1.5.4-1build1 [21.7 kB]
245s Get:490 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave-io ppc64el 2.7.0-3 [273 kB]
245s Get:491 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave-statistics-common all 1.7.6-2 [1008 kB]
245s Get:492 http://ftpmaster.internal/ubuntu resolute/universe ppc64el octave-statistics ppc64el 1.7.6-2 [211 kB]
245s Get:493 http://ftpmaster.internal/ubuntu resolute/main ppc64el x11-xkb-utils ppc64el 7.7+9build1 [202 kB]
245s Get:494 http://ftpmaster.internal/ubuntu resolute/main ppc64el xserver-common all 2:21.1.21-1ubuntu1 [34.8 kB]
245s Get:495 http://ftpmaster.internal/ubuntu resolute/universe ppc64el xvfb ppc64el 2:21.1.21-1ubuntu1 [1135 kB]
246s Fetched 212 MB in 1min 15s (2843 kB/s)
246s Selecting previously unselected package libfyaml0:ppc64el.
247s (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 ... 82008 files and directories currently installed.)
247s Preparing to unpack .../000-libfyaml0_0.9.3-1_ppc64el.deb ...
247s Unpacking libfyaml0:ppc64el (0.9.3-1) ...
247s Selecting previously unselected package libstemmer0d:ppc64el.
247s Preparing to unpack .../001-libstemmer0d_3.0.1-1_ppc64el.deb ...
247s Unpacking libstemmer0d:ppc64el (3.0.1-1) ...
247s Selecting previously unselected package libappstream5:ppc64el.
247s Preparing to unpack .../002-libappstream5_1.1.1-1_ppc64el.deb ...
247s Unpacking libappstream5:ppc64el (1.1.1-1) ...
247s Selecting previously unselected package appstream.
247s Preparing to unpack .../003-appstream_1.1.1-1_ppc64el.deb ...
247s Unpacking appstream (1.1.1-1) ...
247s Selecting previously unselected package m4.
247s Preparing to unpack .../004-m4_1.4.20-2_ppc64el.deb ...
247s Unpacking m4 (1.4.20-2) ...
247s Selecting previously unselected package autoconf.
247s Preparing to unpack .../005-autoconf_2.72-3.1ubuntu1_all.deb ...
247s Unpacking autoconf (2.72-3.1ubuntu1) ...
247s Selecting previously unselected package autotools-dev.
247s Preparing to unpack .../006-autotools-dev_20240727.1_all.deb ...
247s Unpacking autotools-dev (20240727.1) ...
247s Selecting previously unselected package automake.
247s Preparing to unpack .../007-automake_1%3a1.18.1-3build1_all.deb ...
247s Unpacking automake (1:1.18.1-3build1) ...
247s Selecting previously unselected package autopoint.
247s Preparing to unpack .../008-autopoint_0.23.2-1_all.deb ...
247s Unpacking autopoint (0.23.2-1) ...
247s Selecting previously unselected package libcapture-tiny-perl.
247s Preparing to unpack .../009-libcapture-tiny-perl_0.50-1_all.deb ...
247s Unpacking libcapture-tiny-perl (0.50-1) ...
247s Selecting previously unselected package libparams-util-perl.
247s Preparing to unpack .../010-libparams-util-perl_1.102-3build1_ppc64el.deb ...
247s Unpacking libparams-util-perl (1.102-3build1) ...
247s Selecting previously unselected package libsub-install-perl.
247s Preparing to unpack .../011-libsub-install-perl_0.929-1_all.deb ...
247s Unpacking libsub-install-perl (0.929-1) ...
247s Selecting previously unselected package libdata-optlist-perl.
247s Preparing to unpack .../012-libdata-optlist-perl_0.114-1_all.deb ...
247s Unpacking libdata-optlist-perl (0.114-1) ...
247s Selecting previously unselected package libb-hooks-op-check-perl:ppc64el.
247s Preparing to unpack .../013-libb-hooks-op-check-perl_0.22-3build2_ppc64el.deb ...
247s Unpacking libb-hooks-op-check-perl:ppc64el (0.22-3build2) ...
247s Selecting previously unselected package libdynaloader-functions-perl.
247s Preparing to unpack .../014-libdynaloader-functions-perl_0.004-2_all.deb ...
247s Unpacking libdynaloader-functions-perl (0.004-2) ...
247s Selecting previously unselected package libdevel-callchecker-perl:ppc64el.
247s Preparing to unpack .../015-libdevel-callchecker-perl_0.009-2_ppc64el.deb ...
247s Unpacking libdevel-callchecker-perl:ppc64el (0.009-2) ...
247s Selecting previously unselected package libparams-classify-perl:ppc64el.
247s Preparing to unpack .../016-libparams-classify-perl_0.015-2build6_ppc64el.deb ...
247s Unpacking libparams-classify-perl:ppc64el (0.015-2build6) ...
247s Selecting previously unselected package libmodule-runtime-perl.
247s Preparing to unpack .../017-libmodule-runtime-perl_0.018-1_all.deb ...
247s Unpacking libmodule-runtime-perl (0.018-1) ...
247s Selecting previously unselected package libtry-tiny-perl.
247s Preparing to unpack .../018-libtry-tiny-perl_0.32-1_all.deb ...
247s Unpacking libtry-tiny-perl (0.32-1) ...
247s Selecting previously unselected package libmodule-implementation-perl.
247s Preparing to unpack .../019-libmodule-implementation-perl_0.09-2_all.deb ...
247s Unpacking libmodule-implementation-perl (0.09-2) ...
247s Selecting previously unselected package libpackage-stash-perl.
247s Preparing to unpack .../020-libpackage-stash-perl_0.40-1_all.deb ...
247s Unpacking libpackage-stash-perl (0.40-1) ...
247s Selecting previously unselected package libclass-load-perl.
247s Preparing to unpack .../021-libclass-load-perl_0.25-2_all.deb ...
247s Unpacking libclass-load-perl (0.25-2) ...
247s Selecting previously unselected package libio-stringy-perl.
247s Preparing to unpack .../022-libio-stringy-perl_2.113-2_all.deb ...
247s Unpacking libio-stringy-perl (2.113-2) ...
247s Selecting previously unselected package libparams-validate-perl:ppc64el.
247s Preparing to unpack .../023-libparams-validate-perl_1.31-2build4_ppc64el.deb ...
247s Unpacking libparams-validate-perl:ppc64el (1.31-2build4) ...
247s Selecting previously unselected package libsub-exporter-perl.
247s Preparing to unpack .../024-libsub-exporter-perl_0.990-1_all.deb ...
247s Unpacking libsub-exporter-perl (0.990-1) ...
247s Selecting previously unselected package libgetopt-long-descriptive-perl.
247s Preparing to unpack .../025-libgetopt-long-descriptive-perl_0.116-2_all.deb ...
247s Unpacking libgetopt-long-descriptive-perl (0.116-2) ...
247s Selecting previously unselected package libio-tiecombine-perl.
247s Preparing to unpack .../026-libio-tiecombine-perl_1.005-3_all.deb ...
247s Unpacking libio-tiecombine-perl (1.005-3) ...
247s Selecting previously unselected package libmodule-pluggable-perl.
247s Preparing to unpack .../027-libmodule-pluggable-perl_5.2-5_all.deb ...
247s Unpacking libmodule-pluggable-perl (5.2-5) ...
247s Selecting previously unselected package libstring-rewriteprefix-perl.
247s Preparing to unpack .../028-libstring-rewriteprefix-perl_0.009-1_all.deb ...
247s Unpacking libstring-rewriteprefix-perl (0.009-1) ...
247s Selecting previously unselected package libapp-cmd-perl.
247s Preparing to unpack .../029-libapp-cmd-perl_0.338-1_all.deb ...
247s Unpacking libapp-cmd-perl (0.338-1) ...
247s Selecting previously unselected package libboolean-perl.
247s Preparing to unpack .../030-libboolean-perl_0.46-3_all.deb ...
247s Unpacking libboolean-perl (0.46-3) ...
247s Selecting previously unselected package libsub-uplevel-perl.
247s Preparing to unpack .../031-libsub-uplevel-perl_0.2800-3_all.deb ...
247s Unpacking libsub-uplevel-perl (0.2800-3) ...
247s Selecting previously unselected package libtest-exception-perl.
247s Preparing to unpack .../032-libtest-exception-perl_0.43-3_all.deb ...
247s Unpacking libtest-exception-perl (0.43-3) ...
247s Selecting previously unselected package libcarp-assert-more-perl.
247s Preparing to unpack .../033-libcarp-assert-more-perl_2.9.0-1_all.deb ...
247s Unpacking libcarp-assert-more-perl (2.9.0-1) ...
247s Selecting previously unselected package libfile-which-perl.
247s Preparing to unpack .../034-libfile-which-perl_1.27-2_all.deb ...
247s Unpacking libfile-which-perl (1.27-2) ...
247s Selecting previously unselected package libfile-homedir-perl.
247s Preparing to unpack .../035-libfile-homedir-perl_1.006-2_all.deb ...
247s Unpacking libfile-homedir-perl (1.006-2) ...
247s Selecting previously unselected package libclone-choose-perl.
247s Preparing to unpack .../036-libclone-choose-perl_0.010-2_all.deb ...
247s Unpacking libclone-choose-perl (0.010-2) ...
247s Selecting previously unselected package libhash-merge-perl.
247s Preparing to unpack .../037-libhash-merge-perl_0.302-1_all.deb ...
247s Unpacking libhash-merge-perl (0.302-1) ...
247s Selecting previously unselected package libjson-perl.
247s Preparing to unpack .../038-libjson-perl_4.10000-1_all.deb ...
247s Unpacking libjson-perl (4.10000-1) ...
247s Selecting previously unselected package libexporter-tiny-perl.
247s Preparing to unpack .../039-libexporter-tiny-perl_1.006003-1_all.deb ...
247s Unpacking libexporter-tiny-perl (1.006003-1) ...
247s Selecting previously unselected package liblist-moreutils-xs-perl.
247s Preparing to unpack .../040-liblist-moreutils-xs-perl_0.430-4build1_ppc64el.deb ...
247s Unpacking liblist-moreutils-xs-perl (0.430-4build1) ...
247s Selecting previously unselected package liblist-moreutils-perl.
247s Preparing to unpack .../041-liblist-moreutils-perl_0.430-2_all.deb ...
247s Unpacking liblist-moreutils-perl (0.430-2) ...
247s Selecting previously unselected package liblog-log4perl-perl.
247s Preparing to unpack .../042-liblog-log4perl-perl_1.57-1_all.deb ...
247s Unpacking liblog-log4perl-perl (1.57-1) ...
247s Selecting previously unselected package libmouse-perl:ppc64el.
247s Preparing to unpack .../043-libmouse-perl_2.6.1-1_ppc64el.deb ...
247s Unpacking libmouse-perl:ppc64el (2.6.1-1) ...
247s Selecting previously unselected package libmousex-nativetraits-perl.
247s Preparing to unpack .../044-libmousex-nativetraits-perl_1.09-3_all.deb ...
247s Unpacking libmousex-nativetraits-perl (1.09-3) ...
247s Selecting previously unselected package libmousex-strictconstructor-perl.
247s Preparing to unpack .../045-libmousex-strictconstructor-perl_0.02-3_all.deb ...
247s Unpacking libmousex-strictconstructor-perl (0.02-3) ...
247s Selecting previously unselected package libparse-recdescent-perl.
247s Preparing to unpack .../046-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
247s Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
247s Selecting previously unselected package libpath-tiny-perl.
247s Preparing to unpack .../047-libpath-tiny-perl_0.148-1_all.deb ...
247s Unpacking libpath-tiny-perl (0.148-1) ...
247s Selecting previously unselected package libpod-pom-perl.
247s Preparing to unpack .../048-libpod-pom-perl_2.01-4_all.deb ...
247s Unpacking libpod-pom-perl (2.01-4) ...
247s Selecting previously unselected package libregexp-common-perl.
247s Preparing to unpack .../049-libregexp-common-perl_2024080801-1_all.deb ...
247s Unpacking libregexp-common-perl (2024080801-1) ...
247s Selecting previously unselected package libyaml-tiny-perl.
247s Preparing to unpack .../050-libyaml-tiny-perl_1.76-1_all.deb ...
247s Unpacking libyaml-tiny-perl (1.76-1) ...
247s Selecting previously unselected package libconfig-model-perl.
247s Preparing to unpack .../051-libconfig-model-perl_2.155-1_all.deb ...
247s Unpacking libconfig-model-perl (2.155-1) ...
248s Selecting previously unselected package libyaml-pp-perl.
248s Preparing to unpack .../052-libyaml-pp-perl_0.39.0-1_all.deb ...
248s Unpacking libyaml-pp-perl (0.39.0-1) ...
248s Selecting previously unselected package cme.
248s Preparing to unpack .../053-cme_1.044-2_all.deb ...
248s Unpacking cme (1.044-2) ...
248s Selecting previously unselected package libisl23:ppc64el.
248s Preparing to unpack .../054-libisl23_0.27-1build1_ppc64el.deb ...
248s Unpacking libisl23:ppc64el (0.27-1build1) ...
248s Selecting previously unselected package libmpc3:ppc64el.
248s Preparing to unpack .../055-libmpc3_1.3.1-2_ppc64el.deb ...
248s Unpacking libmpc3:ppc64el (1.3.1-2) ...
248s Selecting previously unselected package cpp-15-powerpc64le-linux-gnu.
248s Preparing to unpack .../056-cpp-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking cpp-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package cpp-15.
248s Preparing to unpack .../057-cpp-15_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking cpp-15 (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package cpp-powerpc64le-linux-gnu.
248s Preparing to unpack .../058-cpp-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
248s Unpacking cpp-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ...
248s Selecting previously unselected package cpp.
248s Preparing to unpack .../059-cpp_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
248s Unpacking cpp (4:15.2.0-4ubuntu1) ...
248s Selecting previously unselected package libdebhelper-perl.
248s Preparing to unpack .../060-libdebhelper-perl_13.28ubuntu1_all.deb ...
248s Unpacking libdebhelper-perl (13.28ubuntu1) ...
248s Selecting previously unselected package libcc1-0:ppc64el.
248s Preparing to unpack .../061-libcc1-0_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libcc1-0:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libgomp1:ppc64el.
248s Preparing to unpack .../062-libgomp1_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libgomp1:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libitm1:ppc64el.
248s Preparing to unpack .../063-libitm1_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libitm1:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libasan8:ppc64el.
248s Preparing to unpack .../064-libasan8_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libasan8:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package liblsan0:ppc64el.
248s Preparing to unpack .../065-liblsan0_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking liblsan0:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libtsan2:ppc64el.
248s Preparing to unpack .../066-libtsan2_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libtsan2:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libubsan1:ppc64el.
248s Preparing to unpack .../067-libubsan1_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libubsan1:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libquadmath0:ppc64el.
248s Preparing to unpack .../068-libquadmath0_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libquadmath0:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package libgcc-15-dev:ppc64el.
248s Preparing to unpack .../069-libgcc-15-dev_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking libgcc-15-dev:ppc64el (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package gcc-15-powerpc64le-linux-gnu.
248s Preparing to unpack .../070-gcc-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking gcc-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package gcc-15.
248s Preparing to unpack .../071-gcc-15_15.2.0-12ubuntu1_ppc64el.deb ...
248s Unpacking gcc-15 (15.2.0-12ubuntu1) ...
248s Selecting previously unselected package gcc-powerpc64le-linux-gnu.
248s Preparing to unpack .../072-gcc-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
248s Unpacking gcc-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ...
248s Selecting previously unselected package gcc.
248s Preparing to unpack .../073-gcc_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
248s Unpacking gcc (4:15.2.0-4ubuntu1) ...
248s Selecting previously unselected package libc-dev-bin.
248s Preparing to unpack .../074-libc-dev-bin_2.42-2ubuntu5_ppc64el.deb ...
248s Unpacking libc-dev-bin (2.42-2ubuntu5) ...
249s Selecting previously unselected package linux-libc-dev:ppc64el.
249s Preparing to unpack .../075-linux-libc-dev_6.18.0-9.9_ppc64el.deb ...
249s Unpacking linux-libc-dev:ppc64el (6.18.0-9.9) ...
249s Selecting previously unselected package libcrypt-dev:ppc64el.
249s Preparing to unpack .../076-libcrypt-dev_1%3a4.5.1-1_ppc64el.deb ...
249s Unpacking libcrypt-dev:ppc64el (1:4.5.1-1) ...
249s Selecting previously unselected package rpcsvc-proto.
249s Preparing to unpack .../077-rpcsvc-proto_1.4.3-1build1_ppc64el.deb ...
249s Unpacking rpcsvc-proto (1.4.3-1build1) ...
249s Selecting previously unselected package libc6-dev:ppc64el.
249s Preparing to unpack .../078-libc6-dev_2.42-2ubuntu5_ppc64el.deb ...
249s Unpacking libc6-dev:ppc64el (2.42-2ubuntu5) ...
249s Selecting previously unselected package libtool.
249s Preparing to unpack .../079-libtool_2.5.4-9_all.deb ...
249s Unpacking libtool (2.5.4-9) ...
249s Selecting previously unselected package dh-autoreconf.
249s Preparing to unpack .../080-dh-autoreconf_21_all.deb ...
249s Unpacking dh-autoreconf (21) ...
249s Selecting previously unselected package libarchive-zip-perl.
249s Preparing to unpack .../081-libarchive-zip-perl_1.68-1_all.deb ...
249s Unpacking libarchive-zip-perl (1.68-1) ...
249s Selecting previously unselected package libfile-stripnondeterminism-perl.
249s Preparing to unpack .../082-libfile-stripnondeterminism-perl_1.15.0-1build1_all.deb ...
249s Unpacking libfile-stripnondeterminism-perl (1.15.0-1build1) ...
249s Selecting previously unselected package dh-strip-nondeterminism.
249s Preparing to unpack .../083-dh-strip-nondeterminism_1.15.0-1build1_all.deb ...
249s Unpacking dh-strip-nondeterminism (1.15.0-1build1) ...
249s Selecting previously unselected package debugedit.
249s Preparing to unpack .../084-debugedit_1%3a5.2-3build1_ppc64el.deb ...
249s Unpacking debugedit (1:5.2-3build1) ...
249s Selecting previously unselected package dwz.
249s Preparing to unpack .../085-dwz_0.16-2_ppc64el.deb ...
249s Unpacking dwz (0.16-2) ...
249s Selecting previously unselected package gettext.
249s Preparing to unpack .../086-gettext_0.23.2-1_ppc64el.deb ...
249s Unpacking gettext (0.23.2-1) ...
249s Selecting previously unselected package intltool-debian.
249s Preparing to unpack .../087-intltool-debian_0.35.0+20060710.6build1_all.deb ...
249s Unpacking intltool-debian (0.35.0+20060710.6build1) ...
249s Selecting previously unselected package po-debconf.
249s Preparing to unpack .../088-po-debconf_1.0.22_all.deb ...
249s Unpacking po-debconf (1.0.22) ...
249s Selecting previously unselected package debhelper.
249s Preparing to unpack .../089-debhelper_13.28ubuntu1_all.deb ...
249s Unpacking debhelper (13.28ubuntu1) ...
249s Selecting previously unselected package aglfn.
249s Preparing to unpack .../090-aglfn_1.7+git20191031.4036a9c-2build1_all.deb ...
249s Unpacking aglfn (1.7+git20191031.4036a9c-2build1) ...
249s Selecting previously unselected package gnuplot-data.
249s Preparing to unpack .../091-gnuplot-data_6.0.2+dfsg1-2ubuntu1_all.deb ...
249s Unpacking gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
249s Selecting previously unselected package fonts-freefont-otf.
249s Preparing to unpack .../092-fonts-freefont-otf_20211204+svn4273-4build1_all.deb ...
249s Unpacking fonts-freefont-otf (20211204+svn4273-4build1) ...
249s Selecting previously unselected package fontconfig-config.
249s Preparing to unpack .../093-fontconfig-config_2.17.1-3ubuntu1_ppc64el.deb ...
249s Unpacking fontconfig-config (2.17.1-3ubuntu1) ...
249s Selecting previously unselected package libfontconfig1:ppc64el.
249s Preparing to unpack .../094-libfontconfig1_2.17.1-3ubuntu1_ppc64el.deb ...
249s Unpacking libfontconfig1:ppc64el (2.17.1-3ubuntu1) ...
250s Selecting previously unselected package libpixman-1-0:ppc64el.
250s Preparing to unpack .../095-libpixman-1-0_0.46.4-1_ppc64el.deb ...
250s Unpacking libpixman-1-0:ppc64el (0.46.4-1) ...
250s Selecting previously unselected package libxcb-render0:ppc64el.
250s Preparing to unpack .../096-libxcb-render0_1.17.0-2ubuntu1_ppc64el.deb ...
250s Unpacking libxcb-render0:ppc64el (1.17.0-2ubuntu1) ...
250s Selecting previously unselected package libxcb-shm0:ppc64el.
250s Preparing to unpack .../097-libxcb-shm0_1.17.0-2ubuntu1_ppc64el.deb ...
250s Unpacking libxcb-shm0:ppc64el (1.17.0-2ubuntu1) ...
250s Selecting previously unselected package libxrender1:ppc64el.
250s Preparing to unpack .../098-libxrender1_1%3a0.9.12-1_ppc64el.deb ...
250s Unpacking libxrender1:ppc64el (1:0.9.12-1) ...
250s Selecting previously unselected package libcairo2:ppc64el.
250s Preparing to unpack .../099-libcairo2_1.18.4-3_ppc64el.deb ...
250s Unpacking libcairo2:ppc64el (1.18.4-3) ...
250s Selecting previously unselected package libsharpyuv0:ppc64el.
250s Preparing to unpack .../100-libsharpyuv0_1.5.0-0.1build1_ppc64el.deb ...
250s Unpacking libsharpyuv0:ppc64el (1.5.0-0.1build1) ...
250s Selecting previously unselected package libaom3:ppc64el.
250s Preparing to unpack .../101-libaom3_3.13.1-2_ppc64el.deb ...
250s Unpacking libaom3:ppc64el (3.13.1-2) ...
250s Selecting previously unselected package libheif-plugin-aomdec:ppc64el.
250s Preparing to unpack .../102-libheif-plugin-aomdec_1.21.2-1_ppc64el.deb ...
250s Unpacking libheif-plugin-aomdec:ppc64el (1.21.2-1) ...
250s Selecting previously unselected package libde265-0:ppc64el.
250s Preparing to unpack .../103-libde265-0_1.0.16-1build1_ppc64el.deb ...
250s Unpacking libde265-0:ppc64el (1.0.16-1build1) ...
250s Selecting previously unselected package libheif-plugin-libde265:ppc64el.
250s Preparing to unpack .../104-libheif-plugin-libde265_1.21.2-1_ppc64el.deb ...
250s Unpacking libheif-plugin-libde265:ppc64el (1.21.2-1) ...
250s Selecting previously unselected package libheif1:ppc64el.
250s Preparing to unpack .../105-libheif1_1.21.2-1_ppc64el.deb ...
250s Unpacking libheif1:ppc64el (1.21.2-1) ...
250s Selecting previously unselected package libimagequant0:ppc64el.
250s Preparing to unpack .../106-libimagequant0_2.18.0-1build1_ppc64el.deb ...
250s Unpacking libimagequant0:ppc64el (2.18.0-1build1) ...
250s Selecting previously unselected package libjpeg-turbo8:ppc64el.
250s Preparing to unpack .../107-libjpeg-turbo8_2.1.5-4ubuntu2_ppc64el.deb ...
250s Unpacking libjpeg-turbo8:ppc64el (2.1.5-4ubuntu2) ...
250s Selecting previously unselected package libjpeg8:ppc64el.
250s Preparing to unpack .../108-libjpeg8_8c-2ubuntu11_ppc64el.deb ...
250s Unpacking libjpeg8:ppc64el (8c-2ubuntu11) ...
250s Selecting previously unselected package libdeflate0:ppc64el.
250s Preparing to unpack .../109-libdeflate0_1.23-2_ppc64el.deb ...
250s Unpacking libdeflate0:ppc64el (1.23-2) ...
250s Selecting previously unselected package libjbig0:ppc64el.
250s Preparing to unpack .../110-libjbig0_2.1-6.1ubuntu3_ppc64el.deb ...
250s Unpacking libjbig0:ppc64el (2.1-6.1ubuntu3) ...
250s Selecting previously unselected package liblerc4:ppc64el.
250s Preparing to unpack .../111-liblerc4_4.0.0+ds-5ubuntu2_ppc64el.deb ...
250s Unpacking liblerc4:ppc64el (4.0.0+ds-5ubuntu2) ...
250s Selecting previously unselected package libwebp7:ppc64el.
250s Preparing to unpack .../112-libwebp7_1.5.0-0.1build1_ppc64el.deb ...
250s Unpacking libwebp7:ppc64el (1.5.0-0.1build1) ...
250s Selecting previously unselected package libtiff6:ppc64el.
250s Preparing to unpack .../113-libtiff6_4.7.0-3ubuntu3_ppc64el.deb ...
250s Unpacking libtiff6:ppc64el (4.7.0-3ubuntu3) ...
250s Selecting previously unselected package libxpm4:ppc64el.
250s Preparing to unpack .../114-libxpm4_1%3a3.5.17-1build3_ppc64el.deb ...
250s Unpacking libxpm4:ppc64el (1:3.5.17-1build3) ...
250s Selecting previously unselected package libgd3:ppc64el.
250s Preparing to unpack .../115-libgd3_2.3.3-13ubuntu1_ppc64el.deb ...
250s Unpacking libgd3:ppc64el (2.3.3-13ubuntu1) ...
250s Selecting previously unselected package liblua5.4-0:ppc64el.
250s Preparing to unpack .../116-liblua5.4-0_5.4.8-1_ppc64el.deb ...
250s Unpacking liblua5.4-0:ppc64el (5.4.8-1) ...
250s Selecting previously unselected package fontconfig.
250s Preparing to unpack .../117-fontconfig_2.17.1-3ubuntu1_ppc64el.deb ...
250s Unpacking fontconfig (2.17.1-3ubuntu1) ...
250s Selecting previously unselected package libgraphite2-3:ppc64el.
250s Preparing to unpack .../118-libgraphite2-3_1.3.14-11ubuntu1_ppc64el.deb ...
250s Unpacking libgraphite2-3:ppc64el (1.3.14-11ubuntu1) ...
250s Selecting previously unselected package libharfbuzz0b:ppc64el.
250s Preparing to unpack .../119-libharfbuzz0b_12.3.2-1_ppc64el.deb ...
250s Unpacking libharfbuzz0b:ppc64el (12.3.2-1) ...
250s Selecting previously unselected package libthai-data.
250s Preparing to unpack .../120-libthai-data_0.1.30-1_all.deb ...
250s Unpacking libthai-data (0.1.30-1) ...
250s Selecting previously unselected package libdatrie1:ppc64el.
250s Preparing to unpack .../121-libdatrie1_0.2.14-1_ppc64el.deb ...
250s Unpacking libdatrie1:ppc64el (0.2.14-1) ...
250s Selecting previously unselected package libthai0:ppc64el.
250s Preparing to unpack .../122-libthai0_0.1.30-1_ppc64el.deb ...
250s Unpacking libthai0:ppc64el (0.1.30-1) ...
250s Selecting previously unselected package libpango-1.0-0:ppc64el.
250s Preparing to unpack .../123-libpango-1.0-0_1.57.0-1_ppc64el.deb ...
250s Unpacking libpango-1.0-0:ppc64el (1.57.0-1) ...
250s Selecting previously unselected package libpangoft2-1.0-0:ppc64el.
250s Preparing to unpack .../124-libpangoft2-1.0-0_1.57.0-1_ppc64el.deb ...
250s Unpacking libpangoft2-1.0-0:ppc64el (1.57.0-1) ...
250s Selecting previously unselected package libpangocairo-1.0-0:ppc64el.
250s Preparing to unpack .../125-libpangocairo-1.0-0_1.57.0-1_ppc64el.deb ...
250s Unpacking libpangocairo-1.0-0:ppc64el (1.57.0-1) ...
250s Selecting previously unselected package libwebpmux3:ppc64el.
250s Preparing to unpack .../126-libwebpmux3_1.5.0-0.1build1_ppc64el.deb ...
250s Unpacking libwebpmux3:ppc64el (1.5.0-0.1build1) ...
250s Selecting previously unselected package gnuplot-nox.
250s Preparing to unpack .../127-gnuplot-nox_6.0.2+dfsg1-2ubuntu1_ppc64el.deb ...
250s Unpacking gnuplot-nox (6.0.2+dfsg1-2ubuntu1) ...
250s Selecting previously unselected package dh-octave-autopkgtest.
250s Preparing to unpack .../128-dh-octave-autopkgtest_1.14.0_all.deb ...
250s Unpacking dh-octave-autopkgtest (1.14.0) ...
250s Selecting previously unselected package libapt-pkg-perl.
250s Preparing to unpack .../129-libapt-pkg-perl_0.1.43_ppc64el.deb ...
250s Unpacking libapt-pkg-perl (0.1.43) ...
250s Selecting previously unselected package libarray-intspan-perl.
250s Preparing to unpack .../130-libarray-intspan-perl_2.004-2_all.deb ...
250s Unpacking libarray-intspan-perl (2.004-2) ...
250s Selecting previously unselected package libconfig-inifiles-perl.
250s Preparing to unpack .../131-libconfig-inifiles-perl_3.000003-4_all.deb ...
250s Unpacking libconfig-inifiles-perl (3.000003-4) ...
250s Selecting previously unselected package libyaml-libyaml-perl.
250s Preparing to unpack .../132-libyaml-libyaml-perl_0.904.0+ds-1_ppc64el.deb ...
250s Unpacking libyaml-libyaml-perl (0.904.0+ds-1) ...
250s Selecting previously unselected package libconfig-model-backend-yaml-perl.
250s Preparing to unpack .../133-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
250s Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
250s Selecting previously unselected package libexporter-lite-perl.
250s Preparing to unpack .../134-libexporter-lite-perl_0.09-2_all.deb ...
250s Unpacking libexporter-lite-perl (0.09-2) ...
250s Selecting previously unselected package libencode-locale-perl.
250s Preparing to unpack .../135-libencode-locale-perl_1.05-3_all.deb ...
250s Unpacking libencode-locale-perl (1.05-3) ...
250s Selecting previously unselected package libtimedate-perl.
250s Preparing to unpack .../136-libtimedate-perl_2.3300-2_all.deb ...
250s Unpacking libtimedate-perl (2.3300-2) ...
250s Selecting previously unselected package libhttp-date-perl.
250s Preparing to unpack .../137-libhttp-date-perl_6.06-1_all.deb ...
250s Unpacking libhttp-date-perl (6.06-1) ...
250s Selecting previously unselected package libfile-listing-perl.
250s Preparing to unpack .../138-libfile-listing-perl_6.16-1_all.deb ...
250s Unpacking libfile-listing-perl (6.16-1) ...
250s Selecting previously unselected package libhtml-tagset-perl.
250s Preparing to unpack .../139-libhtml-tagset-perl_3.24-1_all.deb ...
250s Unpacking libhtml-tagset-perl (3.24-1) ...
250s Selecting previously unselected package liburi-perl.
250s Preparing to unpack .../140-liburi-perl_5.34-2build1_all.deb ...
250s Unpacking liburi-perl (5.34-2build1) ...
250s Selecting previously unselected package libhtml-parser-perl:ppc64el.
250s Preparing to unpack .../141-libhtml-parser-perl_3.83-1build1_ppc64el.deb ...
250s Unpacking libhtml-parser-perl:ppc64el (3.83-1build1) ...
250s Selecting previously unselected package libhtml-tree-perl.
250s Preparing to unpack .../142-libhtml-tree-perl_5.07-3_all.deb ...
250s Unpacking libhtml-tree-perl (5.07-3) ...
250s Selecting previously unselected package libclone-perl:ppc64el.
250s Preparing to unpack .../143-libclone-perl_0.47-1_ppc64el.deb ...
250s Unpacking libclone-perl:ppc64el (0.47-1) ...
250s Selecting previously unselected package libio-html-perl.
250s Preparing to unpack .../144-libio-html-perl_1.004-3_all.deb ...
250s Unpacking libio-html-perl (1.004-3) ...
250s Selecting previously unselected package liblwp-mediatypes-perl.
250s Preparing to unpack .../145-liblwp-mediatypes-perl_6.04-2_all.deb ...
250s Unpacking liblwp-mediatypes-perl (6.04-2) ...
250s Selecting previously unselected package libhttp-message-perl.
250s Preparing to unpack .../146-libhttp-message-perl_7.01-1ubuntu1_all.deb ...
250s Unpacking libhttp-message-perl (7.01-1ubuntu1) ...
250s Selecting previously unselected package libhttp-cookies-perl.
250s Preparing to unpack .../147-libhttp-cookies-perl_6.11-1_all.deb ...
250s Unpacking libhttp-cookies-perl (6.11-1) ...
250s Selecting previously unselected package libhttp-negotiate-perl.
250s Preparing to unpack .../148-libhttp-negotiate-perl_6.01-2_all.deb ...
250s Unpacking libhttp-negotiate-perl (6.01-2) ...
250s Selecting previously unselected package perl-openssl-defaults:ppc64el.
251s Preparing to unpack .../149-perl-openssl-defaults_7build4_ppc64el.deb ...
251s Unpacking perl-openssl-defaults:ppc64el (7build4) ...
251s Selecting previously unselected package libnet-ssleay-perl:ppc64el.
251s Preparing to unpack .../150-libnet-ssleay-perl_1.94-3_ppc64el.deb ...
251s Unpacking libnet-ssleay-perl:ppc64el (1.94-3) ...
251s Selecting previously unselected package libio-socket-ssl-perl.
251s Preparing to unpack .../151-libio-socket-ssl-perl_2.098-1_all.deb ...
251s Unpacking libio-socket-ssl-perl (2.098-1) ...
251s Selecting previously unselected package libnet-http-perl.
251s Preparing to unpack .../152-libnet-http-perl_6.24-1build1_all.deb ...
251s Unpacking libnet-http-perl (6.24-1build1) ...
251s Selecting previously unselected package liblwp-protocol-https-perl.
251s Preparing to unpack .../153-liblwp-protocol-https-perl_6.14-1_all.deb ...
251s Unpacking liblwp-protocol-https-perl (6.14-1) ...
251s Selecting previously unselected package libwww-robotrules-perl.
251s Preparing to unpack .../154-libwww-robotrules-perl_6.02-1build1_all.deb ...
251s Unpacking libwww-robotrules-perl (6.02-1build1) ...
251s Selecting previously unselected package libwww-perl.
251s Preparing to unpack .../155-libwww-perl_6.81-1build1_all.deb ...
251s Unpacking libwww-perl (6.81-1build1) ...
251s Selecting previously unselected package liberror-perl.
251s Preparing to unpack .../156-liberror-perl_0.17030-1_all.deb ...
251s Unpacking liberror-perl (0.17030-1) ...
251s Selecting previously unselected package libparse-debcontrol-perl.
251s Preparing to unpack .../157-libparse-debcontrol-perl_2.005-6_all.deb ...
251s Unpacking libparse-debcontrol-perl (2.005-6) ...
251s Selecting previously unselected package libsoftware-copyright-perl.
251s Preparing to unpack .../158-libsoftware-copyright-perl_0.015-1_all.deb ...
251s Unpacking libsoftware-copyright-perl (0.015-1) ...
251s Selecting previously unselected package libalgorithm-c3-perl.
251s Preparing to unpack .../159-libalgorithm-c3-perl_0.11-2_all.deb ...
251s Unpacking libalgorithm-c3-perl (0.11-2) ...
251s Selecting previously unselected package libclass-c3-perl.
251s Preparing to unpack .../160-libclass-c3-perl_0.35-2_all.deb ...
251s Unpacking libclass-c3-perl (0.35-2) ...
251s Selecting previously unselected package libmro-compat-perl.
251s Preparing to unpack .../161-libmro-compat-perl_0.15-2_all.deb ...
251s Unpacking libmro-compat-perl (0.15-2) ...
251s Selecting previously unselected package libdata-section-perl.
251s Preparing to unpack .../162-libdata-section-perl_0.200008-1_all.deb ...
251s Unpacking libdata-section-perl (0.200008-1) ...
251s Selecting previously unselected package libtext-template-perl.
251s Preparing to unpack .../163-libtext-template-perl_1.61-1_all.deb ...
251s Unpacking libtext-template-perl (1.61-1) ...
251s Selecting previously unselected package libsoftware-license-perl.
251s Preparing to unpack .../164-libsoftware-license-perl_0.104007-1_all.deb ...
251s Unpacking libsoftware-license-perl (0.104007-1) ...
251s Selecting previously unselected package libsoftware-licensemoreutils-perl.
251s Preparing to unpack .../165-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
251s Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
251s Selecting previously unselected package libsort-versions-perl.
251s Preparing to unpack .../166-libsort-versions-perl_1.62-3_all.deb ...
251s Unpacking libsort-versions-perl (1.62-3) ...
251s Selecting previously unselected package libtext-reform-perl.
251s Preparing to unpack .../167-libtext-reform-perl_1.20-5_all.deb ...
251s Unpacking libtext-reform-perl (1.20-5) ...
251s Selecting previously unselected package libtext-autoformat-perl.
251s Preparing to unpack .../168-libtext-autoformat-perl_1.750000-2_all.deb ...
251s Unpacking libtext-autoformat-perl (1.750000-2) ...
251s Selecting previously unselected package libtext-levenshtein-damerau-perl.
251s Preparing to unpack .../169-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
251s Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
251s Selecting previously unselected package libtoml-tiny-perl.
251s Preparing to unpack .../170-libtoml-tiny-perl_0.20-1_all.deb ...
251s Unpacking libtoml-tiny-perl (0.20-1) ...
251s Selecting previously unselected package libclass-inspector-perl.
251s Preparing to unpack .../171-libclass-inspector-perl_1.36-3_all.deb ...
251s Unpacking libclass-inspector-perl (1.36-3) ...
251s Selecting previously unselected package libfile-sharedir-perl.
251s Preparing to unpack .../172-libfile-sharedir-perl_1.118-3_all.deb ...
251s Unpacking libfile-sharedir-perl (1.118-3) ...
251s Selecting previously unselected package libindirect-perl.
251s Preparing to unpack .../173-libindirect-perl_0.39-2build5_ppc64el.deb ...
251s Unpacking libindirect-perl (0.39-2build5) ...
251s Selecting previously unselected package libxs-parse-keyword-perl.
251s Preparing to unpack .../174-libxs-parse-keyword-perl_0.49-1_ppc64el.deb ...
251s Unpacking libxs-parse-keyword-perl (0.49-1) ...
251s Selecting previously unselected package libxs-parse-sublike-perl:ppc64el.
251s Preparing to unpack .../175-libxs-parse-sublike-perl_0.41-1_ppc64el.deb ...
251s Unpacking libxs-parse-sublike-perl:ppc64el (0.41-1) ...
251s Selecting previously unselected package libobject-pad-perl.
251s Preparing to unpack .../176-libobject-pad-perl_0.823-2_ppc64el.deb ...
251s Unpacking libobject-pad-perl (0.823-2) ...
251s Selecting previously unselected package libsyntax-keyword-try-perl.
251s Preparing to unpack .../177-libsyntax-keyword-try-perl_0.31-1_ppc64el.deb ...
251s Unpacking libsyntax-keyword-try-perl (0.31-1) ...
251s Selecting previously unselected package libio-interactive-perl.
251s Preparing to unpack .../178-libio-interactive-perl_1.027-1_all.deb ...
251s Unpacking libio-interactive-perl (1.027-1) ...
251s Selecting previously unselected package liblog-any-perl.
251s Preparing to unpack .../179-liblog-any-perl_1.718-1build1_all.deb ...
251s Unpacking liblog-any-perl (1.718-1build1) ...
251s Selecting previously unselected package liblog-any-adapter-screen-perl.
251s Preparing to unpack .../180-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
251s Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
251s Selecting previously unselected package libsub-exporter-progressive-perl.
251s Preparing to unpack .../181-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
251s Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
251s Selecting previously unselected package libvariable-magic-perl.
251s Preparing to unpack .../182-libvariable-magic-perl_0.64-1build1_ppc64el.deb ...
251s Unpacking libvariable-magic-perl (0.64-1build1) ...
251s Selecting previously unselected package libb-hooks-endofscope-perl.
251s Preparing to unpack .../183-libb-hooks-endofscope-perl_0.28-2_all.deb ...
251s Unpacking libb-hooks-endofscope-perl (0.28-2) ...
251s Selecting previously unselected package libsub-identify-perl.
251s Preparing to unpack .../184-libsub-identify-perl_0.14-4_ppc64el.deb ...
251s Unpacking libsub-identify-perl (0.14-4) ...
251s Selecting previously unselected package libsub-name-perl:ppc64el.
251s Preparing to unpack .../185-libsub-name-perl_0.28-1_ppc64el.deb ...
251s Unpacking libsub-name-perl:ppc64el (0.28-1) ...
251s Selecting previously unselected package libnamespace-clean-perl.
251s Preparing to unpack .../186-libnamespace-clean-perl_0.27-2_all.deb ...
251s Unpacking libnamespace-clean-perl (0.27-2) ...
251s Selecting previously unselected package libnumber-compare-perl.
251s Preparing to unpack .../187-libnumber-compare-perl_0.03-3_all.deb ...
251s Unpacking libnumber-compare-perl (0.03-3) ...
251s Selecting previously unselected package libtext-glob-perl.
251s Preparing to unpack .../188-libtext-glob-perl_0.11-3_all.deb ...
251s Unpacking libtext-glob-perl (0.11-3) ...
251s Selecting previously unselected package libpath-iterator-rule-perl.
251s Preparing to unpack .../189-libpath-iterator-rule-perl_1.015-2_all.deb ...
251s Unpacking libpath-iterator-rule-perl (1.015-2) ...
251s Selecting previously unselected package libpod-parser-perl.
251s Preparing to unpack .../190-libpod-parser-perl_1.67-1_all.deb ...
251s Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
251s Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
251s Unpacking libpod-parser-perl (1.67-1) ...
251s Selecting previously unselected package libpod-constants-perl.
251s Preparing to unpack .../191-libpod-constants-perl_0.19-2_all.deb ...
251s Unpacking libpod-constants-perl (0.19-2) ...
251s Selecting previously unselected package libset-intspan-perl.
251s Preparing to unpack .../192-libset-intspan-perl_1.19-3_all.deb ...
251s Unpacking libset-intspan-perl (1.19-3) ...
251s Selecting previously unselected package libstring-copyright-perl.
251s Preparing to unpack .../193-libstring-copyright-perl_0.003014-1_all.deb ...
251s Unpacking libstring-copyright-perl (0.003014-1) ...
251s Selecting previously unselected package libstring-escape-perl.
251s Preparing to unpack .../194-libstring-escape-perl_2010.002-3_all.deb ...
251s Unpacking libstring-escape-perl (2010.002-3) ...
251s Selecting previously unselected package libregexp-pattern-license-perl.
251s Preparing to unpack .../195-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
251s Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
251s Selecting previously unselected package libregexp-pattern-perl.
251s Preparing to unpack .../196-libregexp-pattern-perl_0.2.14-2_all.deb ...
251s Unpacking libregexp-pattern-perl (0.2.14-2) ...
251s Selecting previously unselected package libstring-license-perl.
251s Preparing to unpack .../197-libstring-license-perl_0.0.11-1ubuntu1_all.deb ...
251s Unpacking libstring-license-perl (0.0.11-1ubuntu1) ...
251s Selecting previously unselected package licensecheck.
251s Preparing to unpack .../198-licensecheck_3.3.9-1ubuntu2_all.deb ...
251s Unpacking licensecheck (3.3.9-1ubuntu2) ...
251s Selecting previously unselected package diffstat.
251s Preparing to unpack .../199-diffstat_1.68-1_ppc64el.deb ...
251s Unpacking diffstat (1.68-1) ...
251s Selecting previously unselected package libberkeleydb-perl:ppc64el.
251s Preparing to unpack .../200-libberkeleydb-perl_0.66-2_ppc64el.deb ...
251s Unpacking libberkeleydb-perl:ppc64el (0.66-2) ...
251s Selecting previously unselected package libclass-xsaccessor-perl.
251s Preparing to unpack .../201-libclass-xsaccessor-perl_1.19-4build6_ppc64el.deb ...
251s Unpacking libclass-xsaccessor-perl (1.19-4build6) ...
251s Selecting previously unselected package libconfig-tiny-perl.
251s Preparing to unpack .../202-libconfig-tiny-perl_2.30-1_all.deb ...
251s Unpacking libconfig-tiny-perl (2.30-1) ...
251s Selecting previously unselected package libconst-fast-perl.
251s Preparing to unpack .../203-libconst-fast-perl_0.014-2_all.deb ...
251s Unpacking libconst-fast-perl (0.014-2) ...
251s Selecting previously unselected package libcpanel-json-xs-perl:ppc64el.
251s Preparing to unpack .../204-libcpanel-json-xs-perl_4.40-1_ppc64el.deb ...
251s Unpacking libcpanel-json-xs-perl:ppc64el (4.40-1) ...
252s Selecting previously unselected package libaliased-perl.
252s Preparing to unpack .../205-libaliased-perl_0.34-3_all.deb ...
252s Unpacking libaliased-perl (0.34-3) ...
252s Selecting previously unselected package libclass-data-inheritable-perl.
252s Preparing to unpack .../206-libclass-data-inheritable-perl_0.10-1_all.deb ...
252s Unpacking libclass-data-inheritable-perl (0.10-1) ...
252s Selecting previously unselected package libdevel-stacktrace-perl.
252s Preparing to unpack .../207-libdevel-stacktrace-perl_2.0500-1_all.deb ...
252s Unpacking libdevel-stacktrace-perl (2.0500-1) ...
252s Selecting previously unselected package libexception-class-perl.
252s Preparing to unpack .../208-libexception-class-perl_1.45-1_all.deb ...
252s Unpacking libexception-class-perl (1.45-1) ...
252s Selecting previously unselected package libiterator-perl.
252s Preparing to unpack .../209-libiterator-perl_0.03+ds1-2_all.deb ...
252s Unpacking libiterator-perl (0.03+ds1-2) ...
252s Selecting previously unselected package libiterator-util-perl.
252s Preparing to unpack .../210-libiterator-util-perl_0.02+ds1-2_all.deb ...
252s Unpacking libiterator-util-perl (0.02+ds1-2) ...
252s Selecting previously unselected package libdata-dpath-perl.
252s Preparing to unpack .../211-libdata-dpath-perl_0.60-1_all.deb ...
252s Unpacking libdata-dpath-perl (0.60-1) ...
252s Selecting previously unselected package libnet-domain-tld-perl.
252s Preparing to unpack .../212-libnet-domain-tld-perl_1.75-4_all.deb ...
252s Unpacking libnet-domain-tld-perl (1.75-4) ...
252s Selecting previously unselected package libdata-validate-domain-perl.
252s Preparing to unpack .../213-libdata-validate-domain-perl_0.15-1_all.deb ...
252s Unpacking libdata-validate-domain-perl (0.15-1) ...
252s Selecting previously unselected package libnet-ipv6addr-perl.
252s Preparing to unpack .../214-libnet-ipv6addr-perl_1.02-1_all.deb ...
252s Unpacking libnet-ipv6addr-perl (1.02-1) ...
252s Selecting previously unselected package libnet-netmask-perl.
252s Preparing to unpack .../215-libnet-netmask-perl_2.0003-1build1_all.deb ...
252s Unpacking libnet-netmask-perl (2.0003-1build1) ...
252s Selecting previously unselected package libnetaddr-ip-perl.
252s Preparing to unpack .../216-libnetaddr-ip-perl_4.079+dfsg-2build5_ppc64el.deb ...
252s Unpacking libnetaddr-ip-perl (4.079+dfsg-2build5) ...
252s Selecting previously unselected package libdata-validate-ip-perl.
252s Preparing to unpack .../217-libdata-validate-ip-perl_0.31-1_all.deb ...
252s Unpacking libdata-validate-ip-perl (0.31-1) ...
252s Selecting previously unselected package libdata-validate-uri-perl.
252s Preparing to unpack .../218-libdata-validate-uri-perl_0.07-3_all.deb ...
252s Unpacking libdata-validate-uri-perl (0.07-3) ...
252s Selecting previously unselected package libdevel-size-perl.
252s Preparing to unpack .../219-libdevel-size-perl_0.85-1_ppc64el.deb ...
252s Unpacking libdevel-size-perl (0.85-1) ...
252s Selecting previously unselected package libemail-address-xs-perl.
252s Preparing to unpack .../220-libemail-address-xs-perl_1.05-1build5_ppc64el.deb ...
252s Unpacking libemail-address-xs-perl (1.05-1build5) ...
252s Selecting previously unselected package libipc-system-simple-perl.
252s Preparing to unpack .../221-libipc-system-simple-perl_1.30-2_all.deb ...
252s Unpacking libipc-system-simple-perl (1.30-2) ...
252s Selecting previously unselected package libfile-basedir-perl.
252s Preparing to unpack .../222-libfile-basedir-perl_0.09-2_all.deb ...
252s Unpacking libfile-basedir-perl (0.09-2) ...
252s Selecting previously unselected package libfile-find-rule-perl.
252s Preparing to unpack .../223-libfile-find-rule-perl_0.35-1build1_all.deb ...
252s Unpacking libfile-find-rule-perl (0.35-1build1) ...
252s Selecting previously unselected package libio-string-perl.
252s Preparing to unpack .../224-libio-string-perl_1.08-4_all.deb ...
252s Unpacking libio-string-perl (1.08-4) ...
252s Selecting previously unselected package libfont-ttf-perl.
252s Preparing to unpack .../225-libfont-ttf-perl_1.06-2_all.deb ...
252s Unpacking libfont-ttf-perl (1.06-2) ...
252s Selecting previously unselected package libhtml-html5-entities-perl.
252s Preparing to unpack .../226-libhtml-html5-entities-perl_0.004-3_all.deb ...
252s Unpacking libhtml-html5-entities-perl (0.004-3) ...
252s Selecting previously unselected package libhtml-tokeparser-simple-perl.
252s Preparing to unpack .../227-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
252s Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
252s Selecting previously unselected package libipc-run3-perl.
252s Preparing to unpack .../228-libipc-run3-perl_0.049-1_all.deb ...
252s Unpacking libipc-run3-perl (0.049-1) ...
252s Selecting previously unselected package libjson-maybexs-perl.
252s Preparing to unpack .../229-libjson-maybexs-perl_1.004008-1_all.deb ...
252s Unpacking libjson-maybexs-perl (1.004008-1) ...
252s Selecting previously unselected package liblist-compare-perl.
252s Preparing to unpack .../230-liblist-compare-perl_0.55-2_all.deb ...
252s Unpacking liblist-compare-perl (0.55-2) ...
252s Selecting previously unselected package liblist-someutils-perl.
252s Preparing to unpack .../231-liblist-someutils-perl_0.59-1_all.deb ...
252s Unpacking liblist-someutils-perl (0.59-1) ...
252s Selecting previously unselected package liblist-utilsby-perl.
252s Preparing to unpack .../232-liblist-utilsby-perl_0.12-2_all.deb ...
252s Unpacking liblist-utilsby-perl (0.12-2) ...
252s Selecting previously unselected package libmldbm-perl.
252s Preparing to unpack .../233-libmldbm-perl_2.05-4_all.deb ...
252s Unpacking libmldbm-perl (2.05-4) ...
252s Selecting previously unselected package libclass-method-modifiers-perl.
252s Preparing to unpack .../234-libclass-method-modifiers-perl_2.15-1_all.deb ...
252s Unpacking libclass-method-modifiers-perl (2.15-1) ...
252s Selecting previously unselected package libimport-into-perl.
252s Preparing to unpack .../235-libimport-into-perl_1.002005-2_all.deb ...
252s Unpacking libimport-into-perl (1.002005-2) ...
252s Selecting previously unselected package librole-tiny-perl.
252s Preparing to unpack .../236-librole-tiny-perl_2.002004-1_all.deb ...
252s Unpacking librole-tiny-perl (2.002004-1) ...
252s Selecting previously unselected package libsub-quote-perl.
252s Preparing to unpack .../237-libsub-quote-perl_2.006009-1ubuntu1_all.deb ...
252s Unpacking libsub-quote-perl (2.006009-1ubuntu1) ...
252s Selecting previously unselected package libmoo-perl.
252s Preparing to unpack .../238-libmoo-perl_2.005005-1_all.deb ...
252s Unpacking libmoo-perl (2.005005-1) ...
252s Selecting previously unselected package libstrictures-perl.
252s Preparing to unpack .../239-libstrictures-perl_2.000006-1build1_all.deb ...
252s Unpacking libstrictures-perl (2.000006-1build1) ...
252s Selecting previously unselected package libmoox-aliases-perl.
252s Preparing to unpack .../240-libmoox-aliases-perl_0.001006-2_all.deb ...
252s Unpacking libmoox-aliases-perl (0.001006-2) ...
252s Selecting previously unselected package libperlio-gzip-perl.
252s Preparing to unpack .../241-libperlio-gzip-perl_0.20-1build5_ppc64el.deb ...
252s Unpacking libperlio-gzip-perl (0.20-1build5) ...
252s Selecting previously unselected package libperlio-utf8-strict-perl.
252s Preparing to unpack .../242-libperlio-utf8-strict-perl_0.010-1build4_ppc64el.deb ...
252s Unpacking libperlio-utf8-strict-perl (0.010-1build4) ...
252s Selecting previously unselected package libproc-processtable-perl:ppc64el.
252s Preparing to unpack .../243-libproc-processtable-perl_0.637-1_ppc64el.deb ...
252s Unpacking libproc-processtable-perl:ppc64el (0.637-1) ...
252s Selecting previously unselected package libregexp-wildcards-perl.
252s Preparing to unpack .../244-libregexp-wildcards-perl_1.05-3_all.deb ...
252s Unpacking libregexp-wildcards-perl (1.05-3) ...
252s Selecting previously unselected package libsereal-decoder-perl.
252s Preparing to unpack .../245-libsereal-decoder-perl_5.004+ds-1build5_ppc64el.deb ...
252s Unpacking libsereal-decoder-perl (5.004+ds-1build5) ...
252s Selecting previously unselected package libsereal-encoder-perl.
252s Preparing to unpack .../246-libsereal-encoder-perl_5.004+ds-1build4_ppc64el.deb ...
252s Unpacking libsereal-encoder-perl (5.004+ds-1build4) ...
252s Selecting previously unselected package libterm-readkey-perl.
252s Preparing to unpack .../247-libterm-readkey-perl_2.38-2build5_ppc64el.deb ...
252s Unpacking libterm-readkey-perl (2.38-2build5) ...
252s Selecting previously unselected package libtext-levenshteinxs-perl.
252s Preparing to unpack .../248-libtext-levenshteinxs-perl_0.03-5build5_ppc64el.deb ...
252s Unpacking libtext-levenshteinxs-perl (0.03-5build5) ...
252s Selecting previously unselected package libmarkdown2:ppc64el.
252s Preparing to unpack .../249-libmarkdown2_2.2.7-2.1build1_ppc64el.deb ...
252s Unpacking libmarkdown2:ppc64el (2.2.7-2.1build1) ...
252s Selecting previously unselected package libtext-markdown-discount-perl.
252s Preparing to unpack .../250-libtext-markdown-discount-perl_0.18-1_ppc64el.deb ...
252s Unpacking libtext-markdown-discount-perl (0.18-1) ...
252s Selecting previously unselected package libdata-messagepack-perl.
252s Preparing to unpack .../251-libdata-messagepack-perl_1.02-3_ppc64el.deb ...
252s Unpacking libdata-messagepack-perl (1.02-3) ...
252s Selecting previously unselected package libtext-xslate-perl:ppc64el.
252s Preparing to unpack .../252-libtext-xslate-perl_3.5.9-2build1_ppc64el.deb ...
252s Unpacking libtext-xslate-perl:ppc64el (3.5.9-2build1) ...
252s Selecting previously unselected package libtime-duration-perl.
252s Preparing to unpack .../253-libtime-duration-perl_1.21-2_all.deb ...
252s Unpacking libtime-duration-perl (1.21-2) ...
252s Selecting previously unselected package libtime-moment-perl.
252s Preparing to unpack .../254-libtime-moment-perl_0.46-1_ppc64el.deb ...
252s Unpacking libtime-moment-perl (0.46-1) ...
252s Selecting previously unselected package libunicode-utf8-perl.
252s Preparing to unpack .../255-libunicode-utf8-perl_0.63-1_ppc64el.deb ...
252s Unpacking libunicode-utf8-perl (0.63-1) ...
252s Selecting previously unselected package libcgi-pm-perl.
252s Preparing to unpack .../256-libcgi-pm-perl_4.71-1build1_all.deb ...
252s Unpacking libcgi-pm-perl (4.71-1build1) ...
252s Selecting previously unselected package libhtml-form-perl.
252s Preparing to unpack .../257-libhtml-form-perl_6.13-1build1_all.deb ...
252s Unpacking libhtml-form-perl (6.13-1build1) ...
252s Selecting previously unselected package libwww-mechanize-perl.
252s Preparing to unpack .../258-libwww-mechanize-perl_2.20-1ubuntu1_all.deb ...
252s Unpacking libwww-mechanize-perl (2.20-1ubuntu1) ...
252s Selecting previously unselected package libxml-namespacesupport-perl.
252s Preparing to unpack .../259-libxml-namespacesupport-perl_1.12-2_all.deb ...
252s Unpacking libxml-namespacesupport-perl (1.12-2) ...
252s Selecting previously unselected package libxml-sax-base-perl.
252s Preparing to unpack .../260-libxml-sax-base-perl_1.09-3_all.deb ...
252s Unpacking libxml-sax-base-perl (1.09-3) ...
252s Selecting previously unselected package libxml-sax-perl.
252s Preparing to unpack .../261-libxml-sax-perl_1.02+dfsg-4_all.deb ...
252s Unpacking libxml-sax-perl (1.02+dfsg-4) ...
252s Selecting previously unselected package libxml-libxml-perl.
252s Preparing to unpack .../262-libxml-libxml-perl_2.0207+dfsg+really+2.0207-0ubuntu7_ppc64el.deb ...
252s Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0207-0ubuntu7) ...
252s Selecting previously unselected package lzip.
252s Preparing to unpack .../263-lzip_1.25-4_ppc64el.deb ...
252s Unpacking lzip (1.25-4) ...
253s Selecting previously unselected package lzop.
253s Preparing to unpack .../264-lzop_1.04-2build4_ppc64el.deb ...
253s Unpacking lzop (1.04-2build4) ...
253s Selecting previously unselected package patchutils.
253s Preparing to unpack .../265-patchutils_0.4.3-1_ppc64el.deb ...
253s Unpacking patchutils (0.4.3-1) ...
253s Selecting previously unselected package t1utils.
253s Preparing to unpack .../266-t1utils_1.41-4build4_ppc64el.deb ...
253s Unpacking t1utils (1.41-4build4) ...
253s Selecting previously unselected package unzip.
253s Preparing to unpack .../267-unzip_6.0-29ubuntu1_ppc64el.deb ...
253s Unpacking unzip (6.0-29ubuntu1) ...
253s Selecting previously unselected package lintian.
253s Preparing to unpack .../268-lintian_2.127.0ubuntu1_all.deb ...
253s Unpacking lintian (2.127.0ubuntu1) ...
253s Selecting previously unselected package libconfig-model-dpkg-perl.
253s Preparing to unpack .../269-libconfig-model-dpkg-perl_3.016_all.deb ...
253s Unpacking libconfig-model-dpkg-perl (3.016) ...
253s Selecting previously unselected package libconvert-binhex-perl.
253s Preparing to unpack .../270-libconvert-binhex-perl_1.125-3_all.deb ...
253s Unpacking libconvert-binhex-perl (1.125-3) ...
253s Selecting previously unselected package libnet-smtp-ssl-perl.
253s Preparing to unpack .../271-libnet-smtp-ssl-perl_1.04-2_all.deb ...
253s Unpacking libnet-smtp-ssl-perl (1.04-2) ...
253s Selecting previously unselected package libmailtools-perl.
253s Preparing to unpack .../272-libmailtools-perl_2.22-1_all.deb ...
253s Unpacking libmailtools-perl (2.22-1) ...
253s Selecting previously unselected package libmime-tools-perl.
253s Preparing to unpack .../273-libmime-tools-perl_5.515-1_all.deb ...
253s Unpacking libmime-tools-perl (5.515-1) ...
253s Selecting previously unselected package libb-keywords-perl.
253s Preparing to unpack .../274-libb-keywords-perl_1.29-1_all.deb ...
253s Unpacking libb-keywords-perl (1.29-1) ...
253s Selecting previously unselected package libclass-tiny-perl.
253s Preparing to unpack .../275-libclass-tiny-perl_1.008-2_all.deb ...
253s Unpacking libclass-tiny-perl (1.008-2) ...
253s Selecting previously unselected package liblingua-en-inflect-perl.
253s Preparing to unpack .../276-liblingua-en-inflect-perl_1.905-2_all.deb ...
253s Unpacking liblingua-en-inflect-perl (1.905-2) ...
253s Selecting previously unselected package libpod-spell-perl.
253s Preparing to unpack .../277-libpod-spell-perl_1.27-1_all.deb ...
253s Unpacking libpod-spell-perl (1.27-1) ...
253s Selecting previously unselected package libsafe-isa-perl.
253s Preparing to unpack .../278-libsafe-isa-perl_1.000010-1build1_all.deb ...
253s Unpacking libsafe-isa-perl (1.000010-1build1) ...
253s Selecting previously unselected package libtask-weaken-perl.
253s Preparing to unpack .../279-libtask-weaken-perl_1.06-2_all.deb ...
253s Unpacking libtask-weaken-perl (1.06-2) ...
253s Selecting previously unselected package libppi-perl.
253s Preparing to unpack .../280-libppi-perl_1.284-1_all.deb ...
253s Unpacking libppi-perl (1.284-1) ...
253s Selecting previously unselected package libreadonly-perl.
253s Preparing to unpack .../281-libreadonly-perl_2.050-3_all.deb ...
253s Unpacking libreadonly-perl (2.050-3) ...
253s Selecting previously unselected package libppix-quotelike-perl.
253s Preparing to unpack .../282-libppix-quotelike-perl_0.023-1_all.deb ...
253s Unpacking libppix-quotelike-perl (0.023-1) ...
253s Selecting previously unselected package libppix-regexp-perl.
253s Preparing to unpack .../283-libppix-regexp-perl_0.091-1_all.deb ...
253s Unpacking libppix-regexp-perl (0.091-1) ...
253s Selecting previously unselected package libppix-utils-perl.
253s Preparing to unpack .../284-libppix-utils-perl_0.003-2_all.deb ...
253s Unpacking libppix-utils-perl (0.003-2) ...
253s Selecting previously unselected package libstring-format-perl.
253s Preparing to unpack .../285-libstring-format-perl_1.18-1build1_all.deb ...
253s Unpacking libstring-format-perl (1.18-1build1) ...
253s Selecting previously unselected package perltidy.
253s Preparing to unpack .../286-perltidy_20250105-1build1_all.deb ...
253s Unpacking perltidy (20250105-1build1) ...
253s Selecting previously unselected package libperl-critic-perl.
253s Preparing to unpack .../287-libperl-critic-perl_1.156-1_all.deb ...
253s Unpacking libperl-critic-perl (1.156-1) ...
253s Selecting previously unselected package libtext-wrapper-perl.
253s Preparing to unpack .../288-libtext-wrapper-perl_1.05-4_all.deb ...
253s Unpacking libtext-wrapper-perl (1.05-4) ...
253s Selecting previously unselected package libsuitesparseconfig7:ppc64el.
253s Preparing to unpack .../289-libsuitesparseconfig7_1%3a7.12.1+dfsg-1_ppc64el.deb ...
253s Unpacking libsuitesparseconfig7:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libamd3:ppc64el.
254s Preparing to unpack .../290-libamd3_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libamd3:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libblas3:ppc64el.
254s Preparing to unpack .../291-libblas3_3.12.1-7ubuntu1_ppc64el.deb ...
254s Unpacking libblas3:ppc64el (3.12.1-7ubuntu1) ...
254s Selecting previously unselected package libgfortran5:ppc64el.
254s Preparing to unpack .../292-libgfortran5_15.2.0-12ubuntu1_ppc64el.deb ...
254s Unpacking libgfortran5:ppc64el (15.2.0-12ubuntu1) ...
254s Selecting previously unselected package liblapack3:ppc64el.
254s Preparing to unpack .../293-liblapack3_3.12.1-7ubuntu1_ppc64el.deb ...
254s Unpacking liblapack3:ppc64el (3.12.1-7ubuntu1) ...
254s Selecting previously unselected package libarpack2t64:ppc64el.
254s Preparing to unpack .../294-libarpack2t64_3.9.1-6_ppc64el.deb ...
254s Unpacking libarpack2t64:ppc64el (3.9.1-6) ...
254s Selecting previously unselected package libccolamd3:ppc64el.
254s Preparing to unpack .../295-libccolamd3_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libccolamd3:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libcamd3:ppc64el.
254s Preparing to unpack .../296-libcamd3_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libcamd3:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libcolamd3:ppc64el.
254s Preparing to unpack .../297-libcolamd3_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libcolamd3:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libcholmod5:ppc64el.
254s Preparing to unpack .../298-libcholmod5_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libcholmod5:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libcxsparse4:ppc64el.
254s Preparing to unpack .../299-libcxsparse4_1%3a7.12.1+dfsg-1_ppc64el.deb ...
254s Unpacking libcxsparse4:ppc64el (1:7.12.1+dfsg-1) ...
254s Selecting previously unselected package libfftw3-double3:ppc64el.
254s Preparing to unpack .../300-libfftw3-double3_3.3.10-2fakesync1build2_ppc64el.deb ...
254s Unpacking libfftw3-double3:ppc64el (3.3.10-2fakesync1build2) ...
254s Selecting previously unselected package libfftw3-single3:ppc64el.
254s Preparing to unpack .../301-libfftw3-single3_3.3.10-2fakesync1build2_ppc64el.deb ...
254s Unpacking libfftw3-single3:ppc64el (3.3.10-2fakesync1build2) ...
254s Selecting previously unselected package libxfixes3:ppc64el.
254s Preparing to unpack .../302-libxfixes3_1%3a6.0.0-2build2_ppc64el.deb ...
254s Unpacking libxfixes3:ppc64el (1:6.0.0-2build2) ...
254s Selecting previously unselected package libxcursor1:ppc64el.
254s Preparing to unpack .../303-libxcursor1_1%3a1.2.3-1build1_ppc64el.deb ...
254s Unpacking libxcursor1:ppc64el (1:1.2.3-1build1) ...
254s Selecting previously unselected package libxft2:ppc64el.
254s Preparing to unpack .../304-libxft2_2.3.6-1build2_ppc64el.deb ...
254s Unpacking libxft2:ppc64el (2.3.6-1build2) ...
254s Selecting previously unselected package libxinerama1:ppc64el.
254s Preparing to unpack .../305-libxinerama1_2%3a1.1.4-3build2_ppc64el.deb ...
254s Unpacking libxinerama1:ppc64el (2:1.1.4-3build2) ...
254s Selecting previously unselected package libfltk1.3t64:ppc64el.
254s Preparing to unpack .../306-libfltk1.3t64_1.3.11-3_ppc64el.deb ...
254s Unpacking libfltk1.3t64:ppc64el (1.3.11-3) ...
254s Selecting previously unselected package libglvnd0:ppc64el.
254s Preparing to unpack .../307-libglvnd0_1.7.0-3_ppc64el.deb ...
254s Unpacking libglvnd0:ppc64el (1.7.0-3) ...
254s Selecting previously unselected package libx11-xcb1:ppc64el.
254s Preparing to unpack .../308-libx11-xcb1_2%3a1.8.12-1build1_ppc64el.deb ...
254s Unpacking libx11-xcb1:ppc64el (2:1.8.12-1build1) ...
254s Selecting previously unselected package libxcb-dri3-0:ppc64el.
254s Preparing to unpack .../309-libxcb-dri3-0_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-dri3-0:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxcb-present0:ppc64el.
254s Preparing to unpack .../310-libxcb-present0_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-present0:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxcb-randr0:ppc64el.
254s Preparing to unpack .../311-libxcb-randr0_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-randr0:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxcb-sync1:ppc64el.
254s Preparing to unpack .../312-libxcb-sync1_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-sync1:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxcb-xfixes0:ppc64el.
254s Preparing to unpack .../313-libxcb-xfixes0_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-xfixes0:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxshmfence1:ppc64el.
254s Preparing to unpack .../314-libxshmfence1_1.3.3-1build1_ppc64el.deb ...
254s Unpacking libxshmfence1:ppc64el (1.3.3-1build1) ...
254s Selecting previously unselected package mesa-libgallium:ppc64el.
254s Preparing to unpack .../315-mesa-libgallium_25.3.3-1ubuntu1_ppc64el.deb ...
254s Unpacking mesa-libgallium:ppc64el (25.3.3-1ubuntu1) ...
254s Selecting previously unselected package libgbm1:ppc64el.
254s Preparing to unpack .../316-libgbm1_25.3.3-1ubuntu1_ppc64el.deb ...
254s Unpacking libgbm1:ppc64el (25.3.3-1ubuntu1) ...
254s Selecting previously unselected package libvulkan1:ppc64el.
254s Preparing to unpack .../317-libvulkan1_1.4.335.0-1_ppc64el.deb ...
254s Unpacking libvulkan1:ppc64el (1.4.335.0-1) ...
254s Selecting previously unselected package libgl1-mesa-dri:ppc64el.
254s Preparing to unpack .../318-libgl1-mesa-dri_25.3.3-1ubuntu1_ppc64el.deb ...
254s Unpacking libgl1-mesa-dri:ppc64el (25.3.3-1ubuntu1) ...
254s Selecting previously unselected package libxcb-glx0:ppc64el.
254s Preparing to unpack .../319-libxcb-glx0_1.17.0-2ubuntu1_ppc64el.deb ...
254s Unpacking libxcb-glx0:ppc64el (1.17.0-2ubuntu1) ...
254s Selecting previously unselected package libxxf86vm1:ppc64el.
254s Preparing to unpack .../320-libxxf86vm1_1%3a1.1.4-2_ppc64el.deb ...
254s Unpacking libxxf86vm1:ppc64el (1:1.1.4-2) ...
254s Selecting previously unselected package libglx-mesa0:ppc64el.
254s Preparing to unpack .../321-libglx-mesa0_25.3.3-1ubuntu1_ppc64el.deb ...
254s Unpacking libglx-mesa0:ppc64el (25.3.3-1ubuntu1) ...
254s Selecting previously unselected package libglx0:ppc64el.
254s Preparing to unpack .../322-libglx0_1.7.0-3_ppc64el.deb ...
254s Unpacking libglx0:ppc64el (1.7.0-3) ...
254s Selecting previously unselected package libgl1:ppc64el.
254s Preparing to unpack .../323-libgl1_1.7.0-3_ppc64el.deb ...
254s Unpacking libgl1:ppc64el (1.7.0-3) ...
254s Selecting previously unselected package libfltk-gl1.3t64:ppc64el.
254s Preparing to unpack .../324-libfltk-gl1.3t64_1.3.11-3_ppc64el.deb ...
254s Unpacking libfltk-gl1.3t64:ppc64el (1.3.11-3) ...
254s Selecting previously unselected package libgl2ps1.4:ppc64el.
254s Preparing to unpack .../325-libgl2ps1.4_1.4.2+dfsg1-4_ppc64el.deb ...
254s Unpacking libgl2ps1.4:ppc64el (1.4.2+dfsg1-4) ...
254s Selecting previously unselected package libltdl7:ppc64el.
254s Preparing to unpack .../326-libltdl7_2.5.4-9_ppc64el.deb ...
254s Unpacking libltdl7:ppc64el (2.5.4-9) ...
254s Selecting previously unselected package libglpk40:ppc64el.
254s Preparing to unpack .../327-libglpk40_5.0-2_ppc64el.deb ...
254s Unpacking libglpk40:ppc64el (5.0-2) ...
254s Selecting previously unselected package libopengl0:ppc64el.
254s Preparing to unpack .../328-libopengl0_1.7.0-3_ppc64el.deb ...
254s Unpacking libopengl0:ppc64el (1.7.0-3) ...
254s Selecting previously unselected package libglu1-mesa:ppc64el.
254s Preparing to unpack .../329-libglu1-mesa_9.0.2-1.1build2_ppc64el.deb ...
254s Unpacking libglu1-mesa:ppc64el (9.0.2-1.1build2) ...
254s Selecting previously unselected package libhwy1t64:ppc64el.
254s Preparing to unpack .../330-libhwy1t64_1.3.0-2_ppc64el.deb ...
254s Unpacking libhwy1t64:ppc64el (1.3.0-2) ...
255s Selecting previously unselected package liblcms2-2:ppc64el.
255s Preparing to unpack .../331-liblcms2-2_2.17-1_ppc64el.deb ...
255s Unpacking liblcms2-2:ppc64el (2.17-1) ...
255s Selecting previously unselected package libjxl0.11:ppc64el.
255s Preparing to unpack .../332-libjxl0.11_0.11.1-6ubuntu1_ppc64el.deb ...
255s Unpacking libjxl0.11:ppc64el (0.11.1-6ubuntu1) ...
255s Selecting previously unselected package libwmflite-0.2-7:ppc64el.
255s Preparing to unpack .../333-libwmflite-0.2-7_0.2.13-2_ppc64el.deb ...
255s Unpacking libwmflite-0.2-7:ppc64el (0.2.13-2) ...
255s Selecting previously unselected package libgraphicsmagick-q16-3t64.
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255s Selecting previously unselected package libwacom9:ppc64el.
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257s Preparing to unpack .../427-libkadm5clnt-mit12_1.22.1-2_ppc64el.deb ...
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257s Selecting previously unselected package libkdb5-10t64:ppc64el.
257s Preparing to unpack .../428-libkdb5-10t64_1.22.1-2_ppc64el.deb ...
257s Unpacking libkdb5-10t64:ppc64el (1.22.1-2) ...
257s Selecting previously unselected package libkadm5srv-mit12:ppc64el.
257s Preparing to unpack .../429-libkadm5srv-mit12_1.22.1-2_ppc64el.deb ...
257s Unpacking libkadm5srv-mit12:ppc64el (1.22.1-2) ...
257s Selecting previously unselected package krb5-multidev:ppc64el.
257s Preparing to unpack .../430-krb5-multidev_1.22.1-2_ppc64el.deb ...
257s Unpacking krb5-multidev:ppc64el (1.22.1-2) ...
257s Selecting previously unselected package libkrb5-dev:ppc64el.
257s Preparing to unpack .../431-libkrb5-dev_1.22.1-2_ppc64el.deb ...
257s Unpacking libkrb5-dev:ppc64el (1.22.1-2) ...
257s Selecting previously unselected package libldap-dev:ppc64el.
257s Preparing to unpack .../432-libldap-dev_2.6.10+dfsg-1ubuntu5_ppc64el.deb ...
257s Unpacking libldap-dev:ppc64el (2.6.10+dfsg-1ubuntu5) ...
258s Selecting previously unselected package libpkgconf3:ppc64el.
258s Preparing to unpack .../433-libpkgconf3_1.8.1-4build1_ppc64el.deb ...
258s Unpacking libpkgconf3:ppc64el (1.8.1-4build1) ...
258s Selecting previously unselected package pkgconf-bin.
258s Preparing to unpack .../434-pkgconf-bin_1.8.1-4build1_ppc64el.deb ...
258s Unpacking pkgconf-bin (1.8.1-4build1) ...
258s Selecting previously unselected package pkgconf:ppc64el.
258s Preparing to unpack .../435-pkgconf_1.8.1-4build1_ppc64el.deb ...
258s Unpacking pkgconf:ppc64el (1.8.1-4build1) ...
258s Selecting previously unselected package libnghttp2-dev:ppc64el.
258s Preparing to unpack .../436-libnghttp2-dev_1.64.0-1.1ubuntu1_ppc64el.deb ...
258s Unpacking libnghttp2-dev:ppc64el (1.64.0-1.1ubuntu1) ...
258s Selecting previously unselected package libpsl-dev:ppc64el.
258s Preparing to unpack .../437-libpsl-dev_0.21.2-1.1build2_ppc64el.deb ...
258s Unpacking libpsl-dev:ppc64el (0.21.2-1.1build2) ...
258s Selecting previously unselected package libgmpxx4ldbl:ppc64el.
258s Preparing to unpack .../438-libgmpxx4ldbl_2%3a6.3.0+dfsg-5ubuntu1_ppc64el.deb ...
258s Unpacking libgmpxx4ldbl:ppc64el (2:6.3.0+dfsg-5ubuntu1) ...
258s Selecting previously unselected package libgmp-dev:ppc64el.
258s Preparing to unpack .../439-libgmp-dev_2%3a6.3.0+dfsg-5ubuntu1_ppc64el.deb ...
258s Unpacking libgmp-dev:ppc64el (2:6.3.0+dfsg-5ubuntu1) ...
258s Selecting previously unselected package libevent-2.1-7t64:ppc64el.
258s Preparing to unpack .../440-libevent-2.1-7t64_2.1.12-stable-10build1_ppc64el.deb ...
258s Unpacking libevent-2.1-7t64:ppc64el (2.1.12-stable-10build1) ...
258s Selecting previously unselected package libunbound8:ppc64el.
258s Preparing to unpack .../441-libunbound8_1.24.2-1ubuntu1_ppc64el.deb ...
258s Unpacking libunbound8:ppc64el (1.24.2-1ubuntu1) ...
258s Selecting previously unselected package libgnutls-dane0t64:ppc64el.
258s Preparing to unpack .../442-libgnutls-dane0t64_3.8.10-3ubuntu1_ppc64el.deb ...
258s Unpacking libgnutls-dane0t64:ppc64el (3.8.10-3ubuntu1) ...
258s Selecting previously unselected package libgnutls-openssl27t64:ppc64el.
258s Preparing to unpack .../443-libgnutls-openssl27t64_3.8.10-3ubuntu1_ppc64el.deb ...
258s Unpacking libgnutls-openssl27t64:ppc64el (3.8.10-3ubuntu1) ...
258s Selecting previously unselected package libp11-kit-dev:ppc64el.
258s Preparing to unpack .../444-libp11-kit-dev_0.25.10-1_ppc64el.deb ...
258s Unpacking libp11-kit-dev:ppc64el (0.25.10-1) ...
258s Selecting previously unselected package libtasn1-6-dev:ppc64el.
258s Preparing to unpack .../445-libtasn1-6-dev_4.21.0-2_ppc64el.deb ...
258s Unpacking libtasn1-6-dev:ppc64el (4.21.0-2) ...
258s Selecting previously unselected package nettle-dev:ppc64el.
258s Preparing to unpack .../446-nettle-dev_3.10.2-1_ppc64el.deb ...
258s Unpacking nettle-dev:ppc64el (3.10.2-1) ...
258s Selecting previously unselected package libgnutls28-dev:ppc64el.
258s Preparing to unpack .../447-libgnutls28-dev_3.8.10-3ubuntu1_ppc64el.deb ...
258s Unpacking libgnutls28-dev:ppc64el (3.8.10-3ubuntu1) ...
258s Selecting previously unselected package librtmp-dev:ppc64el.
258s Preparing to unpack .../448-librtmp-dev_2.4+20151223.gitfa8646d.1-3_ppc64el.deb ...
258s Unpacking librtmp-dev:ppc64el (2.4+20151223.gitfa8646d.1-3) ...
258s Selecting previously unselected package libssl-dev:ppc64el.
258s Preparing to unpack .../449-libssl-dev_3.5.3-1ubuntu2_ppc64el.deb ...
258s Unpacking libssl-dev:ppc64el (3.5.3-1ubuntu2) ...
258s Selecting previously unselected package libssh2-1-dev:ppc64el.
258s Preparing to unpack .../450-libssh2-1-dev_1.11.1-1build1_ppc64el.deb ...
258s Unpacking libssh2-1-dev:ppc64el (1.11.1-1build1) ...
258s Selecting previously unselected package libzstd-dev:ppc64el.
258s Preparing to unpack .../451-libzstd-dev_1.5.7+dfsg-3_ppc64el.deb ...
258s Unpacking libzstd-dev:ppc64el (1.5.7+dfsg-3) ...
258s Selecting previously unselected package libcurl4-openssl-dev:ppc64el.
258s Preparing to unpack .../452-libcurl4-openssl-dev_8.18.0-1ubuntu1_ppc64el.deb ...
258s Unpacking libcurl4-openssl-dev:ppc64el (8.18.0-1ubuntu1) ...
258s Selecting previously unselected package hdf5-helpers.
258s Preparing to unpack .../453-hdf5-helpers_1.14.6+repack-2_ppc64el.deb ...
258s Unpacking hdf5-helpers (1.14.6+repack-2) ...
258s Selecting previously unselected package libhdf5-dev.
258s Preparing to unpack .../454-libhdf5-dev_1.14.6+repack-2_ppc64el.deb ...
258s Unpacking libhdf5-dev (1.14.6+repack-2) ...
258s Selecting previously unselected package xorg-sgml-doctools.
258s Preparing to unpack .../455-xorg-sgml-doctools_1%3a1.11-1.1build1_all.deb ...
258s Unpacking xorg-sgml-doctools (1:1.11-1.1build1) ...
258s Selecting previously unselected package x11proto-dev.
258s Preparing to unpack .../456-x11proto-dev_2025.1-1_all.deb ...
258s Unpacking x11proto-dev (2025.1-1) ...
258s Selecting previously unselected package libxau-dev:ppc64el.
258s Preparing to unpack .../457-libxau-dev_1%3a1.0.11-1build1_ppc64el.deb ...
258s Unpacking libxau-dev:ppc64el (1:1.0.11-1build1) ...
258s Selecting previously unselected package libxdmcp-dev:ppc64el.
258s Preparing to unpack .../458-libxdmcp-dev_1%3a1.1.5-2_ppc64el.deb ...
258s Unpacking libxdmcp-dev:ppc64el (1:1.1.5-2) ...
258s Selecting previously unselected package xtrans-dev.
258s Preparing to unpack .../459-xtrans-dev_1.6.0-1build1_all.deb ...
258s Unpacking xtrans-dev (1.6.0-1build1) ...
258s Selecting previously unselected package libxcb1-dev:ppc64el.
258s Preparing to unpack .../460-libxcb1-dev_1.17.0-2ubuntu1_ppc64el.deb ...
258s Unpacking libxcb1-dev:ppc64el (1.17.0-2ubuntu1) ...
258s Selecting previously unselected package libx11-dev:ppc64el.
258s Preparing to unpack .../461-libx11-dev_2%3a1.8.12-1build1_ppc64el.deb ...
258s Unpacking libx11-dev:ppc64el (2:1.8.12-1build1) ...
258s Selecting previously unselected package libglx-dev:ppc64el.
258s Preparing to unpack .../462-libglx-dev_1.7.0-3_ppc64el.deb ...
258s Unpacking libglx-dev:ppc64el (1.7.0-3) ...
258s Selecting previously unselected package libgl-dev:ppc64el.
258s Preparing to unpack .../463-libgl-dev_1.7.0-3_ppc64el.deb ...
258s Unpacking libgl-dev:ppc64el (1.7.0-3) ...
258s Selecting previously unselected package libblas-dev:ppc64el.
258s Preparing to unpack .../464-libblas-dev_3.12.1-7ubuntu1_ppc64el.deb ...
258s Unpacking libblas-dev:ppc64el (3.12.1-7ubuntu1) ...
258s Selecting previously unselected package liblapack-dev:ppc64el.
258s Preparing to unpack .../465-liblapack-dev_3.12.1-7ubuntu1_ppc64el.deb ...
258s Unpacking liblapack-dev:ppc64el (3.12.1-7ubuntu1) ...
259s Selecting previously unselected package libfftw3-long3:ppc64el.
259s Preparing to unpack .../466-libfftw3-long3_3.3.10-2fakesync1build2_ppc64el.deb ...
259s Unpacking libfftw3-long3:ppc64el (3.3.10-2fakesync1build2) ...
259s Selecting previously unselected package libfftw3-bin.
259s Preparing to unpack .../467-libfftw3-bin_3.3.10-2fakesync1build2_ppc64el.deb ...
259s Unpacking libfftw3-bin (3.3.10-2fakesync1build2) ...
259s Selecting previously unselected package libfftw3-dev:ppc64el.
259s Preparing to unpack .../468-libfftw3-dev_3.3.10-2fakesync1build2_ppc64el.deb ...
259s Unpacking libfftw3-dev:ppc64el (3.3.10-2fakesync1build2) ...
259s Selecting previously unselected package libgfortran-15-dev:ppc64el.
259s Preparing to unpack .../469-libgfortran-15-dev_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking libgfortran-15-dev:ppc64el (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package gfortran-15-powerpc64le-linux-gnu.
259s Preparing to unpack .../470-gfortran-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking gfortran-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package gfortran-15.
259s Preparing to unpack .../471-gfortran-15_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking gfortran-15 (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package gfortran-powerpc64le-linux-gnu.
259s Preparing to unpack .../472-gfortran-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
259s Unpacking gfortran-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ...
259s Selecting previously unselected package gfortran.
259s Preparing to unpack .../473-gfortran_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
259s Unpacking gfortran (4:15.2.0-4ubuntu1) ...
259s Selecting previously unselected package libstdc++-15-dev:ppc64el.
259s Preparing to unpack .../474-libstdc++-15-dev_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking libstdc++-15-dev:ppc64el (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package g++-15-powerpc64le-linux-gnu.
259s Preparing to unpack .../475-g++-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking g++-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package g++-15.
259s Preparing to unpack .../476-g++-15_15.2.0-12ubuntu1_ppc64el.deb ...
259s Unpacking g++-15 (15.2.0-12ubuntu1) ...
259s Selecting previously unselected package g++-powerpc64le-linux-gnu.
259s Preparing to unpack .../477-g++-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
259s Unpacking g++-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ...
259s Selecting previously unselected package g++.
259s Preparing to unpack .../478-g++_4%3a15.2.0-4ubuntu1_ppc64el.deb ...
259s Unpacking g++ (4:15.2.0-4ubuntu1) ...
259s Selecting previously unselected package octave-dev.
259s Preparing to unpack .../479-octave-dev_10.3.0-3_ppc64el.deb ...
259s Unpacking octave-dev (10.3.0-3) ...
260s Selecting previously unselected package dh-octave.
260s Preparing to unpack .../480-dh-octave_1.14.0_all.deb ...
260s Unpacking dh-octave (1.14.0) ...
260s Selecting previously unselected package libfontenc1:ppc64el.
260s Preparing to unpack .../481-libfontenc1_1%3a1.1.8-1build2_ppc64el.deb ...
260s Unpacking libfontenc1:ppc64el (1:1.1.8-1build2) ...
260s Selecting previously unselected package libunwind8:ppc64el.
260s Preparing to unpack .../482-libunwind8_1.8.3-0ubuntu1_ppc64el.deb ...
260s Unpacking libunwind8:ppc64el (1.8.3-0ubuntu1) ...
260s Selecting previously unselected package libxt6t64:ppc64el.
260s Preparing to unpack .../483-libxt6t64_1%3a1.2.1-1.3_ppc64el.deb ...
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260s Selecting previously unselected package libxmu6:ppc64el.
260s Preparing to unpack .../484-libxmu6_2%3a1.1.3-4_ppc64el.deb ...
260s Unpacking libxmu6:ppc64el (2:1.1.3-4) ...
260s Selecting previously unselected package libxaw7:ppc64el.
260s Preparing to unpack .../485-libxaw7_2%3a1.0.16-1build1_ppc64el.deb ...
260s Unpacking libxaw7:ppc64el (2:1.0.16-1build1) ...
260s Selecting previously unselected package libxfont2:ppc64el.
260s Preparing to unpack .../486-libxfont2_1%3a2.0.6-2_ppc64el.deb ...
260s Unpacking libxfont2:ppc64el (1:2.0.6-2) ...
260s Selecting previously unselected package libxkbfile1:ppc64el.
260s Preparing to unpack .../487-libxkbfile1_1%3a1.1.0-1build5_ppc64el.deb ...
260s Unpacking libxkbfile1:ppc64el (1:1.1.0-1build5) ...
260s Selecting previously unselected package libxrandr2:ppc64el.
260s Preparing to unpack .../488-libxrandr2_2%3a1.5.4-1build1_ppc64el.deb ...
260s Unpacking libxrandr2:ppc64el (2:1.5.4-1build1) ...
260s Selecting previously unselected package octave-io:ppc64el.
260s Preparing to unpack .../489-octave-io_2.7.0-3_ppc64el.deb ...
260s Unpacking octave-io:ppc64el (2.7.0-3) ...
260s Selecting previously unselected package octave-statistics-common.
260s Preparing to unpack .../490-octave-statistics-common_1.7.6-2_all.deb ...
260s Unpacking octave-statistics-common (1.7.6-2) ...
260s Selecting previously unselected package octave-statistics.
260s Preparing to unpack .../491-octave-statistics_1.7.6-2_ppc64el.deb ...
260s Unpacking octave-statistics (1.7.6-2) ...
260s Selecting previously unselected package x11-xkb-utils.
260s Preparing to unpack .../492-x11-xkb-utils_7.7+9build1_ppc64el.deb ...
260s Unpacking x11-xkb-utils (7.7+9build1) ...
260s Selecting previously unselected package xserver-common.
260s Preparing to unpack .../493-xserver-common_2%3a21.1.21-1ubuntu1_all.deb ...
260s Unpacking xserver-common (2:21.1.21-1ubuntu1) ...
260s Selecting previously unselected package xvfb.
260s Preparing to unpack .../494-xvfb_2%3a21.1.21-1ubuntu1_ppc64el.deb ...
260s Unpacking xvfb (2:21.1.21-1ubuntu1) ...
260s Setting up libconfig-inifiles-perl (3.000003-4) ...
260s Setting up libapt-pkg-perl (0.1.43) ...
260s Setting up libhwy1t64:ppc64el (1.3.0-2) ...
260s Setting up libmodule-pluggable-perl (5.2-5) ...
260s Setting up libgraphite2-3:ppc64el (1.3.14-11ubuntu1) ...
260s Setting up libstring-escape-perl (2010.002-3) ...
260s Setting up libgnutls-openssl27t64:ppc64el (3.8.10-3ubuntu1) ...
260s Setting up libxcb-dri3-0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up liblcms2-2:ppc64el (2.17-1) ...
260s Setting up libberkeleydb-perl:ppc64el (0.66-2) ...
260s Setting up libpixman-1-0:ppc64el (0.46.4-1) ...
260s Setting up libsharpyuv0:ppc64el (1.5.0-0.1build1) ...
260s Setting up libaom3:ppc64el (3.13.1-2) ...
260s Setting up liblingua-en-inflect-perl (1.905-2) ...
260s Setting up libx11-xcb1:ppc64el (2:1.8.12-1build1) ...
260s Setting up libfile-which-perl (1.27-2) ...
260s Setting up libdouble-conversion3:ppc64el (3.4.0-1) ...
260s Setting up libsafe-isa-perl (1.000010-1build1) ...
260s Setting up libtask-weaken-perl (1.06-2) ...
260s Setting up libunicode-utf8-perl (0.63-1) ...
260s Setting up libset-intspan-perl (1.19-3) ...
260s Setting up libxcb-xfixes0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libogg0:ppc64el (1.3.6-2) ...
260s Setting up libmouse-perl:ppc64el (2.6.1-1) ...
260s Setting up libzstd-dev:ppc64el (1.5.7+dfsg-3) ...
260s Setting up liblerc4:ppc64el (4.0.0+ds-5ubuntu2) ...
260s Setting up libpod-pom-perl (2.01-4) ...
260s Setting up libxpm4:ppc64el (1:3.5.17-1build3) ...
260s Setting up hdf5-helpers (1.14.6+repack-2) ...
260s Setting up libwmflite-0.2-7:ppc64el (0.2.13-2) ...
260s Setting up libregexp-pattern-perl (0.2.14-2) ...
260s Setting up libdata-messagepack-perl (1.02-3) ...
260s Setting up libclass-inspector-perl (1.36-3) ...
260s Setting up libxcb-xinput0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libxrender1:ppc64el (1:0.9.12-1) ...
260s Setting up libdynaloader-functions-perl (0.004-2) ...
260s Setting up libdatrie1:ppc64el (0.2.14-1) ...
260s Setting up libtext-glob-perl (0.11-3) ...
260s Setting up libclass-method-modifiers-perl (2.15-1) ...
260s Setting up liblist-compare-perl (0.55-2) ...
260s Setting up libxcb-render0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libclone-perl:ppc64el (0.47-1) ...
260s Setting up libarchive-zip-perl (1.68-1) ...
260s Setting up libsub-identify-perl (0.14-4) ...
260s Setting up libcpanel-json-xs-perl:ppc64el (4.40-1) ...
260s Setting up libglvnd0:ppc64el (1.7.0-3) ...
260s Setting up libio-stringy-perl (2.113-2) ...
260s Setting up libhtml-tagset-perl (3.24-1) ...
260s Setting up libts0t64:ppc64el (1.22-1.1build2) ...
260s Setting up liblog-any-perl (1.718-1build1) ...
260s Setting up libyaml-pp-perl (0.39.0-1) ...
260s Setting up libxcb-glx0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libdevel-size-perl (0.85-1) ...
260s Setting up unzip (6.0-29ubuntu1) ...
260s Setting up libdebhelper-perl (13.28ubuntu1) ...
260s Setting up libregexp-pattern-license-perl (3.11.2-1) ...
260s Setting up libconvert-binhex-perl (1.125-3) ...
260s Setting up liblwp-mediatypes-perl (6.04-2) ...
260s Setting up libyaml-libyaml-perl (0.904.0+ds-1) ...
260s Setting up fonts-freefont-otf (20211204+svn4273-4build1) ...
260s Setting up libio-interactive-perl (1.027-1) ...
260s Setting up libxcb-keysyms1:ppc64el (0.4.1-1build1) ...
260s Setting up libxcb-shape0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up x11-common (1:7.7+24ubuntu1) ...
260s Setting up libtry-tiny-perl (0.32-1) ...
260s Setting up libdeflate0:ppc64el (1.23-2) ...
260s Setting up perl-openssl-defaults:ppc64el (7build4) ...
260s Setting up libmldbm-perl (2.05-4) ...
260s Setting up linux-libc-dev:ppc64el (6.18.0-9.9) ...
260s Setting up libxml-namespacesupport-perl (1.12-2) ...
260s Setting up m4 (1.4.20-2) ...
260s Setting up libevent-2.1-7t64:ppc64el (2.1.12-stable-10build1) ...
260s Setting up libclone-choose-perl (0.010-2) ...
260s Setting up libqhull-r8.0:ppc64el (2020.2-8) ...
260s Setting up libxcb-render-util0:ppc64el (0.3.10-1build1) ...
260s Setting up libtime-moment-perl (0.46-1) ...
260s Setting up libencode-locale-perl (1.05-3) ...
260s Setting up libxcb-shm0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libxcb-icccm4:ppc64el (0.4.2-1build1) ...
260s Setting up texinfo-lib (7.2-5) ...
260s Setting up libtext-wrapper-perl (1.05-4) ...
260s Setting up libmpg123-0t64:ppc64el (1.33.3-2) ...
260s Setting up libgomp1:ppc64el (15.2.0-12ubuntu1) ...
260s Setting up libconfig-tiny-perl (2.30-1) ...
260s Setting up libsereal-encoder-perl (5.004+ds-1build4) ...
260s Setting up libunwind8:ppc64el (1.8.3-0ubuntu1) ...
260s Setting up liblist-utilsby-perl (0.12-2) ...
260s Setting up libyaml-tiny-perl (1.76-1) ...
260s Setting up libjbig0:ppc64el (2.1-6.1ubuntu3) ...
260s Setting up octave-common (10.3.0-3) ...
260s Setting up libregexp-common-perl (2024080801-1) ...
260s Setting up libpcre2-16-0:ppc64el (10.46-1) ...
260s Setting up libaec0:ppc64el (1.1.5-1) ...
260s Setting up libnet-netmask-perl (2.0003-1build1) ...
260s Setting up libopengl0:ppc64el (1.7.0-3) ...
260s Setting up libsub-install-perl (0.929-1) ...
260s Setting up libxcb-util1:ppc64el (0.4.1-1build1) ...
260s Setting up libpsl-dev:ppc64el (0.21.2-1.1build2) ...
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260s Setting up libindirect-perl (0.39-2build5) ...
260s Setting up libfyaml0:ppc64el (0.9.3-1) ...
260s Setting up libxcb-xkb1:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up libxcb-image0:ppc64el (0.4.0-2build2) ...
260s Setting up libnumber-compare-perl (0.03-3) ...
260s Setting up libxcb-present0:ppc64el (1.17.0-2ubuntu1) ...
260s Setting up liberror-perl (0.17030-1) ...
260s Setting up libasound2-data (1.2.15.3-1ubuntu1) ...
260s Setting up patchutils (0.4.3-1) ...
260s Setting up tex-common (6.20) ...
261s update-language: texlive-base not installed and configured, doing nothing!
261s Setting up libjson-maybexs-perl (1.004008-1) ...
261s Setting up libxml-sax-base-perl (1.09-3) ...
261s Setting up libio-string-perl (1.08-4) ...
261s Setting up libreadonly-perl (2.050-3) ...
261s Setting up libboolean-perl (0.46-3) ...
261s Setting up libnetaddr-ip-perl (4.079+dfsg-2build5) ...
261s Setting up xtrans-dev (1.6.0-1build1) ...
261s Setting up libfontenc1:ppc64el (1:1.1.8-1build2) ...
261s Setting up autotools-dev (20240727.1) ...
261s Setting up libblas3:ppc64el (3.12.1-7ubuntu1) ...
261s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/blas/libblas.so.3 to provide /usr/lib/powerpc64le-linux-gnu/libblas.so.3 (libblas.so.3-powerpc64le-linux-gnu) in auto mode
261s Setting up libclass-data-inheritable-perl (0.10-1) ...
261s Setting up libunbound8:ppc64el (1.24.2-1ubuntu1) ...
261s Setting up libpkgconf3:ppc64el (1.8.1-4build1) ...
261s Setting up libgmpxx4ldbl:ppc64el (2:6.3.0+dfsg-5ubuntu1) ...
261s Setting up libalgorithm-c3-perl (0.11-2) ...
261s Setting up rpcsvc-proto (1.4.3-1build1) ...
261s Setting up libasound2t64:ppc64el (1.2.15.3-1ubuntu1) ...
261s Setting up liblog-log4perl-perl (1.57-1) ...
261s Setting up libtext-reform-perl (1.20-5) ...
261s Setting up libclass-tiny-perl (1.008-2) ...
261s Setting up libgnutls-dane0t64:ppc64el (3.8.10-3ubuntu1) ...
261s Setting up libfile-find-rule-perl (0.35-1build1) ...
261s Setting up libxfixes3:ppc64el (1:6.0.0-2build2) ...
261s Setting up libxcb-sync1:ppc64el (1.17.0-2ubuntu1) ...
261s Setting up libipc-system-simple-perl (1.30-2) ...
261s Setting up libio-tiecombine-perl (1.005-3) ...
261s Setting up libnet-domain-tld-perl (1.75-4) ...
261s Setting up libgssrpc4t64:ppc64el (1.22.1-2) ...
261s Setting up libperlio-utf8-strict-perl (0.010-1build4) ...
261s Setting up libldap-dev:ppc64el (2.6.10+dfsg-1ubuntu5) ...
261s Setting up aglfn (1.7+git20191031.4036a9c-2build1) ...
261s Setting up libstring-format-perl (1.18-1build1) ...
261s Setting up libxcb-cursor0:ppc64el (0.1.5-1build1) ...
261s Setting up lzip (1.25-4) ...
261s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip (lzip) in auto mode
261s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-compressor (lzip-compressor) in auto mode
261s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
261s Setting up libavahi-common-data:ppc64el (0.8-17ubuntu2) ...
261s Setting up libopus0:ppc64el (1.6.1-1) ...
261s Setting up t1utils (1.41-4build4) ...
261s Setting up libquadmath0:ppc64el (15.2.0-12ubuntu1) ...
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261s Setting up diffstat (1.68-1) ...
261s Setting up libimagequant0:ppc64el (2.18.0-1build1) ...
261s Setting up libxkbcommon-x11-0:ppc64el (1.12.3-1) ...
261s Setting up libssl-dev:ppc64el (3.5.3-1ubuntu2) ...
261s Setting up libmpc3:ppc64el (1.3.1-2) ...
261s Setting up libvorbis0a:ppc64el (1.3.7-3build1) ...
261s Setting up libvariable-magic-perl (0.64-1build1) ...
261s Setting up libio-html-perl (1.004-3) ...
261s Setting up libxrandr2:ppc64el (2:1.5.4-1build1) ...
261s Setting up libtext-template-perl (1.61-1) ...
261s Setting up libpod-parser-perl (1.67-1) ...
261s Setting up autopoint (0.23.2-1) ...
261s Setting up libb-hooks-op-check-perl:ppc64el (0.22-3build2) ...
261s Setting up libflac14:ppc64el (1.5.0+ds-5) ...
261s Setting up liblist-moreutils-xs-perl (0.430-4build1) ...
261s Setting up pkgconf-bin (1.8.1-4build1) ...
261s Setting up libjpeg-turbo8:ppc64el (2.1.5-4ubuntu2) ...
261s Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-2) ...
261s Setting up libltdl7:ppc64el (2.5.4-9) ...
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261s Setting up libfftw3-double3:ppc64el (3.3.10-2fakesync1build2) ...
261s Setting up libb-keywords-perl (1.29-1) ...
261s Setting up libparams-util-perl (1.102-3build1) ...
261s Setting up libgfortran5:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libvulkan1:ppc64el (1.4.335.0-1) ...
261s Setting up libtime-duration-perl (1.21-2) ...
261s Setting up autoconf (2.72-3.1ubuntu1) ...
261s Setting up libtext-xslate-perl:ppc64el (3.5.9-2build1) ...
261s Setting up libsub-exporter-progressive-perl (0.001013-3) ...
261s Setting up libwebp7:ppc64el (1.5.0-0.1build1) ...
261s Setting up libarray-intspan-perl (2.004-2) ...
261s Setting up libcapture-tiny-perl (0.50-1) ...
261s Setting up libtimedate-perl (2.3300-2) ...
261s Setting up libexporter-lite-perl (0.09-2) ...
261s Setting up libubsan1:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libsub-name-perl:ppc64el (0.28-1) ...
261s Setting up dwz (0.16-2) ...
261s Setting up libdata-validate-domain-perl (0.15-1) ...
261s Setting up libproc-processtable-perl:ppc64el (0.637-1) ...
261s Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
261s Setting up libmtdev1t64:ppc64el (1.1.7-1build1) ...
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261s Setting up libasyncns0:ppc64el (0.8-7) ...
261s Setting up libxshmfence1:ppc64el (1.3.3-1build1) ...
261s Setting up libcrypt-dev:ppc64el (1:4.5.1-1) ...
261s Setting up libxcb-randr0:ppc64el (1.17.0-2ubuntu1) ...
261s Setting up libpath-tiny-perl (0.148-1) ...
261s Setting up libasan8:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up lzop (1.04-2build4) ...
261s Setting up libjson-perl (4.10000-1) ...
261s Setting up liblog-any-adapter-screen-perl (0.141-1) ...
261s Setting up librole-tiny-perl (2.002004-1) ...
261s Setting up libintl-perl (1.35-1) ...
261s Setting up debugedit (1:5.2-3build1) ...
261s Setting up libipc-run3-perl (0.049-1) ...
261s Setting up libmd4c0:ppc64el (0.5.2-2build1) ...
261s Setting up libregexp-wildcards-perl (1.05-3) ...
261s Setting up libmousex-strictconstructor-perl (0.02-3) ...
261s Setting up libfile-sharedir-perl (1.118-3) ...
261s Setting up libsub-uplevel-perl (0.2800-3) ...
261s Setting up libsuitesparseconfig7:ppc64el (1:7.12.1+dfsg-1) ...
261s Setting up liblua5.4-0:ppc64el (5.4.8-1) ...
261s Setting up libaliased-perl (0.34-3) ...
261s Setting up libharfbuzz0b:ppc64el (12.3.2-1) ...
261s Setting up libthai-data (0.1.30-1) ...
261s Setting up xorg-sgml-doctools (1:1.11-1.1build1) ...
261s Setting up libstrictures-perl (2.000006-1build1) ...
261s Setting up libsub-quote-perl (2.006009-1ubuntu1) ...
261s Setting up libdevel-stacktrace-perl (2.0500-1) ...
261s Setting up libclass-xsaccessor-perl (1.19-4build6) ...
261s Setting up libpod-spell-perl (1.27-1) ...
261s Setting up libtext-autoformat-perl (1.750000-2) ...
261s Setting up libglu1-mesa:ppc64el (9.0.2-1.1build2) ...
261s Setting up libtoml-tiny-perl (0.20-1) ...
261s Setting up libstemmer0d:ppc64el (3.0.1-1) ...
261s Setting up libxkbfile1:ppc64el (1:1.1.0-1build5) ...
261s Setting up libsort-versions-perl (1.62-3) ...
261s Setting up libtsan2:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libexporter-tiny-perl (1.006003-1) ...
261s Setting up libterm-readkey-perl (2.38-2build5) ...
261s Setting up libisl23:ppc64el (0.27-1build1) ...
261s Setting up libtext-unidecode-perl (1.30-3) ...
261s Setting up libde265-0:ppc64el (1.0.16-1build1) ...
261s Setting up libc-dev-bin (2.42-2ubuntu5) ...
261s Setting up libfont-ttf-perl (1.06-2) ...
261s Setting up libfile-homedir-perl (1.006-2) ...
261s Setting up libsamplerate0:ppc64el (0.2.2-4build2) ...
261s Setting up libtasn1-6-dev:ppc64el (4.21.0-2) ...
261s Setting up libwebpmux3:ppc64el (1.5.0-0.1build1) ...
261s Setting up libtext-levenshteinxs-perl (0.03-5build5) ...
261s Setting up libperlio-gzip-perl (0.20-1build5) ...
261s Setting up libjxl0.11:ppc64el (0.11.1-6ubuntu1) ...
261s Setting up libxfont2:ppc64el (1:2.0.6-2) ...
261s Setting up libhtml-html5-entities-perl (0.004-3) ...
261s Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
261s Setting up libsereal-decoder-perl (5.004+ds-1build5) ...
261s Setting up libmarkdown2:ppc64el (2.2.7-2.1build1) ...
261s Setting up libcc1-0:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up liburi-perl (5.34-2build1) ...
261s Setting up libnet-ipv6addr-perl (1.02-1) ...
261s Setting up libbrotli-dev:ppc64el (1.1.0-2build6) ...
261s Setting up liblsan0:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up perltidy (20250105-1build1) ...
261s Setting up libp11-kit-dev:ppc64el (0.25.10-1) ...
261s Setting up libmp3lame0:ppc64el (3.100-6build2) ...
261s Setting up libblas-dev:ppc64el (3.12.1-7ubuntu1) ...
261s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/blas/libblas.so to provide /usr/lib/powerpc64le-linux-gnu/libblas.so (libblas.so-powerpc64le-linux-gnu) in auto mode
261s Setting up libsz2:ppc64el (1.1.5-1) ...
261s Setting up libitm1:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libvorbisenc2:ppc64el (1.3.7-3build1) ...
261s Setting up libkadm5clnt-mit12:ppc64el (1.22.1-2) ...
261s Setting up libdata-validate-ip-perl (0.31-1) ...
261s Setting up libwacom-common (2.16.1-1) ...
261s Setting up libmousex-nativetraits-perl (1.09-3) ...
261s Setting up libemail-address-xs-perl (1.05-1build5) ...
261s Setting up libwayland-client0:ppc64el (1.24.0-2) ...
261s Setting up libnet-ssleay-perl:ppc64el (1.94-3) ...
261s Setting up libjpeg8:ppc64el (8c-2ubuntu11) ...
261s Setting up automake (1:1.18.1-3build1) ...
261s update-alternatives: using /usr/bin/automake-1.18 to provide /usr/bin/automake (automake) in auto mode
261s Setting up libb2-1:ppc64el (0.98.1-1.1build2) ...
261s Setting up x11proto-dev (2025.1-1) ...
261s Setting up libfile-stripnondeterminism-perl (1.15.0-1build1) ...
261s Setting up gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
261s Setting up libice6:ppc64el (2:1.1.1-1build1) ...
261s Setting up libqt6core6t64:ppc64el (6.9.2+dfsg-3ubuntu2) ...
261s Setting up libhttp-date-perl (6.06-1) ...
261s Setting up mesa-libgallium:ppc64el (25.3.3-1ubuntu1) ...
261s Setting up liblapack3:ppc64el (3.12.1-7ubuntu1) ...
261s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/powerpc64le-linux-gnu/liblapack.so.3 (liblapack.so.3-powerpc64le-linux-gnu) in auto mode
261s Setting up libproxy1v5:ppc64el (0.5.12-1) ...
261s Setting up libfile-basedir-perl (0.09-2) ...
261s Setting up gettext (0.23.2-1) ...
261s Setting up libarpack2t64:ppc64el (3.9.1-6) ...
261s Setting up libfftw3-single3:ppc64el (3.3.10-2fakesync1build2) ...
261s Setting up libgcc-15-dev:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libgmp-dev:ppc64el (2:6.3.0+dfsg-5ubuntu1) ...
261s Setting up libamd3:ppc64el (1:7.12.1+dfsg-1) ...
261s Setting up libfile-listing-perl (6.16-1) ...
261s Setting up libxau-dev:ppc64el (1:1.0.11-1build1) ...
261s Setting up nettle-dev:ppc64el (3.10.2-1) ...
261s Setting up libkdb5-10t64:ppc64el (1.22.1-2) ...
261s Setting up libgbm1:ppc64el (25.3.3-1ubuntu1) ...
261s Setting up libcolamd3:ppc64el (1:7.12.1+dfsg-1) ...
261s Setting up libwacom9:ppc64el (2.16.1-1) ...
261s Setting up fontconfig-config (2.17.1-3ubuntu1) ...
261s Setting up liblist-moreutils-perl (0.430-2) ...
261s Setting up libxcursor1:ppc64el (1:1.2.3-1build1) ...
261s Setting up libpod-constants-perl (0.19-2) ...
261s Setting up libgl1-mesa-dri:ppc64el (25.3.3-1ubuntu1) ...
261s Setting up libhash-merge-perl (0.302-1) ...
261s Setting up libsoftware-copyright-perl (0.015-1) ...
261s Setting up libaec-dev:ppc64el (1.1.5-1) ...
261s Setting up libavahi-common3:ppc64el (0.8-17ubuntu2) ...
261s Setting up libcxsparse4:ppc64el (1:7.12.1+dfsg-1) ...
261s Setting up libfftw3-long3:ppc64el (3.3.10-2fakesync1build2) ...
261s Setting up libnet-http-perl (6.24-1build1) ...
261s Setting up libpath-iterator-rule-perl (1.015-2) ...
261s Setting up libtext-markdown-discount-perl (0.18-1) ...
261s Setting up libappstream5:ppc64el (1.1.1-1) ...
261s Setting up libexception-class-perl (1.45-1) ...
261s Setting up libclass-c3-perl (0.35-2) ...
261s Setting up libqrupdate1:ppc64el (1.1.5-3) ...
261s Setting up libdevel-callchecker-perl:ppc64el (0.009-2) ...
261s Setting up libgfortran-15-dev:ppc64el (15.2.0-12ubuntu1) ...
261s Setting up libxml-sax-perl (1.02+dfsg-4) ...
261s update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
261s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
261s Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
262s Setting up libcamd3:ppc64el (1:7.12.1+dfsg-1) ...
262s Setting up pkgconf:ppc64el (1.8.1-4build1) ...
262s Setting up libinput-bin (1.30.1-1) ...
262s Setting up libxs-parse-sublike-perl:ppc64el (0.41-1) ...
262s Setting up intltool-debian (0.35.0+20060710.6build1) ...
262s Setting up libthai0:ppc64el (0.1.30-1) ...
262s Setting up libxdmcp-dev:ppc64el (1:1.1.5-2) ...
262s Setting up cpp-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
262s Setting up libegl-mesa0:ppc64el (25.3.3-1ubuntu1) ...
262s Setting up libdata-validate-uri-perl (0.07-3) ...
262s Setting up libxs-parse-keyword-perl (0.49-1) ...
262s Setting up libtest-exception-perl (0.43-3) ...
262s Setting up appstream (1.1.1-1) ...
262s ✔ Metadata cache was updated successfully.
262s Setting up libqt6xml6:ppc64el (6.9.2+dfsg-3ubuntu2) ...
262s Setting up libglpk40:ppc64el (5.0-2) ...
262s Setting up libqt6sql6:ppc64el (6.9.2+dfsg-3ubuntu2) ...
262s Setting up libstring-copyright-perl (0.003014-1) ...
262s Setting up libppi-perl (1.284-1) ...
262s Setting up liblapack-dev:ppc64el (3.12.1-7ubuntu1) ...
262s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/lapack/liblapack.so to provide /usr/lib/powerpc64le-linux-gnu/liblapack.so (liblapack.so-powerpc64le-linux-gnu) in auto mode
262s Setting up libdata-optlist-perl (0.114-1) ...
262s Setting up libccolamd3:ppc64el (1:7.12.1+dfsg-1) ...
262s Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0207-0ubuntu7) ...
262s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
262s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
262s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
262s Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
262s Setting up dh-strip-nondeterminism (1.15.0-1build1) ...
262s Setting up libwww-robotrules-perl (6.02-1build1) ...
262s Setting up libsyntax-keyword-try-perl (0.31-1) ...
262s Setting up libjack-jackd2-0:ppc64el (1.9.22~dfsg-5) ...
262s Setting up libhdf5-310:ppc64el (1.14.6+repack-2) ...
262s Setting up cpp-15 (15.2.0-12ubuntu1) ...
262s Setting up libtiff6:ppc64el (4.7.0-3ubuntu3) ...
262s Setting up libhtml-parser-perl:ppc64el (3.83-1build1) ...
262s Setting up libppix-regexp-perl (0.091-1) ...
262s Setting up libkadm5srv-mit12:ppc64el (1.22.1-2) ...
262s Setting up libegl1:ppc64el (1.7.0-3) ...
262s Setting up libc6-dev:ppc64el (2.42-2ubuntu5) ...
262s Setting up libqt6core5compat6:ppc64el (6.9.2-3build1) ...
262s Setting up libfontconfig1:ppc64el (2.17.1-3ubuntu1) ...
262s Setting up libsndfile1:ppc64el (1.2.2-4) ...
262s Setting up libmro-compat-perl (0.15-2) ...
262s Setting up libhdf5-fortran-310:ppc64el (1.14.6+repack-2) ...
262s Setting up libsm6:ppc64el (2:1.2.6-1) ...
262s Setting up libavahi-client3:ppc64el (0.8-17ubuntu2) ...
262s Setting up libio-socket-ssl-perl (2.098-1) ...
262s Setting up cpp-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ...
262s Setting up libsub-exporter-perl (0.990-1) ...
262s Setting up libqt6dbus6:ppc64el (6.9.2+dfsg-3ubuntu2) ...
262s Setting up libppix-quotelike-perl (0.023-1) ...
262s Setting up libhttp-message-perl (7.01-1ubuntu1) ...
262s Setting up libhtml-form-perl (6.13-1build1) ...
262s Setting up libhdf5-cpp-310:ppc64el (1.14.6+repack-2) ...
262s Setting up libiterator-perl (0.03+ds1-2) ...
262s Setting up libgnutls28-dev:ppc64el (3.8.10-3ubuntu1) ...
262s Setting up libinput10:ppc64el (1.30.1-1) ...
262s Setting up libnghttp2-dev:ppc64el (1.64.0-1.1ubuntu1) ...
262s Setting up libhdf5-hl-310:ppc64el (1.14.6+repack-2) ...
262s Setting up libhttp-negotiate-perl (6.01-2) ...
262s Setting up fontconfig (2.17.1-3ubuntu1) ...
264s Regenerating fonts cache... done.
264s Setting up libjpeg-turbo8-dev:ppc64el (2.1.5-4ubuntu2) ...
264s Setting up libcarp-assert-more-perl (2.9.0-1) ...
264s Setting up libcholmod5:ppc64el (1:7.12.1+dfsg-1) ...
264s Setting up libxft2:ppc64el (2.3.6-1build2) ...
264s Setting up libncurses-dev:ppc64el (6.6+20251231-1) ...
264s Setting up libppix-utils-perl (0.003-2) ...
264s Setting up libglx-mesa0:ppc64el (25.3.3-1ubuntu1) ...
264s Setting up libxcb1-dev:ppc64el (1.17.0-2ubuntu1) ...
264s Setting up libiterator-util-perl (0.02+ds1-2) ...
264s Setting up libglx0:ppc64el (1.7.0-3) ...
264s Setting up libhttp-cookies-perl (6.11-1) ...
264s Setting up libspqr4:ppc64el (1:7.12.1+dfsg-1) ...
264s Setting up libpulse0:ppc64el (1:17.0+dfsg1-2ubuntu4) ...
264s Setting up gcc-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ...
264s Setting up libfftw3-bin (3.3.10-2fakesync1build2) ...
264s Setting up po-debconf (1.0.22) ...
264s Setting up libhtml-tree-perl (5.07-3) ...
264s Setting up libparams-classify-perl:ppc64el (0.015-2build6) ...
264s Setting up libpango-1.0-0:ppc64el (1.57.0-1) ...
264s Setting up libcgi-pm-perl (4.71-1build1) ...
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264s Processing triggers for libc-bin (2.42-2ubuntu5) ...
265s Processing triggers for man-db (2.13.1-1) ...
268s Processing triggers for udev (259-1ubuntu3) ...
268s Processing triggers for install-info (7.2-5) ...
295s autopkgtest [08:53:42]: test command1: DH_OCTAVE_TEST_ENV="xvfb-run -a" /usr/bin/dh_octave_check --use-installed-package
295s autopkgtest [08:53:42]: test command1: [-----------------------
296s Checking package...
297s Run the unit tests...
298s Checking m files ...
298s [inst/slicesample.m]
298s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/slicesample.m
298s ***** demo
298s  ## Define function to sample
298s  d = 2;
298s  mu = [-1; 2];
298s  rand ("seed", 5)  # for reproducibility
298s  Sigma = rand (d);
298s  Sigma = (Sigma + Sigma');
298s  Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
298s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
298s 
298s  ## Inputs
298s  start = ones (1,2);
298s  nsamples = 500;
298s  K = 500;
298s  m = 10;
298s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
298s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
298s  figure;
298s  hold on;
298s  plot (smpl(:,1), smpl(:,2), 'x');
298s  [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
298s  z = reshape (pdf ([x(:), y(:)]), size(x));
298s  mesh (x, y, z, "facecolor", "None");
298s 
298s  ## Using sample points to find the volume of half a sphere with radius of .5
298s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
298s  int = mean (f (smpl) ./ pdf (smpl));
298s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
298s  trueerr = abs (2/3*pi*.25^(3/2)-int);
298s  fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
298s  fprintf ("Monte Carlo integral error estimate %f\n", errest);
298s  fprintf ("The actual error %f\n", trueerr);
298s  mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
298s ***** demo
298s  ## Integrate truncated normal distribution to find normalization constant
298s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
298s  nsamples = 1e3;
298s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
298s  [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
298s  f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
298s  x = linspace (-3, 3, 1000);
298s  area (x, f(x));
298s  xlabel ("x");
298s  ylabel ("f(x)");
298s  int = mean (f (smpl) ./ pdf (smpl));
298s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
298s  trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
298s  fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
298s  fprintf("Monte Carlo integral error estimate %f\n", errest);
298s  fprintf("The actual error %f\n", trueerr);
298s ***** test
298s  start = 0.5;
298s  nsamples = 1e3;
298s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
298s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
298s  assert (mean (smpl, 1), 1, .15);
298s  assert (var (smpl, 1), 1, .25);
300s ***** error slicesample ();
300s ***** error slicesample (1);
300s ***** error slicesample (1, 1);
300s 4 tests, 4 passed, 0 known failure, 0 skipped
300s [inst/dist_wrap/makedist.m]
300s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/makedist.m
300s ***** test
300s  pd = makedist ("beta");
300s  assert (class (pd), "BetaDistribution");
300s  assert (pd.a, 1);
300s  assert (pd.b, 1);
300s ***** test
300s  pd = makedist ("beta", "a", 5);
300s  assert (pd.a, 5);
300s  assert (pd.b, 1);
300s ***** test
300s  pd = makedist ("beta", "b", 5);
300s  assert (pd.a, 1);
300s  assert (pd.b, 5);
300s ***** test
300s  pd = makedist ("beta", "a", 3, "b", 5);
300s  assert (pd.a, 3);
300s  assert (pd.b, 5);
300s ***** test
300s  pd = makedist ("binomial");
300s  assert (class (pd), "BinomialDistribution");
300s  assert (pd.N, 1);
300s  assert (pd.p, 0.5);
300s ***** test
300s  pd = makedist ("binomial", "N", 5);
300s  assert (pd.N, 5);
300s  assert (pd.p, 0.5);
300s ***** test
300s  pd = makedist ("binomial", "p", 0.2);
300s  assert (pd.N, 1);
300s  assert (pd.p, 0.2);
300s ***** test
300s  pd = makedist ("binomial", "N", 3, "p", 0.3);
300s  assert (pd.N, 3);
300s  assert (pd.p, 0.3);
300s ***** test
300s  pd = makedist ("birnbaumsaunders");
300s  assert (class (pd), "BirnbaumSaundersDistribution");
300s  assert (pd.beta, 1);
300s  assert (pd.gamma, 1);
300s ***** test
300s  pd = makedist ("birnbaumsaunders", "beta", 5);
300s  assert (pd.beta, 5);
300s  assert (pd.gamma, 1);
300s ***** test
300s  pd = makedist ("birnbaumsaunders", "gamma", 5);
300s  assert (pd.beta, 1);
300s  assert (pd.gamma, 5);
300s ***** test
300s  pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
300s  assert (pd.beta, 3);
300s  assert (pd.gamma, 5);
300s ***** test
300s  pd = makedist ("burr");
300s  assert (class (pd), "BurrDistribution");
300s  assert (pd.alpha, 1);
300s  assert (pd.c, 1);
300s  assert (pd.k, 1);
300s ***** test
300s  pd = makedist ("burr", "k", 5);
300s  assert (pd.alpha, 1);
300s  assert (pd.c, 1);
300s  assert (pd.k, 5);
300s ***** test
300s  pd = makedist ("burr", "c", 5);
300s  assert (pd.alpha, 1);
300s  assert (pd.c, 5);
300s  assert (pd.k, 1);
300s ***** test
300s  pd = makedist ("burr", "alpha", 3, "c", 5);
300s  assert (pd.alpha, 3);
300s  assert (pd.c, 5);
300s  assert (pd.k, 1);
300s ***** test
300s  pd = makedist ("burr", "k", 3, "c", 5);
300s  assert (pd.alpha, 1);
300s  assert (pd.c, 5);
300s  assert (pd.k, 3);
300s ***** test
300s  pd = makedist ("exponential");
300s  assert (class (pd), "ExponentialDistribution");
300s  assert (pd.mu, 1);
300s ***** test
300s  pd = makedist ("exponential", "mu", 5);
300s  assert (pd.mu, 5);
300s ***** test
300s  pd = makedist ("extremevalue");
300s  assert (class (pd), "ExtremeValueDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("extremevalue", "mu", 5);
300s  assert (class (pd), "ExtremeValueDistribution");
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("ev", "sigma", 5);
300s  assert (class (pd), "ExtremeValueDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("ev", "mu", -3, "sigma", 5);
300s  assert (class (pd), "ExtremeValueDistribution");
300s  assert (pd.mu, -3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("gamma");
300s  assert (class (pd), "GammaDistribution");
300s  assert (pd.a, 1);
300s  assert (pd.b, 1);
300s ***** test
300s  pd = makedist ("gamma", "a", 5);
300s  assert (pd.a, 5);
300s  assert (pd.b, 1);
300s ***** test
300s  pd = makedist ("gamma", "b", 5);
300s  assert (pd.a, 1);
300s  assert (pd.b, 5);
300s ***** test
300s  pd = makedist ("gamma", "a", 3, "b", 5);
300s  assert (pd.a, 3);
300s  assert (pd.b, 5);
300s ***** test
300s  pd = makedist ("GeneralizedExtremeValue");
300s  assert (class (pd), "GeneralizedExtremeValueDistribution");
300s  assert (pd.k, 0);
300s  assert (pd.sigma, 1);
300s  assert (pd.mu, 0);
300s ***** test
300s  pd = makedist ("GeneralizedExtremeValue", "k", 5);
300s  assert (pd.k, 5);
300s  assert (pd.sigma, 1);
300s  assert (pd.mu, 0);
300s ***** test
300s  pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
300s  assert (pd.k, 0);
300s  assert (pd.sigma, 5);
300s  assert (pd.mu, 0);
300s ***** test
300s  pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
300s  assert (pd.k, 3);
300s  assert (pd.sigma, 5);
300s  assert (pd.mu, 0);
300s ***** test
300s  pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
300s  assert (pd.k, 0);
300s  assert (pd.sigma, 5);
300s  assert (pd.mu, 3);
300s ***** test
300s  pd = makedist ("GeneralizedPareto");
300s  assert (class (pd), "GeneralizedParetoDistribution");
300s  assert (pd.k, 1);
300s  assert (pd.sigma, 1);
300s  assert (pd.theta, 1);
300s ***** test
300s  pd = makedist ("GeneralizedPareto", "k", 5);
300s  assert (pd.k, 5);
300s  assert (pd.sigma, 1);
300s  assert (pd.theta, 1);
300s ***** test
300s  pd = makedist ("GeneralizedPareto", "sigma", 5);
300s  assert (pd.k, 1);
300s  assert (pd.sigma, 5);
300s  assert (pd.theta, 1);
300s ***** test
300s  pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
300s  assert (pd.k, 3);
300s  assert (pd.sigma, 5);
300s  assert (pd.theta, 1);
300s ***** test
300s  pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
300s  assert (pd.k, 1);
300s  assert (pd.sigma, 5);
300s  assert (pd.theta, 3);
300s ***** test
300s  pd = makedist ("HalfNormal");
300s  assert (class (pd), "HalfNormalDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("HalfNormal", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("HalfNormal", "sigma", 5);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
300s  assert (pd.mu, 3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("InverseGaussian");
300s  assert (class (pd), "InverseGaussianDistribution");
300s  assert (pd.mu, 1);
300s  assert (pd.lambda, 1);
300s ***** test
300s  pd = makedist ("InverseGaussian", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.lambda, 1);
300s ***** test
300s  pd = makedist ("InverseGaussian", "lambda", 5);
300s  assert (pd.mu, 1);
300s  assert (pd.lambda, 5);
300s ***** test
300s  pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
300s  assert (pd.mu, 3);
300s  assert (pd.lambda, 5);
300s ***** test
300s  pd = makedist ("logistic");
300s  assert (class (pd), "LogisticDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("logistic", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("logistic", "sigma", 5);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("logistic", "mu", 3, "sigma", 5);
300s  assert (pd.mu, 3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("loglogistic");
300s  assert (class (pd), "LoglogisticDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("loglogistic", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("loglogistic", "sigma", 5);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
300s  assert (pd.mu, 3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("Lognormal");
300s  assert (class (pd), "LognormalDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Lognormal", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Lognormal", "sigma", 5);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
300s  assert (pd.mu, -3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("Loguniform");
300s  assert (class (pd), "LoguniformDistribution");
300s  assert (pd.Lower, 1);
300s  assert (pd.Upper, 4);
300s ***** test
300s  pd = makedist ("Loguniform", "Lower", 2);
300s  assert (pd.Lower, 2);
300s  assert (pd.Upper, 4);
300s ***** test
300s  pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
300s  assert (pd.Lower, 1);
300s  assert (pd.Upper, 3);
300s ***** test
300s  pd = makedist ("Multinomial");
300s  assert (class (pd), "MultinomialDistribution");
300s  assert (pd.Probabilities, [0.5, 0.5]);
300s ***** test
300s  pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
300s  assert (class (pd), "MultinomialDistribution");
300s  assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
300s ***** test
300s  pd = makedist ("Nakagami");
300s  assert (class (pd), "NakagamiDistribution");
300s  assert (pd.mu, 1);
300s  assert (pd.omega, 1);
300s ***** test
300s  pd = makedist ("Nakagami", "mu", 5);
300s  assert (class (pd), "NakagamiDistribution");
300s  assert (pd.mu, 5);
300s  assert (pd.omega, 1);
300s ***** test
300s  pd = makedist ("Nakagami", "omega", 0.3);
300s  assert (class (pd), "NakagamiDistribution");
300s  assert (pd.mu, 1);
300s  assert (pd.omega, 0.3);
300s ***** test
300s  pd = makedist ("NegativeBinomial");
300s  assert (class (pd), "NegativeBinomialDistribution");
300s  assert (pd.R, 1);
300s  assert (pd.P, 0.5);
300s ***** test
300s  pd = makedist ("NegativeBinomial", "R", 5);
300s  assert (class (pd), "NegativeBinomialDistribution");
300s  assert (pd.R, 5);
300s  assert (pd.P, 0.5);
300s ***** test
300s  pd = makedist ("NegativeBinomial", "p", 0.3);
300s  assert (class (pd), "NegativeBinomialDistribution");
300s  assert (pd.R, 1);
300s  assert (pd.P, 0.3);
300s ***** test
300s  pd = makedist ("Normal");
300s  assert (class (pd), "NormalDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Normal", "mu", 5);
300s  assert (class (pd), "NormalDistribution");
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Normal", "sigma", 5);
300s  assert (class (pd), "NormalDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("Normal", "mu", -3, "sigma", 5);
300s  assert (class (pd), "NormalDistribution");
300s  assert (pd.mu, -3);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("PiecewiseLinear");
300s  assert (class (pd), "PiecewiseLinearDistribution");
300s  assert (pd.x, [0; 1]);
300s  assert (pd.Fx, [0; 1]);
300s ***** test
300s  pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
300s  assert (pd.x, [0; 1; 2]);
300s  assert (pd.Fx, [0; 0.5; 1]);
300s ***** test
300s  pd = makedist ("Poisson");
300s  assert (class (pd), "PoissonDistribution");
300s  assert (pd.lambda, 1);
300s ***** test
300s  pd = makedist ("Poisson", "lambda", 5);
300s  assert (pd.lambda, 5);
300s ***** test
300s  pd = makedist ("Rayleigh");
300s  assert (class (pd), "RayleighDistribution");
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Rayleigh", "sigma", 5);
300s  assert (pd.sigma, 5);
300s ***** test
300s  pd = makedist ("Rician");
300s  assert (class (pd), "RicianDistribution");
300s  assert (pd.s, 1);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Rician", "s", 3);
300s  assert (pd.s, 3);
300s  assert (pd.sigma, 1);
300s ***** test
300s  pd = makedist ("Rician", "sigma", 3);
300s  assert (pd.s, 1);
300s  assert (pd.sigma, 3);
300s ***** test
300s  pd = makedist ("Rician", "s", 2, "sigma", 3);
300s  assert (pd.s, 2);
300s  assert (pd.sigma, 3);
300s ***** warning
300s  pd = makedist ("stable");
300s  assert (class (pd), "double");
300s  assert (isempty (pd), true);
300s ***** test
300s  pd = makedist ("tlocationscale");
300s  assert (class (pd), "tLocationScaleDistribution");
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 1);
300s  assert (pd.nu, 5);
300s ***** test
300s  pd = makedist ("tlocationscale", "mu", 5);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 1);
300s  assert (pd.nu, 5);
300s ***** test
300s  pd = makedist ("tlocationscale", "sigma", 2);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 2);
300s  assert (pd.nu, 5);
300s ***** test
300s  pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
300s  assert (pd.mu, 5);
300s  assert (pd.sigma, 2);
300s  assert (pd.nu, 5);
300s ***** test
300s  pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
300s  assert (pd.mu, 0);
300s  assert (pd.sigma, 2);
300s  assert (pd.nu, 1);
300s ***** test
300s  pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
300s  assert (pd.mu, -2);
300s  assert (pd.sigma, 3);
300s  assert (pd.nu, 1);
300s ***** test
300s  pd = makedist ("Triangular");
300s  assert (class (pd), "TriangularDistribution");
300s  assert (pd.A, 0);
300s  assert (pd.B, 0.5);
300s  assert (pd.C, 1);
300s ***** test
300s  pd = makedist ("Triangular", "A", -2);
300s  assert (pd.A, -2);
300s  assert (pd.B, 0.5);
300s  assert (pd.C, 1);
300s ***** test
300s  pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
300s  assert (pd.A, 0.5);
300s  assert (pd.B, 0.9);
300s  assert (pd.C, 1);
300s ***** test
300s  pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
300s  assert (pd.A, 1);
300s  assert (pd.B, 2);
300s  assert (pd.C, 5);
300s ***** test
300s  pd = makedist ("Uniform");
300s  assert (class (pd), "UniformDistribution");
300s  assert (pd.Lower, 0);
300s  assert (pd.Upper, 1);
300s ***** test
300s  pd = makedist ("Uniform", "Lower", -2);
300s  assert (pd.Lower, -2);
300s  assert (pd.Upper, 1);
300s ***** test
300s  pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
300s  assert (pd.Lower, 1);
300s  assert (pd.Upper, 3);
300s ***** test
300s  pd = makedist ("Weibull");
300s  assert (class (pd), "WeibullDistribution");
300s  assert (pd.lambda, 1);
300s  assert (pd.k, 1);
300s ***** test
300s  pd = makedist ("Weibull", "lambda", 3);
300s  assert (pd.lambda, 3);
300s  assert (pd.k, 1);
300s ***** test
300s  pd = makedist ("Weibull", "lambda", 3, "k", 2);
300s  assert (pd.lambda, 3);
300s  assert (pd.k, 2);
300s ***** error <makedist: DISTNAME must be a character vector.> makedist (1)
300s ***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
300s ***** error <makedist: unrecognized distribution name.> makedist ("some")
300s ***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
300s  makedist ("Beta", "a")
300s ***** error <makedist: unknown parameter for 'Beta' distribution.> ...
300s  makedist ("Beta", "a", 1, "Q", 23)
300s ***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
300s  makedist ("Binomial", "N", 1, "Q", 23)
300s ***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
300s  makedist ("BirnbaumSaunders", "N", 1)
300s ***** error <makedist: unknown parameter for 'Burr' distribution.> ...
300s  makedist ("Burr", "lambda", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
300s  makedist ("extremevalue", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
300s  makedist ("exponential", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
300s  makedist ("Gamma", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
300s  makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
300s  makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
300s  makedist ("HalfNormal", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
300s  makedist ("InverseGaussian", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
300s  makedist ("Logistic", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
300s  makedist ("Loglogistic", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
300s  makedist ("Lognormal", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
300s  makedist ("Loguniform", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
300s  makedist ("Multinomial", "k", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
300s  makedist ("Nakagami", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
300s  makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Normal' distribution.> ...
300s  makedist ("Normal", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
300s  makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
300s  makedist ("Poisson", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
300s  makedist ("Rayleigh", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Rician' distribution.> ...
300s  makedist ("Rician", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Stable' distribution.> ...
300s  makedist ("Stable", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
300s  makedist ("tLocationScale", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
300s  makedist ("Triangular", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
300s  makedist ("Uniform", "mu", 1, "sdfs", 34)
300s ***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
300s  makedist ("Weibull", "mu", 1, "sdfs", 34)
300s 131 tests, 131 passed, 0 known failure, 0 skipped
300s [inst/dist_wrap/cdf.m]
300s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/cdf.m
300s ***** shared x
300s  x = [1:5];
300s ***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
300s ***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
300s ***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
300s ***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
300s ***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
300s ***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
300s ***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
301s ***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
301s ***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
301s ***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
301s ***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
301s ***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
301s ***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
301s ***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
301s ***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
301s ***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
301s ***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
301s ***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
301s ***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
301s ***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
301s ***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
301s ***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
301s ***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
301s ***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
301s ***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
301s ***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
301s ***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
301s ***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
301s ***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
301s ***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
301s ***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
301s ***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
301s ***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
301s ***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
301s ***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
301s ***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
301s ***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
301s ***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
301s ***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
301s ***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
301s ***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
301s ***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
301s ***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
301s ***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
301s ***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
301s ***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
301s ***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
301s ***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
301s ***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
301s ***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
301s ***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
302s ***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
302s ***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
302s ***** error<cdf: distribution NAME must be a char string.> cdf (1)
302s ***** error<cdf: distribution NAME must be a char string.> cdf ({"beta"})
302s ***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
302s ***** error<cdf: X must be numeric.> cdf ("beta", "text")
302s ***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
302s ***** error<cdf: distribution parameters must be numeric.> ...
302s  cdf ("Beta", x, "a", 2)
302s ***** error<cdf: distribution parameters must be numeric.> ...
302s  cdf ("Beta", x, 5, "")
302s ***** error<cdf: distribution parameters must be numeric.> ...
302s  cdf ("Beta", x, 5, {2})
302s ***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
302s ***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
302s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
302s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
302s 86 tests, 86 passed, 0 known failure, 0 skipped
302s [inst/dist_wrap/fitdist.m]
302s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/fitdist.m
302s ***** test
302s  x = betarnd (1, 1, 100, 1);
302s  pd = fitdist (x, "Beta");
302s  [phat, pci] = betafit (x);
302s  assert ([pd.a, pd.b], phat);
302s  assert (paramci (pd), pci);
302s ***** test
302s  x1 = betarnd (1, 1, 100, 1);
302s  x2 = betarnd (5, 2, 100, 1);
302s  pd = fitdist ([x1; x2], "Beta", "By", [ones(100, 1); 2*ones(100, 1)]);
302s  [phat, pci] = betafit (x1);
302s  assert ([pd{1}.a, pd{1}.b], phat);
302s  assert (paramci (pd{1}), pci);
302s  [phat, pci] = betafit (x2);
302s  assert ([pd{2}.a, pd{2}.b], phat);
302s  assert (paramci (pd{2}), pci);
302s ***** warning <fitdist: no data in group '2' to fit a 'Beta' distribution.> ...
302s  fitdist ([betarnd(1, 1, 100, 1); nan(100, 1)], "Beta", ...
302s           "By", [ones(100, 1); 2*ones(100, 1)]);
302s ***** test
302s  N = 1;
302s  x = binornd (N, 0.5, 100, 1);
302s  pd = fitdist (x, "binomial");
302s  [phat, pci] = binofit (sum (x), numel (x));
302s  assert ([pd.N, pd.p], [N, phat]);
302s  assert (paramci (pd), pci);
302s ***** test
302s  N = 3;
302s  x = binornd (N, 0.4, 100, 1);
302s  pd = fitdist (x, "binomial", "ntrials", N);
302s  [phat, pci] = binofit (sum (x), numel (x) * N);
302s  assert ([pd.N, pd.p], [N, phat]);
302s  assert (paramci (pd), pci);
302s ***** test
302s  N = 1;
302s  x1 = binornd (N, 0.5, 100, 1);
302s  x2 = binornd (N, 0.7, 100, 1);
302s  pd = fitdist ([x1; x2], "binomial", "By", [ones(100, 1); 2*ones(100, 1)]);
302s  [phat, pci] = binofit (sum (x1), numel (x1));
302s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
302s  assert (paramci (pd{1}), pci);
302s  [phat, pci] = binofit (sum (x2), numel (x2));
302s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
302s  assert (paramci (pd{2}), pci);
302s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
302s  fitdist ([binornd(1, 0.5, 100, 1); nan(100, 1)], "binomial", ...
302s           "By", [ones(100, 1); 2*ones(100, 1)]);
302s ***** test
302s  N = 5;
302s  x1 = binornd (N, 0.5, 100, 1);
302s  x2 = binornd (N, 0.8, 100, 1);
302s  pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
302s                "By", [ones(100, 1); 2*ones(100, 1)]);
302s  [phat, pci] = binofit (sum (x1), numel (x1) * N);
302s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
302s  assert (paramci (pd{1}), pci);
302s  [phat, pci] = binofit (sum (x2), numel (x2) * N);
302s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
302s  assert (paramci (pd{2}), pci);
302s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
302s  fitdist ([binornd(5, 0.5, 100, 1); nan(100, 1)], "binomial", "ntrials", 5, ...
302s           "By", [ones(100, 1); 2*ones(100, 1)]);
302s ***** test
302s  x = bisarnd (1, 1, 100, 1);
302s  pd = fitdist (x, "BirnbaumSaunders");
302s  [phat, pci] = bisafit (x);
302s  assert ([pd.beta, pd.gamma], phat);
302s  assert (paramci (pd), pci);
303s ***** test
303s  x1 = bisarnd (1, 1, 100, 1);
303s  x2 = bisarnd (5, 2, 100, 1);
303s  pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
303s  [phat, pci] = bisafit (x1);
303s  assert ([pd{1}.beta, pd{1}.gamma], phat);
303s  assert (paramci (pd{1}), pci);
303s  [phat, pci] = bisafit (x2);
303s  assert ([pd{2}.beta, pd{2}.gamma], phat);
303s  assert (paramci (pd{2}), pci);
303s ***** warning <fitdist: no data in group '2' to fit a 'bisa' distribution.> ...
303s  fitdist ([bisarnd(1, 1, 100, 1); nan(100, 1)], "bisa", ...
303s           "By", [ones(100, 1); 2*ones(100, 1)]);
303s ***** test
303s  x = burrrnd (1, 2, 1, 100, 1);
303s  pd = fitdist (x, "Burr");
303s  [phat, pci] = burrfit (x);
303s  assert ([pd.alpha, pd.c, pd.k], phat);
303s  assert (paramci (pd), pci);
304s ***** test
304s  rand ("seed", 4);   # for reproducibility
304s  x1 = burrrnd (1, 2, 1, 100, 1);
304s  rand ("seed", 3);   # for reproducibility
304s  x2 = burrrnd (1, 0.5, 2, 100, 1);
304s  pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
304s  [phat, pci] = burrfit (x1);
304s  assert ([pd{1}.alpha, pd{1}.c, pd{1}.k], phat);
304s  assert (paramci (pd{1}), pci);
304s  [phat, pci] = burrfit (x2);
304s  assert ([pd{2}.alpha, pd{2}.c, pd{2}.k], phat);
304s  assert (paramci (pd{2}), pci);
305s ***** warning <fitdist: no data in group '2' to fit a 'burr' distribution.> ...
305s  fitdist ([burrrnd(1, 2, 1, 100, 1); nan(100, 1)], "burr", ...
305s           "By", [ones(100, 1); 2*ones(100, 1)]);
305s ***** test
305s  x = exprnd (1, 100, 1);
305s  pd = fitdist (x, "exponential");
305s  [muhat, muci] = expfit (x);
305s  assert ([pd.mu], muhat);
305s  assert (paramci (pd), muci);
305s ***** test
305s  x1 = exprnd (1, 100, 1);
305s  x2 = exprnd (5, 100, 1);
305s  pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
305s  [muhat, muci] = expfit (x1);
305s  assert ([pd{1}.mu], muhat);
305s  assert (paramci (pd{1}), muci);
305s  [muhat, muci] = expfit (x2);
305s  assert ([pd{2}.mu], muhat);
305s  assert (paramci (pd{2}), muci);
305s ***** warning <fitdist: no data in group '2' to fit a 'exponential' distribution.> ...
305s  fitdist ([exprnd(1, 100, 1); nan(100, 1)], "exponential", ...
305s           "By", [ones(100, 1); 2*ones(100, 1)]);
305s ***** test
305s  x = evrnd (1, 1, 100, 1);
305s  pd = fitdist (x, "ev");
305s  [phat, pci] = evfit (x);
305s  assert ([pd.mu, pd.sigma], phat);
305s  assert (paramci (pd), pci);
305s ***** test
305s  x1 = evrnd (1, 1, 100, 1);
305s  x2 = evrnd (5, 2, 100, 1);
305s  pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
305s  [phat, pci] = evfit (x1);
305s  assert ([pd{1}.mu, pd{1}.sigma], phat);
305s  assert (paramci (pd{1}), pci);
305s  [phat, pci] = evfit (x2);
305s  assert ([pd{2}.mu, pd{2}.sigma], phat);
305s  assert (paramci (pd{2}), pci);
305s ***** warning <fitdist: no data in group '2' to fit a 'extremevalue' distribution.> ...
305s  fitdist ([evrnd(1, 1, 100, 1); nan(100, 1)], "extremevalue", ...
305s           "By", [ones(100, 1); 2*ones(100, 1)]);
305s ***** test
305s  x = gamrnd (1, 1, 100, 1);
305s  pd = fitdist (x, "Gamma");
305s  [phat, pci] = gamfit (x);
305s  assert ([pd.a, pd.b], phat);
305s  assert (paramci (pd), pci);
305s ***** test
305s  x1 = gamrnd (1, 1, 100, 1);
305s  x2 = gamrnd (5, 2, 100, 1);
305s  pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
305s  [phat, pci] = gamfit (x1);
305s  assert ([pd{1}.a, pd{1}.b], phat);
305s  assert (paramci (pd{1}), pci);
305s  [phat, pci] = gamfit (x2);
305s  assert ([pd{2}.a, pd{2}.b], phat);
305s  assert (paramci (pd{2}), pci);
306s ***** warning <fitdist: no data in group '2' to fit a 'Gamma' distribution.> ...
306s  fitdist ([gamrnd(1, 1, 100, 1); nan(100, 1)], "Gamma", ...
306s           "By", [ones(100, 1); 2*ones(100, 1)]);
306s ***** test
306s  rand ("seed", 4);   # for reproducibility
306s  x = gevrnd (-0.5, 1, 2, 1000, 1);
306s  pd = fitdist (x, "generalizedextremevalue");
306s  [phat, pci] = gevfit (x);
306s  assert ([pd.k, pd.sigma, pd.mu], phat);
306s  assert (paramci (pd), pci);
306s ***** test
306s  rand ("seed", 5);   # for reproducibility
306s  x1 = gevrnd (-0.5, 1, 2, 1000, 1);
306s  rand ("seed", 9);   # for reproducibility
306s  x2 = gevrnd (0, 1, -4, 1000, 1);
306s  pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
306s  [phat, pci] = gevfit (x1);
306s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.mu], phat);
306s  assert (paramci (pd{1}), pci);
306s  [phat, pci] = gevfit (x2);
306s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.mu], phat);
306s  assert (paramci (pd{2}), pci);
307s ***** warning <fitdist: no data in group '2' to fit a 'gev' distribution.> ...
307s  fitdist ([gevrnd(-0.5, 1, 2, 1000, 1); nan(1000, 1)], "gev", ...
307s           "By", [ones(1000, 1); 2*ones(1000, 1)]);
307s ***** test
307s  x = gprnd (1, 1, 1, 100, 1);
307s  pd = fitdist (x, "GeneralizedPareto");
307s  [phat, pci] = gpfit (x, 1);
307s  assert ([pd.k, pd.sigma, pd.theta], phat);
307s  assert (paramci (pd), pci);
307s ***** test
307s  x = gprnd (1, 1, 2, 100, 1);
307s  pd = fitdist (x, "GeneralizedPareto", "theta", 2);
307s  [phat, pci] = gpfit (x, 2);
307s  assert ([pd.k, pd.sigma, pd.theta], phat);
307s  assert (paramci (pd), pci);
307s ***** test
307s  x1 = gprnd (1, 1, 1, 100, 1);
307s  x2 = gprnd (0, 2, 1, 100, 1);
307s  pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
307s  [phat, pci] = gpfit (x1, 1);
307s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
307s  assert (paramci (pd{1}), pci);
307s  [phat, pci] = gpfit (x2, 1);
307s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
307s  assert (paramci (pd{2}), pci);
308s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
308s  fitdist ([gprnd(1, 1, 1, 100, 1); nan(100, 1)], "gp", ...
308s           "By", [ones(100, 1); 2*ones(100, 1)]);
308s ***** test
308s  x1 = gprnd (3, 2, 2, 100, 1);
308s  x2 = gprnd (2, 3, 2, 100, 1);
308s  pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
308s                "By", [ones(100,1); 2*ones(100,1)]);
308s  [phat, pci] = gpfit (x1, 2);
308s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
308s  assert (paramci (pd{1}), pci);
308s  [phat, pci] = gpfit (x2, 2);
308s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
308s  assert (paramci (pd{2}), pci);
308s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
308s  fitdist ([gprnd(3, 2, 2, 100, 1); nan(100, 1)], "gp", "theta", 2, ...
308s           "By", [ones(100, 1); 2*ones(100, 1)]);
308s ***** test
308s  x = hnrnd (0, 1, 100, 1);
308s  pd = fitdist (x, "HalfNormal");
308s  [phat, pci] = hnfit (x, 0);
308s  assert ([pd.mu, pd.sigma], phat);
308s  assert (paramci (pd), pci);
308s ***** test
308s  x = hnrnd (1, 1, 100, 1);
308s  pd = fitdist (x, "HalfNormal", "mu", 1);
308s  [phat, pci] = hnfit (x, 1);
308s  assert ([pd.mu, pd.sigma], phat);
308s  assert (paramci (pd), pci);
308s ***** test
308s  x1 = hnrnd (0, 1, 100, 1);
308s  x2 = hnrnd (0, 2, 100, 1);
308s  pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
308s  [phat, pci] = hnfit (x1, 0);
308s  assert ([pd{1}.mu, pd{1}.sigma], phat);
308s  assert (paramci (pd{1}), pci);
308s  [phat, pci] = hnfit (x2, 0);
308s  assert ([pd{2}.mu, pd{2}.sigma], phat);
308s  assert (paramci (pd{2}), pci);
309s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
309s  fitdist ([hnrnd(0, 1, 100, 1); nan(100, 1)], "HalfNormal", ...
309s           "By", [ones(100, 1); 2*ones(100, 1)]);
309s ***** test
309s  x1 = hnrnd (2, 1, 100, 1);
309s  x2 = hnrnd (2, 2, 100, 1);
309s  pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
309s                "By", [ones(100,1); 2*ones(100,1)]);
309s  [phat, pci] = hnfit (x1, 2);
309s  assert ([pd{1}.mu, pd{1}.sigma], phat);
309s  assert (paramci (pd{1}), pci);
309s  [phat, pci] = hnfit (x2, 2);
309s  assert ([pd{2}.mu, pd{2}.sigma], phat);
309s  assert (paramci (pd{2}), pci);
309s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
309s  fitdist ([hnrnd(2, 1, 100, 1); nan(100, 1)], "HalfNormal", "mu", 2, ...
309s           "By", [ones(100, 1); 2*ones(100, 1)]);
309s ***** test
309s  x = invgrnd (1, 1, 100, 1);
309s  pd = fitdist (x, "InverseGaussian");
309s  [phat, pci] = invgfit (x);
309s  assert ([pd.mu, pd.lambda], phat);
309s  assert (paramci (pd), pci);
309s ***** test
309s  x1 = invgrnd (1, 1, 100, 1);
309s  x2 = invgrnd (5, 2, 100, 1);
309s  pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
309s  [phat, pci] = invgfit (x1);
309s  assert ([pd{1}.mu, pd{1}.lambda], phat);
309s  assert (paramci (pd{1}), pci);
309s  [phat, pci] = invgfit (x2);
309s  assert ([pd{2}.mu, pd{2}.lambda], phat);
309s  assert (paramci (pd{2}), pci);
309s ***** warning <fitdist: no data in group '2' to fit a 'InverseGaussian' distribution.> ...
309s  fitdist ([invgrnd(1, 1, 100, 1); nan(100, 1)], "InverseGaussian", ...
309s           "By", [ones(100, 1); 2*ones(100, 1)]);
310s ***** test
310s  x = logirnd (1, 1, 100, 1);
310s  pd = fitdist (x, "logistic");
310s  [phat, pci] = logifit (x);
310s  assert ([pd.mu, pd.sigma], phat);
310s  assert (paramci (pd), pci);
310s ***** test
310s  x1 = logirnd (1, 1, 100, 1);
310s  x2 = logirnd (5, 2, 100, 1);
310s  pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
310s  [phat, pci] = logifit (x1);
310s  assert ([pd{1}.mu, pd{1}.sigma], phat);
310s  assert (paramci (pd{1}), pci);
310s  [phat, pci] = logifit (x2);
310s  assert ([pd{2}.mu, pd{2}.sigma], phat);
310s  assert (paramci (pd{2}), pci);
310s ***** warning <fitdist: no data in group '2' to fit a 'logistic' distribution.> ...
310s  fitdist ([logirnd(1, 1, 100, 1); nan(100, 1)], "logistic", ...
310s           "By", [ones(100, 1); 2*ones(100, 1)]);
310s ***** test
310s  x = loglrnd (1, 1, 100, 1);
310s  pd = fitdist (x, "loglogistic");
310s  [phat, pci] = loglfit (x);
310s  assert ([pd.mu, pd.sigma], phat);
310s  assert (paramci (pd), pci);
310s ***** test
310s  x1 = loglrnd (1, 1, 100, 1);
310s  x2 = loglrnd (5, 2, 100, 1);
310s  pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
310s  [phat, pci] = loglfit (x1);
310s  assert ([pd{1}.mu, pd{1}.sigma], phat);
310s  assert (paramci (pd{1}), pci);
310s  [phat, pci] = loglfit (x2);
310s  assert ([pd{2}.mu, pd{2}.sigma], phat);
310s  assert (paramci (pd{2}), pci);
311s ***** warning <fitdist: no data in group '2' to fit a 'loglogistic' distribution.> ...
311s  fitdist ([loglrnd(1, 1, 100, 1); nan(100, 1)], "loglogistic", ...
311s           "By", [ones(100, 1); 2*ones(100, 1)]);
311s ***** test
311s  x = lognrnd (1, 1, 100, 1);
311s  pd = fitdist (x, "lognormal");
311s  [phat, pci] = lognfit (x);
311s  assert ([pd.mu, pd.sigma], phat);
311s  assert (paramci (pd), pci);
311s ***** test
311s  x1 = lognrnd (1, 1, 100, 1);
311s  x2 = lognrnd (5, 2, 100, 1);
311s  pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
311s  [phat, pci] = lognfit (x1);
311s  assert ([pd{1}.mu, pd{1}.sigma], phat);
311s  assert (paramci (pd{1}), pci);
311s  [phat, pci] = lognfit (x2);
311s  assert ([pd{2}.mu, pd{2}.sigma], phat);
311s  assert (paramci (pd{2}), pci);
312s ***** warning <fitdist: no data in group '2' to fit a 'lognormal' distribution.> ...
312s  fitdist ([lognrnd(1, 1, 100, 1); nan(100, 1)], "lognormal", ...
312s           "By", [ones(100, 1); 2*ones(100, 1)]);
313s ***** test
313s  x = nakarnd (2, 0.5, 100, 1);
313s  pd = fitdist (x, "Nakagami");
313s  [phat, pci] = nakafit (x);
313s  assert ([pd.mu, pd.omega], phat);
313s  assert (paramci (pd), pci);
313s ***** test
313s  x1 = nakarnd (2, 0.5, 100, 1);
313s  x2 = nakarnd (5, 0.8, 100, 1);
313s  pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
313s  [phat, pci] = nakafit (x1);
313s  assert ([pd{1}.mu, pd{1}.omega], phat);
313s  assert (paramci (pd{1}), pci);
313s  [phat, pci] = nakafit (x2);
313s  assert ([pd{2}.mu, pd{2}.omega], phat);
313s  assert (paramci (pd{2}), pci);
313s ***** warning <fitdist: no data in group '2' to fit a 'Nakagami' distribution.> ...
313s  fitdist ([nakarnd(2, 0.5, 100, 1); nan(100, 1)], "Nakagami", ...
313s           "By", [ones(100, 1); 2*ones(100, 1)]);
313s ***** test
313s  randp ("seed", 123);
313s  randg ("seed", 321);
313s  x = nbinrnd (2, 0.5, 100, 1);
313s  pd = fitdist (x, "negativebinomial");
313s  [phat, pci] = nbinfit (x);
313s  assert ([pd.R, pd.P], phat);
313s  assert (paramci (pd), pci);
313s ***** test
313s  randp ("seed", 345);
313s  randg ("seed", 543);
313s  x1 = nbinrnd (2, 0.5, 100, 1);
313s  randp ("seed", 432);
313s  randg ("seed", 234);
313s  x2 = nbinrnd (5, 0.8, 100, 1);
313s  pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
313s  [phat, pci] = nbinfit (x1);
313s  assert ([pd{1}.R, pd{1}.P], phat);
313s  assert (paramci (pd{1}), pci);
313s  [phat, pci] = nbinfit (x2);
313s  assert ([pd{2}.R, pd{2}.P], phat);
313s  assert (paramci (pd{2}), pci);
313s ***** warning <fitdist: no data in group '2' to fit a 'nbin' distribution.> ...
313s  fitdist ([nbinrnd(2, 0.5, 100, 1); nan(100, 1)], "nbin", ...
313s           "By", [ones(100, 1); 2*ones(100, 1)]);
313s ***** test
313s  x = normrnd (1, 1, 100, 1);
313s  pd = fitdist (x, "normal");
313s  [muhat, sigmahat, muci, sigmaci] = normfit (x);
313s  assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
313s  assert (paramci (pd), [muci, sigmaci]);
313s ***** test
313s  x1 = normrnd (1, 1, 100, 1);
313s  x2 = normrnd (5, 2, 100, 1);
313s  pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
313s  [muhat, sigmahat, muci, sigmaci] = normfit (x1);
313s  assert ([pd{1}.mu, pd{1}.sigma], [muhat, sigmahat]);
313s  assert (paramci (pd{1}), [muci, sigmaci]);
313s  [muhat, sigmahat, muci, sigmaci] = normfit (x2);
313s  assert ([pd{2}.mu, pd{2}.sigma], [muhat, sigmahat]);
313s  assert (paramci (pd{2}), [muci, sigmaci]);
314s ***** warning <fitdist: no data in group '2' to fit a 'normal' distribution.> ...
314s  fitdist ([normrnd(1, 1, 100, 1); nan(100, 1)], "normal", ...
314s           "By", [ones(100, 1); 2*ones(100, 1)]);
314s ***** test
314s  x = poissrnd (1, 100, 1);
314s  pd = fitdist (x, "poisson");
314s  [phat, pci] = poissfit (x);
314s  assert (pd.lambda, phat);
314s  assert (paramci (pd), pci);
314s ***** test
314s  x1 = poissrnd (1, 100, 1);
314s  x2 = poissrnd (5, 100, 1);
314s  pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
314s  [phat, pci] = poissfit (x1);
314s  assert (pd{1}.lambda, phat);
314s  assert (paramci (pd{1}), pci);
314s  [phat, pci] = poissfit (x2);
314s  assert (pd{2}.lambda, phat);
314s  assert (paramci (pd{2}), pci);
315s ***** warning <fitdist: no data in group '2' to fit a 'poisson' distribution.> ...
315s  fitdist ([poissrnd(1, 100, 1); nan(100, 1)], "poisson", ...
315s           "By", [ones(100, 1); 2*ones(100, 1)]);
315s ***** test
315s  x = raylrnd (1, 100, 1);
315s  pd = fitdist (x, "rayleigh");
315s  [phat, pci] = raylfit (x);
315s  assert (pd.sigma, phat);
315s  assert (paramci (pd), pci);
315s ***** test
315s  x1 = raylrnd (1, 100, 1);
315s  x2 = raylrnd (5, 100, 1);
315s  pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
315s  [phat, pci] = raylfit (x1);
315s  assert (pd{1}.sigma, phat);
315s  assert (paramci (pd{1}), pci);
315s  [phat, pci] = raylfit (x2);
315s  assert (pd{2}.sigma, phat);
315s  assert (paramci (pd{2}), pci);
315s ***** warning <fitdist: no data in group '2' to fit a 'rayleigh' distribution.> ...
315s  fitdist ([raylrnd(1, 100, 1); nan(100, 1)], "rayleigh", ...
315s           "By", [ones(100, 1); 2*ones(100, 1)]);
315s ***** test
315s  x = ricernd (1, 1, 100, 1);
315s  pd = fitdist (x, "rician");
315s  [phat, pci] = ricefit (x);
315s  assert ([pd.s, pd.sigma], phat);
315s  assert (paramci (pd), pci);
315s ***** test
315s  x1 = ricernd (1, 1, 100, 1);
315s  x2 = ricernd (5, 2, 100, 1);
315s  pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
315s  [phat, pci] = ricefit (x1);
315s  assert ([pd{1}.s, pd{1}.sigma], phat);
315s  assert (paramci (pd{1}), pci);
315s  [phat, pci] = ricefit (x2);
315s  assert ([pd{2}.s, pd{2}.sigma], phat);
315s  assert (paramci (pd{2}), pci);
316s ***** warning <fitdist: no data in group '2' to fit a 'rician' distribution.> ...
316s  fitdist ([ricernd(1, 1, 100, 1); nan(100, 1)], "rician", ...
316s           "By", [ones(100, 1); 2*ones(100, 1)]);
316s ***** warning <fitdist: 'Stable' distribution not supported yet.> ...
316s  fitdist ([1 2 3 4 5], "Stable");
316s ***** test
316s  x = tlsrnd (0, 1, 1, 100, 1);
316s  pd = fitdist (x, "tlocationscale");
316s  [phat, pci] = tlsfit (x);
316s  assert ([pd.mu, pd.sigma, pd.nu], phat);
316s  assert (paramci (pd), pci);
316s ***** test
316s  x1 = tlsrnd (0, 1, 1, 100, 1);
316s  x2 = tlsrnd (5, 2, 1, 100, 1);
316s  pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
316s  [phat, pci] = tlsfit (x1);
316s  assert ([pd{1}.mu, pd{1}.sigma, pd{1}.nu], phat);
316s  assert (paramci (pd{1}), pci);
316s  [phat, pci] = tlsfit (x2);
316s  assert ([pd{2}.mu, pd{2}.sigma, pd{2}.nu], phat);
316s  assert (paramci (pd{2}), pci);
317s ***** warning <fitdist: no data in group '2' to fit a 'tlocationscale' distribution.> ...
317s  fitdist ([tlsrnd(0, 1, 1, 100, 1); nan(100, 1)], "tlocationscale", ...
317s           "By", [ones(100, 1); 2*ones(100, 1)]);
317s ***** test
317s  x = [1 2 3 4 5];
317s  pd = fitdist (x, "weibull");
317s  [phat, pci] = wblfit (x);
317s  assert ([pd.lambda, pd.k], phat);
317s  assert (paramci (pd), pci);
317s ***** test
317s  x = [1 2 3 4 5 6 7 8 9 10];
317s  pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
317s  [phat, pci] = wblfit (x(1:5));
317s  assert ([pd{1}.lambda, pd{1}.k], phat);
317s  assert (paramci (pd{1}), pci);
317s  [phat, pci] = wblfit (x(6:10));
317s  assert ([pd{2}.lambda, pd{2}.k], phat);
317s  assert (paramci (pd{2}), pci);
318s ***** warning <fitdist: no data in group '2' to fit a 'weibull' distribution.> ...
318s  fitdist ([1 2 3 4 5 NaN NaN NaN NaN NaN], "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
318s ***** error <fitdist: DISTNAME is required.> fitdist (1)
318s ***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
318s ***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
318s ***** error <fitdist: X must be a numeric vector of real values.> ...
318s  fitdist (ones (2), "normal")
318s ***** error <fitdist: X must be a numeric vector of real values.> ...
318s  fitdist ([i, 2, 3], "normal")
318s ***** error <fitdist: X must be a numeric vector of real values.> ...
318s  fitdist (["a", "s", "d"], "normal")
318s ***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
318s  fitdist ([1, 2, 3], "normal", "By")
318s ***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
318s  fitdist ([1, 2, 3], "normal", "By", [1, 2])
318s ***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
318s  fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
318s ***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
318s  fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
318s ***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
318s  fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
318s ***** error <fitdist: invalid value for 'alpha' argument.> ...
318s  fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
318s ***** error <fitdist: invalid value for 'alpha' argument.> ...
318s  fitdist ([1, 2, 3], "normal", "alpha", i)
318s ***** error <fitdist: invalid value for 'alpha' argument.> ...
318s  fitdist ([1, 2, 3], "normal", "alpha", -0.5)
318s ***** error <fitdist: invalid value for 'alpha' argument.> ...
318s  fitdist ([1, 2, 3], "normal", "alpha", 1.5)
318s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
318s  fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
318s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
318s  fitdist ([1, 2, 3], "normal", "ntrials", 0)
318s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
318s  fitdist ([1, 2, 3], "normal", "options", 0)
318s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
318s  fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
318s ***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
318s ***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
318s ***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
318s ***** error <fitdist: unknown parameter name.> ...
318s  fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
318s ***** error <fitdist: no data in X to fit a 'normal' distribution.> ...
318s  fitdist (nan (100,1), "normal");
318s ***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
318s  [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
318s ***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
318s  fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
318s ***** error <fitdist: invalid MU value for half-normal distribution.> ...
318s  fitdist ([1, 2, 3], "halfnormal", "mu", 2);
318s 103 tests, 103 passed, 0 known failure, 0 skipped
318s [inst/dist_wrap/random.m]
318s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/random.m
318s ***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
318s ***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
318s ***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
318s ***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
318s ***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
318s ***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
318s ***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
318s ***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
318s ***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
318s ***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
318s ***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
318s ***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
318s ***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
318s ***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
318s ***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
318s ***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
318s ***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
318s ***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
318s ***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
318s ***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
318s ***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
318s ***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
318s ***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
318s ***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
318s ***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
318s ***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
318s ***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
318s ***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
318s ***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
318s ***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
318s ***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
318s ***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
318s ***** error<random: distribution NAME must be a char string.> random (1)
318s ***** error<random: distribution NAME must be a char string.> random ({"beta"})
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", "a", 2)
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", 5, "")
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", 5, {2})
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", "a", 2, 2, 10)
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", 5, "", 2, 10)
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", 5, {2}, 2, 10)
318s ***** error<random: distribution parameters must be numeric.> ...
318s  random ("Beta", 5, "", 2, 10)
318s ***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
318s ***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
318s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
318s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
318s 87 tests, 87 passed, 0 known failure, 0 skipped
318s [inst/dist_wrap/icdf.m]
318s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/icdf.m
318s ***** shared p
318s  p = [0.05:0.05:0.5];
318s ***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
318s ***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
318s ***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
318s ***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
318s ***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
318s ***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
318s ***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
318s ***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
318s ***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
318s ***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
318s ***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
318s ***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
318s ***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
318s ***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
318s ***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
318s ***** assert (icdf ("exp", p, 5), expinv (p, 5))
318s ***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
318s ***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
318s ***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
318s ***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
319s ***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
319s ***** assert (icdf ("geo", p, 5), geoinv (p, 5))
319s ***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
319s ***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
319s ***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
319s ***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
319s ***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
319s ***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
319s ***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
319s ***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
319s ***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
319s ***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
319s ***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
319s ***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
319s ***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
319s ***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
319s ***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
319s ***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
319s ***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
319s ***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
319s ***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
319s ***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
319s ***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
319s ***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
319s ***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
319s ***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
319s ***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
319s ***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
319s ***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
320s ***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
321s ***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
321s ***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
322s ***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
322s ***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
322s ***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
322s ***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
322s ***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
322s ***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
322s ***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
323s ***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
325s ***** assert (icdf ("Student T", p, 5), tinv (p, 5))
325s ***** assert (icdf ("t", p, 5), tinv (p, 5))
325s ***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
325s ***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
325s ***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
325s ***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
325s ***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
325s ***** assert (icdf ("unid", p, 5), unidinv (p, 5))
325s ***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
325s ***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
325s ***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
329s ***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
333s ***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
333s ***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
333s ***** error<icdf: distribution NAME must be a char string.> icdf (1)
333s ***** error<icdf: distribution NAME must be a char string.> icdf ({"beta"})
333s ***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
333s ***** error<icdf: P must be numeric.> icdf ("beta", "text")
333s ***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
333s ***** error<icdf: distribution parameters must be numeric.> ...
333s  icdf ("Beta", p, "a", 2)
333s ***** error<icdf: distribution parameters must be numeric.> ...
333s  icdf ("Beta", p, 5, "")
333s ***** error<icdf: distribution parameters must be numeric.> ...
333s  icdf ("Beta", p, 5, {2})
333s ***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
333s ***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
333s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
333s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
333s 86 tests, 86 passed, 0 known failure, 0 skipped
333s [inst/dist_wrap/pdf.m]
333s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/pdf.m
333s ***** shared x
333s  x = [1:5];
333s ***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
333s ***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
333s ***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
333s ***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
333s ***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
333s ***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
333s ***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
333s ***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
333s ***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
333s ***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
333s ***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
333s ***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
333s ***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
333s ***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
333s ***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
333s ***** assert (pdf ("exp", x, 5), exppdf (x, 5))
333s ***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
333s ***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
336s ***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
336s ***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
336s ***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
336s ***** assert (pdf ("geo", x, 5), geopdf (x, 5))
336s ***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
336s ***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
336s ***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
336s ***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
336s ***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
336s ***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
336s ***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
336s ***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
336s ***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
336s ***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
336s ***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
336s ***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
336s ***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
336s ***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
336s ***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
336s ***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
336s ***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
336s ***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
336s ***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
336s ***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
336s ***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
336s ***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
336s ***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
336s ***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
336s ***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
336s ***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
336s ***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
336s ***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
336s ***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
336s ***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
336s ***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
336s ***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
336s ***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
336s ***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
336s ***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
336s ***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
336s ***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
336s ***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
336s ***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
336s ***** assert (pdf ("t", x, 5), tpdf (x, 5))
336s ***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
336s ***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
336s ***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
336s ***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
336s ***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
336s ***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
336s ***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
336s ***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
336s ***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
336s ***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
336s ***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
336s ***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
336s ***** error<pdf: distribution NAME must a char string.> pdf (1)
336s ***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
336s ***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
336s ***** error<pdf: X must be numeric.> pdf ("beta", "text")
336s ***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
336s ***** error<pdf: distribution parameters must be numeric.> ...
336s  pdf ("Beta", x, "a", 2)
336s ***** error<pdf: distribution parameters must be numeric.> ...
336s  pdf ("Beta", x, 5, "")
336s ***** error<pdf: distribution parameters must be numeric.> ...
336s  pdf ("Beta", x, 5, {2})
336s ***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
336s ***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
336s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
336s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
336s 86 tests, 86 passed, 0 known failure, 0 skipped
336s [inst/dist_wrap/mle.m]
336s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_wrap/mle.m
336s ***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
336s ***** error <mle: X must be a numeric vector of real values.> mle ("text")
336s ***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
336s ***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
336s  mle ([1:50], "distribution")
336s ***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
336s  mle ([1:50], "censoring", logical ([1,0,1,0]))
336s ***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
336s  mle ([1:50], "frequency", [1,0,1,0])
336s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
336s  mle ([1 0 1 0], "frequency", [-1 1 0 0])
336s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
336s  mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
336s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
336s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
336s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
336s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
336s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
336s  mle ([1:50], "ntrials", -1)
336s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
336s  mle ([1:50], "ntrials", [20, 50])
336s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
336s  mle ([1:50], "ntrials", [20.3])
336s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
336s  mle ([1:50], "ntrials", 3i)
336s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
336s  mle ([1:50], "options", 4)
336s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
336s  mle ([1:50], "options", struct ("x", 3))
336s ***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
336s ***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
336s  mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
336s ***** error <mle: invalid data for the Bernoulli distribution.> ...
336s  mle ([1 2 1 0], "distribution", "bernoulli")
336s ***** error <mle: censoring is not supported for the Beta distribution.> ...
336s  mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
336s ***** error <mle: censoring is not supported for the Binomial distribution.> ...
336s  mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
336s ***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
336s  mle ([1 0 1 0], "distribution", "bino")
336s ***** error <mle: censoring is not supported for the Geometric distribution.> ...
336s  mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
336s ***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
336s  mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
336s ***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
336s  mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
336s ***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
336s  mle ([1 0 -1 0], "distribution", "gp")
336s ***** error <mle: censoring is not supported for the Half Normal distribution.> ...
336s  mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
336s ***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
336s  mle ([1 0 -1 0], "distribution", "hn")
336s ***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
336s  mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
336s ***** error <mle: censoring is not supported for the Poisson distribution.> ...
336s  mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
338s ***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
338s  mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
338s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
338s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
338s ***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
338s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
338s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
338s 36 tests, 36 passed, 0 known failure, 0 skipped
338s [inst/glmval.m]
338s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/glmval.m
338s ***** demo
338s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
338s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
338s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
338s  b = glmfit (x, [y n], "binomial", "Link", "probit");
338s  yfit = glmval (b, x, "probit", "Size", n);
338s  plot (x, y./n, 'o', x, yfit ./ n, '-')
338s ***** error <glmval: too few input arguments.> glmval ()
338s ***** error <glmval: too few input arguments.> glmval (1)
338s ***** error <glmval: too few input arguments.> glmval (1, 2)
338s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
338s  glmval ("asd", [1; 1; 1], 'probit')
338s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
338s  glmval ([], [1; 1; 1], 'probit')
338s ***** error <glmval: X must be a numeric matrix.> ...
338s  glmval ([0.1; 0.3; 0.4], [], 'probit')
338s ***** error <glmval: X must be a numeric matrix.> ...
338s  glmval ([0.1; 0.3; 0.4], "asd", 'probit')
338s ***** error <glmval: structure with custom link functions must be a scalar.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
338s ***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
338s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
338s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
338s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
338s ***** error <glmval: invalid custom 'Link' function.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
338s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
338s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
338s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
338s ***** error <glmval: invalid custom 'Derivative' function.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
338s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
338s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
338s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
338s ***** error <glmval: invalid custom 'Inverse' function.> ...
338s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
338s ***** error <glmval: cell array with custom link functions must have three elements.> ...
338s  glmval (rand (3,1), rand (5,2), {'log'})
338s ***** error <glmval: cell array with custom link functions must have three elements.> ...
338s  glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
338s ***** error <glmval: cell array with custom link functions must have three elements.> ...
338s  glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
338s ***** error <glmval: bad 'Link' function in custom link function cell array.> ...
338s  glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
338s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
338s ***** error <glmval: invalid custom 'Link' function.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
338s ***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
338s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
338s ***** error <glmval: invalid custom 'Derivative' function.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
338s ***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
338s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
338s ***** error <glmval: invalid custom 'Inverse' function.> ...
338s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
338s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
338s  glmval (rand (3,1), rand (5,2), NaN)
338s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
338s  glmval (rand (3,1), rand (5,2), [1, 2])
338s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
338s  glmval (rand (3,1), rand (5,2), [1i])
338s ***** error <glmval: canonical link function name must be a character vector.> ...
338s  glmval (rand (3,1), rand (5,2), ["log"; "log1"])
338s ***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
338s  glmval (rand (3,1), rand (5,2), 'somelinkfunction')
338s ***** error <glmval: invalid value for custom link function.> ...
338s  glmval (rand (3,1), rand (5,2), true)
338s ***** error <glmval: invalid 'stats' structure.> ...
338s  glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
338s ***** error <glmval: Name-Value arguments must be in pairs.> ...
338s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
338s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
338s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
338s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
338s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
338s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
338s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
338s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
338s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
338s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
338s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
338s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
338s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
338s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
338s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
338s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
338s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
338s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
338s ***** error <glmval: unknown parameter name.> ...
338s  glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
338s ***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
338s  [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
338s 57 tests, 57 passed, 0 known failure, 0 skipped
338s [inst/Classification/ClassificationNeuralNetwork.m]
338s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationNeuralNetwork.m
338s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
338s  ClassificationNeuralNetwork ()
338s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
338s  ClassificationNeuralNetwork (ones(10,2))
338s ***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones (5,1))
338s ***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
338s  ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
338s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
338s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
338s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
338s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
338s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
338s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
338s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
338s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
338s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
338s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
338s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
338s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", {1})
338s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
338s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
338s  ClassificationNeuralNetwork (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
338s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
338s  ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
338s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
338s  ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
338s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
338s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
338s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
338s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
338s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
338s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
338s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
338s ***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
338s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
338s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
338s  "Activations", {"sigmoid", "unsupported_type"})
338s ***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
338s ***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
338s ***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
338s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
338s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
338s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
338s ***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
338s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
338s ***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
338s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
338s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
338s  ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
338s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
338s  ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
338s ***** shared x, y, objST, Mdl
338s  load fisheriris
338s  x = meas;
338s  y = grp2idx (species);
338s  Mdl = fitcnet (x, y, "IterationLimit", 100);
338s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
338s  Mdl.ScoreTransform = "a";
338s ***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
338s  predict (Mdl)
338s ***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
338s  predict (Mdl, [])
338s ***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
338s  predict (Mdl, 1)
338s ***** test
338s  CVMdl = crossval (Mdl, "KFold", 5);
338s  assert (class (CVMdl), "ClassificationPartitionedModel")
338s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
338s  assert (CVMdl.KFold == 5)
338s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
338s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
338s ***** test
338s  CVMdl = crossval (Mdl, "HoldOut", 0.2);
338s  assert (class (CVMdl), "ClassificationPartitionedModel")
338s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
338s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
338s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
338s ***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
338s  crossval (Mdl, "KFold")
338s ***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
338s  crossval (Mdl, "KFold", 5, "leaveout", 'on')
338s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
338s  crossval (Mdl, "KFold", 'a')
338s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
338s  crossval (Mdl, "KFold", 1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
338s  crossval (Mdl, "KFold", -1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
338s  crossval (Mdl, "KFold", 11.5)
338s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
338s  crossval (Mdl, "KFold", [1,2])
338s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
338s  crossval (Mdl, "Holdout", 'a')
338s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
338s  crossval (Mdl, "Holdout", 11.5)
338s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
338s  crossval (Mdl, "Holdout", -1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
338s  crossval (Mdl, "Holdout", 0)
338s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
338s  crossval (Mdl, "Holdout", 1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
338s  crossval (Mdl, "Leaveout", 1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
338s  crossval (Mdl, "CVPartition", 1)
338s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
338s  crossval (Mdl, "CVPartition", 'a')
338s ***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
338s  crossval (Mdl, "some", "some")
338s 58 tests, 58 passed, 0 known failure, 0 skipped
338s [inst/Classification/CompactClassificationNeuralNetwork.m]
338s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/CompactClassificationNeuralNetwork.m
338s ***** demo
338s  ## Create a neural network classifier and its compact version
338s  # and compare their size
338s 
338s  load fisheriris
338s  X = meas;
338s  Y = species;
338s 
338s  Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
338s  CMdl = crossval (Mdl)
338s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
338s  CompactClassificationDiscriminant (1)
338s ***** shared x, y, CMdl
338s  load fisheriris
338s  x = meas;
338s  y = grp2idx (species);
338s  Mdl = fitcnet (x, y, "IterationLimit", 100);
338s  CMdl = compact (Mdl);
338s ***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
338s  predict (CMdl)
338s ***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
338s  predict (CMdl, [])
338s ***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
338s  predict (CMdl, 1)
338s ***** error<CompactClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
338s  CMdl.ScoreTransform = "a";
338s 5 tests, 5 passed, 0 known failure, 0 skipped
338s [inst/Classification/ClassificationPartitionedModel.m]
338s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationPartitionedModel.m
338s ***** demo
338s 
338s  load fisheriris
338s  x = meas;
338s  y = species;
338s 
338s  ## Create a KNN classifier model
338s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
338s 
338s  ## Create a partition for 5-fold cross-validation
338s  partition = cvpartition (y, "KFold", 5);
338s 
338s  ## Create the ClassificationPartitionedModel object
338s  cvModel = crossval (obj, 'cvPartition', partition)
338s ***** demo
338s 
338s  load fisheriris
338s  x = meas;
338s  y = species;
338s 
338s  ## Create a KNN classifier model
338s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
338s 
338s  ## Create the ClassificationPartitionedModel object
338s  cvModel = crossval (obj);
338s 
338s  ## Predict the class labels for the observations not used for training
338s  [label, score, cost] = kfoldPredict (cvModel);
338s  fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
338s           sum (strcmp (label, y)) / numel (y) *100, ...
338s           sum (strcmp (label, y)), numel (y))
338s ***** test
338s  load fisheriris
338s  a = fitcdiscr (meas, species, "gamma", 0.3);
338s  cvModel = crossval (a, "KFold", 5);
338s  assert (class (cvModel), "ClassificationPartitionedModel");
338s  assert (cvModel.NumObservations, 150);
338s  assert (numel (cvModel.Trained), 5);
338s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
338s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
338s  assert (cvModel.KFold, 5);
338s ***** test
338s  load fisheriris
338s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
338s  cvModel = crossval (a, "HoldOut", 0.3);
338s  assert (class (cvModel), "ClassificationPartitionedModel");
338s  assert ({cvModel.X, cvModel.Y}, {meas, species});
338s  assert (cvModel.NumObservations, 150);
338s  assert (numel (cvModel.Trained), 1);
338s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
338s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
338s ***** test
338s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
338s  y = ["a"; "a"; "b"; "b"];
338s  a = fitcgam (x, y, "Interactions", "all");
338s  cvModel = crossval (a, "KFold", 2);
338s  assert (class (cvModel), "ClassificationPartitionedModel");
338s  assert (cvModel.NumObservations, 4);
338s  assert (numel (cvModel.Trained), 2);
338s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
338s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
338s  assert (cvModel.KFold, 2);
353s ***** test
353s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
353s  y = ["a"; "a"; "b"; "b"];
353s  a = fitcgam (x, y);
353s  cvModel = crossval (a, "LeaveOut", "on");
353s  assert (class (cvModel), "ClassificationPartitionedModel");
353s  assert ({cvModel.X, cvModel.Y}, {x, y});
353s  assert (cvModel.NumObservations, 4);
353s  assert (numel (cvModel.Trained), 4);
353s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
353s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
364s ***** test
364s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
364s  y = ["a"; "a"; "b"; "b"];
364s  a = fitcknn (x, y);
364s  partition = cvpartition (y, "KFold", 2);
364s  cvModel = ClassificationPartitionedModel (a, partition);
364s  assert (class (cvModel), "ClassificationPartitionedModel");
364s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
364s  assert (cvModel.NumObservations, 4);
364s  assert (cvModel.ModelParameters.NumNeighbors, 1);
364s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
364s  assert (cvModel.ModelParameters.Distance, "euclidean");
364s  assert (! cvModel.ModelParameters.Standardize);
364s ***** test
364s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
364s  y = ["a"; "a"; "b"; "b"];
364s  a = fitcknn (x, y, "NSMethod", "exhaustive");
364s  partition = cvpartition (y, "HoldOut", 0.2);
364s  cvModel = ClassificationPartitionedModel (a, partition);
364s  assert (class (cvModel), "ClassificationPartitionedModel");
364s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
364s  assert ({cvModel.X, cvModel.Y}, {x, y});
364s  assert (cvModel.NumObservations, 4);
364s  assert (cvModel.ModelParameters.NumNeighbors, 1);
364s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
364s  assert (cvModel.ModelParameters.Distance, "euclidean");
364s  assert (! cvModel.ModelParameters.Standardize);
364s ***** test
364s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
364s  y = ["a"; "a"; "b"; "b"];
364s  k = 2;
364s  a = fitcknn (x, y, "NumNeighbors" ,k);
364s  partition = cvpartition (numel (y), "LeaveOut");
364s  cvModel = ClassificationPartitionedModel (a, partition);
364s  assert (class (cvModel), "ClassificationPartitionedModel");
364s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
364s  assert ({cvModel.X, cvModel.Y}, {x, y});
364s  assert (cvModel.NumObservations, 4);
364s  assert (cvModel.ModelParameters.NumNeighbors, k);
364s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
364s  assert (cvModel.ModelParameters.Distance, "euclidean");
364s  assert (! cvModel.ModelParameters.Standardize);
364s ***** test
364s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
364s  y = {"a"; "a"; "b"; "b"};
364s  a = fitcnet (x, y, "IterationLimit", 50);
364s  cvModel = crossval (a, "KFold", 2);
364s  assert (class (cvModel), "ClassificationPartitionedModel");
364s  assert (cvModel.NumObservations, 4);
364s  assert (numel (cvModel.Trained), 2);
364s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
364s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
364s  assert (cvModel.KFold, 2);
364s ***** test
364s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
364s  y = {"a"; "a"; "b"; "b"};
364s  a = fitcnet (x, y, "LayerSizes", [5, 3]);
364s  cvModel = crossval (a, "LeaveOut", "on");
364s  assert (class (cvModel), "ClassificationPartitionedModel");
364s  assert ({cvModel.X, cvModel.Y}, {x, y});
364s  assert (cvModel.NumObservations, 4);
364s  assert (numel (cvModel.Trained), 4);
364s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
364s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
364s ***** test
364s  load fisheriris
364s  inds = ! strcmp (species, 'setosa');
364s  x = meas(inds, 3:4);
364s  y = grp2idx (species(inds));
364s  SVMModel = fitcsvm (x,y);
364s  CVMdl = crossval (SVMModel, "KFold", 5);
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (CVMdl.KFold == 5)
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
364s ***** test
364s  load fisheriris
364s  inds = ! strcmp (species, 'setosa');
364s  x = meas(inds, 3:4);
364s  y = grp2idx (species(inds));
364s  obj = fitcsvm (x, y);
364s  CVMdl = crossval (obj, "HoldOut", 0.2);
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
364s ***** test
364s  load fisheriris
364s  inds = ! strcmp (species, 'setosa');
364s  x = meas(inds, 3:4);
364s  y = grp2idx (species(inds));
364s  obj = fitcsvm (x, y);
364s  CVMdl = crossval (obj, "LeaveOut", 'on');
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
365s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
365s  ClassificationPartitionedModel ()
365s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
365s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
365s ***** error<ClassificationPartitionedModel: unsupported model type.> ...
365s  ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
365s  randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
366s ***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
366s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
366s  ones (4,1)), 'Holdout')
366s ***** test
366s  load fisheriris
366s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
366s  cvModel = crossval (a, "Kfold", 4);
366s  [label, score, cost] = kfoldPredict (cvModel);
366s  assert (class(cvModel), "ClassificationPartitionedModel");
366s  assert ({cvModel.X, cvModel.Y}, {meas, species});
366s  assert (cvModel.NumObservations, 150);
366s ***** # assert (label, {"b"; "b"; "a"; "a"});
366s ***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
366s ***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
366s ***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
366s ***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
366s ***** test
366s  x = ones(4, 11);
366s  y = {"a"; "a"; "b"; "b"};
366s  k = 3;
366s  a = fitcknn (x, y, "NumNeighbors", k);
366s  partition = cvpartition (numel (y), "LeaveOut");
366s  cvModel = ClassificationPartitionedModel (a, partition);
366s  [label, score, cost] = kfoldPredict (cvModel);
366s  assert (class(cvModel), "ClassificationPartitionedModel");
366s  assert ({cvModel.X, cvModel.Y}, {x, y});
366s  assert (cvModel.NumObservations, 4);
366s  assert (cvModel.ModelParameters.NumNeighbors, k);
366s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
366s  assert (cvModel.ModelParameters.Distance, "euclidean");
366s  assert (! cvModel.ModelParameters.Standardize);
366s  assert (label, {"b"; "b"; "a"; "a"});
366s  assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
366s           0.6667, 0.3333], 1e-4);
366s  assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
366s           0.3333, 0.6667], 1e-4);
366s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
366s  [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
366s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
366s  [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
369s 20 tests, 20 passed, 0 known failure, 0 skipped
369s [inst/Classification/ConfusionMatrixChart.m]
369s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ConfusionMatrixChart.m
369s ***** demo
369s  ## Create a simple ConfusionMatrixChart Object
369s 
369s  cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
369s  NormalizedValues = cm.NormalizedValues
369s  ClassLabels = cm.ClassLabels
369s ***** test
369s  hf = figure ("visible", "off");
369s  unwind_protect
369s    cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
369s    assert (isa (cm, "ConfusionMatrixChart"), true);
369s  unwind_protect_cleanup
369s    close (hf);
369s  end_unwind_protect
369s warning: using the gnuplot graphics toolkit is discouraged
369s 
369s The gnuplot graphics toolkit is not actively maintained and has a number
369s of limitations that are unlikely to be fixed.  Communication with gnuplot
369s uses a one-directional pipe and limited information is passed back to the
369s Octave interpreter so most changes made interactively in the plot window
369s will not be reflected in the graphics properties managed by Octave.  For
369s example, if the plot window is closed with a mouse click, Octave will not
369s be notified and will not update its internal list of open figure windows.
369s The qt toolkit is recommended instead.
370s 1 test, 1 passed, 0 known failure, 0 skipped
370s [inst/Classification/CompactClassificationDiscriminant.m]
370s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/CompactClassificationDiscriminant.m
370s ***** demo
370s  ## Create a discriminant analysis classifier and its compact version
370s  # and compare their size
370s 
370s  load fisheriris
370s  X = meas;
370s  Y = species;
370s 
370s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
370s  CMdl = crossval (Mdl)
370s ***** test
370s  load fisheriris
370s  x = meas;
370s  y = species;
370s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
370s  Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
370s  CMdl = compact (Mdl);
370s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
370s           0.092721, 0.115388, 0.055244, 0.032710; ...
370s           0.167514, 0.055244, 0.185188, 0.042665; ...
370s           0.038401, 0.032710, 0.042665, 0.041882];
370s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
370s        5.9360, 2.7700, 4.2600, 1.3260; ...
370s        6.5880, 2.9740, 5.5520, 2.0260];
370s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
370s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
370s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
370s  assert (class (CMdl), "CompactClassificationDiscriminant");
370s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
370s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
370s  assert (CMdl.ClassNames, unique (species))
370s  assert (CMdl.Sigma, sigma, 1e-6)
370s  assert (CMdl.Mu, mu, 1e-14)
370s  assert (CMdl.LogDetSigma, -9.9585, 1e-4)
370s  assert (CMdl.PredictorNames, PredictorNames)
370s ***** test
370s  load fisheriris
370s  x = meas;
370s  y = species;
370s  Mdl = fitcdiscr (x, y, "Gamma", 0.5);
370s  CMdl = compact (Mdl);
370s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
370s           0.046361, 0.115388, 0.027622, 0.016355; ...
370s           0.083757, 0.027622, 0.185188, 0.021333; ...
370s           0.019201, 0.016355, 0.021333, 0.041882];
370s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
370s        5.9360, 2.7700, 4.2600, 1.3260; ...
370s        6.5880, 2.9740, 5.5520, 2.0260];
370s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
370s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
370s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
370s  assert (class (CMdl), "CompactClassificationDiscriminant");
370s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
370s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
370s  assert (CMdl.ClassNames, unique (species))
370s  assert (CMdl.Sigma, sigma, 1e-6)
370s  assert (CMdl.Mu, mu, 1e-14)
370s  assert (CMdl.LogDetSigma, -8.6884, 1e-4)
370s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
370s  CompactClassificationDiscriminant (1)
370s ***** test
370s  load fisheriris
370s  x = meas;
370s  y = species;
370s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
370s  CMdl = compact (Mdl);
370s  [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
370s  assert (label, {'versicolor'})
370s  assert (score, [0, 0.9999, 0.0001], 1e-4)
370s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
370s  [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
370s  assert (label, {'versicolor'})
370s  assert (score, [0, 0.6368, 0.3632], 1e-4)
370s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
370s ***** test
370s  load fisheriris
370s  x = meas;
370s  y = species;
370s  xc = [min(x); mean(x); max(x)];
370s  Mdl = fitcdiscr (x, y);
370s  CMdl = compact (Mdl);
370s  [label, score, cost] = predict (CMdl, xc);
370s  l = {'setosa'; 'versicolor'; 'virginica'};
370s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
370s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
370s  assert (label, l)
370s  assert (score, s, 1e-4)
370s  assert (cost, c, 1e-4)
370s ***** shared MODEL
370s  X = rand (10,2);
370s  Y = [ones(5,1);2*ones(5,1)];
370s  MODEL = compact (ClassificationDiscriminant (X, Y));
370s ***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
370s  predict (MODEL)
370s ***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
370s  predict (MODEL, [])
370s ***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
370s  predict (MODEL, 1)
370s ***** test
370s  load fisheriris
370s  model = fitcdiscr (meas, species);
370s  x = mean (meas);
370s  y = {'versicolor'};
370s  L = loss (model, x, y);
370s  assert (L, 0)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6];
370s  y = {'A'; 'B'; 'A'};
370s  model = fitcdiscr (x, y, "Gamma", 0.4);
370s  x_test = [1, 6; 3, 3];
370s  y_test = {'A'; 'B'};
370s  L = loss (model, x_test, y_test);
370s  assert (L, 0.3333, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6; 7, 8];
370s  y = ['1'; '2'; '3'; '1'];
370s  model = fitcdiscr (x, y, "gamma" , 0.5);
370s  x_test = [3, 3];
370s  y_test = ['1'];
370s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
370s  assert (L, 0.2423, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6; 7, 8];
370s  y = ['1'; '2'; '3'; '1'];
370s  model = fitcdiscr (x, y, "gamma" , 0.5);
370s  x_test = [3, 3; 5, 7];
370s  y_test = ['1'; '2'];
370s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
370s  assert (L, 0.3333, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6; 7, 8];
370s  y = ['1'; '2'; '3'; '1'];
370s  model = fitcdiscr (x, y, "gamma" , 0.5);
370s  x_test = [3, 3; 5, 7];
370s  y_test = ['1'; '2'];
370s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
370s  assert (L, 0.5886, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6; 7, 8];
370s  y = ['1'; '2'; '3'; '1'];
370s  model = fitcdiscr (x, y, "gamma" , 0.5);
370s  x_test = [3, 3; 5, 7];
370s  y_test = ['1'; '2'];
370s  W = [1; 2];
370s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
370s  assert (L, 0.5107, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6];
370s  y = {'A'; 'B'; 'A'};
370s  model = fitcdiscr (x, y, "gamma" , 0.5);
370s  x_with_nan = [1, 2; NaN, 4];
370s  y_test = {'A'; 'B'};
370s  L = loss (model, x_with_nan, y_test);
370s  assert (L, 0.3333, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6];
370s  y = {'A'; 'B'; 'A'};
370s  model = fitcdiscr (x, y);
370s  x_with_nan = [1, 2; NaN, 4];
370s  y_test = {'A'; 'B'};
370s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
370s  assert (isnan (L))
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6];
370s  y = {'A'; 'B'; 'A'};
370s  model = fitcdiscr (x, y);
370s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
370s  L = loss (model, x, y, 'LossFun', customLossFun);
370s  assert (L, 0.8889, 1e-4)
370s ***** test
370s  x = [1, 2; 3, 4; 5, 6];
370s  y = [1; 2; 1];
370s  model = fitcdiscr (x, y);
370s  L = loss (model, x, y, 'LossFun', 'classiferror');
370s  assert (L, 0.3333, 1e-4)
370s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
370s  loss (MODEL)
370s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
370s  loss (MODEL, ones (4,2))
370s ***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
370s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
370s ***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
370s  loss (MODEL, ones (4,2), ones (3,1))
370s ***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
370s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
370s ***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
370s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
370s  load fisheriris
370s  mdl = fitcdiscr (meas, species);
370s  X = mean (meas);
370s  Y = {'versicolor'};
370s  m = margin (mdl, X, Y);
370s  assert (m, 1, 1e-6)
370s ***** test
370s  X = [1, 2; 3, 4; 5, 6];
370s  Y = [1; 2; 1];
370s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
370s  m = margin (mdl, X, Y);
370s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
370s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
370s  margin (MODEL)
370s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
370s  margin (MODEL, ones (4,2))
370s ***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
370s  margin (MODEL, ones (4,2), ones (3,1))
370s 28 tests, 28 passed, 0 known failure, 0 skipped
370s [inst/Classification/ClassificationSVM.m]
370s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationSVM.m
370s ***** demo
370s  ## Create a Support Vector Machine classifier and determine margin for test
370s  ## data.
370s  load fisheriris
370s  rng(1);  ## For reproducibility
370s 
370s  ## Select indices of the non-setosa species
370s  inds = !strcmp(species, 'setosa');
370s 
370s   ## Select features and labels for non-setosa species
370s  X = meas(inds, 3:4);
370s  Y = grp2idx(species(inds));
370s 
370s  ##  Convert labels to +1 and -1
370s  unique_classes = unique(Y);
370s  Y(Y == unique_classes(1)) = -1;
370s  Y(Y == unique_classes(2)) = 1;
370s 
370s  ## Partition data for training and testing
370s  cv = cvpartition(Y, 'HoldOut', 0.15);
370s  X_train = X(training(cv), :);
370s  Y_train = Y(training(cv));
370s  X_test = X(test(cv), :);
370s  Y_test = Y(test(cv));
370s 
370s  ## Train the SVM model
370s  CVSVMModel = fitcsvm(X_train, Y_train);
370s 
370s  ## Calculate margins
370s  m = margin(CVSVMModel, X_test, Y_test);
370s  disp(m);
370s ***** demo
370s  ## Create a Support Vector Machine classifier and determine loss for test
370s  ## data.
370s  load fisheriris
370s  rng(1);  ## For reproducibility
370s 
370s   ## Select indices of the non-setosa species
370s  inds = !strcmp(species, 'setosa');
370s 
370s   ## Select features and labels for non-setosa species
370s  X = meas(inds, 3:4);
370s  Y = grp2idx(species(inds));
370s 
370s  ##  Convert labels to +1 and -1
370s  unique_classes = unique(Y);
370s  Y(Y == unique_classes(1)) = -1;
370s  Y(Y == unique_classes(2)) = 1;
370s 
370s  ## Randomly partition the data into training and testing sets
370s  cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training
370s 
370s  X_train = X(training(cv), :);
370s  Y_train = Y(training(cv));
370s 
370s  X_test = X(test(cv), :);
370s  Y_test = Y(test(cv));
370s 
370s  ## Train the SVM model
370s  SVMModel = fitcsvm(X_train, Y_train);
370s 
370s  ## Calculate loss
370s 
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
370s  L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
370s ***** test
370s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
370s  3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
370s  y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
370s  a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
370s  assert (class (a), "ClassificationSVM");
370s  assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
370s  assert ({a.X, a.Y}, {x, y})
370s  assert (a.NumObservations, 5)
370s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
370s  assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
370s ***** test
370s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
370s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
370s  a = ClassificationSVM (x, y);
370s  assert (class (a), "ClassificationSVM");
370s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
370s  assert (a.ModelParameters.BoxConstraint, 1)
370s  assert (a.ClassNames, [1; -1])
370s  assert (a.ModelParameters.KernelOffset, 0)
370s ***** test
370s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
370s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
370s  a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
370s  "KernelOffset", 2);
370s  assert (class (a), "ClassificationSVM");
370s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
370s  assert (a.ModelParameters.BoxConstraint, 2)
370s  assert (a.ModelParameters.KernelOffset, 2)
370s ***** test
370s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
370s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
370s  a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
370s  "PolynomialOrder", 3);
370s  assert (class (a), "ClassificationSVM");
370s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
370s  assert (a.ModelParameters.PolynomialOrder, 3)
370s ***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
370s ***** error<ClassificationSVM: too few input arguments.> ...
370s  ClassificationSVM (ones(10,2))
370s ***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
370s  ClassificationSVM (ones(10,2), ones (5,1))
370s ***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
370s ***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
370s ***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
370s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
370s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
370s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
370s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", {1})
370s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
370s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
370s  ClassificationSVM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
370s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
370s  ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
370s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
370s  ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
370s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
370s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
370s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
370s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
370s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
370s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
370s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
370s ***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
370s ***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
370s ***** error<ClassificationSVM: unsupported Kernel function.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
370s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
370s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
370s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
370s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
370s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
370s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
370s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
370s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
370s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
370s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
370s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
370s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
370s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
370s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
370s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
370s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
370s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
370s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
370s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
370s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
370s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
370s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
370s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
370s ***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
370s ***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
370s  ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
370s ***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
370s  ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
370s ***** error<ClassificationSVM: invalid values in X.> ...
370s  ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
370s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
370s  ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
370s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
370s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
370s ***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
370s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
370s ***** shared x, y, x_train, x_test, y_train, y_test, objST
370s  load fisheriris
370s  inds = ! strcmp (species, 'setosa');
370s  x = meas(inds, 3:4);
370s  y = grp2idx (species(inds));
370s ***** test
370s  xc = [min(x); mean(x); max(x)];
370s  obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
370s  assert (isempty (obj.Alpha), true)
370s  assert (sum (obj.IsSupportVector), numel (obj.Beta))
370s  [label, score] = predict (obj, xc);
370s  assert (label, [1; 2; 2]);
370s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
370s  assert (score(:,1), -score(:,2), eps)
370s  obj = fitPosterior (obj);
370s  [label, probs] = predict (obj, xc);
370s  assert (probs(:,2), [0.97555; 0.428164; 0.030385], 3e-2);
370s  assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
371s ***** test
371s  obj = fitcsvm (x, y);
371s  assert (isempty (obj.Beta), true)
371s  assert (sum (obj.IsSupportVector), numel (obj.Alpha))
371s  assert (numel (obj.Alpha), 24)
371s  assert (obj.Bias, -14.415, 1e-3)
371s  xc = [min(x); mean(x); max(x)];
371s  label = predict (obj, xc);
371s  assert (label, [1; 2; 2]);
371s ***** error<ClassificationSVM.predict: too few input arguments.> ...
371s  predict (ClassificationSVM (ones (40,2), ones (40,1)))
371s ***** error<ClassificationSVM.predict: XC is empty.> ...
371s  predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
371s ***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
371s  predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
371s ***** test
371s  objST = fitcsvm (x, y);
371s  objST.ScoreTransform = "a";
371s ***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
371s  [labels, scores] = predict (objST, x);
371s ***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
371s  [labels, scores] = resubPredict (objST);
371s ***** test
371s  rand ("seed", 1);
371s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
371s                        'Tolerance', 1e-7);
371s  obj = CVSVMModel.Trained{1};
371s  testInds = test (CVSVMModel.Partition);
371s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
371s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
371s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
371s  computed_margin = margin (obj, x(testInds,:), y(testInds,:));
371s  assert (computed_margin, expected_margin, 1e-4);
371s ***** error<ClassificationSVM.margin: too few input arguments.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
371s ***** error<ClassificationSVM.margin: too few input arguments.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
371s ***** error<ClassificationSVM.margin: X is empty.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
371s ***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
371s ***** error<ClassificationSVM.margin: Y is empty.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
371s ***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
371s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
371s ***** test
371s  rand ("seed", 1);
371s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
371s  obj = CVSVMModel.Trained{1};
371s  testInds = test (CVSVMModel.Partition);
371s  L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
371s  L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
371s  L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
371s  L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
371s  L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
371s  L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
371s  assert (L1, 2.8711, 1e-4);
371s  assert (L2, 0.5333, 1e-4);
371s  assert (L3, 10.9685, 1e-4);
371s  assert (L4, 1.9827, 1e-4);
371s  assert (L5, 1.5849, 1e-4);
371s  assert (L6, 7.6739, 1e-4);
371s ***** error<ClassificationSVM.loss: too few input arguments.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
371s ***** error<ClassificationSVM.loss: too few input arguments.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
371s ***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones(2,1), "LossFun")
371s ***** error<ClassificationSVM.loss: X is empty.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
371s ***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
371s ***** error<ClassificationSVM.loss: Y is empty.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
371s ***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
371s ***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "LossFun", 1)
371s ***** error<ClassificationSVM.loss: unsupported Loss function.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "LossFun", "some")
371s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "Weights", ['a','b'])
371s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "Weights", 'a')
371s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "Weights", [1,2,3])
371s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "Weights", 3)
371s ***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
371s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
371s  ones (2,1), "some", "some")
371s ***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
371s ***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
371s ***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
371s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
371s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
371s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
371s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
371s ***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
371s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
371s ***** test
371s  SVMModel = fitcsvm (x, y);
371s  CVMdl = crossval (SVMModel, "KFold", 5);
371s  assert (class (CVMdl), "ClassificationPartitionedModel")
371s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
371s  assert (CVMdl.KFold == 5)
371s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
371s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
371s ***** test
371s  obj = fitcsvm (x, y);
371s  CVMdl = crossval (obj, "HoldOut", 0.2);
371s  assert (class (CVMdl), "ClassificationPartitionedModel")
371s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
371s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
371s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
371s ***** test
371s  obj = fitcsvm (x, y);
371s  CVMdl = crossval (obj, "LeaveOut", 'on');
371s  assert (class (CVMdl), "ClassificationPartitionedModel")
371s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
371s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
371s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
372s ***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
372s ***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
372s  "KFold", 5, "leaveout", 'on')
372s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
372s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
372s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
372s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
372s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
372s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
372s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
372s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
372s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
372s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
372s ***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
372s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
372s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
372s ***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
372s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
372s 115 tests, 115 passed, 0 known failure, 0 skipped
372s [inst/Classification/ClassificationKNN.m]
373s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationKNN.m
373s ***** demo
373s  ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
373s  ## Evaluate some model predictions on new data.
373s 
373s  load fisheriris
373s  x = meas;
373s  y = species;
373s  xc = [min(x); mean(x); max(x)];
373s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
373s  [label, score, cost] = predict (obj, xc)
373s ***** demo
373s  load fisheriris
373s  x = meas;
373s  y = species;
373s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
373s 
373s  ## Create a cross-validated model
373s  CVMdl = crossval (obj)
373s ***** demo
373s  load fisheriris
373s  x = meas;
373s  y = species;
373s  covMatrix = cov (x);
373s 
373s  ## Fit the k-NN model using the 'mahalanobis' distance
373s  ## and the custom covariance matrix
373s  obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
373s  'Cov', covMatrix);
373s 
373s  ## Create a partition model using cvpartition
373s  Partition = cvpartition (size (x, 1), 'kfold', 12);
373s 
373s  ## Create cross-validated model using 'cvPartition' name-value argument
373s  CVMdl = crossval (obj, 'cvPartition', Partition)
373s 
373s  ## Access the trained model from first fold of cross-validation
373s  CVMdl.Trained{1}
373s ***** demo
373s  X = [1, 2; 3, 4; 5, 6];
373s  Y = {'A'; 'B'; 'A'};
373s  model = fitcknn (X, Y);
373s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
373s  ## Calculate loss using custom loss function
373s  L = loss (model, X, Y, 'LossFun', customLossFun)
373s ***** demo
373s  X = [1, 2; 3, 4; 5, 6];
373s  Y = {'A'; 'B'; 'A'};
373s  model = fitcknn (X, Y);
373s  ## Calculate loss using 'mincost' loss function
373s  L = loss (model, X, Y, 'LossFun', 'mincost')
373s ***** demo
373s  X = [1, 2; 3, 4; 5, 6];
373s  Y = ['1'; '2'; '3'];
373s  model = fitcknn (X, Y);
373s  X_test = [3, 3; 5, 7];
373s  Y_test = ['1'; '2'];
373s  ## Specify custom Weights
373s  W = [1; 2];
373s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
373s ***** demo
373s  load fisheriris
373s  mdl = fitcknn (meas, species);
373s  X = mean (meas);
373s  Y = {'versicolor'};
373s  m = margin (mdl, X, Y)
373s ***** demo
373s  X = [1, 2; 4, 5; 7, 8; 3, 2];
373s  Y = [2; 1; 3; 2];
373s  ## Train the model
373s  mdl = fitcknn (X, Y);
373s  ## Specify Vars and Labels
373s  Vars = 1;
373s  Labels = 2;
373s  ## Calculate partialDependence
373s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
373s ***** demo
373s  X = [1, 2; 4, 5; 7, 8; 3, 2];
373s  Y = [2; 1; 3; 2];
373s  ## Train the model
373s  mdl = fitcknn (X, Y);
373s  ## Specify Vars and Labels
373s  Vars = 1;
373s  Labels = 1;
373s  queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
373s  ## Calculate partialDependence using queryPoints
373s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
373s  queryPoints)
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y);
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  k = 10;
373s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = ones (4, 11);
373s  y = ["a"; "a"; "b"; "b"];
373s  k = 10;
373s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  k = 10;
373s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  k = 10;
373s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  weights = ones (4,1);
373s  a = ClassificationKNN (x, y, "Standardize", 1);
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.Standardize}, {true})
373s  assert ({a.Sigma}, {std(x, [], 1)})
373s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  weights = ones (4,1);
373s  a = ClassificationKNN (x, y, "Standardize", false);
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.Standardize}, {false})
373s  assert ({a.Sigma}, {[]})
373s  assert ({a.Mu}, {[]})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  s = ones (1, 3);
373s  a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.DistParameter}, {s})
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
373s  assert (class (a), "ClassificationKNN");
373s  assert (a.DistParameter, 5)
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
373s                         "NSMethod", "exhaustive");
373s  assert (class (a), "ClassificationKNN");
373s  assert (a.DistParameter, 5)
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
373s  assert (class (a), "ClassificationKNN");
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
373s  assert ({a.BucketSize}, {20})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y, "IncludeTies", true);
373s  assert (class (a), "ClassificationKNN");
373s  assert (a.IncludeTies, true);
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y);
373s  assert (class (a), "ClassificationKNN");
373s  assert (a.IncludeTies, false);
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  a = ClassificationKNN (x, y);
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Prior, [0.5; 0.5])
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  prior = [0.5; 0.5];
373s  a = ClassificationKNN (x, y, "Prior", "empirical");
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Prior, prior)
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "a"; "b"];
373s  prior = [0.75; 0.25];
373s  a = ClassificationKNN (x, y, "Prior", "empirical");
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Prior, prior)
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "a"; "b"];
373s  prior = [0.5; 0.5];
373s  a = ClassificationKNN (x, y, "Prior", "uniform");
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Prior, prior)
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  cost = eye (2);
373s  a = ClassificationKNN (x, y, "Cost", cost);
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Cost, [1, 0; 0, 1])
373s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
373s  y = ["a"; "a"; "b"; "b"];
373s  cost = eye (2);
373s  a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
373s  assert (class (a), "ClassificationKNN")
373s  assert (a.Cost, [1, 0; 0, 1])
373s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
373s  assert ({a.BucketSize}, {50})
373s ***** test
373s  x = [1, 2; 3, 4; 5,6; 5, 8];
373s  y = {'9'; '9'; '6'; '7'};
373s  a = ClassificationKNN (x, y);
373s  assert (a.Prior, [0.25; 0.25; 0.5])
373s ***** test
373s  load fisheriris
373s  x = meas;
373s  y = species;
373s  ClassNames = {'setosa', 'versicolor', 'virginica'};
373s  a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
373s  assert (a.ClassNames, ClassNames')
373s ***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
373s ***** error<ClassificationKNN: too few input arguments.> ...
373s  ClassificationKNN (ones(4, 1))
373s ***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
373s  ClassificationKNN (ones (4,2), ones (1,4))
373s ***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
373s  ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
373s ***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
373s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
373s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
373s ***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
373s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
373s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
373s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
373s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
373s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
373s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", {1})
373s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
373s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
373s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
373s  ClassificationKNN (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
373s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
373s  ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
373s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
373s  ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
373s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
373s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
373s ***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
373s ***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
373s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
373s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
373s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
373s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
373s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
373s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
373s ***** error<ClassificationKNN: unsupported distance metric.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
373s ***** error<ClassificationKNN: invalid function handle for distance metric.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
373s                     @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
373s ***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
373s                     @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
373s ***** error<ClassificationKNN: invalid distance metric.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
373s ***** error<ClassificationKNN: invalid distance metric.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
373s ***** error<ClassificationKNN: invalid distance metric.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
373s ***** error<ClassificationKNN: function handle for distance weight must return the> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
373s ***** error<ClassificationKNN: invalid distance weight.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
373s ***** error<ClassificationKNN: invalid distance weight.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
373s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
373s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
373s ***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
373s ***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
373s ***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
373s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
373s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
373s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
373s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
373s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
373s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
373s ***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
373s ***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
373s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
373s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
373s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
373s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
373s ***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
373s ***** error<ClassificationKNN: invalid values in X.> ...
373s  ClassificationKNN ([1;2;3;'a';4], ones (5,1))
373s ***** error<ClassificationKNN: invalid values in X.> ...
373s  ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
373s ***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
373s ***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
373s ***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
373s ***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
373s ***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
373s ***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
373s ***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
373s ***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
373s ***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
373s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
373s ***** shared x, y
373s  load fisheriris
373s  x = meas;
373s  y = species;
373s ***** test
373s  xc = [min(x); mean(x); max(x)];
373s  obj = fitcknn (x, y, "NumNeighbors", 5);
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"setosa"; "versicolor"; "virginica"})
373s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
373s  assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
373s ***** test
373s  xc = [min(x); mean(x); max(x)];
373s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"; "versicolor"; "virginica"})
373s  assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
373s  assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
373s ***** test
373s  xc = [min(x); mean(x); max(x)];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
373s  [l, s, c] = predict (obj, xc);
373s  assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
373s  assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
373s ***** test
373s  xc = [min(x); mean(x); max(x)];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"setosa"; "versicolor"; "virginica"})
373s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
373s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
373s ***** test
373s  xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
373s          5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
373s  obj = fitcknn (x, y, "NumNeighbors", 5);
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
373s  assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
373s  assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 5);
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 0.6, 0.4], 1e-4)
373s  assert (c, [1, 0.4, 0.6], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 0.5, 0.5], 1e-4)
373s  assert (c, [1, 0.5, 0.5], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"setosa"})
373s  assert (s, [0.9, 0.1, 0], 1e-4)
373s  assert (c, [0.1, 0.9, 1], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
373s  assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"setosa"})
373s  assert (s, [0.8, 0.2, 0], 1e-4)
373s  assert (c, [0.2, 0.8, 1], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 1, 0], 1e-4)
373s  assert (c, [1, 0, 1], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 0.7, 0.3], 1e-4)
373s  assert (c, [1, 0.3, 0.7], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 0.6, 0.4], 1e-4)
373s  assert (c, [1, 0.4, 0.6], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"virginica"})
373s  assert (s, [0, 0.1, 0.9], 1e-4)
373s  assert (c, [1, 0.9, 0.1], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"virginica"})
373s  assert (s, [0, 0.1, 0.9], 1e-4)
373s  assert (c, [1, 0.9, 0.1], 1e-4)
373s ***** test
373s  xc = [5, 3, 5, 1.45];
373s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
373s  [l, s, c] = predict (obj, xc);
373s  assert (l, {"versicolor"})
373s  assert (s, [0, 1, 0], 1e-4)
373s  assert (c, [1, 0, 1], 1e-4)
374s ***** test
374s  xc = [5, 3, 5, 1.45];
374s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
374s  [l, s, c] = predict (obj, xc);
374s  assert (l, {"setosa"})
374s  assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
374s  assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
374s ***** test
374s  xc = [5, 3, 5, 1.45];
374s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
374s  [l, s, c] = predict (obj, xc);
374s  assert (l, {"setosa"})
374s  assert (s, [0.8, 0.2, 0], 1e-4)
374s  assert (c, [0.2, 0.8, 1], 1e-4)
374s ***** test
374s  xc = [min(x); mean(x); max(x)];
374s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
374s  [l, s, c] = predict (obj, xc);
374s  assert (l, {"setosa"; "versicolor"; "virginica"})
374s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
374s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
374s ***** test
374s  xc = [min(x); mean(x); max(x)];
374s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
374s  [l, s, c] = predict (obj, xc);
374s  assert (l, {"setosa";"setosa";"setosa"})
374s  assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
374s  assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
374s ***** error<ClassificationKNN.predict: too few input arguments.> ...
374s  predict (ClassificationKNN (ones (4,2), ones (4,1)))
374s ***** error<ClassificationKNN.predict: XC is empty.> ...
374s  predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
374s ***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
374s  predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
374s ***** test
374s  load fisheriris
374s  model = fitcknn (meas, species, 'NumNeighbors', 5);
374s  X = mean (meas);
374s  Y = {'versicolor'};
374s  L = loss (model, X, Y);
374s  assert (L, 0)
374s ***** test
374s  load fisheriris
374s  model = fitcknn (meas, species, 'NumNeighbors', 5);
374s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
374s  assert (L, 0.1413, 1e-4)
374s ***** test
374s  load fisheriris
374s  model = fitcknn (meas, species);
374s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
374s  assert (L, 0.1269, 1e-4)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = {'A'; 'B'; 'A'};
374s  model = fitcknn (X, Y);
374s  X_test = [1, 6; 3, 3];
374s  Y_test = {'A'; 'B'};
374s  L = loss (model, X_test, Y_test);
374s  assert (abs (L - 0.6667) > 1e-5)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = {'A'; 'B'; 'A'};
374s  model = fitcknn (X, Y);
374s  X_with_nan = [1, 2; NaN, 4];
374s  Y_test = {'A'; 'B'};
374s  L = loss (model, X_with_nan, Y_test);
374s  assert (abs (L - 0.3333) < 1e-4)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = {'A'; 'B'; 'A'};
374s  model = fitcknn (X, Y);
374s  X_with_nan = [1, 2; NaN, 4];
374s  Y_test = {'A'; 'B'};
374s  L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
374s  assert (isnan (L))
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = {'A'; 'B'; 'A'};
374s  model = fitcknn (X, Y);
374s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
374s  L = loss (model, X, Y, 'LossFun', customLossFun);
374s  assert (L, 0)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = [1; 2; 1];
374s  model = fitcknn (X, Y);
374s  L = loss (model, X, Y, 'LossFun', 'classiferror');
374s  assert (L, 0)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = [true; false; true];
374s  model = fitcknn (X, Y);
374s  L = loss (model, X, Y, 'LossFun', 'binodeviance');
374s  assert (abs (L - 0.1269) < 1e-4)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = ['1'; '2'; '1'];
374s  model = fitcknn (X, Y);
374s  L = loss (model, X, Y, 'LossFun', 'classiferror');
374s  assert (L, 0)
374s ***** test
374s  X = [1, 2; 3, 4; 5, 6];
374s  Y = ['1'; '2'; '3'];
374s  model = fitcknn (X, Y);
374s  X_test = [3, 3];
374s  Y_test = ['1'];
374s  L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
374s  assert (L, 1)
375s ***** test
375s  X = [1, 2; 3, 4; 5, 6];
375s  Y = ['1'; '2'; '3'];
375s  model = fitcknn (X, Y);
375s  X_test = [3, 3; 5, 7];
375s  Y_test = ['1'; '2'];
375s  L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
375s  assert (L, 1)
375s ***** test
375s  X = [1, 2; 3, 4; 5, 6];
375s  Y = ['1'; '2'; '3'];
375s  model = fitcknn (X, Y);
375s  X_test = [3, 3; 5, 7];
375s  Y_test = ['1'; '2'];
375s  L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
375s  assert (L, 1)
375s ***** test
375s  X = [1, 2; 3, 4; 5, 6];
375s  Y = ['1'; '2'; '3'];
375s  model = fitcknn (X, Y);
375s  X_test = [3, 3; 5, 7];
375s  Y_test = ['1'; '2'];
375s  W = [1; 2];
375s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
375s  assert (abs (L - 0.6931) < 1e-4)
375s ***** error<ClassificationKNN.loss: too few input arguments.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)))
375s ***** error<ClassificationKNN.loss: too few input arguments.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
375s ***** error<ClassificationKNN.loss: X is empty.> ...
375s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
375s ***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
375s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
375s ***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
375s         ones (4,1), 'LossFun')
375s ***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
375s ***** error<ClassificationKNN.loss: invalid loss function.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
375s         ones (4,1), 'LossFun', 'a')
375s ***** error<ClassificationKNN.loss: invalid Weights.> ...
375s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
375s         ones (4,1), 'Weights', 'w')
375s ***** test
375s  load fisheriris
375s  mdl = fitcknn (meas, species, 'NumNeighbors', 5);
375s  X = mean (meas);
375s  Y = {'versicolor'};
375s  m = margin (mdl, X, Y);
375s  assert (m, 1)
375s ***** test
375s  X = [1, 2; 3, 4; 5, 6];
375s  Y = [1; 2; 3];
375s  mdl = fitcknn (X, Y);
375s  m = margin (mdl, X, Y);
375s  assert (m, [1; 1; 1])
375s ***** test
375s  X = [7, 8; 9, 10];
375s  Y = ['1'; '2'];
375s  mdl = fitcknn (X, Y);
375s  m = margin (mdl, X, Y);
375s  assert (m, [1; 1])
375s ***** test
375s  X = [11, 12];
375s  Y = {'1'};
375s  mdl = fitcknn (X, Y);
375s  m = margin (mdl, X, Y);
375s  assert (isnan (m))
375s ***** test
375s  X = [1, 2; 3, 4; 5, 6];
375s  Y = [1; 2; 3];
375s  mdl = fitcknn (X, Y);
375s  X1 = [15, 16];
375s  Y1 = [1];
375s  m = margin (mdl, X1, Y1);
375s  assert (m, -1)
375s ***** error<ClassificationKNN.margin: too few input arguments.> ...
375s  margin (ClassificationKNN (ones (4,2), ones (4,1)))
375s ***** error<ClassificationKNN.margin: too few input arguments.> ...
375s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
375s ***** error<ClassificationKNN.margin: X is empty.> ...
375s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
375s ***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
375s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
375s ***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
375s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
375s ***** shared X, Y, mdl
375s  X = [1, 2; 4, 5; 7, 8; 3, 2];
375s  Y = [2; 1; 3; 2];
375s  mdl = fitcknn (X, Y);
375s ***** test
375s  Vars = 1;
375s  Labels = 2;
375s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
375s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
375s  0.5000, 0.5000];
375s  assert (pd, pdm)
375s ***** test
375s  Vars = 1;
375s  Labels = 2;
375s  [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
375s  'NumObservationsToSample', 5);
375s  pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
375s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
375s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
375s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
375s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
375s  assert (abs (pdm - pd) < 1)
376s ***** test
376s  Vars = 1;
376s  Labels = 2;
376s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
376s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
376s  0.5000, 0.5000];
376s  assert (pd, pdm)
377s ***** test
377s  Vars = [1, 2];
377s  Labels = 1;
377s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
377s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
377s                             queryPoints, 'UseParallel', true);
377s  pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
377s  assert (pd, pdm)
377s ***** test
377s  Vars = 1;
377s  Labels = [1; 2];
377s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
377s  pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
377s  0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
377s  0.5000, 0.5000, 0.5000];
377s  assert (pd, pdm)
377s ***** test
377s  Vars = [1, 2];
377s  Labels = [1; 2];
377s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
377s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
377s  pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
377s  pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
377s  pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
377s  assert (pd, pdm)
378s ***** test
378s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
378s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
378s  X = [X1, double(X2)];
378s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
378s  mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
378s  Vars = 1;
378s  Labels = 1;
378s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
378s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
378s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
378s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
378s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
378s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
378s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
378s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
378s  0.7500, 0.7500, 0.7500];
378s  assert (pd, pdm)
379s ***** test
379s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
379s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
379s  X = [X1, double(X2)];
379s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
379s  predictorNames = {'Feature1', 'Feature2'};
379s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
379s  Vars = 'Feature1';
379s  Labels = 1;
379s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
379s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
379s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
379s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
379s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
379s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
379s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
379s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
379s  0.7500, 0.7500, 0.7500];
379s  assert (pd, pdm)
380s ***** test
380s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
380s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
380s  X = [X1, double(X2)];
380s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
380s  predictorNames = {'Feature1', 'Feature2'};
380s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
380s  new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
380s  new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
380s  new_X = [new_X1, double(new_X2)];
380s  Vars = 'Feature1';
380s  Labels = 1;
380s  [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
380s  pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
380s  0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
380s  1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
380s  1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
380s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
380s  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];
380s  assert (pd, pdm)
381s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
381s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
381s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
381s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
381s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
381s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
381s  ones (4,1), 'NumObservationsToSample')
381s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
381s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
381s  ones (4,1), 2)
381s ***** shared x, y, obj
381s  load fisheriris
381s  x = meas;
381s  y = species;
381s  covMatrix = cov (x);
381s  obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
381s       'mahalanobis', 'Cov', covMatrix);
381s ***** test
381s  CVMdl = crossval (obj);
381s  assert (class (CVMdl), "ClassificationPartitionedModel")
381s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
381s  assert (CVMdl.KFold == 10)
381s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
381s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
381s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
381s  assert (!CVMdl.ModelParameters.Standardize)
381s ***** test
381s  CVMdl = crossval (obj, "KFold", 5);
381s  assert (class (CVMdl), "ClassificationPartitionedModel")
381s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
381s  assert (CVMdl.KFold == 5)
381s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
381s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
381s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
381s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
381s ***** test
381s  obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
381s  CVMdl = crossval (obj, "HoldOut", 0.2);
381s  assert (class (CVMdl), "ClassificationPartitionedModel")
381s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
381s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
381s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
381s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
381s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
381s ***** test
381s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
381s  CVMdl = crossval (obj, "LeaveOut", 'on');
381s  assert (class (CVMdl), "ClassificationPartitionedModel")
381s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
381s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
381s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
381s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
381s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
382s ***** test
382s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
382s  partition = cvpartition (y, 'KFold', 3);
382s  CVMdl = crossval (obj, 'cvPartition', partition);
382s  assert (class (CVMdl), "ClassificationPartitionedModel")
382s  assert (CVMdl.KFold == 3)
382s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
382s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
382s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
382s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
382s ***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
382s ***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
382s ***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
382s ***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
382s ***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
382s ***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
382s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
382s 165 tests, 165 passed, 0 known failure, 0 skipped
382s [inst/Classification/CompactClassificationSVM.m]
382s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/CompactClassificationSVM.m
382s ***** demo
382s  ## Create a support vectors machine classifier and its compact version
382s  # and compare their size
382s 
382s  load fisheriris
382s  X = meas;
382s  Y = species;
382s 
382s  Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
382s  CMdl = crossval (Mdl)
382s ***** error<CompactClassificationSVM: invalid classification object.> ...
382s  CompactClassificationSVM (1)
382s ***** shared x, y, CMdl
382s  load fisheriris
382s  inds = ! strcmp (species, 'setosa');
382s  x = meas(inds, 3:4);
382s  y = grp2idx (species(inds));
382s ***** test
382s  xc = [min(x); mean(x); max(x)];
382s  Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
382s  CMdl = compact (Mdl);
382s  assert (isempty (CMdl.Alpha), true)
382s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
382s  [label, score] = predict (CMdl, xc);
382s  assert (label, [1; 2; 2]);
382s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
382s  assert (score(:,1), -score(:,2), eps)
382s ***** test
382s  Mdl = fitcsvm (x, y);
382s  CMdl = compact (Mdl);
382s  assert (isempty (CMdl.Beta), true)
382s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
382s  assert (numel (CMdl.Alpha), 24)
382s  assert (CMdl.Bias, -14.415, 1e-3)
382s  xc = [min(x); mean(x); max(x)];
382s  label = predict (CMdl, xc);
382s  assert (label, [1; 2; 2]);
382s ***** error<CompactClassificationSVM.predict: too few input arguments.> ...
382s  predict (CMdl)
382s ***** error<CompactClassificationSVM.predict: XC is empty.> ...
382s  predict (CMdl, [])
382s ***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
382s  predict (CMdl, 1)
382s ***** test
382s  CMdl.ScoreTransform = "a";
382s ***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
382s  [labels, scores] = predict (CMdl, x);
382s ***** test
382s  rand ("seed", 1);
382s  C = cvpartition (y, 'HoldOut', 0.15);
382s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
382s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
382s  CMdl = compact (Mdl);
382s  testInds = test (C);
382s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
382s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
382s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
382s  computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
382s  assert (computed_margin, expected_margin, 1e-4);
382s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
382s  margin (CMdl)
382s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
382s  margin (CMdl, zeros (2))
382s ***** error<CompactClassificationSVM.margin: X is empty.> ...
382s  margin (CMdl, [], 1)
382s ***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
382s  margin (CMdl, 1, 1)
382s ***** error<CompactClassificationSVM.margin: Y is empty.> ...
382s  margin (CMdl, [1, 2], [])
382s ***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
382s  margin (CMdl, [1, 2], [1; 2])
382s ***** test
382s  rand ("seed", 1);
382s  C = cvpartition (y, 'HoldOut', 0.15);
382s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
382s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
382s  CMdl = compact (Mdl);
382s  testInds = test (C);
382s  L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
382s  L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
382s  L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
382s  L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
382s  L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
382s  L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
382s  assert (L1, 2.8711, 1e-4);
382s  assert (L2, 0.5333, 1e-4);
382s  assert (L3, 10.9685, 1e-4);
382s  assert (L4, 1.9827, 1e-4);
382s  assert (L5, 1.5849, 1e-4);
382s  assert (L6, 7.6739, 1e-4);
382s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
382s  loss (CMdl)
382s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
382s  loss (CMdl, zeros (2))
382s ***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
382s  loss (CMdl, [1, 2], 1, "LossFun")
382s ***** error<CompactClassificationSVM.loss: X is empty.> ...
382s  loss (CMdl, [], zeros (2))
382s ***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
382s  loss (CMdl, 1, zeros (2))
382s ***** error<CompactClassificationSVM.loss: Y is empty.> ...
382s  loss (CMdl, [1, 2], [])
382s ***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
382s  loss (CMdl, [1, 2], [1; 2])
383s ***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
383s  loss (CMdl, [1, 2], 1, "LossFun", 1)
383s ***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
383s  loss (CMdl, [1, 2], 1, "LossFun", "some")
383s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
383s  loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
383s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
383s  loss (CMdl, [1, 2], 1, "Weights", 'a')
383s ***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
383s  loss (CMdl, [1, 2], 1, "Weights", [1, 2])
383s ***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
383s  loss (CMdl, [1, 2], 1, "some", "some")
383s 29 tests, 29 passed, 0 known failure, 0 skipped
383s [inst/Classification/CompactClassificationGAM.m]
383s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/CompactClassificationGAM.m
383s ***** demo
383s  ## Create a generalized additive model classifier and its compact version
383s  # and compare their size
383s 
383s  load fisheriris
383s  X = meas;
383s  Y = species;
383s 
383s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
383s  CMdl = crossval (Mdl)
383s ***** test
383s  Mdl = CompactClassificationGAM ();
383s  assert (class (Mdl), "CompactClassificationGAM")
383s ***** test
383s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
383s  y = [0; 0; 1; 1];
383s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
383s  Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
383s  CMdl = compact (Mdl);
383s  assert (class (CMdl), "CompactClassificationGAM");
383s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
383s  assert (CMdl.ClassNames, {'0'; '1'})
383s  assert (CMdl.PredictorNames, PredictorNames)
383s  assert (CMdl.BaseModel.Intercept, 0)
385s ***** test
385s  load fisheriris
385s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
385s  X = meas(inds, :);
385s  Y = species(inds, :)';
385s  Y = strcmp (Y, 'virginica')';
385s  Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
385s  CMdl = compact (Mdl);
385s  assert (class (CMdl), "CompactClassificationGAM");
385s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
385s  assert (CMdl.ClassNames, {'0'; '1'})
385s  assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
385s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
385s  assert (CMdl.ModelwInt.Intercept, 0)
392s ***** test
392s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
392s  Y = [0; 1; 0; 1; 1];
392s  Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
392s  CMdl = compact (Mdl);
392s  assert (class (CMdl), "CompactClassificationGAM");
392s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
392s  assert (CMdl.ClassNames, {'0'; '1'})
392s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
392s  assert (CMdl.Knots, [4, 4, 4])
392s  assert (CMdl.Order, [3, 3, 3])
392s  assert (CMdl.DoF, [7, 7, 7])
392s  assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
394s ***** error<CompactClassificationGAM: invalid classification object.> ...
394s  CompactClassificationGAM (1)
394s ***** test
394s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
394s  y = [1; 0; 1; 0; 1];
394s  Mdl = fitcgam (x, y, "interactions", "all");
394s  CMdl = compact (Mdl);
394s  l = {'0'; '0'; '0'; '0'; '0'};
394s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
394s       0.4259, 0.5741; 0.3760, 0.6240];
394s  [labels, scores] = predict (CMdl, x);
394s  assert (class (CMdl), "CompactClassificationGAM");
394s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
394s  assert (CMdl.ClassNames, {'1'; '0'})
394s  assert (CMdl.PredictorNames, {'x1', 'x2'})
394s  assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
394s  assert (labels, l)
394s  assert (scores, s, 1e-1)
398s ***** test
398s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
398s  y = [0; 0; 1; 1];
398s  interactions = [false, true, false; true, false, true; false, true, false];
398s  Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
398s  CMdl = compact (Mdl);
398s  [label, score] = predict (CMdl, x, "includeinteractions", true);
398s  l = {'0'; '0'; '1'; '1'};
398s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
398s  assert (class (CMdl), "CompactClassificationGAM");
398s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
398s  assert (CMdl.ClassNames, {'0'; '1'})
398s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
398s  assert (CMdl.ModelwInt.Intercept, 0)
398s  assert (label, l)
398s  assert (score, s, 1e-1)
405s ***** shared CMdl
405s  Mdl = fitcgam (ones (4,2), ones (4,1));
405s  CMdl = compact (Mdl);
406s ***** error<CompactClassificationGAM.predict: too few input arguments.> ...
406s  predict (CMdl)
406s ***** error<CompactClassificationGAM.predict: XC is empty.> ...
406s  predict (CMdl, [])
406s ***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
406s  predict (CMdl, 1)
406s 10 tests, 10 passed, 0 known failure, 0 skipped
406s [inst/Classification/ClassificationGAM.m]
406s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationGAM.m
406s ***** demo
406s  ## Train a GAM classifier for binary classification
406s  ## using specific data and plot the decision boundaries.
406s 
406s  ## Define specific data
406s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
406s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
406s  Y = [0; 0; 0; 0; 0; ...
406s      1; 1; 1; 1; 1];
406s 
406s  ## Train the GAM model
406s  obj = fitcgam (X, Y, "Interactions", "all")
406s 
406s  ## Create a grid of values for prediction
406s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
406s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
406s  [x1G, x2G] = meshgrid (x1, x2);
406s  XGrid = [x1G(:), x2G(:)];
406s  [labels, score] = predict (obj, XGrid);
406s ***** test
406s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
406s  y = [0; 0; 1; 1];
406s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
406s  a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
406s  assert (class (a), "ClassificationGAM");
406s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
406s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
406s  assert (a.ClassNames, {'0'; '1'})
406s  assert (a.PredictorNames, PredictorNames)
406s  assert (a.BaseModel.Intercept, 0)
409s ***** test
409s  load fisheriris
409s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
409s  X = meas(inds, :);
409s  Y = species(inds, :)';
409s  Y = strcmp (Y, 'virginica')';
409s  a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
409s  assert (class (a), "ClassificationGAM");
409s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
409s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
409s  assert (a.ClassNames, {'0'; '1'})
409s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
409s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
409s  assert (a.ModelwInt.Intercept, 0)
419s ***** test
419s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
419s  Y = [0; 1; 0; 1; 1];
419s  a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
419s  assert (class (a), "ClassificationGAM");
419s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
419s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
419s  assert (a.ClassNames, {'0'; '1'})
419s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
419s  assert (a.Knots, [4, 4, 4])
419s  assert (a.Order, [3, 3, 3])
419s  assert (a.DoF, [7, 7, 7])
419s  assert (a.BaseModel.Intercept, 0.4055, 1e-1)
419s ***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
419s ***** error<ClassificationGAM: too few input arguments.> ...
419s  ClassificationGAM (ones(4, 1))
419s ***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
419s  ClassificationGAM (ones (4,2), ones (1,4))
419s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
419s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
419s ***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
419s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
419s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
419s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
419s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
419s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
419s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", {1})
419s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
419s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
419s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
419s  ClassificationGAM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
419s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
419s  ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
419s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
419s  ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
419s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
419s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
419s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
419s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
419s ***** test
419s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
419s  y = [1; 0; 1; 0; 1];
419s  a = ClassificationGAM (x, y, "interactions", "all");
419s  l = {'0'; '0'; '0'; '0'; '0'};
419s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
419s       0.4259, 0.5741; 0.3760, 0.6240];
419s  [labels, scores] = predict (a, x);
419s  assert (class (a), "ClassificationGAM");
419s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
419s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
419s  assert (a.ClassNames, {'1'; '0'})
419s  assert (a.PredictorNames, {'x1', 'x2'})
419s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
419s  assert (labels, l)
419s  assert (scores, s, 1e-1)
422s ***** test
422s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
422s  y = [0; 0; 1; 1];
422s  interactions = [false, true, false; true, false, true; false, true, false];
422s  a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
422s  [label, score] = predict (a, x, "includeinteractions", true);
422s  l = {'0'; '0'; '1'; '1'};
422s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
422s  assert (class (a), "ClassificationGAM");
422s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
422s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
422s  assert (a.ClassNames, {'0'; '1'})
422s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
422s  assert (a.ModelwInt.Intercept, 0)
422s  assert (label, l)
422s  assert (score, s, 1e-1)
429s ***** error<ClassificationGAM.predict: too few input arguments.> ...
429s  predict (ClassificationGAM (ones (4,2), ones (4,1)))
430s ***** error<ClassificationGAM.predict: XC is empty.> ...
430s  predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
432s ***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
432s  predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
433s ***** shared x, y, obj
433s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6];
433s  y = [0; 0; 1; 1; 0];
433s  obj = fitcgam (x, y);
436s ***** test
436s  CVMdl = crossval (obj);
436s  assert (class (CVMdl), "ClassificationPartitionedModel")
436s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
436s  assert (CVMdl.KFold == 4)
436s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
436s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
436s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
436s warning: called from
436s     cvpartition at line 764 column 19
436s     crossval at line 792 column 9
436s     __test__ at line 3 column 2
436s     test at line 685 column 11
436s     /tmp/tmp.vpN5xdrKMr at line 158 column 2
436s 
451s ***** test
451s  CVMdl = crossval (obj, "KFold", 2);
451s  assert (class (CVMdl), "ClassificationPartitionedModel")
451s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
451s  assert (CVMdl.KFold == 2)
451s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
451s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
451s ***** test
451s  CVMdl = crossval (obj, "HoldOut", 0.2);
451s  assert (class (CVMdl), "ClassificationPartitionedModel")
451s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
451s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
451s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
452s ***** test
452s  partition = cvpartition (y, 'KFold', 3);
452s  CVMdl = crossval (obj, 'cvPartition', partition);
452s  assert (class (CVMdl), "ClassificationPartitionedModel")
452s  assert (CVMdl.KFold == 3)
452s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
452s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
452s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
452s warning: called from
452s     cvpartition at line 764 column 19
452s     __test__ at line 3 column 2
452s     test at line 685 column 11
452s     /tmp/tmp.vpN5xdrKMr at line 158 column 2
452s 
459s ***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
459s  crossval (obj, "kfold")
459s ***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
459s  crossval (obj, "kfold", 12, "holdout", 0.2)
459s ***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
459s  crossval (obj, "kfold", 'a')
459s ***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
459s  crossval (obj, "holdout", 2)
459s ***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
459s  crossval (obj, "leaveout", 1)
459s ***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
459s  crossval (obj, "cvpartition", 1)
459s 35 tests, 35 passed, 0 known failure, 0 skipped
459s [inst/Classification/ClassificationDiscriminant.m]
459s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Classification/ClassificationDiscriminant.m
459s ***** demo
459s  ## Create discriminant classifier
459s  ## Evaluate some model predictions on new data.
459s 
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  xc = [min(x); mean(x); max(x)];
459s  obj = fitcdiscr (x, y);
459s  [label, score, cost] = predict (obj, xc);
459s ***** demo
459s  load fisheriris
459s  model = fitcdiscr (meas, species);
459s  X = mean (meas);
459s  Y = {'versicolor'};
459s  ## Compute loss for discriminant model
459s  L = loss (model, X, Y)
459s ***** demo
459s  load fisheriris
459s  mdl = fitcdiscr (meas, species);
459s  X = mean (meas);
459s  Y = {'versicolor'};
459s  ## Margin for discriminant model
459s  m = margin (mdl, X, Y)
459s ***** demo
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  obj = fitcdiscr (x, y, "gamma", 0.4);
459s  ## Cross-validation for discriminant model
459s  CVMdl = crossval (obj)
459s ***** test
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
459s  Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
459s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
459s           0.092721, 0.115388, 0.055244, 0.032710; ...
459s           0.167514, 0.055244, 0.185188, 0.042665; ...
459s           0.038401, 0.032710, 0.042665, 0.041882];
459s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
459s        5.9360, 2.7700, 4.2600, 1.3260; ...
459s        6.5880, 2.9740, 5.5520, 2.0260];
459s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
459s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
459s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
459s  assert (class (Mdl), "ClassificationDiscriminant");
459s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
459s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
459s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
459s  assert (Mdl.ClassNames, unique (species))
459s  assert (Mdl.Sigma, sigma, 1e-6)
459s  assert (Mdl.Mu, mu, 1e-14)
459s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
459s  assert (Mdl.LogDetSigma, -9.9585, 1e-4)
459s  assert (Mdl.PredictorNames, PredictorNames)
459s ***** test
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
459s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
459s           0.046361, 0.115388, 0.027622, 0.016355; ...
459s           0.083757, 0.027622, 0.185188, 0.021333; ...
459s           0.019201, 0.016355, 0.021333, 0.041882];
459s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
459s        5.9360, 2.7700, 4.2600, 1.3260; ...
459s        6.5880, 2.9740, 5.5520, 2.0260];
459s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
459s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
459s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
459s  assert (class (Mdl), "ClassificationDiscriminant");
459s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
459s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
459s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
459s  assert (Mdl.ClassNames, unique (species))
459s  assert (Mdl.Sigma, sigma, 1e-6)
459s  assert (Mdl.Mu, mu, 1e-14)
459s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
459s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
459s ***** shared X, Y, MODEL
459s  X = rand (10,2);
459s  Y = [ones(5,1);2*ones(5,1)];
459s  MODEL = ClassificationDiscriminant (X, Y);
459s ***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
459s ***** error<ClassificationDiscriminant: too few input arguments.> ...
459s  ClassificationDiscriminant (ones(4, 1))
459s ***** error<ClassificationDiscriminant: Name-Value arguments must be in pairs.> ...
459s  ClassificationDiscriminant (X, Y, "prior")
459s ***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
459s  ClassificationDiscriminant (ones (4,2), ones (1,4))
459s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
459s  ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
459s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
459s  ClassificationDiscriminant (X, Y, "PredictorNames", "A")
459s ***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
459s  ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
459s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
459s  ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
459s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
459s  ClassificationDiscriminant (X, Y, "ResponseName", 1)
459s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
459s  ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
459s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
459s  ClassificationDiscriminant (X, Y, "ClassNames", {1})
459s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
459s  ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
459s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
459s  ClassificationDiscriminant ([1;2;3;4;5], ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
459s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
459s  ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
459s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
459s  ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
459s ***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
459s  ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
459s ***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
459s  ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
459s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
459s  ClassificationDiscriminant (X, Y, "Cost", [1, 2])
459s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
459s  ClassificationDiscriminant (X, Y, "Cost", "string")
459s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
459s  ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
459s ***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
459s  ClassificationDiscriminant (X, Y, "Cost", ones (3))
459s ***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
459s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
459s ***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
459s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
459s ***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
459s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
459s ***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
459s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
459s ***** test
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
459s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
459s  assert (label, {'versicolor'})
459s  assert (score, [0, 0.9999, 0.0001], 1e-4)
459s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
459s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
459s  assert (label, {'versicolor'})
459s  assert (score, [0, 0.6368, 0.3632], 1e-4)
459s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
459s ***** test
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  xc = [min(x); mean(x); max(x)];
459s  Mdl = fitcdiscr (x, y);
459s  [label, score, cost] = predict (Mdl, xc);
459s  l = {'setosa'; 'versicolor'; 'virginica'};
459s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
459s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
459s  assert (label, l)
459s  assert (score, s, 1e-4)
459s  assert (cost, c, 1e-4)
459s ***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
459s  predict (MODEL)
459s ***** error<ClassificationDiscriminant.predict: XC is empty.> ...
459s  predict (MODEL, [])
459s ***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
459s  predict (MODEL, 1)
459s ***** test
459s  load fisheriris
459s  model = fitcdiscr (meas, species);
459s  x = mean (meas);
459s  y = {'versicolor'};
459s  L = loss (model, x, y);
459s  assert (L, 0)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6];
459s  y = {'A'; 'B'; 'A'};
459s  model = fitcdiscr (x, y, "Gamma", 0.4);
459s  x_test = [1, 6; 3, 3];
459s  y_test = {'A'; 'B'};
459s  L = loss (model, x_test, y_test);
459s  assert (L, 0.3333, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6; 7, 8];
459s  y = ['1'; '2'; '3'; '1'];
459s  model = fitcdiscr (x, y, "gamma" , 0.5);
459s  x_test = [3, 3];
459s  y_test = ['1'];
459s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
459s  assert (L, 0.2423, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6; 7, 8];
459s  y = ['1'; '2'; '3'; '1'];
459s  model = fitcdiscr (x, y, "gamma" , 0.5);
459s  x_test = [3, 3; 5, 7];
459s  y_test = ['1'; '2'];
459s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
459s  assert (L, 0.3333, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6; 7, 8];
459s  y = ['1'; '2'; '3'; '1'];
459s  model = fitcdiscr (x, y, "gamma" , 0.5);
459s  x_test = [3, 3; 5, 7];
459s  y_test = ['1'; '2'];
459s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
459s  assert (L, 0.5886, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6; 7, 8];
459s  y = ['1'; '2'; '3'; '1'];
459s  model = fitcdiscr (x, y, "gamma" , 0.5);
459s  x_test = [3, 3; 5, 7];
459s  y_test = ['1'; '2'];
459s  W = [1; 2];
459s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
459s  assert (L, 0.5107, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6];
459s  y = {'A'; 'B'; 'A'};
459s  model = fitcdiscr (x, y, "gamma" , 0.5);
459s  x_with_nan = [1, 2; NaN, 4];
459s  y_test = {'A'; 'B'};
459s  L = loss (model, x_with_nan, y_test);
459s  assert (L, 0.3333, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6];
459s  y = {'A'; 'B'; 'A'};
459s  model = fitcdiscr (x, y);
459s  x_with_nan = [1, 2; NaN, 4];
459s  y_test = {'A'; 'B'};
459s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
459s  assert (isnan (L))
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6];
459s  y = {'A'; 'B'; 'A'};
459s  model = fitcdiscr (x, y);
459s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
459s  L = loss (model, x, y, 'LossFun', customLossFun);
459s  assert (L, 0.8889, 1e-4)
459s ***** test
459s  x = [1, 2; 3, 4; 5, 6];
459s  y = [1; 2; 1];
459s  model = fitcdiscr (x, y);
459s  L = loss (model, x, y, 'LossFun', 'classiferror');
459s  assert (L, 0.3333, 1e-4)
459s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
459s  loss (MODEL)
459s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
459s  loss (MODEL, ones (4,2))
459s ***** error<ClassificationDiscriminant.loss: X is empty.> ...
459s  loss (MODEL, [], zeros (2))
459s ***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
459s  loss (MODEL, 1, zeros (2))
459s ***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
459s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
459s ***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
459s  loss (MODEL, ones (4,2), ones (3,1))
459s ***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
459s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
459s ***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
459s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
459s  load fisheriris
459s  mdl = fitcdiscr (meas, species);
459s  X = mean (meas);
459s  Y = {'versicolor'};
459s  m = margin (mdl, X, Y);
459s  assert (m, 1, 1e-6)
459s ***** test
459s  X = [1, 2; 3, 4; 5, 6];
459s  Y = [1; 2; 1];
459s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
459s  m = margin (mdl, X, Y);
459s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
459s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
459s  margin (MODEL)
459s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
459s  margin (MODEL, ones (4,2))
459s ***** error<ClassificationDiscriminant.margin: X is empty.> ...
459s  margin (MODEL, [], zeros (2))
459s ***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
459s  margin (MODEL, 1, zeros (2))
459s ***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
459s  margin (MODEL, ones (4,2), ones (3,1))
459s ***** shared x, y, obj
459s  load fisheriris
459s  x = meas;
459s  y = species;
459s  obj = fitcdiscr (x, y, "gamma", 0.4);
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 (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
459s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
460s ***** test
460s  CVMdl = crossval (obj, "KFold", 3);
460s  assert (class (CVMdl), "ClassificationPartitionedModel")
460s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
460s  assert (CVMdl.KFold == 3)
460s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
460s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
460s ***** test
460s  CVMdl = crossval (obj, "HoldOut", 0.2);
460s  assert (class (CVMdl), "ClassificationPartitionedModel")
460s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
460s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
460s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
460s ***** test
460s  CVMdl = crossval (obj, "LeaveOut", 'on');
460s  assert (class (CVMdl), "ClassificationPartitionedModel")
460s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
460s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
460s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
463s ***** test
463s  partition = cvpartition (y, 'KFold', 3);
463s  CVMdl = crossval (obj, 'cvPartition', partition);
463s  assert (class (CVMdl), "ClassificationPartitionedModel")
463s  assert (CVMdl.KFold == 3)
463s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
463s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
463s ***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
463s  crossval (obj, "kfold")
463s ***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
463s  crossval (obj, "kfold", 12, "holdout", 0.2)
463s ***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
463s  crossval (obj, "kfold", 'a')
463s ***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
463s  crossval (obj, "holdout", 2)
463s ***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
463s  crossval (obj, "leaveout", 1)
463s ***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
463s  crossval (obj, "cvpartition", 1)
463s 67 tests, 67 passed, 0 known failure, 0 skipped
463s [inst/hmmestimate.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hmmestimate.m
463s ***** test
463s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
463s              3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
463s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
463s              1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
463s  [transprobest, outprobest] = hmmestimate (sequence, states);
463s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
463s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
463s  assert (transprobest, expectedtransprob, 0.001);
463s  assert (outprobest, expectedoutprob, 0.001);
463s ***** test
463s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
463s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
463s  states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
463s            "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
463s            "Two", "One", "One", "One", "One", "One", "One"};
463s  symbols = {"A", "B", "C"};
463s  statenames = {"One", "Two"};
463s  [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
463s                                            symbols, "statenames", statenames);
463s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
463s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
463s  assert (transprobest, expectedtransprob, 0.001);
463s  assert (outprobest, expectedoutprob, 0.001);
463s ***** test
463s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
463s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
463s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
463s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
463s  pseudotransitions = [8, 2; 4, 6];
463s  pseudoemissions = [2, 4, 4; 7, 2, 1];
463s  [transprobest, outprobest] = hmmestimate (sequence, states, ...
463s   "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
463s  expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
463s  expectedoutprob = [0.178571, 0.357143, 0.464286; ...
463s                     0.823529, 0.117647, 0.058824];
463s  assert (transprobest, expectedtransprob, 0.001);
463s  assert (outprobest, expectedoutprob, 0.001);
463s 3 tests, 3 passed, 0 known failure, 0 skipped
463s [inst/ttest.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ttest.m
463s ***** test
463s  x = 8:0.1:12;
463s  [h, pval, ci] = ttest (x, 10);
463s  assert (h, 0)
463s  assert (pval, 1, 10*eps)
463s  assert (ci, [9.6219 10.3781], 1E-5)
463s  [h, pval, ci0] = ttest (x, 0);
463s  assert (h, 1)
463s  assert (pval, 0)
463s  assert (ci0, ci, 2e-15)
463s  [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
463s  assert (h, 0)
463s  assert (pval, 0.5, 10*eps)
463s  assert (ci, [9.68498 Inf], 1E-5)
463s ***** error ttest ([8:0.1:12], 10, "tail", "invalid");
463s ***** error ttest ([8:0.1:12], 10, "tail", 25);
463s 3 tests, 3 passed, 0 known failure, 0 skipped
463s [inst/qrandn.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/qrandn.m
463s ***** demo
463s  z = qrandn (-5, 5e6);
463s  [c x] = hist (z,linspace(-1.5,1.5,200),1);
463s  figure(1)
463s  plot(x,c,"r."); axis tight; axis([-1.5,1.5]);
463s 
463s  z = qrandn (-0.14286, 5e6);
463s  [c x] = hist (z,linspace(-2,2,200),1);
463s  figure(2)
463s  plot(x,c,"r."); axis tight; axis([-2,2]);
463s 
463s  z = qrandn (2.75, 5e6);
463s  [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
463s  figure(3)
463s  semilogy(x,c,"r."); axis tight; axis([-100,100]);
463s 
463s  # ---------
463s  # Figures from the reference paper.
463s ***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
463s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
463s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
463s ***** error qrandn (2.5, 1, 2, 3)
463s ***** error qrandn (2.5)
463s ***** test
463s  q = 1.5;
463s  s = [2, 3];
463s  z = qrandn (q, s);
463s  assert (isnumeric (z) && isequal (size (z), s));
463s 6 tests, 6 passed, 0 known failure, 0 skipped
463s [inst/silhouette.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/silhouette.m
463s ***** demo
463s  load fisheriris;
463s  X = meas(:,3:4);
463s  cidcs = kmeans (X, 3, "Replicates", 5);
463s  silhouette (X, cidcs);
463s  y_labels(cidcs([1 51 101])) = unique (species);
463s  set (gca, "yticklabel", y_labels);
463s  title ("Fisher's iris data");
463s ***** error silhouette ();
463s ***** error silhouette ([1 2; 1 1]);
463s ***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
463s ***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
463s 4 tests, 4 passed, 0 known failure, 0 skipped
463s [inst/tiedrank.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/tiedrank.m
463s ***** test
463s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
463s  assert (r, [1, 2.5, 4, 5, 2.5]);
463s  assert (tieadj, 3);
463s ***** test
463s  [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
463s  assert (r, [1; 2.5; 4; 5; 2.5]);
463s  assert (tieadj, 3);
463s ***** test
463s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
463s  assert (r, [1, 2.5, 4, 5, 2.5]);
463s  assert (tieadj, [1; 0; 18]);
463s ***** test
463s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
463s  assert (r, [1, 2.5, 2, 1, 2.5]);
463s  assert (tieadj, 3);
463s ***** test
463s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
463s  assert (r, [1, 2.5, 2, 1, 2.5]);
463s  assert (tieadj, [1; 0; 18]);
463s ***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
463s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], [1, 1])
463s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], "A")
463s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], [true, true])
463s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
463s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], 0, "A")
463s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
463s  tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
463s 12 tests, 12 passed, 0 known failure, 0 skipped
463s [inst/fishertest.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fishertest.m
463s ***** demo
463s  ## A Fisher's exact test example
463s 
463s  x = [3, 1; 1, 3]
463s  [h, p, stats] = fishertest(x)
463s ***** assert (fishertest ([3, 4; 5, 7]), false);
463s ***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
463s ***** test
463s  [h, pval, stats] = fishertest ([3, 4; 5, 7]);
463s  assert (pval, 1, 1e-14);
463s  assert (stats.OddsRatio, 1.05);
463s  CI = [0.159222057151289, 6.92429189601808];
463s  assert (stats.ConfidenceInterval, CI, 1e-14)
463s ***** test
463s  [h, pval, stats] = fishertest ([3, 4; 5, 0]);
463s  assert (pval, 0.08080808080808080, 1e-14);
463s  assert (stats.OddsRatio, 0);
463s  assert (stats.ConfidenceInterval, [-Inf, Inf])
463s ***** error fishertest ();
463s ***** error fishertest (1, 2, 3, 4, 5, 6);
463s ***** error<fishertest: X must be a 2-dimensional matrix.> ...
463s  fishertest (ones (2, 2, 2));
463s ***** error<fishertest: X must contain only non-negative real integers.> ...
463s  fishertest ([1, 2; -3, 4]);
463s ***** error<fishertest: X must contain only non-negative real integers.> ...
463s  fishertest ([1, 2; 3, 4+i]);
463s ***** error<fishertest: X must contain only non-negative real integers.> ...
463s  fishertest ([1, 2; 3, 4.2]);
463s ***** error<fishertest: X must contain only non-negative real integers.> ...
463s  fishertest ([NaN, 2; 3, 4]);
463s ***** error<fishertest: X must contain only non-negative real integers.> ...
463s  fishertest ([1, Inf; 3, 4]);
463s ***** error<fishertest: cannot handle large entries.> ...
463s  fishertest (ones (2) * 1e8);
463s ***** error<fishertest: invalid value for alpha.> ...
463s  fishertest ([1, 2; 3, 4], "alpha", 0);
463s ***** error<fishertest: invalid value for alpha.> ...
463s  fishertest ([1, 2; 3, 4], "alpha", 1.2);
463s ***** error<fishertest: invalid value for alpha.> ...
463s  fishertest ([1, 2; 3, 4], "alpha", "val");
463s ***** error<fishertest: invalid value for tail.>  ...
463s  fishertest ([1, 2; 3, 4], "tail", "val");
463s ***** error<fishertest: invalid value for tail.>  ...
463s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
463s ***** error<fishertest: invalid name for optional arguments.> ...
463s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
463s 19 tests, 19 passed, 0 known failure, 0 skipped
463s [inst/crossval.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/crossval.m
463s ***** demo
463s  ## Determine the optimal number of clusters using cross-validation
463s 
463s  ## Declare a function to compute the sum of squared distances
463s  ## between data points and a varying number of clusters.
463s  function D = dist2clusters (X, Y, k)
463s    [Z, Zmu, Zstd] = zscore (X);
463s    [~, C] = kmeans (Z, k);
463s    ZY = (Y - Zmu) ./ Zstd;
463s    d = pdist2 (C, ZY, 'euclidean', 'Smallest', 1);
463s    D = sum (d .^ 2);
463s  endfunction
463s 
463s  load fisheriris
463s  for k = 1:8
463s    fcn = @(X, Y) dist2clusters (X, Y, k);
463s    distances = crossval (fcn, meas);
463s    cvdist(k) = sum (distances);
463s  endfor
463s 
463s  plot(cvdist)
463s  xlabel('Number of Clusters')
463s  ylabel('CV Sum of Squared Distances')
463s  xlim ([1,8]);
463s ***** test
463s  function yfit = regf (Xtrain, ytrain, Xtest)
463s    b = regress (ytrain, Xtrain);
463s    yfit = Xtest * b;
463s  endfunction
463s 
463s  load carsmall
463s  data = [Acceleration Horsepower Weight MPG];
463s  data(any(isnan(data),2),:) = [];
463s 
463s  y = data(:,4);
463s  X = [ones(length(y),1) data(:,1:3)];
463s  rand ("seed", 3);
463s  cvMSE = crossval('mse',X,y,'Predfun',@regf);
463s  assert (cvMSE, 18.720, 1e-3);
463s ***** error <crossval: criterion must be 'mse' or 'mcr'.> ...
463s  crossval ('fe', rand (10, 1), rand (10, 1), 1);
463s ***** error <crossval: prediction function handle is required for error evaluation.>  ...
463s  crossval ('mse', rand (10, 1), rand (10, 1), 1);
463s ***** error <crossval: X and Y are required for error evaluation.> ...
463s  crossval ('mse', rand (10, 1), 'Predfun', @(x,y) x + y);
463s ***** error <crossval: bad prediction function handle for error evaluation.> ...
463s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y) sum (x + y));
463s ***** error <crossval: prediction function handle must return a column vector with the same rows as XTest.> ...
463s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y,z) sum (x + y));
463s ***** error <crossval: X is required for values evaluation.> crossval (@(x) x);
463s ***** error <crossval: bad function handle to cross-validate.> ...
463s  crossval (@(x) x, rand (10, 3), rand (10, 1));
463s ***** error <crossval: function handle must return a scalar or a row vector.> ...
463s  crossval (@(x,y) [x, y], rand (10, 3), rand (10, 1));
463s ***** error <crossval: invalid first input argument.> crossval ({1}, 1, 1);
463s ***** error <crossval: you can only set one cvpartition type in paired arguments.> ...
463s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Holdout', 0.1, 'Leaveout', true)
463s ***** error <crossval: you cannot specify both 'Partition' and 'Stratify'.> ...
463s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Partition', cvpartition (10, 'Leaveout'), 'Stratify', true)
463s 12 tests, 12 passed, 0 known failure, 0 skipped
463s [inst/regression_ttest.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/regression_ttest.m
463s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
463s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
463s ***** error<regression_ttest: Y must contain finite real numbers.> ...
463s  regression_ttest ([1 2 NaN]', [2 3 4]');
463s ***** error<regression_ttest: Y must contain finite real numbers.> ...
463s  regression_ttest ([1 2 Inf]', [2 3 4]');
463s ***** error<regression_ttest: Y must contain finite real numbers.> ...
463s  regression_ttest ([1 2 3+i]', [2 3 4]');
463s ***** error<regression_ttest: X must contain finite real numbers.> ...
463s  regression_ttest ([1 2 3]', [2 3 NaN]');
463s ***** error<regression_ttest: X must contain finite real numbers.> ...
463s  regression_ttest ([1 2 3]', [2 3 Inf]');
463s ***** error<regression_ttest: X must contain finite real numbers.> ...
463s  regression_ttest ([1 2 3]', [3 4 3+i]');
463s ***** error<regression_ttest: Y and X must be vectors of equal length.> ...
463s  regression_ttest ([1 2 3]', [3 4 4 5]');
463s ***** error<regression_ttest: invalid value for alpha.> ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
463s ***** error<regression_ttest: invalid value for alpha.> ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
463s ***** error<regression_ttest: invalid value for alpha.> ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
463s ***** error<regression_ttest: invalid value for alpha.> ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
463s ***** error<regression_ttest: invalid Name argument.> ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
463s ***** error<regression_ttest: invalid value for tail.>  ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
463s ***** error<regression_ttest: invalid value for tail.>  ...
463s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
463s 16 tests, 16 passed, 0 known failure, 0 skipped
463s [inst/kstest.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/kstest.m
463s ***** demo
463s  ## Use the stock return data set to test the null hypothesis that the data
463s  ## come from a standard normal distribution against the alternative
463s  ## hypothesis that the population CDF of the data is larger that the
463s  ## standard normal CDF.
463s 
463s  load stockreturns;
463s  x = stocks(:,2);
463s  [h, p, k, c] = kstest (x, "Tail", "larger")
463s 
463s  ## Compute the empirical CDF and plot against the standard normal CDF
463s  [f, x_values] = ecdf (x);
463s  h1 = plot (x_values, f);
463s  hold on;
463s  h2 = plot (x_values, normcdf (x_values), 'r--');
463s  set (h1, "LineWidth", 2);
463s  set (h2, "LineWidth", 2);
463s  legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
463s          "Location", "southeast");
463s  title ("Empirical CDF of stock return data against standard normal CDF")
463s ***** error<kstest: too few inputs.> kstest ()
463s ***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
463s ***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
463s ***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
463s ***** error<kstest: optional parameters must be in name/value pairs.> ...
463s  kstest ([2, 3, 4, 5, 6], "tail")
463s ***** error<kstest: alpha must be a numeric scalar in the range> ...
463s  kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
463s ***** error<kstest: alpha must be a numeric scalar in the range> ...
463s  kstest ([2, 3, 4, 5, 6], "alpha", NaN)
463s ***** error<kstest: tail argument must be a string.> ...
463s  kstest ([2, 3, 4, 5, 6], "tail", 0)
463s ***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
463s  kstest ([2,3,4,5,6], "tail", "whatever")
463s ***** error<kstest: invalid function handle.> ...
463s  kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
463s ***** error<kstest: 'somedist' is not a supported distribution.> ...
463s  kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
463s ***** error<kstest: 'CDF' must be a probability distribution object.> ...
463s  kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5, 'resubstitution'))
463s ***** error<kstest: numerical CDF should have only 2 columns.> ...
463s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
463s ***** error<kstest: numerical CDF should have at least one row.> ...
463s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
463s ***** error<kstest: non-incrementing numerical CDF.> ...
463s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
463s ***** error<kstest: wrong duplicates in numerical CDF.> ...
463s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
463s ***** error<kstest: invalid value parsed as CDF optional argument.> ...
463s  kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
463s ***** test
463s  load examgrades
463s  [h, p] = kstest (grades(:,1));
463s  assert (h, true);
463s  assert (p, 7.58603305206105e-107, 1e-14);
463s ***** test
463s  load examgrades
463s  [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
463s  assert (h, false);
463s  assert (p, 0.5612, 1e-4);
463s ***** test
463s  load examgrades
463s  x = grades(:,1);
463s  test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
463s  [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
463s  assert (h, true);
463s  assert (p, 0.0021, 1e-4);
463s ***** test
463s  load stockreturns
463s  x = stocks(:,3);
463s  [h,p,k,c] = kstest (x, "Tail", "larger");
463s  assert (h, true);
463s  assert (p, 5.085438806199252e-05, 1e-14);
463s  assert (k, 0.2197, 1e-4);
463s  assert (c, 0.1207, 1e-4);
463s 21 tests, 21 passed, 0 known failure, 0 skipped
463s [inst/pdist2.m]
463s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/pdist2.m
463s ***** shared x, y, xx
463s  x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
463s  y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
463s  xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
463s ***** test
463s  d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
463s  assert (pdist2 (x, y), d);
463s ***** test
463s  d = [5.1962, 2.2361, 3.3166, 6.7082; ...
463s       3.4641, 2.2361, 3.3166, 5.4772];
463s  i = [3, 1, 1, 1; 2, 3, 3, 2];
463s  [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  d = [1.7321, 1.4142, 2.8284, 4.5826; ...
463s       3.4641, 2.2361, 3.3166, 5.4772];
463s  i = [1, 2, 2, 3;2, 1, 1, 2];
463s  [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  yy = [1 2 3;5 6 7;9 5 1];
463s  d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
463s       3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
463s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
463s  [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  yy = [1 2 3;5 6 7;9 5 1];
463s  d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
463s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
463s  [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  yy = [1 2 3;5 6 7;9 5 1];
463s  d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
463s       1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
463s  i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
463s  [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
463s       3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
463s  assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
463s ***** test
463s  xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
463s  d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
463s  i = [2, 2, 2; 3, 4, 4];
463s  [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
463s  i = [1, 1, 3; 4, 3, 1];
463s  [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
463s  assert ({D, I}, {d, i}, 1e-4);
463s ***** test
463s  d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
463s  assert (pdist2 (x, y, "cityblock"), d);
463s ***** test
463s  d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
463s  assert (pdist2 (x, y, "chebychev"), d);
463s ***** test
463s  d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
463s  assert (pdist2 (x, y, "cosine"), d, 1e-4);
463s ***** test
463s  yy = [1 2 3;5 6 7;9 5 1];
463s  d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
463s  i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
463s  [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
463s  assert ({D, I}, {d, i}, eps);
463s  [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
463s  assert ({D, I}, {d, i}, eps);
463s ***** test
463s  d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
463s  i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
463s  [D, I] = pdist2 (x, y, "hamming", "largest", 4);
463s  assert ({D, I}, {d, i}, eps);
463s  [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
463s  assert ({D, I}, {d, i}, eps);
463s ***** test
463s  xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
463s  yy = [1, 2, 2, 3; 2, 3, 3, 4];
463s  [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
463s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
463s  [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
463s  assert ({D, I}, {d, i});
463s ***** warning<pdist2: matrix is close to singular> ...
463s  pdist2 (xx, xx, "mahalanobis");
463s ***** error<pdist2: too few input arguments.> pdist2 (1)
463s ***** error<pdist2: X and Y must have equal number of columns.> ...
463s  pdist2 (ones (4, 5), ones (4))
463s ***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
463s  pdist2 (ones (4, 2, 3), ones (3, 2))
463s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
463s  pdist2 (ones (3), ones (3), "euclidean", "Largest")
463s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
463s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
463s ***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
463s  pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
463s ***** error<pdist2: you can only use either Smallest or Largest.> ...
463s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
463s ***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
463s  [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
463s ***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
463s  pdist2 (ones (3), ones (3), "seuclidean", 3)
463s ***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
463s  pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
463s ***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
463s  pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
463s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
463s  pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
463s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
463s  pdist2 (ones (3), eye (3), "minkowski", 0)
463s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
463s  pdist2 (ones (3), eye (3), "minkowski", -5)
463s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
463s  pdist2 (ones (3), eye (3), "minkowski", [1, 2])
463s ***** error<pdist2: invalid function handle for distance metric.> ...
463s  pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
463s ***** error<pdist2: custom distance function produces wrong output size.> ...
463s  pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
463s 33 tests, 33 passed, 0 known failure, 0 skipped
463s [inst/chi2test.m]
464s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/chi2test.m
464s ***** error chi2test ();
464s ***** error chi2test ([1, 2, 3, 4, 5]);
464s ***** error chi2test ([1, 2; 2, 1+3i]);
464s ***** error chi2test ([NaN, 6; 34, 12]);
464s ***** error<chi2test: optional arguments are not supported for 2-way> ...
464s  p = chi2test (ones (3, 3), "mutual", []);
464s ***** error<chi2test: invalid model name for testing a 3-way table.> ...
464s  p = chi2test (ones (3, 3, 3), "testtype", 2);
464s ***** error<chi2test: optional arguments must be in pairs.> ...
464s  p = chi2test (ones (3, 3, 3), "mutual");
464s ***** error<chi2test: value must be numeric in optional argument> ...
464s  p = chi2test (ones (3, 3, 3), "joint", ["a"]);
464s ***** error<chi2test: value must be empty or scalar in optional argument> ...
464s  p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
464s ***** error<chi2test: optional arguments are not supported for k> ...
464s  p = chi2test (ones (3, 3, 3, 4), "mutual", [])
464s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
464s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
464s ***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
464s ***** test
464s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
464s  p = chi2test (x);
464s  assert (p, 0.017787, 1e-6);
464s ***** test
464s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
464s  [p, chisq] = chi2test (x);
464s  assert (chisq, 11.9421, 1e-4);
464s ***** test
464s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
464s  [p, chisq, df] = chi2test (x);
464s  assert (df, 4);
464s ***** test
464s ***** shared x
464s  x(:,:,1) = [59, 32; 9,16];
464s  x(:,:,2) = [55, 24;12,33];
464s  x(:,:,3) = [107,80;17,56];%!
464s ***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
464s ***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
464s ***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
464s ***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
464s ***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
464s ***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
464s ***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
464s ***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
464s ***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
464s ***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
464s ***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
464s ***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
464s ***** test
464s  [pval, chisq, df, E] = chi2test (x);
464s  assert (chisq, 64.0982, 1e-4);
464s  assert (df, 7);
464s  assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
464s ***** test
464s  [pval, chisq, df, E] = chi2test (x, "joint", 2);
464s  assert (chisq, 56.0943, 1e-4);
464s  assert (df, 5);
464s  assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
464s ***** test
464s  [pval, chisq, df, E] = chi2test (x, "marginal", 3);
464s  assert (chisq, 146.6058, 1e-4);
464s  assert (df, 9);
464s  assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
464s ***** test
464s  [pval, chisq, df, E] = chi2test (x, "conditional", 3);
464s  assert (chisq, 52.2509, 1e-4);
464s  assert (df, 3);
464s  assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
464s ***** test
464s  [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
464s  assert (chisq, 1.6034, 1e-4);
464s  assert (df, 2);
464s  assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
464s 34 tests, 34 passed, 0 known failure, 0 skipped
464s [inst/wblplot.m]
464s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/wblplot.m
464s ***** demo
464s  x = [16 34 53 75 93 120];
464s  wblplot (x);
464s ***** demo
464s  x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
464s  c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
464s  [h, p] = wblplot (x, c);
464s  p
464s ***** demo
464s  x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
464s  [h, p] = wblplot (x, [], [], 0.05);
464s  p
464s  ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
464s ***** demo
464s  x = [46 64 83 105 123 150 150];
464s  c = [0 0 0 0 0 0 1];
464s  f = [1 1 1 1 1 1 4];
464s  wblplot (x, c, f, 0.05);
464s ***** demo
464s  x = [46 64 83 105 123 150 150];
464s  c = [0 0 0 0 0 0 1];
464s  f = [1 1 1 1 1 1 4];
464s  ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
464s  wblplot (x - 30.92, c, f, 0.05);
464s ***** test
464s  hf = figure ("visible", "off");
464s  unwind_protect
464s    x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
464s    [h, p] = wblplot (x, [], [], 0.05);
464s    assert (numel (h), 4)
464s    assert (p(1), 146.2545, 1E-4)
464s    assert (p(2), 1.1973, 1E-4)
464s    assert (p(3), 0.9999, 5E-5)
464s  unwind_protect_cleanup
464s    close (hf);
464s  end_unwind_protect
465s 1 test, 1 passed, 0 known failure, 0 skipped
465s [inst/logistic_regression.m]
465s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/logistic_regression.m
465s ***** test
465s  # Output compared to following MATLAB commands
465s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
465s  # P = mnrval(B,X)
465s  X = [1.489381332449196, 1.1534152241851305; ...
465s       1.8110085304863965, 0.9449666896938425; ...
465s       -0.04453299665130296, 0.34278203449678646; ...
465s       -0.36616019468850347, 1.130254275908322; ...
465s        0.15339143291005095, -0.7921044310668951; ...
465s       -1.6031878794469698, -1.8343471035233376; ...
465s       -0.14349521143198166, -0.6762996896828459; ...
465s       -0.4403818557740143, -0.7921044310668951; ...
465s       -0.7372685001160434, -0.027793137932169563; ...
465s       -0.11875465773681024, 0.5512305689880763];
465s  Y = [1,1,1,1,1,0,0,0,0,0]';
465s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
465s ***** test
465s  # Output compared to following MATLAB commands
465s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
465s  load carbig
465s  X = [Acceleration Displacement Horsepower Weight];
465s  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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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, ...
465s           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]';
465s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
465s  assert (DEV, 433.197174495549, 1e-05);
465s  assert (INTERCEPT(1), -16.6895155618903, 1e-05);
465s  assert (INTERCEPT(2), -11.7207818178493, 1e-05);
465s  assert (INTERCEPT(3), -8.0605768506075, 1e-05);
465s  assert (SLOPE(1), 0.104762463756714, 1e-05);
465s  assert (SLOPE(2), 0.0103357623191891, 1e-05);
465s  assert (SLOPE(3), 0.0645199313242276, 1e-05);
465s  assert (SLOPE(4), 0.00166377028388103, 1e-05);
465s 2 tests, 2 passed, 0 known failure, 0 skipped
465s [inst/fitrgam.m]
465s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitrgam.m
465s ***** demo
465s  # Train a RegressionGAM Model for synthetic values
465s 
465s  f1 = @(x) cos (3 *x);
465s  f2 = @(x) x .^ 3;
465s 
465s  # generate x1 and x2 for f1 and f2
465s  x1 = 2 * rand (50, 1) - 1;
465s  x2 = 2 * rand (50, 1) - 1;
465s 
465s  # calculate y
465s  y = f1(x1) + f2(x2);
465s 
465s  # add noise
465s  y = y + y .* 0.2 .* rand (50,1);
465s  X = [x1, x2];
465s 
465s  # create an object
465s  a = fitrgam (X, y, "tol", 1e-3)
465s ***** test
465s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
465s  y = [1; 2; 3; 4];
465s  a = fitrgam (x, y);
465s  assert ({a.X, a.Y}, {x, y})
465s  assert ({a.BaseModel.Intercept}, {2.5000})
465s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
465s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
465s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
465s  assert ({a.Formula}, {[]})
465s ***** test
465s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
465s  y = [1; 2; 3; 4];
465s  pnames = {"A", "B", "C", "D"};
465s  formula = "Y ~ A + B + C + D + A:C";
465s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
465s  a = fitrgam (x, y, "predictors", pnames, "formula", formula);
465s  assert ({a.IntMatrix}, {intMat})
465s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
465s  assert ({a.Formula}, {formula})
465s ***** error<fitrgam: too few arguments.> fitrgam ()
465s ***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
465s ***** error<fitrgam: Name-Value arguments must be in pairs.>
465s  fitrgam (ones (4,2), ones (4, 1), "K")
465s ***** error<fitrgam: number of rows in X and Y must be equal.>
465s  fitrgam (ones (4,2), ones (3, 1))
465s ***** error<fitrgam: number of rows in X and Y must be equal.>
465s  fitrgam (ones (4,2), ones (3, 1), "K", 2)
465s 7 tests, 7 passed, 0 known failure, 0 skipped
465s [inst/isoutlier.m]
465s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/isoutlier.m
465s ***** demo
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  TF = isoutlier (A, "mean")
465s ***** demo
465s  ## Use a moving detection method to detect local outliers in a sine wave
465s 
465s  x = -2*pi:0.1:2*pi;
465s  A = sin(x);
465s  A(47) = 0;
465s  time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
465s  TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
465s  plot (time, A)
465s  hold on
465s  plot (time(TF), A(TF), "x")
465s  datetick ('x', 20, 'keepticks')
465s  legend ("Original Data", "Outlier Data")
465s ***** demo
465s  ## Locate an outlier in a vector of data and visualize the outlier
465s 
465s  x = 1:10;
465s  A = [60 59 49 49 58 100 61 57 48 58];
465s  [TF, L, U, C] = isoutlier (A);
465s  plot (x, A);
465s  hold on
465s  plot (x(TF), A(TF), "x");
465s  xlim ([1,10]);
465s  line ([1,10], [L, L], "Linestyle", ":");
465s  text (1.1, L-2, "Lower Threshold");
465s  line ([1,10], [U, U], "Linestyle", ":");
465s  text (1.1, U-2, "Upper Threshold");
465s  line ([1,10], [C, C], "Linestyle", ":");
465s  text (1.1, C-3, "Center Value");
465s  legend ("Original Data", "Outlier Data");
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
465s  assert (isoutlier (A, "median"), ...
465s  logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "mean");
465s  assert (L, -109.2459044922864, 1e-12)
465s  assert (U, 264.9792378256198, 1e-12)
465s  assert (C, 77.8666666666666, 1e-12)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "median");
465s  assert (L, 50.104386688966386, 1e-12)
465s  assert (U, 67.895613311033610, 1e-12)
465s  assert (C, 59)
465s ***** test
465s  A = magic(5) + diag(200*ones(1,5));
465s  T = logical (eye (5));
465s  assert (isoutlier (A, 2), T)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "movmedian", 5);
465s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
465s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
465s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
465s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
465s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
465s  assert (L, l, 1e-4)
465s  assert (U, u, 1e-4)
465s  assert (C, c)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
465s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
465s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
465s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
465s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
465s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
465s  assert (L, l, 1e-4)
465s  assert (U, u, 1e-4)
465s  assert (C, c)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "movmean", 5);
465s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
465s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
465s       52.5979, 51.0627];
465s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
465s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
465s       66.9373];
465s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
465s       60.6, 59.8, 59.25, 59];
465s  assert (L, l, 1e-4)
465s  assert (U, u, 1e-4)
465s  assert (C, c, 1e-4)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
465s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
465s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
465s       52.5979, 51.0627];
465s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
465s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
465s       66.9373];
465s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
465s       60.6, 59.8, 59.25, 59];
465s  assert (L, l, 1e-4)
465s  assert (U, u, 1e-4)
465s  assert (C, c, 1e-4)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "gesd");
465s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 34.235977035439944, 1e-12)
465s  assert (U, 89.764022964560060, 1e-12)
465s  assert (C, 62)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
465s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 31.489256770616173, 1e-12)
465s  assert (U, 92.510743229383820, 1e-12)
465s  assert (C, 62)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
465s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 23.976664158788935, 1e-12)
465s  assert (U, 100.02333584121110, 1e-12)
465s  assert (C, 62)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "grubbs");
465s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 54.642809574646606, 1e-12)
465s  assert (U, 63.511036579199555, 1e-12)
465s  assert (C, 59.076923076923080, 1e-12)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
465s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 54.216083184201850, 1e-12)
465s  assert (U, 63.937762969644310, 1e-12)
465s  assert (C, 59.076923076923080, 1e-12)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
465s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
465s  assert (L, 57)
465s  assert (U, 100)
465s  assert (C, 78.5)
465s ***** test
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s  [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
465s  assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
465s  assert (L, 57.5)
465s  assert (U, 62)
465s  assert (C, 59.75)
465s ***** shared A
465s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", 0);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", []);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", [2 3 4]);
465s ***** error<isoutlier: WINDOW must be a positive integer> ...
465s  isoutlier (A, "movmedian", 1.4);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", [0 1]);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", [2 -1]);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", {2 3});
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmedian", "char");
465s 
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", 0);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", []);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", [2 3 4]);
465s ***** error<isoutlier: WINDOW must be a positive integer> ...
465s  isoutlier (A, "movmean", 1.4);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", [0 1]);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", [2 -1]);
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", {2 3});
465s ***** error<isoutlier: WINDOW must be a positive scalar> ...
465s  isoutlier (A, "movmean", "char");
465s 
465s ***** error<isoutlier: THRESHOLD must be a two-element> ...
465s  isoutlier (A, "percentiles", [-1 90]);
465s ***** error<isoutlier: THRESHOLD must be a two-element> ...
465s  isoutlier (A, "percentiles", [10 -90]);
465s ***** error<isoutlier: THRESHOLD must be a two-element> ...
465s  isoutlier (A, "percentiles", [90]);
465s ***** error<isoutlier: THRESHOLD must be a two-element> ...
465s  isoutlier (A, "percentiles", [90 20]);
466s ***** error<isoutlier: THRESHOLD must be a two-element> ...
466s  isoutlier (A, "percentiles", [90 20]);
466s ***** error<isoutlier: THRESHOLD must be a two-element> ...
466s  isoutlier (A, "percentiles", [10 20 90]);
466s ***** error<isoutlier: THRESHOLD must be a two-element> ...
466s  isoutlier (A, "percentiles", {10 90});
466s ***** error<isoutlier: THRESHOLD must be a two-element> ...
466s  isoutlier (A, "percentiles", "char");
466s 
466s ***** error<isoutlier: sample points must be a vector.> ...
466s  isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
466s ***** error<isoutlier: sample points must be a vector.> ...
466s  isoutlier (A, "movmean", 5, "SamplePoints", 15);
466s ***** error<isoutlier: sample points must be unique.> ...
466s  isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
466s ***** error<isoutlier: sample points must be sorted.> ...
466s  isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
466s ***** error<isoutlier: sample points must have the same size> ...
466s  isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);
466s 
466s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
466s  isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
466s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
466s  isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
466s ***** error<isoutlier: threshold factor must must be in> ...
466s  isoutlier (A, "gesd", "ThresholdFactor", 3);
466s ***** error<isoutlier: threshold factor must must be in> ...
466s  isoutlier (A, "grubbs", "ThresholdFactor", 3);
466s 
466s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
466s  isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
466s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
466s  isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
466s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
466s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
466s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
466s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);
466s 
466s ***** error<isoutlier: invalid input argument.> ...
466s  isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
466s ***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
466s ***** error<isoutlier: invalid input argument.> isoutlier (A, true);
466s ***** error<isoutlier: invalid input argument.> isoutlier (A, false);
466s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
466s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
466s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
466s 59 tests, 59 passed, 0 known failure, 0 skipped
466s [inst/fitcgam.m]
466s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitcgam.m
466s ***** demo
466s  ## Train a GAM classifier for binary classification
466s  ## using specific data and plot the decision boundaries.
466s 
466s  ## Define specific data
466s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
466s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
466s  Y = [0; 0; 0; 0; 0; ...
466s      1; 1; 1; 1; 1];
466s 
466s  ## Train the GAM model
466s  obj = fitcgam (X, Y, "Interactions", "all");
466s 
466s  ## Create a grid of values for prediction
466s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
466s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
466s  [x1G, x2G] = meshgrid (x1, x2);
466s  XGrid = [x1G(:), x2G(:)];
466s  pred = predict (obj, XGrid);
466s 
466s  ## Plot decision boundaries and data points
466s  predNumeric = str2double (pred);
466s  gidx = predNumeric > 0.5;
466s 
466s  figure
466s  scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
466s  hold on
466s  scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
466s  plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
466s  xlabel("Feature 1");
466s  ylabel("Feature 2");
466s  title("Generalized Additive Model (GAM) Decision Boundary");
466s  legend({"Class 1 Region", "Class 0 Region", ...
466s        "Class 1 Samples", "Class 0 Samples"}, ...
466s        "location", "northwest")
466s  axis tight
466s  hold off
466s ***** test
466s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
466s  y = [0; 0; 1; 1];
466s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
466s  a = fitcgam (x, y, "PredictorNames", PredictorNames);
466s  assert (class (a), "ClassificationGAM");
466s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
466s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
466s  assert (a.ClassNames, {'0'; '1'})
466s  assert (a.PredictorNames, PredictorNames)
466s  assert (a.BaseModel.Intercept, 0)
468s ***** test
468s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
468s  y = [1; 0; 1; 0; 1];
468s  a = fitcgam (x, y, "interactions", "all");
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)
471s ***** test
471s  load fisheriris
471s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
471s  X = meas(inds, :);
471s  Y = species(inds, :)';
471s  Y = strcmp (Y, 'virginica')';
471s  a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
471s  assert (class (a), "ClassificationGAM");
471s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
471s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
471s  assert (a.ClassNames, {'0'; '1'})
471s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
471s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
471s  assert (a.ModelwInt.Intercept, 0)
479s ***** error<fitcgam: too few arguments.> fitcgam ()
479s ***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
479s ***** error<fitcgam: name-value arguments must be in pairs.>
479s  fitcgam (ones (4,2), ones (4, 1), "K")
479s ***** error<fitcgam: number of rows in X and Y must be equal.>
479s  fitcgam (ones (4,2), ones (3, 1))
479s ***** error<fitcgam: number of rows in X and Y must be equal.>
479s  fitcgam (ones (4,2), ones (3, 1), "K", 2)
479s 8 tests, 8 passed, 0 known failure, 0 skipped
479s [inst/hist3.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hist3.m
479s ***** demo
479s  X = [
479s     1    1
479s     1    1
479s     1   10
479s     1   10
479s     5    5
479s     5    5
479s     5    5
479s     5    5
479s     5    5
479s     7    3
479s     7    3
479s     7    3
479s    10   10
479s    10   10];
479s  hist3 (X)
479s ***** test
479s  N_exp = [ 0  0  0  5 20
479s            0  0 10 15  0
479s            0 15 10  0  0
479s           20  5  0  0  0];
479s 
479s  n = 100;
479s  x = [1:n]';
479s  y = [n:-1:1]';
479s  D = [x y];
479s  N = hist3 (D, [4 5]);
479s  assert (N, N_exp);
479s ***** test
479s  N_exp = [0  0  0  0  1
479s           0  0  0  0  1
479s           0  0  0  0  1
479s           1  1  1  1 93];
479s 
479s  n = 100;
479s  x = [1:n]';
479s  y = [n:-1:1]';
479s  D = [x y];
479s  C{1} = [1 1.7 3 4];
479s  C{2} = [1:5];
479s  N = hist3 (D, C);
479s  assert (N, N_exp);
479s ***** test
479s  D = [1 1; 3 1; 3 3; 3 1];
479s  [c, nn] = hist3 (D, {0:4, 0:4});
479s  exp_c = zeros (5);
479s  exp_c([7 9 19]) = [1 2 1];
479s  assert (c, exp_c);
479s  assert (nn, {0:4, 0:4});
479s ***** test
479s  for i = 10
479s    assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
479s  endfor
479s ***** test
479s  edge_1 = linspace (0, 10, 10);
479s  edge_2 = linspace (0, 50, 10);
479s  [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
479s  exp_c = zeros (10, 10);
479s  exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
479s  assert (c, exp_c);
479s 
479s  assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
479s  assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
479s ***** shared X
479s  X = [
479s   5  2
479s   5  3
479s   1  4
479s   5  3
479s   4  4
479s   1  2
479s   2  3
479s   3  3
479s   5  4
479s   5  3];
479s ***** test
479s  N = zeros (10);
479s  N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
479s  C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
479s  assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
479s ***** test
479s  N = zeros (5, 7);
479s  N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
479s  C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
479s  assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
479s  assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
479s ***** test
479s  N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
479s  C = {(2:5), (2.5:1:4.5)};
479s  assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
479s ***** test
479s  N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
479s  C = {(1.2:3.2), (0:5)};
479s  assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
479s  assert (nthargout ([1 2], @hist3, X, C), {N C})
479s ***** test
479s  [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
479s                                          [-1 .05 .07 .2 .3 .5 .89 1.2]});
479s  C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
479s           [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
479s  assert (C, C_exp, eps*10^2)
479s ***** test
479s  Xv = repmat ([1:10]', [1 2]);
479s 
479s  ## Test Centers
479s  assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))
479s 
479s  N_exp = eye (6);
479s  N_exp([1 end]) = 3;
479s  assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)
479s 
479s  N_exp = zeros (8, 6);
479s  N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
479s  assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)
479s 
479s  ## Test Edges
479s  assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
479s  assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
479s  assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])
479s 
479s  N_exp = zeros (14);
479s  N_exp(3:12, 3:12) = eye (10);
479s  assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)
479s 
479s  ## Test for Nbins
479s  assert (hist3 (Xv), eye (10))
479s  assert (hist3 (Xv, [10 10]), eye (10))
479s  assert (hist3 (Xv, "nbins", [10 10]), eye (10))
479s  assert (hist3 (Xv, [5 5]), eye (5) * 2)
479s 
479s  N_exp = zeros (7, 5);
479s  N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
479s  assert (hist3 (Xv, [7 5]), N_exp)
479s ***** test # bug #51059
479s  D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
479s  [c, nn] = hist3 (D, {0:4, 0:4});
479s  exp_c = zeros (5);
479s  exp_c([7 9 19]) = [1 2 1];
479s  assert (c, exp_c)
479s  assert (nn, {0:4, 0:4})
479s ***** test
479s  [c, nn] = hist3 ([1 8]);
479s  exp_c = zeros (10, 10);
479s  exp_c(6, 6) = 1;
479s  exp_nn = {-4:5, 3:12};
479s  assert (c, exp_c)
479s  assert (nn, exp_nn, eps)
479s 
479s  [c, nn] = hist3 ([1 8], [10 11]);
479s  exp_c = zeros (10, 11);
479s  exp_c(6, 6) = 1;
479s  exp_nn = {-4:5, 3:13};
479s  assert (c, exp_c)
479s  assert (nn, exp_nn, eps)
479s ***** test
479s  [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
479s  exp_c = zeros (10, 10);
479s  exp_c(2, 1) = 1;
479s  exp_c(8, 10) = 1;
479s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
479s  assert (c, exp_c)
479s  assert (nn, exp_nn, eps*100)
479s ***** test
479s  [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
479s  exp_c = zeros (10, 10);
479s  exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
479s  assert (c, exp_c)
479s  assert (nn, exp_nn, eps*100)
479s ***** test
479s  [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
479s  exp_c = zeros (10, 10);
479s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
479s  assert (c, exp_c)
479s  assert (nn, exp_nn, eps*100)
479s 16 tests, 16 passed, 0 known failure, 0 skipped
479s [inst/barttest.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/barttest.m
479s ***** error<barttest: invalid number of input arguments.> barttest ()
479s ***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
479s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
479s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
479s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
479s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
479s ***** error<barttest: not enough data in X.> barttest (ones (30, 1))
479s ***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
479s ***** test
479s  x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
479s  [ndim, pval, chisq] = barttest (x);
479s  assert (ndim, 2);
479s  assert (pval, 0);
479s  ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
479s ***** test
479s  x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
479s       1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
479s      -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
479s       0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
479s       0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
479s      -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
479s      -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
479s       0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
479s       3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
479s       2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
479s      -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
479s       3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
479s       0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
479s      -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
479s       0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
479s      -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
479s      -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
479s       1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
479s       1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
479s       1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
479s  [ndim, pval, chisq] = barttest (x);
479s  assert (ndim, 3);
479s  assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
479s  chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
479s  assert (chisq, chisq_out, 1e-4);
479s 10 tests, 10 passed, 0 known failure, 0 skipped
479s [inst/pcares.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/pcares.m
479s ***** demo
479s  x = [ 7    26     6    60;
479s        1    29    15    52;
479s       11    56     8    20;
479s       11    31     8    47;
479s        7    52     6    33;
479s       11    55     9    22;
479s        3    71    17     6;
479s        1    31    22    44;
479s        2    54    18    22;
479s       21    47     4    26;
479s        1    40    23    34;
479s       11    66     9    12;
479s       10    68     8    12];
479s 
479s  ## As we increase the number of principal components, the norm
479s  ## of the residuals matrix will decrease
479s  r1 = pcares (x,1);
479s  n1 = norm (r1)
479s  r2 = pcares (x,2);
479s  n2 = norm (r2)
479s  r3 = pcares (x,3);
479s  n3 = norm (r3)
479s  r4 = pcares (x,4);
479s  n4 = norm (r4)
479s ***** test
479s  load hald
479s  r1 = pcares (ingredients,1);
479s  r2 = pcares (ingredients,2);
479s  r3 = pcares (ingredients,3);
479s  assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
479s  assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
479s  assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
479s ***** error<pcares: too few input arguments.> pcares (ones (20, 3))
479s ***** error<pcares: NDIM must be less than or equal to the column of X.> ...
479s  pcares (ones (30, 2), 3)
479s 3 tests, 3 passed, 0 known failure, 0 skipped
479s [inst/factoran.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/factoran.m
479s ***** demo
479s  x = [ 7    26     6    60;
479s        1    29    15    52;
479s       11    56     8    20;
479s       11    31     8    47;
479s        7    52     6    33;
479s       11    55     9    22;
479s        3    71    17     6;
479s        1    31    22    44;
479s        2    54    18    22;
479s       21    47     4    26;
479s        1    40    23    34;
479s       11    66     9    12;
479s       10    68     8    12
479s      ];
479s  [loadings, specvar, fscores] = factoran (x, 2);
479s ***** test
479s  x = [1, 2; 2, 1; 3, 3];
479s  [loadings, specvar, fscores] = factoran (x, 1);
479s  l_out = [0.7071; 0.7071];
479s  s_out = [0.5000; 0.5000];
479s  f_out = [-0.7071; -0.7071; 1.4142];
479s  assert (loadings, l_out, 1.3e-4);
479s  assert (specvar, s_out, 1.3e-4);
479s  assert (fscores, f_out, 1.3e-4);
479s ***** error factoran ()
479s ***** error factoran (ones (5,3), 0)
479s ***** error factoran (ones (5,3), 3)
479s ***** error<factoran: X must be a numeric matrix.> factoran ({1,2}, 1)
479s ***** error<factoran: X must be a numeric matrix.> factoran (ones (2,2,2), 1)
479s ***** error<EIG: matrix contains Inf or NaN values> x=ones (3,2); x(:,2)=0; factoran (x,1)
479s 7 tests, 7 passed, 0 known failure, 0 skipped
479s [inst/fitcdiscr.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitcdiscr.m
479s ***** demo
479s  ## Train a linear discriminant classifier for Gamma = 0.5
479s  ## and plot the decision boundaries.
479s 
479s  load fisheriris
479s  idx = ! strcmp (species, "setosa");
479s  X = meas(idx,3:4);
479s  Y = cast (strcmpi (species(idx), "virginica"), "double");
479s  obj = fitcdiscr (X, Y, "Gamma", 0.5)
479s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
479s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
479s  [x1G, x2G] = meshgrid (x1, x2);
479s  XGrid = [x1G(:), x2G(:)];
479s  pred = predict (obj, XGrid);
479s  gidx = logical (pred);
479s 
479s  figure
479s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
479s  hold on
479s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
479s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
479s  xlabel ("Petal length (cm)");
479s  ylabel ("Petal width (cm)");
479s  title ("Linear Discriminant Analysis Decision Boundary");
479s  legend ({"Versicolor Region", "Virginica Region", ...
479s          "Sampled Versicolor", "Sampled Virginica"}, ...
479s          "location", "northwest")
479s  axis tight
479s  hold off
479s ***** test
479s  load fisheriris
479s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
479s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
479s  assert (label, {'versicolor'})
479s  assert (score, [0, 0.9999, 0.0001], 1e-4)
479s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
479s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
479s  assert (label, {'versicolor'})
479s  assert (score, [0, 0.6368, 0.3632], 1e-4)
479s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
479s  assert (class (Mdl), "ClassificationDiscriminant");
479s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
479s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
479s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
479s  assert (Mdl.ClassNames, unique (species))
479s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
479s           0.046361, 0.115388, 0.027622, 0.016355; ...
479s           0.083757, 0.027622, 0.185188, 0.021333; ...
479s           0.019201, 0.016355, 0.021333, 0.041882];
479s  assert (Mdl.Sigma, sigma, 1e-6)
479s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
479s        5.9360, 2.7700, 4.2600, 1.3260; ...
479s        6.5880, 2.9740, 5.5520, 2.0260];
479s  assert (Mdl.Mu, mu, 1e-14)
479s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
479s ***** error<fitcdiscr: too few arguments.> fitcdiscr ()
479s ***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
479s ***** error<fitcdiscr: name-value arguments must be in pairs.>
479s  fitcdiscr (ones (4,2), ones (4, 1), "K")
479s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
479s  fitcdiscr (ones (4,2), ones (3, 1))
479s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
479s  fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
479s 6 tests, 6 passed, 0 known failure, 0 skipped
479s [inst/squareform.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/squareform.m
479s ***** shared v, m
479s  v = 1:6;
479s  m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
479s ***** assert (squareform (v), m)
479s ***** assert (squareform (squareform (v)), v)
479s ***** assert (squareform (m), v)
479s ***** assert (squareform (v'), m)
479s ***** assert (squareform (1), [0 1;1 0])
479s ***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
479s ***** assert (squareform (0, "tovector"), zeros (1, 0))
479s ***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
479s ***** test
479s  for c = {@single, @double, @uint8, @uint32, @uint64}
479s    f = c{1};
479s    assert (squareform (f (v)), f (m))
479s    assert (squareform (f (m)), f (v))
479s  endfor
479s 9 tests, 9 passed, 0 known failure, 0 skipped
479s [inst/dist_fit/burrlike.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/burrlike.m
479s ***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
479s ***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
479s ***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
479s ***** error<burrlike: PARAMS must be a three-element vector.> ...
479s  burrlike ([1, 2], [1, 3, 5, 7])
479s ***** error<burrlike: PARAMS must be a three-element vector.> ...
479s  burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
479s ***** error<burrlike: X and CENSOR vector mismatch.> ...
479s  burrlike ([1, 2, 3], [1:5], [0, 0, 0])
479s ***** error<burrlike: X and FREQ vector mismatch.> ...
479s  burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
479s ***** error<burrlike: X and FREQ vector mismatch.> ...
479s  burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
479s 8 tests, 8 passed, 0 known failure, 0 skipped
479s [inst/dist_fit/lognlike.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/lognlike.m
479s ***** test
479s  x = 1:50;
479s  [nlogL, avar] = lognlike ([0, 0.25], x);
479s  avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
479s  assert (nlogL, 3962.330333301793, 1e-10);
479s  assert (avar, avar_out, 1e-7);
479s ***** test
479s  x = 1:50;
479s  [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
479s  avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
479s  assert (nlogL, 2473.183051225747, 1e-10);
479s  assert (avar, avar_out, 1e-7);
479s ***** test
479s  x = 1:50;
479s  [nlogL, avar] = lognlike ([0, 0.5], x);
479s  avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
479s  assert (nlogL, 1119.072424020455, 1e-12);
479s  assert (avar, avar_out, 1e-6);
479s ***** test
479s  x = 1:50;
479s  censor = ones (1, 50);
479s  censor([2, 4, 6, 8, 12, 14]) = 0;
479s  [nlogL, avar] = lognlike ([0, 0.5], x, censor);
479s  avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
479s  assert (nlogL, 1091.746371145497, 1e-12);
479s  assert (avar, avar_out, 1e-6);
479s ***** test
479s  x = 1:50;
479s  censor = ones (1, 50);
479s  censor([2, 4, 6, 8, 12, 14]) = 0;
479s  [nlogL, avar] = lognlike ([0, 1], x, censor);
479s  avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
479s  assert (nlogL, 349.3969104144271, 1e-12);
479s  assert (avar, avar_out, 1e-6);
479s ***** error<lognlike: function called with too few input arguments.> ...
479s  lognlike ([12, 15]);
479s ***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
479s ***** error<lognlike: PARAMS must be a two-element vector.> ...
479s  lognlike ([12, 15, 3], [1:50]);
479s ***** error<lognlike: X and CENSOR vectors mismatch.> ...
479s  lognlike ([12, 15], [1:50], [1, 2, 3]);
479s ***** error<lognlike: X and FREQ vectors mismatch.> ...
479s  lognlike ([12, 15], [1:50], [], [1, 2, 3]);
479s 10 tests, 10 passed, 0 known failure, 0 skipped
479s [inst/dist_fit/invgfit.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/invgfit.m
479s ***** demo
479s  ## Sample 3 populations from different inverse Gaussian distributions
479s  rand ("seed", 5); randn ("seed", 5);   # for reproducibility
479s  r1 = invgrnd (1, 0.2, 2000, 1);
479s  rand ("seed", 2); randn ("seed", 2);   # for reproducibility
479s  r2 = invgrnd (1, 3, 2000, 1);
479s  rand ("seed", 7); randn ("seed", 7);   # for reproducibility
479s  r3 = invgrnd (3, 1, 2000, 1);
479s  r = [r1, r2, r3];
479s 
479s  ## Plot them normalized and fix their colors
479s  hist (r, [0.1:0.1:3.2], 9);
479s  h = findobj (gca, "Type", "patch");
479s  set (h(1), "facecolor", "c");
479s  set (h(2), "facecolor", "g");
479s  set (h(3), "facecolor", "r");
479s  ylim ([0, 3]);
479s  xlim ([0, 3]);
479s  hold on
479s 
479s  ## Estimate their MU and LAMBDA parameters
479s  mu_lambdaA = invgfit (r(:,1));
479s  mu_lambdaB = invgfit (r(:,2));
479s  mu_lambdaC = invgfit (r(:,3));
479s 
479s  ## Plot their estimated PDFs
479s  x = [0:0.1:3];
479s  y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
479s  plot (x, y, "-pr");
479s  y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
479s  plot (x, y, "-sg");
479s  y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
479s  plot (x, y, "-^c");
479s  hold off
479s  legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
479s           "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
479s           "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
479s           sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
479s                   mu_lambdaA(1), mu_lambdaA(2)), ...
479s           sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
479s                   mu_lambdaB(1), mu_lambdaB(2)), ...
479s           sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
479s                   mu_lambdaC(1), mu_lambdaC(2))})
479s  title ("Three population samples from different inverse Gaussian distributions")
479s  hold off
479s ***** test
479s  paramhat = invgfit ([1:50]);
479s  paramhat_out = [25.5, 19.6973];
479s  assert (paramhat, paramhat_out, 1e-4);
479s ***** test
479s  paramhat = invgfit ([1:5]);
479s  paramhat_out = [3, 8.1081];
479s  assert (paramhat, paramhat_out, 1e-4);
479s ***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
479s ***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
479s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
479s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
479s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
479s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
479s  invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
479s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
479s  invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
479s ***** error<invgfit: X and FREQ vectors mismatch.> ...
479s  invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
479s ***** error<invgfit: X and FREQ vectors mismatch.> ...
479s  invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
479s ***** error<invgfit: 'options' 5th argument must be a structure> ...
479s  invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
479s 12 tests, 12 passed, 0 known failure, 0 skipped
479s [inst/dist_fit/evfit.m]
479s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/evfit.m
479s ***** demo
479s  ## Sample 3 populations from different extreme value distributions
479s  rand ("seed", 1);    # for reproducibility
479s  r1 = evrnd (2, 5, 400, 1);
479s  rand ("seed", 12);    # for reproducibility
479s  r2 = evrnd (-5, 3, 400, 1);
479s  rand ("seed", 13);    # for reproducibility
479s  r3 = evrnd (14, 8, 400, 1);
479s  r = [r1, r2, r3];
479s 
479s  ## Plot them normalized and fix their colors
479s  hist (r, 25, 0.4);
479s  h = findobj (gca, "Type", "patch");
479s  set (h(1), "facecolor", "c");
479s  set (h(2), "facecolor", "g");
479s  set (h(3), "facecolor", "r");
479s  ylim ([0, 0.28])
479s  xlim ([-30, 30]);
479s  hold on
479s 
479s  ## Estimate their MU and SIGMA parameters
479s  mu_sigmaA = evfit (r(:,1));
479s  mu_sigmaB = evfit (r(:,2));
479s  mu_sigmaC = evfit (r(:,3));
479s 
479s  ## Plot their estimated PDFs
479s  x = [min(r(:)):max(r(:))];
479s  y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
479s  plot (x, y, "-pr");
479s  y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
479s  plot (x, y, "-sg");
479s  y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
479s  plot (x, y, "-^c");
479s  legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
479s           "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
479s           "Normalized HIST of sample 3 with μ=14 and σ=8", ...
479s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
479s                   mu_sigmaA(1), mu_sigmaA(2)), ...
479s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
479s                   mu_sigmaB(1), mu_sigmaB(2)), ...
479s           sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
479s                   mu_sigmaC(1), mu_sigmaC(2))})
479s  title ("Three population samples from different extreme value distributions")
479s  hold off
479s ***** test
479s  x = 1:50;
479s  [paramhat, paramci] = evfit (x);
479s  paramhat_out = [32.6811, 13.0509];
479s  paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
479s  assert (paramhat, paramhat_out, 1e-4);
479s  assert (paramci, paramci_out, 1e-4);
479s ***** test
479s  x = 1:50;
479s  [paramhat, paramci] = evfit (x, 0.01);
479s  paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
479s  assert (paramci, paramci_out, 1e-4);
480s ***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
480s ***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
480s ***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
480s ***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
480s ***** error<evfit: X and FREQ vectors mismatch.>
480s  evfit ([1 2 3], 0.05, [], [1 5])
480s ***** error<evfit: FREQ must not contain negative values.>
480s  evfit ([1 2 3], 0.05, [], [1 5 -1])
480s ***** error<evfit: 'options' 5th argument must be a structure> ...
480s  evfit ([1:10], 0.05, [], [], 5)
480s 9 tests, 9 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/normlike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/normlike.m
480s ***** error<normlike: too few input arguments.> normlike ([12, 15]);
480s ***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
480s ***** error<normlike: PARAMS must be a two-element vector.> ...
480s  normlike ([12, 15, 3], [1:50]);
480s ***** error<normlike: X and CENSOR vectors mismatch.> ...
480s  normlike ([12, 15], [1:50], [1, 2, 3]);
480s ***** error<normlike: X and FREQ vectors mismatch.> ...
480s  normlike ([12, 15], [1:50], [], [1, 2, 3]);
480s ***** error<normlike: FREQ must not contain negative values.> ...
480s  normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
480s ***** test
480s  x = 1:50;
480s  [nlogL, avar] = normlike ([2.3, 1.2], x);
480s  avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
480s  assert (nlogL, 13014.95883783327, 1e-10);
480s  assert (avar, avar_out, 1e-4);
480s ***** test
480s  x = 1:50;
480s  [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
480s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
480s  assert (nlogL, 2854.802587833265, 1e-10);
480s  assert (avar, avar_out, 1e-4);
480s ***** test
480s  x = 1:50;
480s  [nlogL, avar] = normlike ([21, 15], x);
480s  avar_out = [5.460474308300396, -1.600790513833993; ...
480s              -1.600790513833993, 2.667984189723321];
480s  assert (nlogL, 206.738325604233, 1e-12);
480s  assert (avar, avar_out, 1e-14);
480s ***** test
480s  x = 1:50;
480s  censor = ones (1, 50);
480s  censor([2, 4, 6, 8, 12, 14]) = 0;
480s  [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
480s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
480s  assert (nlogL, Inf);
480s  assert (avar, [NaN, NaN; NaN, NaN]);
480s ***** test
480s  x = 1:50;
480s  censor = ones (1, 50);
480s  censor([2, 4, 6, 8, 12, 14]) = 0;
480s  [nlogL, avar] = normlike ([21, 15], x, censor);
480s  avar_out = [24.4824488866131, -10.6649544179636; ...
480s              -10.6649544179636, 6.22827849965737];
480s  assert (nlogL, 86.9254371829733, 1e-12);
480s  assert (avar, avar_out, 8e-14);
480s 11 tests, 11 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/hnlike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/hnlike.m
480s ***** test
480s  x = 1:20;
480s  paramhat = hnfit (x, 0);
480s  [nlogL, acov] = hnlike (paramhat, x);
480s  assert (nlogL, 64.179177404891300, 1e-14);
480s ***** test
480s  x = 1:20;
480s  paramhat = hnfit (x, 0);
480s  [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
480s  assert (nlogL, 64.179177404891300, 1e-14);
480s ***** error<hnlike: function called with too few input arguments.> ...
480s  hnlike ([12, 15]);
480s ***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
480s ***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
480s ***** error<hnlike: X must be a vector of real values.> ...
480s  hnlike ([0, 3], ones (2));
480s ***** error<hnlike: X must be a vector of real values.> ...
480s  hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
480s ***** error<hnlike: X and FREQ vectors mismatch.> ...
480s  hnlike ([1, 2], ones (10, 1), ones (8,1))
480s ***** error<hnlike: FREQ must not contain negative values.> ...
480s  hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
480s 9 tests, 9 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/nbinlike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/nbinlike.m
480s ***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
480s ***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
480s ***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
480s ***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
480s ***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
480s ***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
480s ***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
480s ***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
480s ***** error<nbinlike: PARAMS must be a two-element vector.> ...
480s  nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
480s ***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
480s ***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
480s ***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
480s ***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
480s ***** error<nbinlike: X and FREQ vectors mismatch.> ...
480s  nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
480s ***** error<nbinlike: FREQ must not contain negative values.> ...
480s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
480s ***** error<nbinlike: FREQ must contain integer values.> ...
480s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
480s 16 tests, 16 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/explike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/explike.m
480s ***** test
480s  x = 12;
480s  beta = 5;
480s  [L, V] = explike (beta, x);
480s  expected_L = 4.0094;
480s  expected_V = 6.5789;
480s  assert (L, expected_L, 0.001);
480s  assert (V, expected_V, 0.001);
480s ***** test
480s  x = 1:5;
480s  beta = 2;
480s  [L, V] = explike (beta, x);
480s  expected_L = 10.9657;
480s  expected_V = 0.4;
480s  assert (L, expected_L, 0.001);
480s  assert (V, expected_V, 0.001);
480s ***** error<explike: function called with too few input arguments.> explike ()
480s ***** error<explike: function called with too few input arguments.> explike (2)
480s ***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
480s ***** error<explike: X must be a vector.> explike (3, ones (10, 2))
480s ***** error<explike: X and CENSOR vectors mismatch.> ...
480s  explike (3, [1:50], [1, 2, 3])
480s ***** error<explike: X and FREQ vectors mismatch.> ...
480s  explike (3, [1:50], [], [1, 2, 3])
480s 8 tests, 8 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/betafit.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/betafit.m
480s ***** demo
480s  ## Sample 2 populations from different Beta distributions
480s  randg ("seed", 1);   # for reproducibility
480s  r1 = betarnd (2, 5, 500, 1);
480s  randg ("seed", 2);   # for reproducibility
480s  r2 = betarnd (2, 2, 500, 1);
480s  r = [r1, r2];
480s 
480s  ## Plot them normalized and fix their colors
480s  hist (r, 12, 15);
480s  h = findobj (gca, "Type", "patch");
480s  set (h(1), "facecolor", "c");
480s  set (h(2), "facecolor", "g");
480s  hold on
480s 
480s  ## Estimate their shape parameters
480s  a_b_A = betafit (r(:,1));
480s  a_b_B = betafit (r(:,2));
480s 
480s  ## Plot their estimated PDFs
480s  x = [min(r(:)):0.01:max(r(:))];
480s  y = betapdf (x, a_b_A(1), a_b_A(2));
480s  plot (x, y, "-pr");
480s  y = betapdf (x, a_b_B(1), a_b_B(2));
480s  plot (x, y, "-sg");
480s  ylim ([0, 4])
480s  legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
480s           "Normalized HIST of sample 2 with α=2 and β=2", ...
480s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
480s                   a_b_A(1), a_b_A(2)), ...
480s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
480s                   a_b_B(1), a_b_B(2))})
480s  title ("Two population samples from different Beta distributions")
480s  hold off
480s ***** test
480s  x = 0.01:0.02:0.99;
480s  [paramhat, paramci] = betafit (x);
480s  paramhat_out = [1.0199, 1.0199];
480s  paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
480s  assert (paramhat, paramhat_out, 1e-4);
480s  assert (paramci, paramci_out, 1e-4);
480s ***** test
480s  x = 0.01:0.02:0.99;
480s  [paramhat, paramci] = betafit (x, 0.01);
480s  paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
480s  assert (paramci, paramci_out, 1e-4);
480s ***** test
480s  x = 0.00:0.02:1;
480s  [paramhat, paramci] = betafit (x);
480s  paramhat_out = [0.0875, 0.1913];
480s  paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
480s  assert (paramhat, paramhat_out, 1e-4);
480s  assert (paramci, paramci_out, 1e-4);
480s ***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
480s ***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
480s ***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
480s ***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
480s ***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
480s ***** error<betafit: X and FREQ vectors mismatch.> ...
480s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
480s ***** error<betafit: FREQ must not contain negative values.> ...
480s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
480s ***** error<betafit: FREQ must contain integer values.> ...
480s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
480s ***** error<betafit: 'options' argument must be a structure> ...
480s  betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
480s ***** error<betafit: 'options' argument must be a structure> ...
480s  betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
480s 13 tests, 13 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/normfit.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/normfit.m
480s ***** demo
480s  ## Sample 3 populations from 3 different normal distributions
480s  randn ("seed", 1);    # for reproducibility
480s  r1 = normrnd (2, 5, 5000, 1);
480s  randn ("seed", 2);    # for reproducibility
480s  r2 = normrnd (5, 2, 5000, 1);
480s  randn ("seed", 3);    # for reproducibility
480s  r3 = normrnd (9, 4, 5000, 1);
480s  r = [r1, r2, r3];
480s 
480s  ## Plot them normalized and fix their colors
480s  hist (r, 15, 0.4);
480s  h = findobj (gca, "Type", "patch");
480s  set (h(1), "facecolor", "c");
480s  set (h(2), "facecolor", "g");
480s  set (h(3), "facecolor", "r");
480s  hold on
480s 
480s  ## Estimate their mu and sigma parameters
480s  [muhat, sigmahat] = normfit (r);
480s 
480s  ## Plot their estimated PDFs
480s  x = [min(r(:)):max(r(:))];
480s  y = normpdf (x, muhat(1), sigmahat(1));
480s  plot (x, y, "-pr");
480s  y = normpdf (x, muhat(2), sigmahat(2));
480s  plot (x, y, "-sg");
480s  y = normpdf (x, muhat(3), sigmahat(3));
480s  plot (x, y, "-^c");
480s  ylim ([0, 0.5])
480s  xlim ([-20, 20])
480s  hold off
480s  legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
480s           "Normalized HIST of sample 2 with mu=5, σ=2", ...
480s           "Normalized HIST of sample 3 with mu=9, σ=4", ...
480s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
480s                   muhat(1), sigmahat(1)), ...
480s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
480s                   muhat(2), sigmahat(2)), ...
480s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
480s                   muhat(3), sigmahat(3))}, "location", "northwest")
480s  title ("Three population samples from different normal distributions")
480s  hold off
480s ***** test
480s  load lightbulb
480s  idx = find (lightbulb(:,2) == 0);
480s  censoring = lightbulb(idx,3) == 1;
480s  [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
480s  assert (muHat, 9496.59586737857, 1e-11);
480s  assert (sigmaHat, 3064.021012796456, 2e-12);
480s ***** test
480s  randn ("seed", 234);
480s  x = normrnd (3, 5, [1000, 1]);
480s  [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
480s  assert (muCI(1) < 3);
480s  assert (muCI(2) > 3);
480s  assert (sigmaCI(1) < 5);
480s  assert (sigmaCI(2) > 5);
480s ***** error<normfit: X must not be a multi-dimensional array.> ...
480s  normfit (ones (3,3,3))
480s ***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
480s  normfit (ones (20,3), [], zeros (20,1))
480s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
480s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
480s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
480s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
480s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
480s ***** error<normfit: X and CENSOR vectors mismatch.> ...
480s  normfit (ones (20,1), [], zeros(15,1))
480s ***** error<normfit: X and FREQ vectors mismatch.> ...
480s  normfit (ones (20,1), [], zeros(20,1), ones(25,1))
480s ***** error<normfit: FREQ must not contain negative values.> ...
480s  normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
480s ***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
480s 13 tests, 13 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/ricelike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/ricelike.m
480s ***** test
480s  nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
480s  assert (nlogL, 204.5230311010569, 1e-12);
480s ***** test
480s  nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
480s  assert (nlogL, 8.65562164930058, 1e-12);
480s ***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
480s ***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
480s ***** error<ricelike: PARAMS must be a two-element vector.> ...
480s  ricelike ([1, 0.2, 3], [1, 3, 5, 7])
480s ***** error<ricelike: X and CENSOR vector mismatch.> ...
480s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
480s ***** error<ricelike: X and FREQ vector mismatch.> ...
480s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
480s ***** error<ricelike: X and FREQ vector mismatch.> ...
480s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
480s ***** error<ricelike: FREQ must not contain negative values.> ...
480s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
480s 9 tests, 9 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/logllike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/logllike.m
480s ***** test
480s  [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
480s  assert (nlogL, 211.2965, 1e-4);
480s  assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
480s ***** test
480s  [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
480s  assert (nlogL, 9.2206, 1e-4);
480s  assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
480s ***** error<logllike: function called with too few input arguments.> logllike (3.25)
480s ***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
480s ***** error<logllike: PARAMS must be a two-element vector.> ...
480s  logllike ([1, 0.2, 3], [1, 3, 5, 7])
480s ***** error<logllike: X and CENSOR vector mismatch.> ...
480s  logllike ([1.5, 0.2], [1:5], [0, 0, 0])
480s ***** error<logllike: X and FREQ vector mismatch.> ...
480s  logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
480s ***** error<logllike: X and FREQ vector mismatch.> ...
480s  logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
480s 8 tests, 8 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/wbllike.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/wbllike.m
480s ***** test
480s  x = 1:50;
480s  [nlogL, acov] = wbllike ([2.3, 1.2], x);
480s  avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
480s  assert (nlogL, 945.9589180651594, 1e-12);
480s  assert (acov, avar_out, 1e-4);
480s ***** test
480s  x = 1:50;
480s  [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
480s  avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
480s  assert (nlogL, 424.9879809704742, 6e-14);
480s  assert (acov, avar_out, 1e-4);
480s ***** test
480s  x = 1:50;
480s  [nlogL, acov] = wbllike ([21, 15], x);
480s  avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
480s  assert (nlogL, 1635190.328991511, 1e-8);
480s  assert (acov, avar_out, 1e-8);
480s ***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
480s ***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
480s ***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
480s ***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
480s ***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
480s ***** error<wbllike: FREQ cannot have negative values.> ...
480s  wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
480s 9 tests, 9 passed, 0 known failure, 0 skipped
480s [inst/dist_fit/loglfit.m]
480s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/loglfit.m
480s ***** demo
480s  ## Sample 3 populations from different log-logistic distributions
480s  rand ("seed", 5)  # for reproducibility
480s  r1 = loglrnd (0, 1, 2000, 1);
480s  rand ("seed", 2)   # for reproducibility
480s  r2 = loglrnd (0, 0.5, 2000, 1);
480s  rand ("seed", 7)   # for reproducibility
480s  r3 = loglrnd (0, 0.125, 2000, 1);
480s  r = [r1, r2, r3];
480s 
480s  ## Plot them normalized and fix their colors
480s  hist (r, [0.05:0.1:2.5], 10);
480s  h = findobj (gca, "Type", "patch");
480s  set (h(1), "facecolor", "c");
480s  set (h(2), "facecolor", "g");
480s  set (h(3), "facecolor", "r");
480s  ylim ([0, 3.5]);
480s  xlim ([0, 2.0]);
480s  hold on
480s 
480s  ## Estimate their MU and LAMBDA parameters
480s  a_bA = loglfit (r(:,1));
480s  a_bB = loglfit (r(:,2));
480s  a_bC = loglfit (r(:,3));
480s 
480s  ## Plot their estimated PDFs
480s  x = [0.01:0.1:2.01];
480s  y = loglpdf (x, a_bA(1), a_bA(2));
480s  plot (x, y, "-pr");
480s  y = loglpdf (x, a_bB(1), a_bB(2));
480s  plot (x, y, "-sg");
480s  y = loglpdf (x, a_bC(1), a_bC(2));
480s  plot (x, y, "-^c");
480s  legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
480s           "Normalized HIST of sample 2 with α=1 and β=2", ...
480s           "Normalized HIST of sample 3 with α=1 and β=8", ...
480s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
480s                   a_bA(1), a_bA(2)), ...
480s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
480s                   a_bB(1), a_bB(2)), ...
480s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
480s                   a_bC(1), a_bC(2))})
480s  title ("Three population samples from different log-logistic distributions")
480s  hold off
480s ***** test
480s  [paramhat, paramci] = loglfit ([1:50]);
480s  paramhat_out = [3.09717, 0.468525];
480s  paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
480s  assert (paramhat, paramhat_out, 1e-5);
480s  assert (paramci, paramci_out, 1e-5);
481s ***** test
481s  paramhat = loglfit ([1:5]);
481s  paramhat_out = [1.01124, 0.336449];
481s  assert (paramhat, paramhat_out, 1e-5);
481s ***** test
481s  paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
481s  paramhat_out = [1.01124, 0.336449];
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** test
481s  paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
481s  paramhat_out = loglfit ([1:5, 5]);
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
481s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
481s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
481s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
481s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
481s  loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
481s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
481s  loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
481s ***** error<loglfit: X and FREQ vectors mismatch.> ...
481s  loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
481s ***** error<loglfit: X and FREQ vectors mismatch.> ...
481s  loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
481s ***** error<loglfit: 'options' 5th argument must be a structure> ...
481s  loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
481s 13 tests, 13 passed, 0 known failure, 0 skipped
481s [inst/dist_fit/betalike.m]
481s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/betalike.m
481s ***** test
481s  x = 0.01:0.02:0.99;
481s  [nlogL, avar] = betalike ([2.3, 1.2], x);
481s  avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
481s  assert (nlogL, 17.873477715879040, 3e-14);
481s  assert (avar, avar_out, 1e-7);
481s ***** test
481s  x = 0.01:0.02:0.99;
481s  [nlogL, avar] = betalike ([1, 4], x);
481s  avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
481s  assert (nlogL, 79.648061114839550, 1e-13);
481s  assert (avar, avar_out, 1e-7);
481s ***** test
481s  x = 0.00:0.02:1;
481s  [nlogL, avar] = betalike ([1, 4], x);
481s  avar_out = [0.00000801564765, 0.00000131397245; ...
481s              0.00000131397245, 0.00070827639442];
481s  assert (nlogL, 573.2008434477486, 1e-10);
481s  assert (avar, avar_out, 1e-14);
481s ***** error<betalike: function called with too few input arguments.> ...
481s  betalike ([12, 15]);
481s ***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
481s ***** error<betalike: X and FREQ vectors mismatch.> ...
481s  betalike ([12, 15], ones (10, 1), ones (8,1))
481s ***** error<betalike: FREQ must not contain negative values.> ...
481s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
481s ***** error<betalike: FREQ must contain integer values.> ...
481s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
481s 8 tests, 8 passed, 0 known failure, 0 skipped
481s [inst/dist_fit/unifit.m]
481s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/unifit.m
481s ***** demo
481s  ## Sample 2 populations from different continuous uniform distributions
481s  rand ("seed", 5);    # for reproducibility
481s  r1 = unifrnd (2, 5, 2000, 1);
481s  rand ("seed", 6);    # for reproducibility
481s  r2 = unifrnd (3, 9, 2000, 1);
481s  r = [r1, r2];
481s 
481s  ## Plot them normalized and fix their colors
481s  hist (r, 0:0.5:10, 2);
481s  h = findobj (gca, "Type", "patch");
481s  set (h(1), "facecolor", "c");
481s  set (h(2), "facecolor", "g");
481s  hold on
481s 
481s  ## Estimate their probability of success
481s  a_bA = unifit (r(:,1));
481s  a_bB = unifit (r(:,2));
481s 
481s  ## Plot their estimated PDFs
481s  x = [0:10];
481s  y = unifpdf (x, a_bA(1), a_bA(2));
481s  plot (x, y, "-pg");
481s  y = unifpdf (x, a_bB(1), a_bB(2));
481s  plot (x, y, "-sc");
481s  xlim ([1, 10])
481s  ylim ([0, 0.5])
481s  legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
481s           "Normalized HIST of sample 2 with a=3 and b=9", ...
481s           sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
481s                   a_bA(1), a_bA(2)), ...
481s           sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
481s                   a_bB(1), a_bB(2))})
481s  title ("Two population samples from different continuous uniform distributions")
481s  hold off
481s ***** test
481s  x = 0:5;
481s  [paramhat, paramci] = unifit (x);
481s  assert (paramhat, [0, 5]);
481s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
481s ***** test
481s  x = 0:5;
481s  [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
481s  assert (paramhat, [0, 5]);
481s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
481s ***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
481s ***** error<unifit: function called with too few input arguments.> unifit ()
481s ***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
481s ***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
481s ***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
481s ***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
481s ***** error<unifit: X and FREQ vector mismatch.> ...
481s  unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
481s ***** error<unifit: FREQ cannot have negative values.> ...
481s  unifit ([1.5, 0.2], [], [1, -1])
481s ***** error<unifit: X and FREQ vector mismatch.> ...
481s  unifit ([1.5, 0.2], [], [1, 1, 1])
481s 11 tests, 11 passed, 0 known failure, 0 skipped
481s [inst/dist_fit/logifit.m]
481s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/logifit.m
481s ***** demo
481s  ## Sample 3 populations from different logistic distributions
481s  rand ("seed", 5)  # for reproducibility
481s  r1 = logirnd (2, 1, 2000, 1);
481s  rand ("seed", 2)   # for reproducibility
481s  r2 = logirnd (5, 2, 2000, 1);
481s  rand ("seed", 7)   # for reproducibility
481s  r3 = logirnd (9, 4, 2000, 1);
481s  r = [r1, r2, r3];
481s 
481s  ## Plot them normalized and fix their colors
481s  hist (r, [-6:20], 1);
481s  h = findobj (gca, "Type", "patch");
481s  set (h(1), "facecolor", "c");
481s  set (h(2), "facecolor", "g");
481s  set (h(3), "facecolor", "r");
481s  ylim ([0, 0.3]);
481s  xlim ([-5, 20]);
481s  hold on
481s 
481s  ## Estimate their MU and LAMBDA parameters
481s  mu_sA = logifit (r(:,1));
481s  mu_sB = logifit (r(:,2));
481s  mu_sC = logifit (r(:,3));
481s 
481s  ## Plot their estimated PDFs
481s  x = [-5:0.5:20];
481s  y = logipdf (x, mu_sA(1), mu_sA(2));
481s  plot (x, y, "-pr");
481s  y = logipdf (x, mu_sB(1), mu_sB(2));
481s  plot (x, y, "-sg");
481s  y = logipdf (x, mu_sC(1), mu_sC(2));
481s  plot (x, y, "-^c");
481s  hold off
481s  legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
481s           "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
481s           "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
481s           sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
481s                   mu_sA(1), mu_sA(2)), ...
481s           sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
481s                   mu_sB(1), mu_sB(2)), ...
481s           sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
481s                   mu_sC(1), mu_sC(2))})
481s  title ("Three population samples from different logistic distributions")
481s  hold off
481s ***** test
481s  paramhat = logifit ([1:50]);
481s  paramhat_out = [25.5, 8.7724];
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** test
481s  paramhat = logifit ([1:5]);
481s  paramhat_out = [3, 0.8645];
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** test
481s  paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
481s  paramhat_out = [3, 0.8645];
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** test
481s  paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
481s  paramhat_out = logifit ([1:5, 5]);
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** error<logifit: X must be a vector.> logifit (ones (2,5));
481s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
481s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
481s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
481s ***** error<logifit: X and CENSOR vectors mismatch.> ...
481s  logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
481s ***** error<logifit: X and CENSOR vectors mismatch.> ...
481s  logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
481s ***** error<logifit: X and FREQ vectors mismatch.> ...
481s  logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
481s ***** error<logifit: X and FREQ vectors mismatch.> ...
481s  logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
481s ***** error<logifit: 'options' 5th argument must be a structure> ...
481s  logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
481s 13 tests, 13 passed, 0 known failure, 0 skipped
481s [inst/dist_fit/gumbellike.m]
481s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gumbellike.m
481s ***** test
481s  x = 1:50;
481s  [nlogL, avar] = gumbellike ([2.3, 1.2], x);
481s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
481s  assert (nlogL, 3.242264755689906e+17, 1e-14);
481s  assert (avar, avar_out, 1e-3);
481s ***** test
481s  x = 1:50;
481s  [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
481s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
481s  assert (nlogL, 481898704.0472211, 1e-6);
481s  assert (avar, avar_out, 1e-3);
481s ***** test
481s  x = 1:50;
481s  [nlogL, avar] = gumbellike ([21, 15], x);
481s  avar_out = [11.73913876598908, -5.9546128523121216; ...
481s              -5.954612852312121, 3.708060045170236];
481s  assert (nlogL, 223.7612479380652, 1e-13);
481s  assert (avar, avar_out, 1e-14);
481s ***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
481s ***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
481s ***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
481s ***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
481s ***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
481s 8 tests, 8 passed, 0 known failure, 0 skipped
481s [inst/dist_fit/bisafit.m]
481s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/bisafit.m
481s ***** demo
481s  ## Sample 3 populations from different Birnbaum-Saunders distributions
481s  rand ("seed", 5);    # for reproducibility
481s  r1 = bisarnd (1, 0.5, 2000, 1);
481s  rand ("seed", 2);    # for reproducibility
481s  r2 = bisarnd (2, 0.3, 2000, 1);
481s  rand ("seed", 7);    # for reproducibility
481s  r3 = bisarnd (4, 0.5, 2000, 1);
481s  r = [r1, r2, r3];
481s 
481s  ## Plot them normalized and fix their colors
481s  hist (r, 80, 4.2);
481s  h = findobj (gca, "Type", "patch");
481s  set (h(1), "facecolor", "c");
481s  set (h(2), "facecolor", "g");
481s  set (h(3), "facecolor", "r");
481s  ylim ([0, 1.1]);
481s  xlim ([0, 8]);
481s  hold on
481s 
481s  ## Estimate their α and β parameters
481s  beta_gammaA = bisafit (r(:,1));
481s  beta_gammaB = bisafit (r(:,2));
481s  beta_gammaC = bisafit (r(:,3));
481s 
481s  ## Plot their estimated PDFs
481s  x = [0:0.1:8];
481s  y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
481s  plot (x, y, "-pr");
481s  y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
481s  plot (x, y, "-sg");
481s  y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
481s  plot (x, y, "-^c");
481s  hold off
481s  legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
481s           "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
481s           "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
481s           sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
481s                   beta_gammaA(1), beta_gammaA(2)), ...
481s           sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
481s                   beta_gammaB(1), beta_gammaB(2)), ...
481s           sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
481s                   beta_gammaC(1), beta_gammaC(2))})
481s  title ("Three population samples from different Birnbaum-Saunders distributions")
481s  hold off
481s ***** test
481s  paramhat = bisafit ([1:50]);
481s  paramhat_out = [16.2649, 1.0156];
481s  assert (paramhat, paramhat_out, 1e-4);
481s ***** test
481s  paramhat = bisafit ([1:5]);
481s  paramhat_out = [2.5585, 0.5839];
481s  assert (paramhat, paramhat_out, 1e-4);
482s ***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
482s ***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
482s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
482s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
482s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
482s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
482s  bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
482s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
482s  bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
482s ***** error<bisafit: X and FREQ vectors mismatch.> ...
482s  bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
482s ***** error<bisafit: X and FREQ vectors mismatch.> ...
482s  bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
482s ***** error<bisafit: 'options' 5th argument must be a structure> ...
482s  bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
482s 12 tests, 12 passed, 0 known failure, 0 skipped
482s [inst/dist_fit/burrfit.m]
482s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/burrfit.m
482s ***** demo
482s  ## Sample 3 populations from different Burr type XII distributions
482s  rand ("seed", 4);    # for reproducibility
482s  r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
482s  rand ("seed", 2);    # for reproducibility
482s  r2 = burrrnd (1, 3, 1, 10000, 1);
482s  rand ("seed", 9);    # for reproducibility
482s  r3 = burrrnd (0.5, 2, 3, 10000, 1);
482s  r = [r1, r2, r3];
482s 
482s  ## Plot them normalized and fix their colors
482s  hist (r, [0.1:0.2:20], [18, 5, 3]);
482s  h = findobj (gca, "Type", "patch");
482s  set (h(1), "facecolor", "c");
482s  set (h(2), "facecolor", "g");
482s  set (h(3), "facecolor", "r");
482s  ylim ([0, 3]);
482s  xlim ([0, 5]);
482s  hold on
482s 
482s  ## Estimate their α and β parameters
482s  lambda_c_kA = burrfit (r(:,1));
482s  lambda_c_kB = burrfit (r(:,2));
482s  lambda_c_kC = burrfit (r(:,3));
482s 
482s  ## Plot their estimated PDFs
482s  x = [0.01:0.15:15];
482s  y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
482s  plot (x, y, "-pr");
482s  y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
482s  plot (x, y, "-sg");
482s  y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
482s  plot (x, y, "-^c");
482s  hold off
482s  legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
482s           "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
482s           "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
482s   sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
482s           lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
482s   sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
482s           lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
482s   sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
482s           lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
482s  title ("Three population samples from different Burr type XII distributions")
482s  hold off
482s ***** test
482s  l = 1; c = 2; k = 3;
482s  r = burrrnd (l, c, k, 100000, 1);
482s  lambda_c_kA = burrfit (r);
482s  assert (lambda_c_kA(1), l, 0.2);
482s  assert (lambda_c_kA(2), c, 0.2);
482s  assert (lambda_c_kA(3), k, 0.3);
484s ***** test
484s  l = 0.5; c = 1; k = 3;
484s  r = burrrnd (l, c, k, 100000, 1);
484s  lambda_c_kA = burrfit (r);
484s  assert (lambda_c_kA(1), l, 0.2);
484s  assert (lambda_c_kA(2), c, 0.2);
484s  assert (lambda_c_kA(3), k, 0.3);
486s ***** test
486s  l = 1; c = 3; k = 1;
486s  r = burrrnd (l, c, k, 100000, 1);
486s  lambda_c_kA = burrfit (r);
486s  assert (lambda_c_kA(1), l, 0.2);
486s  assert (lambda_c_kA(2), c, 0.2);
486s  assert (lambda_c_kA(3), k, 0.3);
488s ***** test
488s  l = 3; c = 2; k = 1;
488s  r = burrrnd (l, c, k, 100000, 1);
488s  lambda_c_kA = burrfit (r);
488s  assert (lambda_c_kA(1), l, 0.2);
488s  assert (lambda_c_kA(2), c, 0.2);
488s  assert (lambda_c_kA(3), k, 0.3);
490s ***** test
490s  l = 4; c = 2; k = 4;
490s  r = burrrnd (l, c, k, 100000, 1);
490s  lambda_c_kA = burrfit (r);
490s  assert (lambda_c_kA(1), l, 0.2);
490s  assert (lambda_c_kA(2), c, 0.2);
490s  assert (lambda_c_kA(3), k, 0.3);
493s ***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
493s ***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
493s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
493s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
493s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
493s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
493s  burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
493s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
493s  burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
493s ***** error<burrfit: X and FREQ vectors mismatch.>
493s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
493s ***** error<burrfit: FREQ must not contain negative values.>
493s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
493s ***** error<burrfit: 'options' 5th argument must be a structure> ...
493s  burrfit ([1:10], 0.05, [], [], 5)
493s 15 tests, 15 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/nbinfit.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/nbinfit.m
493s ***** demo
493s  ## Sample 2 populations from different negative binomial distributions
493s  randp ("seed", 5); randg ("seed", 5);    # for reproducibility
493s  r1 = nbinrnd (2, 0.15, 5000, 1);
493s  randp ("seed", 8); randg ("seed", 8);    # for reproducibility
493s  r2 = nbinrnd (5, 0.2, 5000, 1);
493s  r = [r1, r2];
493s 
493s  ## Plot them normalized and fix their colors
493s  hist (r, [0:51], 1);
493s  h = findobj (gca, "Type", "patch");
493s  set (h(1), "facecolor", "c");
493s  set (h(2), "facecolor", "g");
493s  hold on
493s 
493s  ## Estimate their probability of success
493s  r_psA = nbinfit (r(:,1));
493s  r_psB = nbinfit (r(:,2));
493s 
493s  ## Plot their estimated PDFs
493s  x = [0:40];
493s  y = nbinpdf (x, r_psA(1), r_psA(2));
493s  plot (x, y, "-pg");
493s  x = [min(r(:,2)):max(r(:,2))];
493s  y = nbinpdf (x, r_psB(1), r_psB(2));
493s  plot (x, y, "-sc");
493s  ylim ([0, 0.1])
493s  xlim ([0, 50])
493s  legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
493s           "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
493s           sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
493s                   r_psA(1), r_psA(2)), ...
493s           sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
493s                   r_psB(1), r_psB(2))})
493s  title ("Two population samples from negative different binomial distributions")
493s  hold off
493s ***** test
493s  [paramhat, paramci] = nbinfit ([1:50]);
493s  assert (paramhat, [2.420857, 0.086704], 1e-6);
493s  assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
493s  assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
493s ***** test
493s  [paramhat, paramci] = nbinfit ([1:20]);
493s  assert (paramhat, [3.588233, 0.254697], 1e-6);
493s  assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
493s  assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
493s ***** test
493s  [paramhat, paramci] = nbinfit ([1:10]);
493s  assert (paramhat, [8.8067, 0.6156], 1e-4);
493s  assert (paramci(:,1), [0; 30.7068], 1e-4);
493s  assert (paramci(:,2), [0.0217; 1], 1e-4);
493s ***** test
493s  [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
493s  assert (paramhat, [8.8067, 0.6156], 1e-4);
493s  assert (paramci(:,1), [0; 30.7068], 1e-4);
493s  assert (paramci(:,2), [0.0217; 1], 1e-4);
493s ***** test
493s  [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
493s  assert (paramhat, [8.8067, 0.6156], 1e-4);
493s  assert (paramci(:,1), [0; 30.7068], 1e-4);
493s  assert (paramci(:,2), [0.0217; 1], 1e-4);
493s ***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
493s ***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
493s ***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
493s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
493s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
493s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
493s ***** error<nbinfit: X and FREQ vectors mismatch.> ...
493s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
493s ***** error<nbinfit: FREQ must not contain negative values.> ...
493s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
493s ***** error<nbinfit: FREQ must contain integer values.> ...
493s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
493s ***** error<nbinfit: 'options' argument must be a structure> ...
493s  nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
493s ***** error<nbinfit: 'options' argument must be a structure> ...
493s  nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
493s 16 tests, 16 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/invglike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/invglike.m
493s ***** test
493s  nlogL = invglike ([25.5, 19.6973], [1:50]);
493s  assert (nlogL, 219.1516, 1e-4);
493s ***** test
493s  nlogL = invglike ([3, 8.1081], [1:5]);
493s  assert (nlogL, 9.0438, 1e-4);
493s ***** error<invglike: function called with too few input arguments.> invglike (3.25)
493s ***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
493s ***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
493s ***** error<invglike: PARAMS must be a two-element vector.> ...
493s  invglike ([1, 0.2, 3], [1, 3, 5, 7])
493s ***** error<invglike: X and CENSOR vector mismatch.> ...
493s  invglike ([1.5, 0.2], [1:5], [0, 0, 0])
493s ***** error<invglike: X and FREQ vector mismatch.> ...
493s  invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
493s ***** error<invglike: X and FREQ vector mismatch.> ...
493s  invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
493s 9 tests, 9 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/rayllike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/rayllike.m
493s ***** test
493s  x = [1 3 2 4 5 4 3 4];
493s  [nlogL, acov] = rayllike (3.25, x);
493s  assert (nlogL, 14.7442, 1e-4)
493s ***** test
493s  x = [1 2 3 4 5];
493s  f = [1 1 2 3 1];
493s  [nlogL, acov] = rayllike (3.25, x, [], f);
493s  assert (nlogL, 14.7442, 1e-4)
493s ***** test
493s  x = [1 2 3 4 5 6];
493s  f = [1 1 2 3 1 0];
493s  [nlogL, acov] = rayllike (3.25, x, [], f);
493s  assert (nlogL, 14.7442, 1e-4)
493s ***** test
493s  x = [1 2 3 4 5 6];
493s  c = [0 0 0 0 0 1];
493s  f = [1 1 2 3 1 0];
493s  [nlogL, acov] = rayllike (3.25, x, c, f);
493s  assert (nlogL, 14.7442, 1e-4)
493s ***** error<rayllike: function called with too few input arguments.> rayllike (1)
493s ***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
493s ***** error<rayllike: X must be a vector of non-negative values.> ...
493s  rayllike (3.25, ones (10, 2))
493s ***** error<rayllike: X must be a vector of non-negative values.> ...
493s  rayllike (3.25, [1 2 3 -4 5])
493s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
493s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
493s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
493s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
493s ***** error<rayllike: X and FREQ vectors mismatch.> ...
493s  rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
493s ***** error<rayllike: X and FREQ vectors mismatch.> ...
493s  rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
493s ***** error<rayllike: FREQ must not contain negative values.> ...
493s  rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
493s 13 tests, 13 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/evlike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/evlike.m
493s ***** test
493s  x = 1:50;
493s  [nlogL, acov] = evlike ([2.3, 1.2], x);
493s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
493s  assert (nlogL, 3.242264755689906e+17, 1e-14);
493s  assert (acov, avar_out, 1e-3);
493s ***** test
493s  x = 1:50;
493s  [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
493s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
493s  assert (nlogL, 481898704.0472211, 1e-6);
493s  assert (acov, avar_out, 1e-3);
493s ***** test
493s  x = 1:50;
493s  [nlogL, acov] = evlike ([21, 15], x);
493s  avar_out = [11.73913876598908, -5.9546128523121216; ...
493s              -5.954612852312121, 3.708060045170236];
493s  assert (nlogL, 223.7612479380652, 1e-13);
493s  assert (acov, avar_out, 1e-14);
493s ***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
493s ***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
493s ***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
493s ***** error<evlike: X and CENSOR vectors mismatch.> ...
493s  evlike ([12, 15], [1:50], [1, 2, 3])
493s ***** error<evlike: X and FREQ vectors mismatch.> ...
493s  evlike ([12, 15], [1:50], [], [1, 2, 3])
493s 8 tests, 8 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/bisalike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/bisalike.m
493s ***** test
493s  nlogL = bisalike ([16.2649, 1.0156], [1:50]);
493s  assert (nlogL, 215.5905, 1e-4);
493s ***** test
493s  nlogL = bisalike ([2.5585, 0.5839], [1:5]);
493s  assert (nlogL, 8.9950, 1e-4);
493s ***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
493s ***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
493s ***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
493s ***** error<bisalike: PARAMS must be a two-element vector.> ...
493s  bisalike ([1, 0.2, 3], [1, 3, 5, 7])
493s ***** error<bisalike: X and CENSOR vector mismatch.> ...
493s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
493s ***** error<bisalike: X and FREQ vector mismatch.> ...
493s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
493s ***** error<bisalike: X and FREQ vector mismatch.> ...
493s  bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
493s 9 tests, 9 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/gpfit.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gpfit.m
493s ***** demo
493s  ## Sample 2 populations from different generalized Pareto distributions
493s  ## Assume location parameter θ is known
493s  theta = 0;
493s  rand ("seed", 5);    # for reproducibility
493s  r1 = gprnd (1, 2, theta, 20000, 1);
493s  rand ("seed", 2);    # for reproducibility
493s  r2 = gprnd (3, 1, theta, 20000, 1);
493s  r = [r1, r2];
493s 
493s  ## Plot them normalized and fix their colors
493s  hist (r, [0.1:0.2:100], 5);
493s  h = findobj (gca, "Type", "patch");
493s  set (h(1), "facecolor", "r");
493s  set (h(2), "facecolor", "c");
493s  ylim ([0, 1]);
493s  xlim ([0, 5]);
493s  hold on
493s 
493s  ## Estimate their α and β parameters
493s  k_sigmaA = gpfit (r(:,1), theta);
493s  k_sigmaB = gpfit (r(:,2), theta);
493s 
493s  ## Plot their estimated PDFs
493s  x = [0.01, 0.1:0.2:18];
493s  y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
493s  plot (x, y, "-pc");
493s  y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
493s  plot (x, y, "-sr");
493s  hold off
493s  legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
493s           "Normalized HIST of sample 2 with k=2 and σ=2", ...
493s           sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
493s                   k_sigmaA(1), k_sigmaA(2)), ...
493s           sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
493s                   k_sigmaB(1), k_sigmaB(2))})
493s  title ("Two population samples from different generalized Pareto distributions")
493s  text (2, 0.7, "Known location parameter θ = 0")
493s  hold off
493s ***** test
493s  k = 0.8937; sigma = 1.3230; theta = 1;
493s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
493s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
493s  [hat, ci] = gpfit (x, theta);
493s  assert (hat, [k, sigma, theta], 1e-4);
493s  assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
493s ***** error<gpfit: function called with too few input arguments.> gpfit ()
493s ***** error<gpfit: function called with too few input arguments.> gpfit (1)
493s ***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
493s ***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
493s ***** error<gpfit: THETA must be a real scalar value.> ...
493s  gpfit ([0.5, 1.2], [0, 1]);
493s ***** error<gpfit: THETA must be a real scalar value.> ...
493s  gpfit ([0.5, 1.2], 5+i);
493s ***** error<gpfit: X cannot contain values less than THETA.> ...
493s  gpfit ([1:5], 2);
493s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
493s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
493s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
493s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
493s ***** error<gpfit: X and FREQ vectors mismatch.>
493s  gpfit ([1 2 3], 0, [], [1 5])
493s ***** error<gpfit: FREQ must not contain negative values.>
493s  gpfit ([1 2 3], 0, [], [1 5 -1])
493s ***** error<gpfit: 'options' 5th argument must be a structure> ...
493s  gpfit ([1:10], 1, 0.05, [], 5)
493s 15 tests, 15 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/nakalike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/nakalike.m
493s ***** test
493s  nlogL = nakalike ([0.735504, 858.5], [1:50]);
493s  assert (nlogL, 202.8689, 1e-4);
493s ***** test
493s  nlogL = nakalike ([1.17404, 11], [1:5]);
493s  assert (nlogL, 8.6976, 1e-4);
493s ***** test
493s  nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
493s  assert (nlogL, 8.6976, 1e-4);
493s ***** test
493s  nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
493s  assert (nlogL, 8.6976, 1e-4);
493s ***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
493s ***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
493s ***** error<nakalike: PARAMS must be a two-element vector.> ...
493s  nakalike ([1, 0.2, 3], [1, 3, 5, 7])
493s ***** error<nakalike: X and CENSOR vector mismatch.> ...
493s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
493s ***** error<nakalike: X and FREQ vector mismatch.> ...
493s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
493s ***** error<nakalike: X and FREQ vector mismatch.> ...
493s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
493s ***** error<nakalike: FREQ must not contain negative values.> ...
493s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
493s 11 tests, 11 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/binolike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/binolike.m
493s ***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
493s ***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
493s ***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
493s ***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
493s ***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
493s ***** test
493s  [nlogL, acov] = binolike ([3, 0.333], 3);
493s  assert (acov(4), 0.0740, 1e-4)
493s ***** error<binolike: function called with too few input arguments.> binolike (3.25)
493s ***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
493s ***** error<binolike: PARAMS must be a two-element vector.> ...
493s  binolike ([1, 0.2, 3], [1, 3, 5, 7])
493s ***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
493s ***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
493s ***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
493s ***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
493s ***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
493s ***** error<binolike: X and FREQ vectors mismatch.> ...
493s  binolike ([5, 0.5], ones (10, 1), ones (8,1))
493s ***** error<binolike: FREQ must not contain negative values.> ...
493s  binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
493s ***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
493s ***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
493s 18 tests, 18 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/tlslike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/tlslike.m
493s ***** test
493s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
493s  [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
493s  acov_out = [0.2525, 0.0670, 0.0288; ...
493s              0.0670, 0.5724, 0.1786; ...
493s              0.0288, 0.1786, 0.1789];
493s  assert (nlogL, 17.9979636579, 1e-10);
493s  assert (acov, acov_out, 1e-4);
493s ***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
493s ***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
493s ***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
493s ***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
493s ***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
493s ***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
493s 7 tests, 7 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/logilike.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/logilike.m
493s ***** test
493s  nlogL = logilike ([25.5, 8.7725], [1:50]);
493s  assert (nlogL, 206.6769, 1e-4);
493s ***** test
493s  nlogL = logilike ([3, 0.8645], [1:5]);
493s  assert (nlogL, 9.0699, 1e-4);
493s ***** error<logilike: function called with too few input arguments.> logilike (3.25)
493s ***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
493s ***** error<logilike: PARAMS must be a two-element vector.> ...
493s  logilike ([1, 0.2, 3], [1, 3, 5, 7])
493s ***** error<logilike: X and CENSOR vector mismatch.> ...
493s  logilike ([1.5, 0.2], [1:5], [0, 0, 0])
493s ***** error<logilike: X and FREQ vector mismatch.> ...
493s  logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
493s ***** error<logilike: X and FREQ vector mismatch.> ...
493s  logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
493s 8 tests, 8 passed, 0 known failure, 0 skipped
493s [inst/dist_fit/lognfit.m]
493s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/lognfit.m
493s ***** demo
493s  ## Sample 3 populations from 3 different log-normal distributions
493s  randn ("seed", 1);    # for reproducibility
493s  r1 = lognrnd (0, 0.25, 1000, 1);
493s  randn ("seed", 2);    # for reproducibility
493s  r2 = lognrnd (0, 0.5, 1000, 1);
493s  randn ("seed", 3);    # for reproducibility
493s  r3 = lognrnd (0, 1, 1000, 1);
493s  r = [r1, r2, r3];
493s 
493s  ## Plot them normalized and fix their colors
493s  hist (r, 30, 2);
493s  h = findobj (gca, "Type", "patch");
493s  set (h(1), "facecolor", "c");
493s  set (h(2), "facecolor", "g");
493s  set (h(3), "facecolor", "r");
493s  hold on
493s 
493s  ## Estimate their mu and sigma parameters
493s  mu_sigmaA = lognfit (r(:,1));
493s  mu_sigmaB = lognfit (r(:,2));
493s  mu_sigmaC = lognfit (r(:,3));
493s 
493s  ## Plot their estimated PDFs
493s  x = [0:0.1:6];
493s  y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
493s  plot (x, y, "-pr");
493s  y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
493s  plot (x, y, "-sg");
493s  y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
493s  plot (x, y, "-^c");
493s  ylim ([0, 2])
493s  xlim ([0, 6])
493s  hold off
493s  legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
493s           "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
493s           "Normalized HIST of sample 3 with mu=0, σ=1", ...
493s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
493s                   mu_sigmaA(1), mu_sigmaA(2)), ...
493s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
493s                   mu_sigmaB(1), mu_sigmaB(2)), ...
493s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
493s                   mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
493s  title ("Three population samples from different log-normal distributions")
493s  hold off
493s ***** test
493s  randn ("seed", 1);
493s  x = lognrnd (3, 5, [1000, 1]);
493s  [paramhat, paramci] = lognfit (x, 0.01);
493s  assert (paramci(1,1) < 3);
493s  assert (paramci(1,2) > 3);
493s  assert (paramci(2,1) < 5);
493s  assert (paramci(2,2) > 5);
494s ***** error<lognfit: X must be a numeric vector of positive values.> ...
494s  lognfit (ones (20,3))
494s ***** error<lognfit: X must be a numeric vector of positive values.> ...
494s  lognfit ({1, 2, 3, 4, 5})
494s ***** error<lognfit: X must be a numeric vector of positive values.> ...
494s  lognfit ([-1, 2, 3, 4, 5])
494s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
494s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
494s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
494s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
494s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
494s ***** error<lognfit: X and CENSOR vectors mismatch.> ...
494s  lognfit (ones (20,1), [], zeros(15,1))
494s ***** error<lognfit: X and FREQ vectors mismatch.> ...
494s  lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
494s ***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
494s 12 tests, 12 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/gamlike.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gamlike.m
494s ***** test
494s  [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
494s  assert (nlogL, 19.4426, 1e-4);
494s  assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
494s ***** test
494s  [nlogL, acov] = gamlike([2, 3], [5:45]);
494s  assert (nlogL, 305.8070, 1e-4);
494s  assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
494s ***** test
494s  [nlogL, acov] = gamlike([2, 13], [5:45]);
494s  assert (nlogL, 163.2261, 1e-4);
494s  assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
494s ***** error<gamlike: function called with too few input arguments.> ...
494s  gamlike ([12, 15])
494s ***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
494s ***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
494s ***** error<gamlike: X and CENSOR vectors mismatch.> ...
494s  gamlike ([12, 15], [1:50], [1, 2, 3])
494s ***** error<gamlike: X and FREQ vectors mismatch.> ...
494s  gamlike ([12, 15], [1:50], [], [1, 2, 3])
494s 8 tests, 8 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/poissfit.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/poissfit.m
494s ***** demo
494s  ## Sample 3 populations from 3 different Poisson distributions
494s  randp ("seed", 2);    # for reproducibility
494s  r1 = poissrnd (1, 1000, 1);
494s  randp ("seed", 2);    # for reproducibility
494s  r2 = poissrnd (4, 1000, 1);
494s  randp ("seed", 3);    # for reproducibility
494s  r3 = poissrnd (10, 1000, 1);
494s  r = [r1, r2, r3];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, [0:20], 1);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  set (h(3), "facecolor", "r");
494s  hold on
494s 
494s  ## Estimate their lambda parameter
494s  lambdahat = poissfit (r);
494s 
494s  ## Plot their estimated PDFs
494s  x = [0:20];
494s  y = poisspdf (x, lambdahat(1));
494s  plot (x, y, "-pr");
494s  y = poisspdf (x, lambdahat(2));
494s  plot (x, y, "-sg");
494s  y = poisspdf (x, lambdahat(3));
494s  plot (x, y, "-^c");
494s  xlim ([0, 20])
494s  ylim ([0, 0.4])
494s  legend ({"Normalized HIST of sample 1 with λ=1", ...
494s           "Normalized HIST of sample 2 with λ=4", ...
494s           "Normalized HIST of sample 3 with λ=10", ...
494s           sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
494s                   lambdahat(1)), ...
494s           sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
494s                   lambdahat(2)), ...
494s           sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
494s                   lambdahat(3))})
494s  title ("Three population samples from different Poisson distributions")
494s  hold off
494s ***** test
494s  x = [1 3 2 4 5 4 3 4];
494s  [lhat, lci] = poissfit (x);
494s  assert (lhat, 3.25)
494s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
494s ***** test
494s  x = [1 3 2 4 5 4 3 4];
494s  [lhat, lci] = poissfit (x, 0.01);
494s  assert (lhat, 3.25)
494s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
494s ***** test
494s  x = [1 2 3 4 5];
494s  f = [1 1 2 3 1];
494s  [lhat, lci] = poissfit (x, [], f);
494s  assert (lhat, 3.25)
494s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
494s ***** test
494s  x = [1 2 3 4 5];
494s  f = [1 1 2 3 1];
494s  [lhat, lci] = poissfit (x, 0.01, f);
494s  assert (lhat, 3.25)
494s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
494s ***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
494s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
494s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
494s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
494s ***** error<poissfit: X and FREQ vectors mismatch.>
494s  poissfit ([1 2 3], [], [1 5])
494s ***** error<poissfit: FREQ must not contain negative values.>
494s  poissfit ([1 2 3], [], [1 5 -1])
494s 10 tests, 10 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/raylfit.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/raylfit.m
494s ***** demo
494s  ## Sample 3 populations from 3 different Rayleigh distributions
494s  rand ("seed", 2);    # for reproducibility
494s  r1 = raylrnd (1, 1000, 1);
494s  rand ("seed", 2);    # for reproducibility
494s  r2 = raylrnd (2, 1000, 1);
494s  rand ("seed", 3);    # for reproducibility
494s  r3 = raylrnd (4, 1000, 1);
494s  r = [r1, r2, r3];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, [0.5:0.5:10.5], 2);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  set (h(3), "facecolor", "r");
494s  hold on
494s 
494s  ## Estimate their lambda parameter
494s  sigmaA = raylfit (r(:,1));
494s  sigmaB = raylfit (r(:,2));
494s  sigmaC = raylfit (r(:,3));
494s 
494s  ## Plot their estimated PDFs
494s  x = [0:0.1:10];
494s  y = raylpdf (x, sigmaA);
494s  plot (x, y, "-pr");
494s  y = raylpdf (x, sigmaB);
494s  plot (x, y, "-sg");
494s  y = raylpdf (x, sigmaC);
494s  plot (x, y, "-^c");
494s  xlim ([0, 10])
494s  ylim ([0, 0.7])
494s  legend ({"Normalized HIST of sample 1 with σ=1", ...
494s           "Normalized HIST of sample 2 with σ=2", ...
494s           "Normalized HIST of sample 3 with σ=4", ...
494s           sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
494s                   sigmaA), ...
494s           sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
494s                   sigmaB), ...
494s           sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
494s                   sigmaC)})
494s  title ("Three population samples from different Rayleigh distributions")
494s  hold off
494s ***** test
494s  x = [1 3 2 4 5 4 3 4];
494s  [shat, sci] = raylfit (x);
494s  assert (shat, 2.4495, 1e-4)
494s  assert (sci, [1.8243; 3.7279], 1e-4)
494s ***** test
494s  x = [1 3 2 4 5 4 3 4];
494s  [shat, sci] = raylfit (x, 0.01);
494s  assert (shat, 2.4495, 1e-4)
494s  assert (sci, [1.6738; 4.3208], 1e-4)
494s ***** test
494s  x = [1 2 3 4 5];
494s  f = [1 1 2 3 1];
494s  [shat, sci] = raylfit (x, [], [], f);
494s  assert (shat, 2.4495, 1e-4)
494s  assert (sci, [1.8243; 3.7279], 1e-4)
494s ***** test
494s  x = [1 2 3 4 5];
494s  f = [1 1 2 3 1];
494s  [shat, sci] = raylfit (x, 0.01, [], f);
494s  assert (shat, 2.4495, 1e-4)
494s  assert (sci, [1.6738; 4.3208], 1e-4)
494s ***** test
494s  x = [1 2 3 4 5 6];
494s  c = [0 0 0 0 0 1];
494s  f = [1 1 2 3 1 1];
494s  [shat, sci] = raylfit (x, 0.01, c, f);
494s  assert (shat, 2.4495, 1e-4)
494s  assert (sci, [1.6738; 4.3208], 1e-4)
494s ***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
494s ***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
494s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
494s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
494s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
494s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
494s  raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
494s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
494s  raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
494s ***** error<raylfit: X and FREQ vectors mismatch.> ...
494s  raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
494s ***** error<raylfit: X and FREQ vectors mismatch.> ...
494s  raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
494s ***** error<raylfit: X and FREQ vectors mismatch.>
494s  raylfit ([1 2 3], [], [], [1 5])
494s ***** error<raylfit: FREQ must not contain negative values.>
494s  raylfit ([1 2 3], [], [], [1 5 -1])
494s 16 tests, 16 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/poisslike.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/poisslike.m
494s ***** test
494s  x = [1 3 2 4 5 4 3 4];
494s  [nlogL, avar] = poisslike (3.25, x);
494s  assert (nlogL, 13.9533, 1e-4)
494s ***** test
494s  x = [1 2 3 4 5];
494s  f = [1 1 2 3 1];
494s  [nlogL, avar] = poisslike (3.25, x, f);
494s  assert (nlogL, 13.9533, 1e-4)
494s ***** error<poisslike: function called with too few input arguments.> poisslike (1)
494s ***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
494s ***** error<poisslike: X must be a vector of non-negative values.> ...
494s  poisslike (3.25, ones (10, 2))
494s ***** error<poisslike: X must be a vector of non-negative values.> ...
494s  poisslike (3.25, [1 2 3 -4 5])
494s ***** error<poisslike: X and FREQ vectors mismatch.> ...
494s  poisslike (3.25, ones (10, 1), ones (8,1))
494s ***** error<poisslike: FREQ must not contain negative values.> ...
494s  poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
494s 8 tests, 8 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/expfit.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/expfit.m
494s ***** demo
494s  ## Sample 3 populations from 3 different exponential distributions
494s  rande ("seed", 1);   # for reproducibility
494s  r1 = exprnd (2, 4000, 1);
494s  rande ("seed", 2);   # for reproducibility
494s  r2 = exprnd (5, 4000, 1);
494s  rande ("seed", 3);   # for reproducibility
494s  r3 = exprnd (12, 4000, 1);
494s  r = [r1, r2, r3];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, 48, 0.52);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  set (h(3), "facecolor", "r");
494s  hold on
494s 
494s  ## Estimate their mu parameter
494s  muhat = expfit (r);
494s 
494s  ## Plot their estimated PDFs
494s  x = [0:max(r(:))];
494s  y = exppdf (x, muhat(1));
494s  plot (x, y, "-pr");
494s  y = exppdf (x, muhat(2));
494s  plot (x, y, "-sg");
494s  y = exppdf (x, muhat(3));
494s  plot (x, y, "-^c");
494s  ylim ([0, 0.6])
494s  xlim ([0, 40])
494s  legend ({"Normalized HIST of sample 1 with μ=2", ...
494s           "Normalized HIST of sample 2 with μ=5", ...
494s           "Normalized HIST of sample 3 with μ=12", ...
494s           sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
494s           sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
494s           sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
494s  title ("Three population samples from different exponential distributions")
494s  hold off
494s ***** assert (expfit (1), 1)
494s ***** assert (expfit (1:3), 2)
494s ***** assert (expfit ([1:3]'), 2)
494s ***** assert (expfit (1:3, []), 2)
494s ***** assert (expfit (1:3, [], [], []), 2)
494s ***** assert (expfit (magic (3)), [5 5 5])
494s ***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
494s ***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
494s ***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
494s ***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
494s ***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
494s ***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
494s ***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
494s ***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
494s ***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
494s ***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
494s ***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
494s ***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
494s ***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
494s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
494s ***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
494s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
494s  [1.75 4.75 7.75])
494s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
494s  [1.75 4.75 7.75])
494s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
494s  [3.5 19/3 31/3])
494s ***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
494s ***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
494s ***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
494s ***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
494s ***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
494s ***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
494s ***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
494s              1000*eps)
494s ***** assert ([~,muci] = expfit (1:3, 0.1), ...
494s                           [0.953017262058213; 7.337731146400207], 1000*eps)
494s ***** assert ([~,muci] = expfit ([1:3;2:4]), ...
494s              [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
494s              12.385982973214016, 20.643304955356694, 28.900626937499371], ...
494s              1000*eps)
494s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
494s              100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
494s              1.184936706156216, 1.974894510260360, 2.764852314364504], ...
494s              1000*eps)
494s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
494s              [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
494s              4.587722594914109, 8.257900670845396, 11.928078746776684], ...
494s              1000*eps)
494s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
494s              [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
494s              8.081825275395081, 14.547285495711145, 21.012745716027212], ...
494s              1000*eps)
494s ***** test
494s  x = reshape (1:8, [4 2]);
494s  x(4) = NaN;
494s  [muhat,muci] = expfit (x);
494s  assert ({muhat, muci}, {[NaN, 6.5], ...
494s          [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
494s ***** test
494s  x = magic (3);
494s  censor = [0 1 0; 0 1 0; 0 1 0];
494s  freq = [1 1 0; 1 1 0; 1 1 0];
494s  [muhat,muci] = expfit (x, [], censor, freq);
494s  assert ({muhat, muci}, {[5 NaN NaN], ...
494s                  [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
494s ***** error expfit ()
494s ***** error expfit (1,2,3,4,5)
494s ***** error [a b censor] = expfit (1)
494s ***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
494s ***** error <X cannot be negative> expfit ([-1 2 3 4 5])
494s ***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
494s ***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
494s ***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
494s ***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
494s 47 tests, 47 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/binofit.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/binofit.m
494s ***** demo
494s  ## Sample 2 populations from different binomial distributions
494s  rand ("seed", 1);    # for reproducibility
494s  r1 = binornd (50, 0.15, 1000, 1);
494s  rand ("seed", 2);    # for reproducibility
494s  r2 = binornd (100, 0.5, 1000, 1);
494s  r = [r1, r2];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, 23, 0.35);
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 probability of success
494s  pshatA = binofit (r(:,1), 50);
494s  pshatB = binofit (r(:,2), 100);
494s 
494s  ## Plot their estimated PDFs
494s  x = [min(r(:,1)):max(r(:,1))];
494s  y = binopdf (x, 50, mean (pshatA));
494s  plot (x, y, "-pg");
494s  x = [min(r(:,2)):max(r(:,2))];
494s  y = binopdf (x, 100, mean (pshatB));
494s  plot (x, y, "-sc");
494s  ylim ([0, 0.2])
494s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
494s           "Normalized HIST of sample 2 with ps=0.50", ...
494s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
494s                   mean (pshatA)), ...
494s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
494s                   mean (pshatB))})
494s  title ("Two population samples from different binomial distributions")
494s  hold off
494s ***** test
494s  x = 0:3;
494s  [pshat, psci] = binofit (x, 3);
494s  assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
494s  assert (psci(1,:), [0, 0.7076], 1e-4);
494s  assert (psci(2,:), [0.0084, 0.9057], 1e-4);
494s  assert (psci(3,:), [0.0943, 0.9916], 1e-4);
494s  assert (psci(4,:), [0.2924, 1.0000], 1e-4);
494s ***** error<binofit: function called with too few input arguments.> ...
494s  binofit ([1 2 3 4])
494s ***** error<binofit: X cannot have negative values.> ...
494s  binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
494s ***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
494s ***** error<binofit: N must be a non-negative integer.> ...
494s  binofit ([1, 4, 3, 2], [1, 2, -1, 3])
494s ***** error<binofit: N must be a scalar or the same size as X.> ...
494s  binofit ([1, 4, 3, 2], [5, 5, 5])
494s ***** error<binofit: N must be at least as large as X.> ...
494s  binofit ([1, 4, 3, 2], [5, 3, 5, 5])
494s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
494s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
494s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
494s 10 tests, 10 passed, 0 known failure, 0 skipped
494s [inst/dist_fit/ricefit.m]
494s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/ricefit.m
494s ***** demo
494s  ## Sample 3 populations from different Gamma distributions
494s  randg ("seed", 5);    # for reproducibility
494s  randp ("seed", 6);
494s  r1 = ricernd (1, 2, 3000, 1);
494s  randg ("seed", 2);    # for reproducibility
494s  randp ("seed", 8);
494s  r2 = ricernd (2, 4, 3000, 1);
494s  randg ("seed", 7);    # for reproducibility
494s  randp ("seed", 9);
494s  r3 = ricernd (7.5, 1, 3000, 1);
494s  r = [r1, r2, r3];
494s 
494s  ## Plot them normalized and fix their colors
494s  hist (r, 75, 4);
494s  h = findobj (gca, "Type", "patch");
494s  set (h(1), "facecolor", "c");
494s  set (h(2), "facecolor", "g");
494s  set (h(3), "facecolor", "r");
494s  ylim ([0, 0.7]);
494s  xlim ([0, 12]);
494s  hold on
494s 
494s  ## Estimate their α and β parameters
494s  s_sigmaA = ricefit (r(:,1));
494s  s_sigmaB = ricefit (r(:,2));
494s  s_sigmaC = ricefit (r(:,3));
494s 
494s  ## Plot their estimated PDFs
494s  x = [0.01,0.1:0.2:18];
494s  y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
494s  plot (x, y, "-pr");
494s  y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
494s  plot (x, y, "-sg");
494s  y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
494s  plot (x, y, "-^c");
494s  hold off
494s  legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
494s           "Normalized HIST of sample 2 with s=2 and σ=4", ...
494s           "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
494s           sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
494s                   s_sigmaA(1), s_sigmaA(2)), ...
494s           sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
494s                   s_sigmaB(1), s_sigmaB(2)), ...
494s           sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
494s                   s_sigmaC(1), s_sigmaC(2))})
494s  title ("Three population samples from different Rician distributions")
494s  hold off
494s ***** test
494s  [paramhat, paramci] = ricefit ([1:50]);
494s  assert (paramhat, [15.3057, 17.6668], 1e-4);
494s  assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
494s ***** test
494s  [paramhat, paramci] = ricefit ([1:50], 0.01);
494s  assert (paramhat, [15.3057, 17.6668], 1e-4);
494s  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);
495s ***** assert (class (ricefit (single ([1:50]))), "single")
495s ***** error<ricefit: X must be a vector.> ricefit (ones (2))
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
495s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
495s ***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
495s ***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
495s ***** error<ricefit: FREQ cannot have negative values.> ...
495s  ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
495s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
495s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
495s 19 tests, 19 passed, 0 known failure, 0 skipped
495s [inst/dist_fit/gumbelfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gumbelfit.m
498s ***** demo
498s  ## Sample 3 populations from different Gumbel distributions
498s  rand ("seed", 1);    # for reproducibility
498s  r1 = gumbelrnd (2, 5, 400, 1);
498s  rand ("seed", 11);    # for reproducibility
498s  r2 = gumbelrnd (-5, 3, 400, 1);
498s  rand ("seed", 16);    # for reproducibility
498s  r3 = gumbelrnd (14, 8, 400, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 25, 0.32);
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, 0.28])
498s  xlim ([-11, 50]);
498s  hold on
498s 
498s  ## Estimate their MU and BETA parameters
498s  mu_betaA = gumbelfit (r(:,1));
498s  mu_betaB = gumbelfit (r(:,2));
498s  mu_betaC = gumbelfit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [min(r(:)):max(r(:))];
498s  y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
498s  plot (x, y, "-pr");
498s  y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
498s  plot (x, y, "-sg");
498s  y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
498s  plot (x, y, "-^c");
498s  legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
498s           "Normalized HIST of sample 2 with μ=-5 and β=3", ...
498s           "Normalized HIST of sample 3 with μ=14 and β=8", ...
498s           sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
498s                   mu_betaA(1), mu_betaA(2)), ...
498s           sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
498s                   mu_betaB(1), mu_betaB(2)), ...
498s           sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
498s                   mu_betaC(1), mu_betaC(2))})
498s  title ("Three population samples from different Gumbel distributions")
498s  hold off
498s ***** test
498s  x = 1:50;
498s  [paramhat, paramci] = gumbelfit (x);
498s  paramhat_out = [18.3188, 13.0509];
498s  paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
498s  assert (paramhat, paramhat_out, 1e-4);
498s  assert (paramci, paramci_out, 1e-4);
498s ***** test
498s  x = 1:50;
498s  [paramhat, paramci] = gumbelfit (x, 0.01);
498s  paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
498s  assert (paramci, paramci_out, 1e-4);
498s ***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
498s ***** error<gumbelfit: X must be a double-precision vector.> ...
498s  gumbelfit (single (ones (1,5)));
498s ***** error<gumbelfit: X must NOT contain missing values> ...
498s  gumbelfit ([1, 2, 3, 4, NaN]);
498s ***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
498s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<gumbelfit: X and FREQ vectors mismatch.> ...
498s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
498s ***** error<gamfit: FREQ must not contain negative values.>
498s  gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
498s ***** error<gumbelfit: 'options' 5th argument> ...
498s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 10 tests, 10 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/wblfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/wblfit.m
498s ***** demo
498s  ## Sample 3 populations from 3 different Weibull distributions
498s  rande ("seed", 1);    # for reproducibility
498s  r1 = wblrnd(2, 4, 2000, 1);
498s  rande ("seed", 2);    # for reproducibility
498s  r2 = wblrnd(5, 2, 2000, 1);
498s  rande ("seed", 5);    # for reproducibility
498s  r3 = wblrnd(1, 5, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 30, [2.5 2.1 3.2]);
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, 2]);
498s  xlim ([0, 10]);
498s  hold on
498s 
498s  ## Estimate their lambda parameter
498s  lambda_kA = wblfit (r(:,1));
498s  lambda_kB = wblfit (r(:,2));
498s  lambda_kC = wblfit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0:0.1:15];
498s  y = wblpdf (x, lambda_kA(1), lambda_kA(2));
498s  plot (x, y, "-pr");
498s  y = wblpdf (x, lambda_kB(1), lambda_kB(2));
498s  plot (x, y, "-sg");
498s  y = wblpdf (x, lambda_kC(1), lambda_kC(2));
498s  plot (x, y, "-^c");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
498s           "Normalized HIST of sample 2 with λ=5 and k=2", ...
498s           "Normalized HIST of sample 3 with λ=1 and k=5", ...
498s           sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
498s                   lambda_kA(1), lambda_kA(2)), ...
498s           sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
498s                   lambda_kB(1), lambda_kB(2)), ...
498s           sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
498s                   lambda_kC(1), lambda_kC(2))})
498s  title ("Three population samples from different Weibull distributions")
498s  hold off
498s ***** test
498s  x = 1:50;
498s  [paramhat, paramci] = wblfit (x);
498s  paramhat_out = [28.3636, 1.7130];
498s  paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
498s  assert (paramhat, paramhat_out, 1e-4);
498s  assert (paramci, paramci_out, 1e-4);
498s ***** test
498s  x = 1:50;
498s  [paramhat, paramci] = wblfit (x, 0.01);
498s  paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
498s  assert (paramci, paramci_out, 1e-4);
498s ***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
498s ***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
498s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
498s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
498s  wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
498s  wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<wblfit: X and FREQ vectors mismatch.> ...
498s  wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<wblfit: FREQ cannot have negative values.> ...
498s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
498s ***** error<wblfit: X and FREQ vectors mismatch.> ...
498s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<wblfit: 'options' 5th argument must be a structure> ...
498s  wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 13 tests, 13 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gplike.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gplike.m
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  [nlogL, acov] = gplike ([k, sigma, theta], x);
498s  assert (nlogL, 21.736, 1e-3);
498s  assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
498s ***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
498s ***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
498s ***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
498s ***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
498s ***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
498s ***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
498s ***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
498s ***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
498s ***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
498s ***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
498s ***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
498s ***** error<gplike: function called with too few input arguments.> gplike ()
498s ***** error<gplike: function called with too few input arguments.> gplike (1)
498s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
498s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
498s ***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
498s ***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
498s ***** error<gplike: X and FREQ vectors mismatch.> ...
498s  gplike ([1, 2, 0], ones (10, 1), ones (8,1))
498s ***** error<gplike: FREQ must not contain negative values.> ...
498s  gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
498s 20 tests, 20 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gevfit_lmom.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gevfit_lmom.m
498s ***** xtest <31070>
498s  data = 1:50;
498s  [pfit, pci] = gevfit_lmom (data);
498s  expected_p = [-0.28 15.01 20.22]';
498s  assert (pfit, expected_p, 0.1);
498s 1 test, 1 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gevlike.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/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/gamfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gamfit.m
498s ***** demo
498s  ## Sample 3 populations from different Gamma distributions
498s  randg ("seed", 5);    # for reproducibility
498s  r1 = gamrnd (1, 2, 2000, 1);
498s  randg ("seed", 2);    # for reproducibility
498s  r2 = gamrnd (2, 2, 2000, 1);
498s  randg ("seed", 7);    # for reproducibility
498s  r3 = gamrnd (7.5, 1, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 75, 4);
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, 0.62]);
498s  xlim ([0, 12]);
498s  hold on
498s 
498s  ## Estimate their α and β parameters
498s  a_bA = gamfit (r(:,1));
498s  a_bB = gamfit (r(:,2));
498s  a_bC = gamfit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0.01,0.1:0.2:18];
498s  y = gampdf (x, a_bA(1), a_bA(2));
498s  plot (x, y, "-pr");
498s  y = gampdf (x, a_bB(1), a_bB(2));
498s  plot (x, y, "-sg");
498s  y = gampdf (x, a_bC(1), a_bC(2));
498s  plot (x, y, "-^c");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
498s           "Normalized HIST of sample 2 with α=2 and β=2", ...
498s           "Normalized HIST of sample 3 with α=7.5 and β=1", ...
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 Gamma distributions")
498s  hold off
498s ***** shared x
498s  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];
498s ***** test
498s  [paramhat, paramci] = gamfit (x);
498s  assert (paramhat, [3.4248, 0.9752], 1e-4);
498s  assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
498s ***** test
498s  [paramhat, paramci] = gamfit (x, 0.01);
498s  assert (paramhat, [3.4248, 0.9752], 1e-4);
498s  assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
498s ***** test
498s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
498s  [paramhat, paramci] = gamfit (x, [], [], freq);
498s  assert (paramhat, [3.3025, 1.0615], 1e-4);
498s  assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
498s ***** test
498s  [paramhat, paramci] = gamfit (x, [], [], [1:15]);
498s  assert (paramhat, [4.4484, 0.9689], 1e-4);
498s  assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
498s ***** test
498s  [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
498s  assert (paramhat, [4.4484, 0.9689], 1e-4);
498s  assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
498s ***** test
498s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
498s  [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
498s  assert (paramhat, [4.7537, 0.9308], 1e-4);
498s  assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
498s ***** test
498s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
498s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
498s  [paramhat, paramci] = gamfit (x, [], cens, freq);
498s  assert (paramhat, [3.4736, 1.0847], 1e-4);
498s  assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
498s ***** test
498s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
498s  assert (paramhat, [Inf, 0]);
498s  assert (paramci, [Inf, 0; Inf, 0]);
498s ***** test
498s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
498s  assert (paramhat, [NaN, NaN]);
498s  assert (paramci, [NaN, NaN; NaN, NaN]);
498s ***** test
498s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
498s  assert (paramhat, [Inf, 0]);
498s  assert (paramci, [Inf, 0; Inf, 0]);
498s ***** assert (class (gamfit (single (x))), "single")
498s ***** error<gamfit: X must be a vector.> gamfit (ones (2))
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
498s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
498s ***** error<gamfit: X and FREQ vectors mismatch.>
498s  gamfit ([1 2 3], 0.05, [], [1 5])
498s ***** error<gamfit: FREQ must not contain negative values.>
498s  gamfit ([1 2 3], 0.05, [], [1 5 -1])
498s ***** error<gamfit: 'options' 5th argument must be a structure> ...
498s  gamfit ([1:10], 0.05, [], [], 5)
498s ***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
498s ***** error<gamfit: X must contain positive values when censored.> ...
498s  gamfit ([1 2 0], [], [1 0 0])
498s 23 tests, 23 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/tlsfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/tlsfit.m
498s ***** demo
498s  ## Sample 3 populations from 3 different location-scale T distributions
498s  randn ("seed", 1);    # for reproducibility
498s  randg ("seed", 2);    # for reproducibility
498s  r1 = tlsrnd (-4, 3, 1, 2000, 1);
498s  randn ("seed", 3);    # for reproducibility
498s  randg ("seed", 4);    # for reproducibility
498s  r2 = tlsrnd (0, 3, 1, 2000, 1);
498s  randn ("seed", 5);    # for reproducibility
498s  randg ("seed", 6);    # for reproducibility
498s  r3 = tlsrnd (5, 5, 4, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [-21:21], [1, 1, 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, 0.25]);
498s  xlim ([-20, 20]);
498s  hold on
498s 
498s  ## Estimate their lambda parameter
498s  mu_sigma_nuA = tlsfit (r(:,1));
498s  mu_sigma_nuB = tlsfit (r(:,2));
498s  mu_sigma_nuC = tlsfit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [-20:0.1:20];
498s  y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
498s  plot (x, y, "-pr");
498s  y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
498s  plot (x, y, "-sg");
498s  y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
498s  plot (x, y, "-^c");
498s  hold off
498s  legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
498s           "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
498s           "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
498s           sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
498s                   mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
498s           sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
498s                   mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
498s           sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
498s                   mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
498s  title ("Three population samples from different location-scale T distributions")
498s  hold off
498s ***** test
498s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
498s  [paramhat, paramci] = tlsfit (x);
498s  paramhat_out = [0.035893, 0.862711, 0.649261];
498s  paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
498s  assert (paramhat, paramhat_out, 1e-6);
498s  assert (paramci, paramci_out, 1e-5);
498s ***** test
498s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
498s  [paramhat, paramci] = tlsfit (x, 0.01);
498s  paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
498s  assert (paramci, paramci_out, 1e-4);
498s ***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
498s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
498s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
498s  tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
498s  tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
498s  tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
498s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<tlsfit: FREQ cannot have negative values.> ...
498s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
498s ***** error<tlsfit: 'options' 5th argument must be a structure> ...
498s  tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 12 tests, 12 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/hnfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/hnfit.m
498s ***** demo
498s  ## Sample 2 populations from different half-normal distributions
498s  rand ("seed", 1);   # for reproducibility
498s  r1 = hnrnd (0, 5, 5000, 1);
498s  rand ("seed", 2);   # for reproducibility
498s  r2 = hnrnd (0, 2, 5000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [0.5:20], 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 shape parameters
498s  mu_sigmaA = hnfit (r(:,1), 0);
498s  mu_sigmaB = hnfit (r(:,2), 0);
498s 
498s  ## Plot their estimated PDFs
498s  x = [0:0.2:10];
498s  y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
498s  plot (x, y, "-pr");
498s  y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
498s  plot (x, y, "-sg");
498s  xlim ([0, 10])
498s  ylim ([0, 0.5])
498s  legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
498s           "Normalized HIST of sample 2 with μ=0 and σ=2", ...
498s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
498s                   mu_sigmaA(1), mu_sigmaA(2)), ...
498s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
498s                   mu_sigmaB(1), mu_sigmaB(2))})
498s  title ("Two population samples from different half-normal distributions")
498s  hold off
498s ***** test
498s  x = 1:20;
498s  [paramhat, paramci] = hnfit (x, 0);
498s  assert (paramhat, [0, 11.9791], 1e-4);
498s  assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
498s ***** test
498s  x = 1:20;
498s  [paramhat, paramci] = hnfit (x, 0, 0.01);
498s  assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
498s ***** error<hnfit: function called with too few input arguments.> hnfit ()
498s ***** error<hnfit: function called with too few input arguments.> hnfit (1)
498s ***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
498s ***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
498s ***** error<hnfit: MU must be a real scalar value.> ...
498s  hnfit ([0.5, 1.2], [0, 1]);
498s ***** error<hnfit: MU must be a real scalar value.> ...
498s  hnfit ([0.5, 1.2], 5+i);
498s ***** error<hnfit: X cannot contain values less than MU.> ...
498s  hnfit ([1:5], 2);
498s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
498s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
498s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
498s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
498s ***** error<hnfit: X and FREQ vectors mismatch.>
498s  hnfit ([1 2 3], 0, [], [1 5])
498s ***** error<hnfit: FREQ must not contain negative values.>
498s  hnfit ([1 2 3], 0, [], [1 5 -1])
498s 15 tests, 15 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/geofit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/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/nakafit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/nakafit.m
498s ***** demo
498s  ## Sample 3 populations from different Nakagami distributions
498s  randg ("seed", 5)  # for reproducibility
498s  r1 = nakarnd (0.5, 1, 2000, 1);
498s  randg ("seed", 2)   # for reproducibility
498s  r2 = nakarnd (5, 1, 2000, 1);
498s  randg ("seed", 7)   # for reproducibility
498s  r3 = nakarnd (2, 2, 2000, 1);
498s  r = [r1, r2, r3];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, [0.05:0.1:3.5], 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, 2.5]);
498s  xlim ([0, 3.0]);
498s  hold on
498s 
498s  ## Estimate their MU and LAMBDA parameters
498s  mu_omegaA = nakafit (r(:,1));
498s  mu_omegaB = nakafit (r(:,2));
498s  mu_omegaC = nakafit (r(:,3));
498s 
498s  ## Plot their estimated PDFs
498s  x = [0.01:0.1:3.01];
498s  y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
498s  plot (x, y, "-pr");
498s  y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
498s  plot (x, y, "-sg");
498s  y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
498s  plot (x, y, "-^c");
498s  legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
498s           "Normalized HIST of sample 2 with μ=5 and ω=1", ...
498s           "Normalized HIST of sample 3 with μ=2 and ω=2", ...
498s           sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
498s                   mu_omegaA(1), mu_omegaA(2)), ...
498s           sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
498s                   mu_omegaB(1), mu_omegaB(2)), ...
498s           sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
498s                   mu_omegaC(1), mu_omegaC(2))})
498s  title ("Three population samples from different Nakagami distributions")
498s  hold off
498s ***** test
498s  paramhat = nakafit ([1:50]);
498s  paramhat_out = [0.7355, 858.5];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = nakafit ([1:5]);
498s  paramhat_out = [1.1740, 11];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
498s  paramhat_out = [1.1740, 11];
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** test
498s  paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
498s  paramhat_out = nakafit ([1:5, 5]);
498s  assert (paramhat, paramhat_out, 1e-4);
498s ***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
498s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
498s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
498s  nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
498s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
498s  nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
498s ***** error<nakafit: X and FREQ vectors mismatch.> ...
498s  nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
498s ***** error<nakafit: X and FREQ vectors mismatch.> ...
498s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
498s ***** error<nakafit: FREQ must not contain negative values.> ...
498s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
498s ***** error<nakafit: FREQ must contain integer values.> ...
498s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
498s ***** error<nakafit: 'options' 5th argument must be a structure> ...
498s  nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
498s 15 tests, 15 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/gevfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fit/gevfit.m
498s ***** demo
498s  ## Sample 2 populations from 2 different exponential distributions
498s  rand ("seed", 1);   # for reproducibility
498s  r1 = gevrnd (-0.5, 1, 2, 5000, 1);
498s  rand ("seed", 2);   # for reproducibility
498s  r2 = gevrnd (0, 1, -4, 5000, 1);
498s  r = [r1, r2];
498s 
498s  ## Plot them normalized and fix their colors
498s  hist (r, 50, 5);
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 k, sigma, and mu parameters
498s  k_sigma_muA = gevfit (r(:,1));
498s  k_sigma_muB = gevfit (r(:,2));
498s 
498s  ## Plot their estimated PDFs
498s  x = [-10:0.5:20];
498s  y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
498s  plot (x, y, "-pr");
498s  y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
498s  plot (x, y, "-sg");
498s  ylim ([0, 0.7])
498s  xlim ([-7, 5])
498s  legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
498s           "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
498s      sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
498s                  k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
498s      sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
498s                  k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
498s  title ("Two population samples from different exponential distributions")
498s  hold off
498s ***** test
498s  x = 1:50;
498s  [pfit, pci] = gevfit (x);
498s  pfit_out = [-0.4407, 15.1923, 21.5309];
498s  pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
498s  assert (pfit, pfit_out, 1e-3);
498s  assert (pci, pci_out, 1e-3);
498s ***** test
498s  x = 1:2:50;
498s  [pfit, pci] = gevfit (x);
498s  pfit_out = [-0.4434, 15.2024, 21.0532];
498s  pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
498s  assert (pfit, pfit_out, 1e-3);
498s  assert (pci, pci_out, 1e-3);
498s ***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
498s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
498s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
498s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
498s ***** error<gevfit: X and FREQ vectors mismatch.> ...
498s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
498s ***** error<gevfit: FREQ must not contain negative values.> ...
498s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
498s ***** error<gevfit: FREQ must contain integer values.> ...
498s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
498s ***** error<gevfit: 'options' argument must be a structure> ...
498s  gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
498s ***** error<gevfit: 'options' argument must be a structure> ...
498s  gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
498s 11 tests, 11 passed, 0 known failure, 0 skipped
498s [inst/dist_fit/unidfit.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/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/nanmin.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/nanmin.m
498s ***** demo
498s  ## Find the column minimum values and their indices
498s  ## for matrix data with missing values.
498s 
498s  x = magic (3);
498s  x([1, 6:9]) = NaN
498s  [y, ind] = nanmin (x)
498s ***** demo
498s  ## Find the minimum of all the values in an array, ignoring missing values.
498s  ## Create a 2-by-5-by-3 array x with some missing values.
498s 
498s  x = reshape (1:30, [2, 5, 3]);
498s  x([10:12, 25]) = NaN
498s 
498s  ## Find the minimum of the elements of x.
498s 
498s  y = nanmin (x, [], 'all')
498s ***** assert (nanmin ([2, 4, NaN, 7]), 2)
498s ***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
498s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
498s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
498s ***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
498s ***** shared x, y
498s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
498s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
498s  y = x;
498s  y(2,3,1) = 0.51;
498s ***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
498s ***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
498s ***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
498s ***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
498s ***** assert (nanmin (x, [], 'all'), -2.95)
498s ***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
498s ***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
498s ***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
498s ***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
498s ***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
498s ***** test
498s  xx = repmat ([1:20;6:25], [5 2 6 3]);
498s  assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
498s  assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
498s  assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
498s  assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
498s  assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
498s ***** assert (nanmin (ones (2), [], 3), ones (2, 2))
498s ***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
498s ***** assert (nanmin (magic (3), [], 3), magic (3))
498s ***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
498s ***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
498s ***** assert (nanmin (ones (2), 3), ones (2,2))
498s ***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
498s  nanmin (y, [], [1, 1, 2])
498s ***** error <nanmin: a second output is not supported with this syntax.> ...
498s  [v, idx] = nanmin(x, y, [1 2])
498s 24 tests, 24 passed, 0 known failure, 0 skipped
498s [inst/vartest2.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/vartest2.m
498s ***** error<vartest2: too few input arguments.> vartest2 ();
498s ***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
498s ***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
498s  vartest2 (rand (20,1), 5);
498s ***** error<vartest2: invalid value for alpha.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
498s ***** error<vartest2: invalid value for alpha.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
498s ***** error<vartest2: invalid value for alpha.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
498s ***** error<vartest2: invalid value for alpha.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
498s ***** error<vartest2: invalid value for tail.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
498s ***** error<vartest2: invalid value for tail.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
498s ***** error<vartest2: invalid value for operating dimension.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
498s ***** error<vartest2: invalid value for operating dimension.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
498s ***** error<vartest2: invalid name for optional arguments.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
498s ***** error<vartest2: optional arguments must be in name/value pairs.> ...
498s  vartest2 (rand (20,1), rand (25,1)*2, "some");
498s ***** test
498s  load carsmall
498s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
498s  assert (h, 0);
498s  assert (pval, 0.6288022362718455, 1e-13);
498s  assert (ci, [0.4139; 1.7193], 1e-4);
498s  assert (stat.fstat, 0.8384, 1e-4);
498s  assert (stat.df1, 30);
498s  assert (stat.df2, 33);
498s ***** test
498s  load carsmall
498s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
498s                                  "tail", "left");
498s  assert (h, 0);
498s  assert (pval, 0.314401118135922, 1e-13);
498s  assert (ci, [0; 1.5287], 1e-4);
498s  assert (stat.fstat, 0.8384, 1e-4);
498s  assert (stat.df1, 30);
498s  assert (stat.df2, 33);
498s ***** test
498s  load carsmall
498s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
498s                                  "tail", "right");
498s  assert (h, 0);
498s  assert (pval, 0.685598881864077, 1e-13);
498s  assert (ci, [0.4643; Inf], 1e-4);
498s  assert (stat.fstat, 0.8384, 1e-4);
498s  assert (stat.df1, 30);
498s  assert (stat.df2, 33);
498s 16 tests, 16 passed, 0 known failure, 0 skipped
498s [inst/einstein.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/einstein.m
498s ***** demo
498s  einstein (0.4, 0.6)
498s ***** demo
498s  einstein (0.2, 0.5)
498s ***** demo
498s  einstein (0.6, 0.1)
498s ***** test
498s  hf = figure ("visible", "off");
498s  unwind_protect
498s    tiles = einstein (0.4, 0.6);
498s    assert (isstruct (tiles), true);
498s  unwind_protect_cleanup
498s    close (hf);
498s  end_unwind_protect
498s ***** error<Invalid call to einstein.  Correct usage is> einstein
498s ***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
498s ***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
498s ***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
498s ***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
498s 6 tests, 6 passed, 0 known failure, 0 skipped
498s [inst/ridge.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ridge.m
498s ***** demo
498s  ## Perform ridge regression for a range of ridge parameters and observe
498s  ## how the coefficient estimates change based on the acetylene dataset.
498s 
498s  load acetylene
498s 
498s  X = [x1, x2, x3];
498s 
498s  x1x2 = x1 .* x2;
498s  x1x3 = x1 .* x3;
498s  x2x3 = x2 .* x3;
498s 
498s  D = [x1, x2, x3, x1x2, x1x3, x2x3];
498s 
498s  k = 0:1e-5:5e-3;
498s 
498s  b = ridge (y, D, k);
498s 
498s  figure
498s  plot (k, b, "LineWidth", 2)
498s  ylim ([-100, 100])
498s  grid on
498s  xlabel ("Ridge Parameter")
498s  ylabel ("Standardized Coefficient")
498s  title ("Ridge Trace")
498s  legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")
498s 
498s ***** demo
498s 
498s  load carbig
498s  X = [Acceleration Weight Displacement Horsepower];
498s  y = MPG;
498s 
498s  n = length(y);
498s 
498s  rand("seed",1); % For reproducibility
498s 
498s  c = cvpartition(n,'HoldOut',0.3);
498s  idxTrain = training(c,1);
498s  idxTest = ~idxTrain;
498s 
498s  idxTrain = training(c,1);
498s  idxTest = ~idxTrain;
498s 
498s  k = 5;
498s  b = ridge(y(idxTrain),X(idxTrain,:),k,0);
498s 
498s  % Predict MPG values for the test data using the model.
498s  yhat = b(1) + X(idxTest,:)*b(2:end);
498s  scatter(y(idxTest),yhat)
498s 
498s  hold on
498s  plot(y(idxTest),y(idxTest),"r")
498s  xlabel('Actual MPG')
498s  ylabel('Predicted MPG')
498s  hold off
498s 
498s ***** test
498s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
498s  assert (b, [0.5533; 0.5533], 1e-4);
498s ***** test
498s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
498s  assert (b, [0.4841; 0.4841], 1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0);
498s  assert (b,[10.2273;1.97128;-0.601818],1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0.0005);
498s  assert (b,[10.2233;1.9712;-0.6056],1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0.001);
498s  assert (b,[10.2194;1.9711;-0.6094],1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0.002);
498s  assert (b,[10.2116;1.9709;-0.6169],1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0.005);
498s  assert (b,[10.1882;1.9704;-0.6393],1e-4);
498s ***** test
498s  load acetylene
498s  x = [x1, x2, x3];
498s  b = ridge (y, x, 0.01);
498s  assert (b,[10.1497;1.9695;-0.6761],1e-4);
498s ***** error<ridge: function called with too few input arguments.> ridge (1)
498s ***** error<ridge: function called with too few input arguments.> ridge (1, 2)
498s ***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
498s ***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
498s ***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
498s ***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
498s ***** error<ridge: Y and X must contain the same number of rows.> ...
498s  ridge ([1; 2; 3; 4; 5], ones (3), 3)
498s ***** error<ridge: wrong value for SCALED argument.> ...
498s  ridge ([1; 2; 3], ones (3), 3, 2)
498s ***** error<ridge: wrong value for SCALED argument.> ...
498s  ridge ([1; 2; 3], ones (3), 3, "some")
498s 17 tests, 17 passed, 0 known failure, 0 skipped
498s [inst/procrustes.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/procrustes.m
498s ***** demo
498s  ## Create some random points in two dimensions
498s  n = 10;
498s  randn ("seed", 1);
498s  X = normrnd (0, 1, [n, 2]);
498s 
498s  ## Those same points, rotated, scaled, translated, plus some noise
498s  S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
498s  Y = normrnd (0.5*X*S + 2, 0.05, n, 2);
498s 
498s  ## Conform Y to X, plot original X and Y, and transformed Y
498s  [d, Z] = procrustes (X, Y);
498s  plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
498s ***** demo
498s  ## Find Procrustes distance and plot superimposed shape
498s 
498s  X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
498s  Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
498s  plot (X(:,1),X(:,2),"x");
498s  hold on
498s  plot (Y(:,1),Y(:,2),"o");
498s  xlim ([0 100]);
498s  ylim ([0 100]);
498s  legend ("Target shape (X)", "Source shape (Y)");
498s  [d, Z] = procrustes (X, Y)
498s  plot (Z(:,1), Z(:,2), "s");
498s  legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
498s  hold off
498s ***** demo
498s  ## Apply Procrustes transformation to larger set of points
498s 
498s  ## Create matrices with landmark points for two triangles
498s  X = [5, 0; 5, 5; 8, 5];   # target
498s  Y = [0, 0; 1, 0; 1, 1];   # source
498s 
498s  ## Create a matrix with more points on the source triangle
498s  Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
498s          linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
498s          linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];
498s 
498s  ## Plot both shapes, including the larger set of points for the source shape
498s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
498s  hold on
498s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
498s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
498s  xlim ([-1 10]);
498s  ylim ([-1 6]);
498s  legend ("Target shape (X)", "Source shape (Y)", ...
498s          "More points on Y", "Location", "northwest");
498s  hold off
498s 
498s  ## Obtain the Procrustes transformation
498s  [d, Z, transform] = procrustes (X, Y)
498s 
498s  ## Use the Procrustes transformation to superimpose the more points (Y_mp)
498s  ## on the source shape onto the target shape, and then visualize the results.
498s  Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
498s  figure
498s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
498s  hold on
498s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
498s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
498s  xlim ([-1 10]);
498s  ylim ([-1 6]);
498s  plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
498s  plot (Z_mp(:,1),Z_mp(:,2),"ks");
498s  legend ("Target shape (X)", "Source shape (Y)", ...
498s          "More points on Y", "Transformed source shape (Z)", ...
498s          "Transformed additional points", "Location", "northwest");
498s  hold off
498s ***** demo
498s  ## Compare shapes without reflection
498s 
498s  T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
498s       28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
498s  S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
498s       52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
498s  plot (T(:,1), T(:,2), "x-");
498s  hold on
498s  plot (S(:,1), S(:,2), "o-");
498s  legend ("Target shape (d)", "Source shape (b)");
498s  hold off
498s  d_false = procrustes (T, S, "reflection", false);
498s  printf ("Procrustes distance without reflection: %f\n", d_false);
498s  d_true = procrustes (T, S, "reflection", true);
498s  printf ("Procrustes distance with reflection: %f\n", d_true);
498s  d_best = procrustes (T, S, "reflection", "best");
498s  printf ("Procrustes distance with best fit: %f\n", d_true);
498s ***** error procrustes ();
498s ***** error procrustes (1, 2, 3, 4, 5, 6);
498s ***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
498s  procrustes (ones (2, 2, 2), ones (2, 2, 2));
498s ***** error<procrustes: values in X and Y must be real.> ...
498s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
498s ***** error<procrustes: values in X and Y must be real.> ...
498s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
498s ***** error<procrustes: values in X and Y must be real.> ...
498s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
498s ***** error<procrustes: X and Y must have equal number of rows.> ...
498s  procrustes (ones (10 ,3), ones (11, 3));
498s ***** error<procrustes: X must have at least as many columns as Y.> ...
498s  procrustes (ones (10 ,3), ones (10, 4));
498s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "reflection");
498s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), true);
498s ***** error<procrustes: invalid value for scaling.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
498s ***** error<procrustes: invalid value for scaling.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
498s ***** error<procrustes: invalid value for reflection.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
498s ***** error<procrustes: invalid value for reflection.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
498s ***** error<procrustes: invalid name for optional arguments.> ...
498s  procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
498s 15 tests, 15 passed, 0 known failure, 0 skipped
498s [inst/confusionchart.m]
498s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/confusionchart.m
498s ***** demo
498s  close all
498s  ## Setting the chart properties
498s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
498s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
498s  confusionchart (Yt, Yp, "Title", ...
498s    "Demonstration with summaries","Normalization",...
498s    "absolute","ColumnSummary", "column-normalized","RowSummary",...
498s    "row-normalized")
498s ***** demo
498s  close all
498s  ## Cellstr as inputs
498s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
498s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
498s  m = confusionmat (Yt, Yp);
498s  confusionchart (m, {"Positive", "Negative"});
498s  hold off
498s ***** demo
498s  close all
498s  ## Editing the object properties
498s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
498s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
498s  cm = confusionchart (Yt, Yp);
498s  cm.Title = "This is an example with a green diagonal";
498s  cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
498s  hold off
498s ***** demo
498s  close all
498s  ## Confusion chart in a uipanel
498s  h = uipanel ();
498s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
498s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
498s  cm = confusionchart (h, Yt, Yp);
498s  hold off
498s ***** demo
498s  close all
498s  ## Sorting classes
498s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
498s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
498s  cm = confusionchart (Yt, Yp, "Title", ...
498s    "Classes are sorted in ascending order");
498s  cm = confusionchart (Yt, Yp, "Title", ...
498s    "Classes are sorted according to clusters");
498s  sortClasses (cm, "cluster");
498s ***** shared visibility_setting
498s  visibility_setting = get (0, "DefaultFigureVisible");
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ()", "Invalid call");
498s  set (0, "DefaultFigureVisible", visibility_setting);
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
498s  set (0, "DefaultFigureVisible", visibility_setting);
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
498s  set (0, "DefaultFigureVisible", visibility_setting);
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
498s  set (0, "DefaultFigureVisible", visibility_setting);
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
498s        ".* YLabel .* string");
498s  set (0, "DefaultFigureVisible", visibility_setting);
498s ***** test
498s  set (0, "DefaultFigureVisible", "off");
498s  fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
498s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
499s        ".* FontName .* string");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
499s        ".* FontSize .* numeric");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
499s        ".* DiagonalColor .* color");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
499s        ".* OffDiagonalColor .* color");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
499s        ".* invalid .* Normalization");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
499s        ".* invalid .* ColumnSummary");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
499s        ".* invalid .* RowSummary");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
499s        ".* invalid .* GridVisible");
499s  set (0, "DefaultFigureVisible", visibility_setting);
499s ***** test
499s  set (0, "DefaultFigureVisible", "off");
499s  fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
499s        ".* invalid .* HandleVisibility");
499s  set (0, "DefaultFigureVisible", visibility_setting);
500s ***** test
500s  set (0, "DefaultFigureVisible", "off");
500s  fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
500s        ".* invalid .* OuterPosition");
500s  set (0, "DefaultFigureVisible", visibility_setting);
500s ***** test
500s  set (0, "DefaultFigureVisible", "off");
500s  fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
500s        ".* invalid .* Position");
500s  set (0, "DefaultFigureVisible", visibility_setting);
500s ***** test
500s  set (0, "DefaultFigureVisible", "off");
500s  fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
500s  set (0, "DefaultFigureVisible", visibility_setting);
500s 18 tests, 18 passed, 0 known failure, 0 skipped
500s [inst/trimmean.m]
500s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/trimmean.m
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  out = trimmean (x, 10, [1, 2]);
500s  assert (out(1,1,1), 10.3889, 1e-4);
500s  assert (out(1,1,2), 29.6111, 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  out = trimmean (x, 10, 1);
500s  assert (out(:,:,1), [-17.6, 8, 13, 18]);
500s  assert (out(:,:,2), [23, 28, 33, 10.6]);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, 1);
500s  assert (out(:,:,1), [-23, 8, 13, 18]);
500s  assert (out(:,:,2), [23, 28, 33, 3.75]);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  out = trimmean (x, 10, 2);
500s  assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
500s  assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, 2);
500s  assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
500s  assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  out = trimmean (x, 10, [1, 2, 3]);
500s  assert (out, trimmean (x, 10, "all"));
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, [1, 2]);
500s  assert (out(1,1,1), 10.7647, 1e-4);
500s  assert (out(1,1,2), 29.1176, 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, [1, 3]);
500s  assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, [2, 3]);
500s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, [1, 2, 3]);
500s  assert (out, trimmean (x, 10, "all"));
500s ***** test
500s  x = reshape (1:40, [5, 4, 2]);
500s  x([3, 37]) = -100;
500s  x([4, 38]) = NaN;
500s  out = trimmean (x, 10, [2, 3, 5]);
500s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
500s ***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
500s ***** assert (trimmean ([], 10), NaN)
500s ***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
500s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
500s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
500s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
500s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
500s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
500s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
500s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
500s  trimmean ([1 2 3 4], 10, -1)
500s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
500s  trimmean ([1 2 3 4], 10, "floor", -1)
500s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
500s  trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
500s ***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
500s  trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
500s 26 tests, 26 passed, 0 known failure, 0 skipped
500s [inst/knnsearch.m]
500s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/knnsearch.m
500s ***** demo
500s  ## find 10 nearest neighbour of a point using different distance metrics
500s  ## and compare the results by plotting
500s  load fisheriris
500s  X = meas(:,3:4);
500s  Y = species;
500s  point = [5, 1.45];
500s 
500s  ## calculate 10 nearest-neighbours by minkowski distance
500s  [id, d] = knnsearch (X, point, "K", 10);
500s 
500s  ## calculate 10 nearest-neighbours by minkowski distance
500s  [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);
500s 
500s  ## calculate 10 nearest-neighbours by chebychev distance
500s  [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");
500s 
500s  ## plotting the results
500s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
500s  title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
500s  xlabel("Petal length (cm)");
500s  ylabel("Petal width (cm)");
500s 
500s  line (point(1), point(2), "marker", "X", "color", "k", ...
500s        "linewidth", 2, "displayname", "query point")
500s  line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
500s        "linestyle", "none", "markersize", 10, "displayname", "euclidean")
500s  line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
500s        "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
500s  line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
500s        "linestyle", "none", "markersize", 10, "displayname", "chebychev")
500s  xlim ([4.5 5.5]);
500s  ylim ([1 2]);
500s  axis square;
500s ***** demo
500s  ## knnsearch on iris dataset using kdtree method
500s  load fisheriris
500s  X = meas(:,3:4);
500s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
500s  title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");
500s 
500s  ## new point to be predicted
500s  point = [5 1.45];
500s 
500s  line (point(1), point(2), "marker", "X", "color", "k", ...
500s        "linewidth", 2, "displayname", "query point")
500s 
500s  ## knnsearch using kdtree method
500s  [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");
500s 
500s  ## plotting predicted neighbours
500s  line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
500s        "linestyle", "none", "markersize", 10, ...
500s        "displayname", "nearest neighbour")
500s  xlim ([4 6])
500s  ylim ([1 3])
500s  axis square
500s  ## details of predicted labels
500s  tabulate (species(idx))
500s 
500s  ctr = point - d(end);
500s  diameter = 2 * d(end);
500s  ##  Draw a circle around the 10 nearest neighbors.
500s  h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);
500s 
500s  ## here only 8 neighbours are plotted instead of 10 since the dataset
500s  ## contains duplicate values
500s ***** shared X, Y
500s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
500s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * sqrt (2));
500s ***** test
500s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
500s  [idx, D] = knnsearch (X, Y, "Distance", eucldist);
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * sqrt (2));
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
500s  assert (iscell (idx), true);
500s  assert (iscell (D), true)
500s  assert (idx {1}, [1]);
500s  assert (idx {2}', [1, 2]);
500s  assert (D{1}, ones (1, 1) * sqrt (2));
500s  assert (D{2}', ones (1, 2) * sqrt (2));
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
500s  assert (idx, [1, 2; 1, 2]);
500s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * sqrt (2));
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
500s  assert (idx, [1, 2; 1, 2]);
500s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
500s ***** test
500s  xx = [1, 2; 1, 3; 2, 4; 3, 6];
500s  yy = [2, 4; 2, 6];
500s  [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
500s  assert (idx, [3; 2]);
500s  assert (D, [0; 3.162277660168377], 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * sqrt (2));
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
500s  assert (idx, [1; 1]);
500s  assert (D, [2; 2]);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
500s  assert (idx, [1; 1]);
500s  assert (D, [1; 1]);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "cosine");
500s  assert (idx, [2; 3]);
500s  assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "correlation");
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "spearman");
500s  assert (idx, [1; 1]);
500s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "hamming");
500s  assert (idx, [1; 1]);
500s  assert (D, [0.5; 0.5]);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
500s  assert (idx, [1; 1]);
500s  assert (D, [0.5; 0.5]);
500s ***** test
500s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
500s  assert (idx, [1, 2; 1, 2]);
500s  assert (D, [0.5, 1; 0.5, 0.5]);
500s ***** test
500s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
500s  b = [1, 1];
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
500s  assert (iscell (idx), true);
500s  assert (iscell (D), true)
500s  assert (cell2mat (idx)', [4, 2, 3, 6, 1, 5, 7, 9]);
500s  assert (cell2mat (D)', [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
500s ***** test
500s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
500s  b = [1, 1];
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
500s  assert (iscell (idx), true);
500s  assert (iscell (D), true)
500s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
500s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
500s ***** test
500s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
500s  b = [1, 1];
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
500s  assert (iscell (idx), false);
500s  assert (iscell (D), false)
500s  assert (idx, [4, 2, 3, 6, 1]);
500s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
500s ***** test
500s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
500s  b = [1, 1];
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
500s  assert (iscell (idx), false);
500s  assert (iscell (D), false)
500s  assert (idx, [4, 2, 3, 6, 1]);
500s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
500s ***** test
500s  load fisheriris
500s  a = meas;
500s  b = min(meas);
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
500s  assert (idx, [42, 9, 14, 39, 13]);
500s  assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874], 1e-4);
500s ***** test
500s  load fisheriris
500s  a = meas;
500s  b = mean(meas);
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
500s  assert (idx, [65, 83, 89, 72, 100]);
500s  assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625], 1e-4);
500s ***** test
500s  load fisheriris
500s  a = meas;
500s  b = max(meas);
500s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
500s  assert (idx, [118, 132, 110, 106, 136]);
500s  assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
500s 
500s ***** test
500s  load fisheriris
500s  a = meas;
500s  b = max(meas);
500s  [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
500s  assert (iscell (idx), true);
500s  assert (iscell (D), true);
500s  assert (cell2mat (idx)', [118, 132, 110, 106, 136]);
500s  assert (cell2mat (D)', [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
500s ***** error<knnsearch: too few input arguments.> knnsearch (1)
500s ***** error<knnsearch: number of columns in X and Y must match.> ...
500s  knnsearch (ones (4, 5), ones (4))
500s ***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
500s  knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
500s ***** error<knnsearch: only a single distance parameter can be defined.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
500s ***** error<knnsearch: invalid value of K.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "K", 0)
500s ***** error<knnsearch: invalid value of Minkowski Exponent.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "P", -2)
500s ***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
500s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
500s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
500s ***** error<knnsearch: invalid value of bucketsize.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
500s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
500s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
500s 42 tests, 42 passed, 0 known failure, 0 skipped
500s [inst/bar3.m]
500s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/bar3.m
500s ***** demo
500s  ## Plotting 5 bars in the same series.
500s 
500s  z = [50; 40; 30; 20; 10];
500s  bar3 (z);
500s ***** demo
500s  ## Plotting 5 bars in different groups.
500s 
500s  z = [50, 40, 30, 20, 10];
500s  bar3 (z);
500s ***** demo
500s  ## A 3D bar graph with each series corresponding to a column in z.
500s 
500s  z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
500s  bar3 (z);
500s ***** demo
500s  ## Specify y-axis locations as tick names. y must be a column vector!
500s 
500s  y = [1950, 1960, 1970, 1980, 1990]';
500s  z = [16, 8, 4, 2, 1]';
500s  bar3 (y, z);
500s ***** demo
500s  ## Plot 3 series as a grouped plot without any space between the grouped bars
500s 
500s  z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
500s  bar3 (z, 1, 'grouped');
500s ***** demo
500s  ## Plot a stacked style 3D bar graph
500s 
500s  z = [19, 30, 21, 30; 40, 16, 32, 12];
500s  b = bar3 (z, 0.5, 'stacked');
500s ***** error <bar3: Z must be numeric.> bar3 ("A")
500s ***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
500s ***** error <bar3: inconsistent size in Y and Z input arguments.> ...
500s  bar3 ([1,2,3]', ones (2))
500s ***** error <bar3: WIDTH must be a scalar in the range> ...
500s  bar3 ([1:5], 1.2)
500s ***** error <bar3: WIDTH must be a scalar in the range> ...
500s  bar3 ([1:5]', ones (5), 1.2)
500s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
500s  bar3 ([1:5]', ones (5), [0.8, 0.7])
500s ***** error <bar3: missing value for optional argument 'width'.> ...
500s  bar3 (ones (5), 'width')
500s ***** error <bar3: invalid value for optional argument 'width'.> ...
500s  bar3 (ones (5), 'width', 1.2)
500s ***** error <bar3: invalid value for optional argument 'width'.> ...
500s  bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
500s ***** error <bar3: missing value for optional argument 'color'.> ...
500s  bar3 (ones (5), 'color')
500s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
500s  bar3 (ones (5), 'color', [0.8, 0.8])
500s ***** error <bar3: invalid value for optional argument 'color'.> ...
500s  bar3 (ones (5), 'color', "brown")
500s ***** error <bar3: invalid value for optional argument 'color'.> ...
500s  bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
500s ***** error <bar3: missing value for optional argument 'xlabel'.> ...
500s  bar3 (ones (5), 'xlabel')
500s ***** error <bar3: invalid value for optional argument 'xlabel'.> ...
500s  bar3 (ones (5), 'xlabel', 4)
500s ***** error <bar3: missing value for optional argument 'ylabel'.> ...
500s  bar3 (ones (5), 'ylabel')
500s ***** error <bar3: invalid value for optional argument 'ylabel'.> ...
500s  bar3 (ones (5), 'ylabel', 4)
500s ***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
500s ***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
500s  bar3 (ones (5), 'xlabel', {"A", "B", "C"})
500s ***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
500s  bar3 (ones (5), 'ylabel', {"A", "B", "C"})
500s 20 tests, 20 passed, 0 known failure, 0 skipped
500s [inst/normalise_distribution.m]
500s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/normalise_distribution.m
500s ***** test
500s  v = normalise_distribution ([1 2 3], [], 1);
500s  assert (v, [0 0 0])
500s ***** test
500s  v = normalise_distribution ([1 2 3], [], 2);
500s  assert (v, norminv ([1 3 5] / 6), 3 * eps)
500s ***** test
500s  v = normalise_distribution ([1 2 3]', [], 2);
500s  assert (v, [0 0 0]')
500s ***** test
500s  v = normalise_distribution ([1 2 3]', [], 1);
500s  assert (v, norminv ([1 3 5]' / 6), 3 * eps)
500s ***** test
500s  v = normalise_distribution ([1 1 2 2 3 3], [], 2);
500s  assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
500s ***** test
500s  v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
500s  assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
500s ***** test
500s  A = randn ( 10 );
500s  N = normalise_distribution (A, @normcdf);
500s  assert (A, N, 10000 * eps)
500s ***** test
500s  A = exprnd (1, 100);
500s  N = normalise_distribution (A, @(x)(expcdf (x, 1)));
500s  assert (mean (vec (N)), 0, 0.1)
500s  assert (std (vec (N)), 1, 0.1)
501s ***** test
501s  A = rand (1000,1);
501s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
501s  assert (mean (vec (N)), 0, 0.2)
501s  assert (std (vec (N)), 1, 0.1)
501s ***** test
501s  A = [rand(1000,1), randn(1000, 1)];
501s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
501s  assert (mean (N), [0, 0], 0.2)
501s  assert (std (N), [1, 1], 0.1)
501s ***** test
501s  A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
501s  N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
501s  assert (mean (N, 2), [0, 0, 0]', 0.2);
501s  assert (std (N, [], 2), [1, 1, 1]', 0.1);
501s ***** xtest
501s  A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
501s  N = normalise_distribution (A);
501s  assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
501s  assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
501s ***** test
501s ***** error normalise_distribution (zeros (3, 4), ...
501s  {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
501s 14 tests, 14 passed, 0 known failure, 0 skipped
501s [inst/cdfplot.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cdfplot.m
501s ***** demo
501s  x = randn(100,1);
501s  cdfplot (x);
501s ***** test
501s  hf = figure ("visible", "off");
501s  unwind_protect
501s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
501s    [hCDF, stats] = cdfplot (x);
501s    assert (stats.min, 2);
501s    assert (stats.max, 6);
501s    assert (stats.median, 3.5);
501s    assert (stats.std, 1.35400640077266, 1e-14);
501s  unwind_protect_cleanup
501s    close (hf);
501s  end_unwind_protect
501s ***** test
501s  hf = figure ("visible", "off");
501s  unwind_protect
501s    x = randn(100,1);
501s    cdfplot (x);
501s  unwind_protect_cleanup
501s    close (hf);
501s  end_unwind_protect
501s ***** error cdfplot ();
501s ***** error cdfplot ([x',x']);
501s ***** error cdfplot ([NaN, NaN, NaN, NaN]);
501s 5 tests, 5 passed, 0 known failure, 0 skipped
501s [inst/mcnemar_test.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/mcnemar_test.m
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
501s  assert (h, 1);
501s  assert (pval, 3.8151e-06, 1e-10);
501s  assert (chisq, 21.356, 1e-3);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
501s  assert (h, 1);
501s  assert (pval, 0.034690, 1e-6);
501s  assert (isempty (chisq), true);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
501s  assert (h, 0);
501s  assert (pval, 0.034690, 1e-6);
501s  assert (isempty (chisq), true);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
501s  assert (h, 1);
501s  assert (pval, 0.034690, 1e-6);
501s  assert (isempty (chisq), true);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
501s  assert (h, 1);
501s  assert (pval, 0.033006, 1e-6);
501s  assert (chisq, 4.5455, 1e-4);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
501s  assert (h, 0);
501s  assert (pval, 0.052479, 1e-6);
501s  assert (isempty (chisq), true);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
501s  assert (h, 0);
501s  assert (pval, 0.055009, 1e-6);
501s  assert (chisq, 3.6818, 1e-4);
501s ***** test
501s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
501s  assert (h, 1);
501s  assert (pval, 0.055009, 1e-6);
501s  assert (chisq, 3.6818, 1e-4);
501s ***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
501s ***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
501s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
501s  mcnemar_test ([59,6;16,-80])
501s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
501s  mcnemar_test ([59,6;16,4.5])
501s ***** error<mcnemar_test: invalid 2nd input argument.> ...
501s  mcnemar_test ([59,6;16,80], {""})
501s ***** error<mcnemar_test: invalid value for ALPHA.> ...
501s  mcnemar_test ([59,6;16,80], -0.2)
501s ***** error<mcnemar_test: invalid value for ALPHA.> ...
501s  mcnemar_test ([59,6;16,80], [0.05, 0.1])
501s ***** error<mcnemar_test: invalid value for ALPHA.> ...
501s  mcnemar_test ([59,6;16,80], 1)
501s ***** error<mcnemar_test: invalid value for TESTTYPE.> ...
501s  mcnemar_test ([59,6;16,80], "")
501s 17 tests, 17 passed, 0 known failure, 0 skipped
501s [inst/geomean.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/geomean.m
501s ***** test
501s  x = [0:10];
501s  y = [x;x+5;x+10];
501s  assert (geomean (x), 0);
501s  m = [0 9.462942809849169 14.65658770861967];
501s  assert (geomean (y, 2), m', 4e-14);
501s  assert (geomean (y, "all"), 0);
501s  y(2,4) = NaN;
501s  m(2) = 9.623207231679554;
501s  assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
501s  assert (geomean (y', "omitnan"), m, 4e-14);
501s  z = y + 20;
501s  assert (geomean (z, "all"), NaN);
501s  assert (geomean (z, "all", "includenan"), NaN);
501s  assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
501s  m = [24.79790781765634 NaN 34.85638839503932];
501s  assert (geomean (z'), m, 4e-14);
501s  assert (geomean (z', "includenan"), m, 4e-14);
501s  m(2) = 30.02181156156319;
501s  assert (geomean (z', "omitnan"), m, 4e-14);
501s  assert (geomean (z, 2, "omitnan"), m', 4e-14);
501s ***** test
501s  x = repmat ([1:20;6:25], [5 2 6 3]);
501s  assert (size (geomean (x, [3 2])), [10 1 1 3]);
501s  assert (size (geomean (x, [1 2])), [1 1 6 3]);
501s  assert (size (geomean (x, [1 2 4])), [1 1 6]);
501s  assert (size (geomean (x, [1 4 3])), [1 40]);
501s  assert (size (geomean (x, [1 2 3 4])), [1 1]);
501s ***** test
501s  x = repmat ([1:20;6:25], [5 2 6 3]);
501s  m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
501s  assert (geomean (x, [3 2]), m, 4e-13);
501s  x(2,5,6,3) = NaN;
501s  m(2,3) = NaN;
501s  assert (geomean (x, [3 2]), m, 4e-13);
501s  m(2,3) = 14.3292729579901;
501s  assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
501s ***** error <geomean: X must contain real nonnegative values.> geomean ("char")
501s ***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
501s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
501s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
501s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
501s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
501s ***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
501s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
501s 8 tests, 8 passed, 0 known failure, 0 skipped
501s [inst/ppplot.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ppplot.m
501s ***** test
501s  hf = figure ("visible", "off");
501s  unwind_protect
501s    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]);
501s  unwind_protect_cleanup
501s    close (hf);
501s  end_unwind_protect
501s ***** error ppplot ()
501s ***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
501s ***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
501s ***** error <ppplot: DIST must be a string> ppplot ([1 2 3 4], 2)
501s 5 tests, 5 passed, 0 known failure, 0 skipped
501s [inst/plsregress.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/plsregress.m
501s ***** demo
501s  ## Perform Partial Least-Squares Regression
501s 
501s  ## Load the spectra data set and use the near infrared (NIR) spectral
501s  ## intensities (NIR) as the predictor and the corresponding octave
501s  ## ratings (octave) as the response.
501s  load spectra
501s 
501s  ## Perform PLS regression with 10 components
501s  [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);
501s 
501s  ## Plot the percentage of explained variance in the response variable
501s  ## (PCTVAR) as a function of the number of components.
501s  plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
501s  xlim ([1, 10]);
501s  xlabel ("Number of PLS components");
501s  ylabel ("Percentage of Explained Variance in octane");
501s  title ("Explained Variance per PLS components");
501s 
501s  ## Compute the fitted response and display the residuals.
501s  octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
501s  residuals = octane - octane_fitted;
501s  figure
501s  stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
501s  xlabel ("Observations");
501s  ylabel ("Residuals");
501s  title ("Residuals in octane's fitted response");
501s ***** demo
501s  ## Calculate Variable Importance in Projection (VIP) for PLS Regression
501s 
501s  ## Load the spectra data set and use the near infrared (NIR) spectral
501s  ## intensities (NIR) as the predictor and the corresponding octave
501s  ## ratings (octave) as the response.  Variables with a VIP score greater than
501s  ## 1 are considered important for the projection of the PLS regression model.
501s  load spectra
501s 
501s  ## Perform PLS regression with 10 components
501s  [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
501s                                                  plsregress (NIR, octane, 10);
501s 
501s  ## Calculate the normalized PLS weights
501s  W0 = stats.W ./ sqrt(sum(stats.W.^2,1));
501s 
501s  ## Calculate the VIP scores for 10 components
501s  nobs = size (xload, 1);
501s  SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
501s  VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));
501s 
501s  ## Find variables with a VIP score greater than or equal to 1
501s  VIPidx = find (VIPscore >= 1);
501s 
501s  ## Plot the VIP scores
501s  scatter (1:length (VIPscore), VIPscore, "xb");
501s  hold on
501s  scatter (VIPidx, VIPscore (VIPidx), "xr");
501s  plot ([1, length(VIPscore)], [1, 1], "--k");
501s  hold off
501s  axis ("tight");
501s  xlabel ("Predictor Variables");
501s  ylabel ("VIP scores");
501s  title ("VIP scores for each predictor variable with 10 components");
501s ***** test
501s  load spectra
501s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
501s  xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
501s                -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
501s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
501s               0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
501s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
501s                 -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
501s  yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
501s                  -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
501s  assert (xload(1,:), xload1_out, 1e-4);
501s  assert (yload, yload_out, 1e-4);
501s  assert (xscore(1,:), xscore1_out, 1e-4);
501s  assert (yscore(1,:), yscore1_out, 1e-4);
501s ***** test
501s  load spectra
501s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
501s  xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
501s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
501s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
501s  yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
501s  assert (xload(1,:), xload1_out, 1e-4);
501s  assert (yload, yload_out, 1e-4);
501s  assert (xscore(1,:), xscore1_out, 1e-4);
501s  assert (yscore(1,:), yscore1_out, 1e-4);
501s ***** error<plsregress: function called with too few input arguments.>
501s  plsregress (1)
501s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
501s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
501s ***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
501s ***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
501s ***** error<plsregress: X and Y observations mismatch.> ...
501s  plsregress (ones (20,3), ones (15,1))
501s ***** error<plsregress: invalid value for NCOMP.> ...
501s  plsregress (ones (20,3), ones (20,1), 0)
501s ***** error<plsregress: invalid value for NCOMP.> ...
501s  plsregress (ones (20,3), ones (20,1), -5)
501s ***** error<plsregress: invalid value for NCOMP.> ...
501s  plsregress (ones (20,3), ones (20,1), 3.2)
501s ***** error<plsregress: invalid value for NCOMP.> ...
501s  plsregress (ones (20,3), ones (20,1), [2, 3])
501s ***** error<plsregress: NCOMP exceeds maximum components for X.> ...
501s  plsregress (ones (20,3), ones (20,1), 4)
501s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
501s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
501s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
501s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
501s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
501s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
501s ***** error<plsregress: invalid NAME argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
501s ***** error<plsregress: invalid NAME argument.> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
501s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
501s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
501s  plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
501s ***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
501s 24 tests, 24 passed, 0 known failure, 0 skipped
501s [inst/Regression/RegressionGAM.m]
501s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Regression/RegressionGAM.m
501s ***** demo
501s  ## Train a RegressionGAM Model for synthetic values
501s  f1 = @(x) cos (3 * x);
501s  f2 = @(x) x .^ 3;
501s  x1 = 2 * rand (50, 1) - 1;
501s  x2 = 2 * rand (50, 1) - 1;
501s  y = f1(x1) + f2(x2);
501s  y = y + y .* 0.2 .* rand (50,1);
501s  X = [x1, x2];
501s  a = fitrgam (X, y, "tol", 1e-3)
501s ***** demo
501s  ## Declare two different functions
501s  f1 = @(x) cos (3 * x);
501s  f2 = @(x) x .^ 3;
501s 
501s  ## Generate 80 samples for f1 and f2
501s  x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
501s  X1 = f1 (x);
501s  X2 = f2 (x);
501s 
501s  ## Create a synthetic response by adding noise
501s  rand ("seed", 3);
501s  Ytrue = X1 + X2;
501s  Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);
501s 
501s  ## Assemble predictor data
501s  X = [X1, X2];
501s 
501s  ## Train the GAM and test on the same data
501s  a = fitrgam (X, Y, "order", [5, 5]);
501s  [ypred, ySDsd, yInt] = predict (a, X);
501s 
501s  ## Plot the results
501s  figure
501s  [sortedY, indY] = sort (Ytrue);
501s  plot (sortedY, "r-");
501s  xlim ([0, 80]);
501s  hold on
501s  plot (ypred(indY), "g+")
501s  plot (yInt(indY,1), "k:")
501s  plot (yInt(indY,2), "k:")
501s  xlabel ("Predictor samples");
501s  ylabel ("Response");
501s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
501s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
501s 
501s  ## Use 30% Holdout partitioning for training and testing data
501s  C = cvpartition (80, "HoldOut", 0.3);
501s  [ypred, ySDsd, yInt] = predict (a, X(test(C),:));
501s 
501s  ## Plot the results
501s  figure
501s  [sortedY, indY] = sort (Ytrue(test(C)));
501s  plot (sortedY, 'r-');
501s  xlim ([0, sum(test(C))]);
501s  hold on
501s  plot (ypred(indY), "g+")
501s  plot (yInt(indY,1),'k:')
501s  plot (yInt(indY,2),'k:')
501s  xlabel ("Predictor samples");
501s  ylabel ("Response");
501s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
501s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
501s ***** test
501s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
501s  y = [1; 2; 3; 4];
501s  a = RegressionGAM (x, y);
501s  assert ({a.X, a.Y}, {x, y})
501s  assert ({a.BaseModel.Intercept}, {2.5000})
501s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
501s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
501s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
501s  assert ({a.Formula}, {[]})
501s ***** test
501s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
501s  y = [1; 2; 3; 4];
501s  pnames = {"A", "B", "C", "D"};
501s  formula = "Y ~ A + B + C + D + A:C";
501s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
501s  a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
501s  assert ({a.IntMatrix}, {intMat})
501s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
501s  assert ({a.Formula}, {formula})
501s ***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
501s ***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
501s ***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
501s  RegressionGAM (ones(10,2), ones (5,1))
501s ***** error<RegressionGAM: invalid values in X.> ...
501s  RegressionGAM ([1;2;3;"a";4], ones (5,1))
501s ***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
501s  RegressionGAM (ones(10,2), ones (10,1), "some", "some")
501s ***** error<RegressionGAM: Formula must be a string.>
501s  RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
501s ***** error<RegressionGAM: Formula must be a string.>
501s  RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
501s ***** error<RegressionGAM: invalid syntax in Formula.> ...
501s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
501s ***** error<RegressionGAM: no predictor terms in Formula.> ...
501s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
501s ***** error<RegressionGAM: no predictor terms in Formula.> ...
501s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
502s ***** error<RegressionGAM: some predictors have not been identified> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
502s ***** error<RegressionGAM: invalid Interactions parameter.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
502s ***** error<RegressionGAM: invalid Interactions parameter.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
502s ***** error<RegressionGAM: invalid Interactions parameter.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
502s ***** error<RegressionGAM: number of interaction terms requested is larger than> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
502s ***** error<RegressionGAM: Formula has been already defined.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
502s ***** error<RegressionGAM: Interactions have been already defined.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
502s ***** error<RegressionGAM: invalid value for Knots.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
502s ***** error<RegressionGAM: DoF and Order have been set already.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
502s ***** error<RegressionGAM: invalid value for DoF.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
502s ***** error<RegressionGAM: Knots and Order have been set already.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
502s ***** error<RegressionGAM: invalid value for Order.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
502s ***** error<RegressionGAM: DoF and Knots have been set already.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
502s ***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
502s ***** error<RegressionGAM: ResponseName must be a char string.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
502s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
502s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
502s ***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
502s  RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
502s ***** error<RegressionGAM.predict: too few arguments.> ...
502s  predict (RegressionGAM (ones(10,1), ones(10,1)))
502s ***** error<RegressionGAM.predict: Xfit is empty.> ...
502s  predict (RegressionGAM (ones(10,1), ones(10,1)), [])
502s ***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
502s ***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
502s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
502s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
502s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
502s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
502s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
502s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
502s 39 tests, 39 passed, 0 known failure, 0 skipped
502s [inst/cluster.m]
502s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cluster.m
502s ***** error cluster ()
502s ***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
502s ***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
502s ***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
502s ***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
502s ***** test
502s 6 tests, 6 passed, 0 known failure, 0 skipped
502s [inst/hotelling_t2test.m]
502s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hotelling_t2test.m
502s ***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
502s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
502s  hotelling_t2test (1);
502s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
502s  hotelling_t2test (ones(2,2,2));
502s ***** error<hotelling_t2test: invalid value for alpha.> ...
502s  hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
502s ***** error<hotelling_t2test: invalid value for alpha.> ...
502s  hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
502s ***** error<hotelling_t2test: invalid value for alpha.> ...
502s  hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
502s ***** error<hotelling_t2test: invalid value for alpha.> ...
502s  hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
502s ***** error<hotelling_t2test: invalid Name argument.> ...
502s  hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
502s ***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
502s  hotelling_t2test (ones(20,1), [0, 0]);
502s ***** error<hotelling_t2test: X must have more rows than columns.> ...
502s  hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
502s ***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
502s  hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
502s ***** test
502s  randn ("seed", 1);
502s  x = randn (50000, 5);
502s  [h, pval, stats] = hotelling_t2test (x);
502s  assert (h, 0);
502s  assert (stats.df1, 5);
502s  assert (stats.df2, 49995);
502s ***** test
502s  randn ("seed", 1);
502s  x = randn (50000, 5);
502s  [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
502s  assert (h, 1);
502s  assert (stats.df1, 5);
502s  assert (stats.df2, 49995);
502s 13 tests, 13 passed, 0 known failure, 0 skipped
502s [inst/signtest.m]
502s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/signtest.m
502s ***** test
502s  [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
502s  assert (pval, 1.091701889420221e-218, 1e-14);
502s  assert (h, 1);
502s  assert (stats.zval, -31.5437631079266, 1e-14);
502s ***** test
502s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
502s  assert (pval, 0.6875000000000006, 1e-14);
502s  assert (h, 0);
502s  assert (stats.zval, NaN);
502s  assert (stats.sign, 4);
502s ***** test
502s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
502s  assert (pval, 0.6830913983096086, 1e-14);
502s  assert (h, 0);
502s  assert (stats.zval, 0.4082482904638631, 1e-14);
502s  assert (stats.sign, 4);
502s ***** error <signtest: X must be a vector.> signtest (ones (2))
502s ***** error <signtest: Y must be either a scalar of a vector.> ...
502s  signtest ([1, 2, 3, 4], ones (2))
502s ***** error <signtest: X and Y vectors have different lengths.> ...
502s  signtest ([1, 2, 3, 4], [1, 2, 3])
502s ***** error <signtest: optional arguments must be in pairs.> ...
502s  signtest ([1, 2, 3, 4], [], 'tail')
502s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
502s  signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
502s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
502s  signtest ([1, 2, 3, 4], [], 'alpha', 0)
502s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
502s  signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
502s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
502s  signtest ([1, 2, 3, 4], [], 'alpha', "a")
502s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
502s  signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
502s ***** error <signtest: 'tail' argument must be a character vector.> ...
502s  signtest ([1, 2, 3, 4], [], 'tail', 0.01)
502s ***** error <signtest: 'tail' argument must be a character vector.> ...
502s  signtest ([1, 2, 3, 4], [], 'tail', {"both"})
502s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
502s  signtest ([1, 2, 3, 4], [], 'tail', "some")
502s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
502s  signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
502s ***** error <signtest: 'method' argument must be a character vector.> ...
502s  signtest ([1, 2, 3, 4], [], 'method', 0.01)
502s ***** error <signtest: 'method' argument must be a character vector.> ...
502s  signtest ([1, 2, 3, 4], [], 'method', {"exact"})
502s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
502s  signtest ([1, 2, 3, 4], [], 'method', "some")
502s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
502s  signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
502s 20 tests, 20 passed, 0 known failure, 0 skipped
502s [inst/randsample.m]
502s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/randsample.m
502s ***** test
502s  n = 20;
502s  k = 5;
502s  x = randsample(n, k);
502s  assert (size(x), [1 k]);
502s  x = randsample(n, k, true);
502s  assert (size(x), [1 k]);
502s  x = randsample(n, k, false);
502s  assert (size(x), [1 k]);
502s  x = randsample(n, k, true, ones(n, 1));
502s  assert (size(x), [1 k]);
502s  x = randsample(1:n, k);
502s  assert (size(x), [1 k]);
502s  x = randsample(1:n, k, true);
502s  assert (size(x), [1 k]);
502s  x = randsample(1:n, k, false);
502s  assert (size(x), [1 k]);
502s  x = randsample(1:n, k, true, ones(n, 1));
502s  assert (size(x), [1 k]);
502s  x = randsample((1:n)', k);
502s  assert (size(x), [k 1]);
502s  x = randsample((1:n)', k, true);
502s  assert (size(x), [k 1]);
502s  x = randsample((1:n)', k, false);
502s  assert (size(x), [k 1]);
502s  x = randsample((1:n)', k, true, ones(n, 1));
502s  assert (size(x), [k 1]);
502s  n = 10;
502s  k = 100;
502s  x = randsample(n, k, true, 1:n);
502s  assert (size(x), [1 k]);
502s  x = randsample((1:n)', k, true);
502s  assert (size(x), [k 1]);
502s  x = randsample(k, k, false, 1:k);
502s  assert (size(x), [1 k]);
502s 1 test, 1 passed, 0 known failure, 0 skipped
502s [inst/boxplot.m]
502s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/boxplot.m
502s ***** demo
502s  axis ([0, 3]);
502s  randn ("seed", 1);    # for reproducibility
502s  girls = randn (10, 1) * 5 + 140;
502s  randn ("seed", 2);    # for reproducibility
502s  boys = randn (13, 1) * 8 + 135;
502s  boxplot ({girls, boys});
502s  set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
502s  title ("Grade 3 heights");
502s ***** demo
502s  randn ("seed", 7);    # for reproducibility
502s  A = randn (10, 1) * 5 + 140;
502s  randn ("seed", 8);    # for reproducibility
502s  B = randn (25, 1) * 8 + 135;
502s  randn ("seed", 9);    # for reproducibility
502s  C = randn (20, 1) * 6 + 165;
502s  data = [A; B; C];
502s  groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
502s  labels = {"Team A", "Team B", "Team C"};
502s  pos = [2, 1, 3];
502s  boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
502s           "OutlierTags", "on", "BoxStyle", "filled");
502s  title ("Example of Group splitting with paired vectors");
502s ***** demo
502s  randn ("seed", 1);    # for reproducibility
502s  data = randn (100, 9);
502s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
502s           "colors", "ygcwkmb", "whisker", 1.2);
502s  title ("Example of different colors specified with characters");
502s ***** demo
502s  randn ("seed", 5);    # for reproducibility
502s  data = randn (100, 13);
502s  colors = [0.7 0.7 0.7; ...
502s            0.0 0.4 0.9; ...
502s            0.7 0.4 0.3; ...
502s            0.7 0.1 0.7; ...
502s            0.8 0.7 0.4; ...
502s            0.1 0.8 0.5; ...
502s            0.9 0.9 0.2];
502s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
502s           "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
502s  title ("Example of different colors specified as RGB values");
502s ***** error <numerical array or cell array containing> boxplot ("a")
502s ***** error <data cells must contain> boxplot ({[1 2 3], "a"})
502s ***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
502s ***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
502s ***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
502s ***** error <illegal Notch value> boxplot ([1:10], "notch", i)
502s ***** error <illegal Notch value> boxplot ([1:10], "notch", {})
502s ***** error <must be character> boxplot (1, "symbol", 1)
502s ***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
502s ***** error <illegal Orientation value> boxplot (1, "orientation", {})
502s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
502s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
502s ***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
502s ***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
502s ***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
502s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
502s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
502s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
502s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
502s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
502s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
502s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
502s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
502s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
502s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
502s ***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
502s ***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
502s ***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
502s ***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
502s ***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
502s ***** error <with the formalism> boxplot (rand (3, 3), [1 2])
502s ***** test
502s  hf = figure ("visible", "off");
502s  unwind_protect
502s    [a, b] = boxplot (rand (10, 3));
502s    assert (size (a), [7, 3]);
502s    assert (numel (b.box), 3);
502s    assert (numel (b.whisker), 12);
502s    assert (numel (b.median), 3);
502s  unwind_protect_cleanup
502s    close (hf);
502s  end_unwind_protect
502s ***** test
502s  hf = figure ("visible", "off");
502s  unwind_protect
502s    [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
502s    assert (numel (b.box_fill), 3);
502s  unwind_protect_cleanup
502s    close (hf);
502s  end_unwind_protect
503s ***** test
503s  hf = figure ("visible", "off");
503s  unwind_protect
503s    hold on
503s    [a, b] = boxplot (rand (10, 3));
503s    assert (ishold, true);
503s  unwind_protect_cleanup
503s    close (hf);
503s  end_unwind_protect
503s 34 tests, 34 passed, 0 known failure, 0 skipped
503s [inst/anova1.m]
503s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/anova1.m
503s ***** demo
503s  x = meshgrid (1:6);
503s  randn ("seed", 15);    # for reproducibility
503s  x = x + normrnd (0, 1, 6, 6);
503s  anova1 (x, [], 'off');
503s ***** demo
503s  x = meshgrid (1:6);
503s  randn ("seed", 15);    # for reproducibility
503s  x = x + normrnd (0, 1, 6, 6);
503s  [p, atab] = anova1(x);
503s ***** demo
503s  x = ones (50, 4) .* [-2, 0, 1, 5];
503s  randn ("seed", 13);    # for reproducibility
503s  x = x + normrnd (0, 2, 50, 4);
503s  groups = {"A", "B", "C", "D"};
503s  anova1 (x, groups);
503s ***** demo
503s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
503s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
503s  anova1 (y(:), g(:), "on", "unequal");
503s ***** test
503s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
503s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
503s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
503s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
503s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
503s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
503s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
503s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
503s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
503s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
503s  group = [1:10] .* ones (10,10);
503s  group = group(:);
503s  [p, tbl] = anova1 (data, group, "off");
503s  assert (p, 0.022661, 1e-6);
503s  assert (tbl{2,5}, 2.2969, 1e-4);
503s  assert (tbl{2,3}, 9, 0);
503s  assert (tbl{4,2}, 0.003903, 1e-6);
503s  data = reshape (data, 10, 10);
503s  [p, tbl, stats] = anova1 (data, [], "off");
503s  assert (p, 0.022661, 1e-6);
503s  assert (tbl{2,5}, 2.2969, 1e-4);
503s  assert (tbl{2,3}, 9, 0);
503s  assert (tbl{4,2}, 0.003903, 1e-6);
503s  means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
503s  N = 10 * ones (1, 10);
503s  assert (stats.means, means, 1e-6);
503s  assert (length (stats.gnames), 10, 0);
503s  assert (stats.n, N, 0);
503s ***** test
503s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
503s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
503s  [p, tbl] = anova1 (y(:), g(:), "off", "equal");
503s  assert (p, 0.00004163, 1e-6);
503s  assert (tbl{2,5}, 22.573418, 1e-6);
503s  assert (tbl{2,3}, 2, 0);
503s  assert (tbl{3,3}, 14, 0);
503s  [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
503s  assert (p, 0.00208877, 1e-8);
503s  assert (tbl{2,5}, 15.523192, 1e-6);
503s  assert (tbl{2,3}, 2, 0);
503s  assert (tbl{2,4}, 7.5786897, 1e-6);
503s 2 tests, 2 passed, 0 known failure, 0 skipped
503s [inst/binotest.m]
503s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/binotest.m
503s ***** demo
503s  % flip a coin 1000 times, showing 475 heads
503s  % Hypothesis: coin is fair, i.e. p=1/2
503s  [h,p_val,ci] = binotest(475,1000,0.5)
503s  % Result: h = 0 : null hypothesis not rejected, coin could be fair
503s  %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
503s  %         0.444 <= p <= 0.506 with 95% confidence
503s ***** demo
503s  % flip a coin 100 times, showing 65 heads
503s  % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
503s  [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
503s  % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
503s  %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
503s  %         0 <= p <= 0.76 with 99% confidence
503s ***** test #example from https://en.wikipedia.org/wiki/Binomial_test
503s  [h,p_val,ci] = binotest (51,235,1/6);
503s  assert (p_val, 0.0437, 0.00005)
503s  [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
503s  assert (p_val, 0.027, 0.0005)
503s 1 test, 1 passed, 0 known failure, 0 skipped
503s [inst/pdist.m]
503s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/pdist.m
503s ***** shared xy, t, eucl, x
503s  xy = [0 1; 0 2; 7 6; 5 6];
503s  t = 1e-3;
503s  eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
503s  x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
503s ***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
503s ***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
503s ***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
503s ***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
503s ***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
503s ***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
503s ***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
503s ***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
503s ***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
503s ***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
503s ***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
503s ***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
503s ***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
503s ***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
503s ***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
503s ***** assert (pdist (x, "euclidean"), ...
503s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
503s ***** assert (pdist (x, eucl), ...
503s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
503s ***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
503s ***** assert (pdist (x, "seuclidean"), ...
503s         [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
503s ***** warning<pdist: matrix is close to singular> ...
503s  pdist (x, "mahalanobis");
503s ***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
503s ***** assert (pdist (x, "minkowski"), ...
503s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
503s ***** assert (pdist (x, "minkowski", 3), ...
503s         [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
503s ***** assert (pdist (x, "cosine"), ...
503s         [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
503s ***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
503s ***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
503s ***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
503s ***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
503s ***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
503s 29 tests, 29 passed, 0 known failure, 0 skipped
503s [inst/pca.m]
503s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/pca.m
503s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
503s ***** test
503s  x=[7 4 3
503s     4 1 8
503s     6 3 5
503s     8 6 1
503s     8 5 7
503s     7 2 9
503s     5 3 3
503s     9 5 8
503s     7 4 5
503s     8 2 2];
503s  R = corrcoef (x);
503s  [V, lambda] = eig (R);
503s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
503s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
503s ***** 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
503s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
503s  F = zscore(x)*B;
503s  [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
503s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
503s ***** test
503s  x=[1,2,3;2,1,3]';
503s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
503s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
503s  m(:,1) = m(:,1)*sign(COEFF(1,1));
503s  m(:,2) = m(:,2)*sign(COEFF(1,2));
503s ***** assert(COEFF,m(1:2,:),10*eps);
503s ***** assert(SCORE,-m,10*eps);
503s ***** assert(latent,[1.5;.5],10*eps);
503s ***** assert(tsquare,[4;4;4]/3,10*eps);
503s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
503s ***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
503s ***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
503s ***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
503s ***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
503s !!!!! known failure
503s ASSERT errors for:  assert (tsquare,([1.5; 0.5; 1.5]),10 * eps)
503s 
503s   Location  |  Observed  |  Expected  |  Reason
503s     (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
503s     (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
503s     (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
503s ***** test
503s  x=x';
503s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
503s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
503s  m(:,1) = m(:,1)*sign(COEFF(1,1));
503s  m(:,2) = m(:,2)*sign(COEFF(1,2));
503s  m(:,3) = m(:,3)*sign(COEFF(3,3));
503s ***** assert(COEFF,m,10*eps);
503s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
503s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
503s ***** assert(latent,[1;0;0],10*eps);
503s ***** assert(tsquare,[0.5;0.5],10*eps)
503s ***** test
503s  [COEFF,SCORE,latent,tsquare] = pca(x);
503s ***** assert(COEFF,m(:, 1),10*eps);
503s ***** assert(SCORE,-m(1:2,1),10*eps);
503s ***** assert(latent,[1],10*eps);
503s ***** assert(tsquare,[0.5;0.5],10*eps)
503s ***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
503s ***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
503s ***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
503s ***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
503s ***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
503s ***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
503s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
503s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
503s ***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
503s 32 tests, 31 passed, 1 known failure, 0 skipped
503s [inst/rangesearch.m]
503s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/rangesearch.m
503s ***** demo
503s  ## Generate 1000 random 2D points from each of five distinct multivariate
503s  ## normal distributions that form five separate classes
503s  N = 1000;
503s  d = 10;
503s  randn ("seed", 5);
503s  X1 = mvnrnd (d * [0, 0], eye (2), 1000);
503s  randn ("seed", 6);
503s  X2 = mvnrnd (d * [1, 1], eye (2), 1000);
503s  randn ("seed", 7);
503s  X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
503s  randn ("seed", 8);
503s  X4 = mvnrnd (d * [1, -1], eye (2), 1000);
503s  randn ("seed", 8);
503s  X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
503s  X = [X1; X2; X3; X4; X5];
503s 
503s  ## For each point in X, find the points in X that are within a radius d
503s  ## away from the points in X.
503s  Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");
503s 
503s  ## Select the first point in X (corresponding to the first class) and find
503s  ## its nearest neighbors within the radius d.  Display these points in
503s  ## one color and the remaining points in a different color.
503s  x = X(1,:);
503s  nearestPoints = X (Idx{1},:);
503s  nonNearestIdx = true (size (X, 1), 1);
503s  nonNearestIdx(Idx{1}) = false;
503s 
503s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
503s  hold on
503s  scatter (nearestPoints(:,1),nearestPoints(:,2))
503s  scatter (x(1), x(2), "black", "filled")
503s  hold off
503s 
503s  ## Select the last point in X (corresponding to the fifth class) and find
503s  ## its nearest neighbors within the radius d.  Display these points in
503s  ## one color and the remaining points in a different color.
503s  x = X(end,:);
503s  nearestPoints = X (Idx{1},:);
503s  nonNearestIdx = true (size (X, 1), 1);
503s  nonNearestIdx(Idx{1}) = false;
503s 
503s  figure
503s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
503s  hold on
503s  scatter (nearestPoints(:,1),nearestPoints(:,2))
503s  scatter (x(1), x(2), "black", "filled")
503s  hold off
503s ***** shared x, y, X, Y
503s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
503s  y = [2, 3, 4; 1, 4, 3];
503s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
503s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4);
503s  assert (idx, {[1; 4; 2]; [1; 4]});
503s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
503s  assert (idx, {[1, 4, 2]; [1, 4]});
503s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
503s  assert (idx, {[1; 4; 2]; [1; 4]});
503s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
503s  assert (idx, {[1; 4; 2]; [1; 4]});
503s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
503s  assert (idx, {[1; 2; 4]; [1; 4]});
503s  assert (D, {[1.7321; 3.4641; 3.3166]; [2; 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
503s                          "SortIndices", false);
503s  assert (idx, {[1, 2, 4]; [1, 4]});
503s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
503s ***** test
503s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
503s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
503s  assert (idx, {[1, 4, 2]; [1, 4]});
503s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
503s ***** test
503s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
503s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
503s                          "NSMethod", "exhaustive");
503s  assert (idx, {[1, 4, 2]; [1, 4]});
503s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
503s                          "NSMethod", "exhaustive");
503s  assert (idx, {[1, 4, 2]; [1, 4]});
503s  assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
503s                          "NSMethod", "exhaustive", "SortIndices", false);
503s  assert (idx, {[1, 2, 4]; [1, 4]});
503s  assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (X, Y, 4);
503s  assert (idx, {[1; 2]; [1; 2; 3]});
503s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
503s ***** test
503s  [idx, D] = rangesearch (X, Y, 2);
503s  assert (idx, {[1]; [1; 2]});
503s  assert (D, {[1.4142]; [1.4142; 1.4142]}, 1e-4);
503s ***** test
503s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
503s  [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
503s  assert (idx, {[1, 2]; [1, 2, 3]});
503s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
504s ***** test
504s  [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
504s  assert (idx, {[1; 2]; [1; 2; 3]});
504s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
504s ***** test
504s  [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
504s                          "NSMethod", "exhaustive");
504s  assert (idx, {[1, 2]; [1, 2, 3]});
504s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
504s ***** error<rangesearch: too few input arguments.> rangesearch (1)
504s ***** error<rangesearch: too few input arguments.> rangesearch (ones (4, 5))
504s ***** error<rangesearch: too few input arguments.> ...
504s  rangesearch (ones (4, 5), ones (4))
504s ***** error<rangesearch: number of columns in X and Y must match.> ...
504s  rangesearch (ones (4, 5), ones (4), 1)
504s ***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
504s  rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
504s ***** error<rangesearch: only a single distance parameter can be defined.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
504s ***** error<rangesearch: invalid value of Minkowski Exponent.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "P", -2)
504s ***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
504s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
504s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
504s ***** error<rangesearch: invalid value of bucketsize.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
504s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
504s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
504s 33 tests, 33 passed, 0 known failure, 0 skipped
504s [inst/qqplot.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/qqplot.m
504s ***** test
504s  hf = figure ("visible", "off");
504s  unwind_protect
504s    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]);
504s  unwind_protect_cleanup
504s    close (hf);
504s  end_unwind_protect
504s ***** error qqplot ()
504s ***** error <qqplot: X must be a numeric vector> qqplot ({1})
504s ***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
504s ***** error <qqplot: X must be a numeric vector> qqplot (1, "foobar")
504s ***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
504s 6 tests, 6 passed, 0 known failure, 0 skipped
504s [inst/princomp.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/princomp.m
504s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
504s ***** test
504s  x=[7 4 3
504s     4 1 8
504s     6 3 5
504s     8 6 1
504s     8 5 7
504s     7 2 9
504s     5 3 3
504s     9 5 8
504s     7 4 5
504s     8 2 2];
504s  R = corrcoef (x);
504s  [V, lambda] = eig (R);
504s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
504s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
504s  ## contribution of first 2 PCs to each original variable
504s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
504s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
504s  F = zscore(x)*B;
504s  [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
504s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
504s ***** test
504s  x=[1,2,3;2,1,3]';
504s  [COEFF,SCORE,latent,tsquare] = princomp(x);
504s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
504s  m(:,1) = m(:,1)*sign(COEFF(1,1));
504s  m(:,2) = m(:,2)*sign(COEFF(1,2));
504s ***** assert(COEFF,m(1:2,:),10*eps);
504s ***** assert(SCORE,-m,10*eps);
504s ***** assert(latent,[1.5;.5],10*eps);
504s ***** assert(tsquare,[4;4;4]/3,10*eps);
504s ***** test
504s  x=x';
504s  [COEFF,SCORE,latent,tsquare] = princomp(x);
504s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
504s  m(:,1) = m(:,1)*sign(COEFF(1,1));
504s  m(:,2) = m(:,2)*sign(COEFF(1,2));
504s  m(:,3) = m(:,3)*sign(COEFF(3,3));
504s ***** assert(COEFF,m,10*eps);
504s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
504s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
504s ***** assert(latent,[1;0;0],10*eps);
504s ***** assert(tsquare,[0.5;0.5],10*eps)
504s ***** test
504s  [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
504s ***** assert(COEFF,m(:, 1),10*eps);
504s ***** assert(SCORE,-m(1:2,1),10*eps);
504s ***** assert(latent,[1],10*eps);
504s ***** assert(tsquare,[0.5;0.5],10*eps)
504s 19 tests, 19 passed, 0 known failure, 0 skipped
504s [inst/ismissing.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ismissing.m
504s ***** assert (ismissing ([1,NaN,3]), [false,true,false])
504s ***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
504s ***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
504s ***** assert (ismissing ({'x','','y'}), [false,true,false])
504s ***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
504s ***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
504s ***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
504s ***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
504s ***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
504s ***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
504s ***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
504s ***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
504s ***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
504s ***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
504s ***** assert (ismissing (double (NaN)), true)
504s ***** assert (ismissing (single (NaN)), true)
504s ***** assert (ismissing (' '), true)
504s ***** assert (ismissing ({''}), true)
504s ***** assert (ismissing ({' '}), false)
504s ***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
504s ***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
504s ***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
504s ***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
504s ***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
504s ***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
504s ***** assert (ismissing ({'123', '', 123}), [false false false])
504s ***** assert (ismissing (logical ([1 0 1])), [false false false])
504s ***** assert (ismissing (int32 ([1 2 3])), [false false false])
504s ***** assert (ismissing (uint32 ([1 2 3])), [false false false])
504s ***** assert (ismissing ({1, 2, 3}), [false false false])
504s ***** assert (ismissing ([struct struct struct]), [false false false])
504s ***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
504s ***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
504s ***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
504s ***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
504s ***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
504s ***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
504s ***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
504s ***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
504s ***** assert (ismissing ([]), logical([]))
504s ***** assert (ismissing (''), logical([]))
504s ***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
504s ***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
504s ***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
504s ***** error ismissing ()
504s ***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
504s ***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
504s ***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
504s ***** error <indicators not supported for data type> ismissing (struct, 1)
504s 49 tests, 49 passed, 0 known failure, 0 skipped
504s [inst/clusterdata.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/clusterdata.m
504s ***** demo
504s  randn ("seed", 1)  # for reproducibility
504s  r1 = randn (10, 2) * 0.25 + 1;
504s  randn ("seed", 5)  # for reproducibility
504s  r2 = randn (20, 2) * 0.5 - 1;
504s  X = [r1; r2];
504s 
504s  wnl = warning ("off", "Octave:linkage_savemem", "local");
504s  T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
504s  scatter (X(:,1), X(:,2), 36, T, "filled");
504s ***** error<clusterdata: function called with too few input arguments.> ...
504s  clusterdata ()
504s ***** error<clusterdata: function called with too few input arguments.> ...
504s  clusterdata (1)
504s ***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
504s ***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
504s 4 tests, 4 passed, 0 known failure, 0 skipped
504s [inst/inconsistent.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/inconsistent.m
504s ***** error inconsistent ()
504s ***** error inconsistent ([1 2 1], 2, 3)
504s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
504s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
504s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
504s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
504s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
504s ***** test
504s  load fisheriris;
504s  Z = linkage(meas, 'average', 'chebychev');
504s  assert (cond (inconsistent (Z)), 39.9, 1e-3);
504s 8 tests, 8 passed, 0 known failure, 0 skipped
504s [inst/nansum.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/nansum.m
504s ***** demo
504s  ## Find the column sums for a matrix with missing values.,
504s 
504s  x = magic (3);
504s  x([1, 4, 7:9]) = NaN
504s  s = nansum (x)
504s ***** demo
504s  ## Find the row sums for a matrix with missing values.,
504s 
504s  x = magic (3);
504s  x([1, 4, 7:9]) = NaN
504s  s = nansum (x, 2)
504s ***** demo
504s  ## Find the sum of all the values in a multidimensional array
504s  ## with missing values.
504s 
504s  x = reshape (1:30, [2, 5, 3]);
504s  x([10:12, 25]) = NaN
504s  s = nansum (x, "all")
504s ***** demo
504s  ## Find the sum of a multidimensional array with missing values over
504s  ## multiple dimensions.
504s 
504s  x = reshape (1:30, [2, 5, 3]);
504s  x([10:12, 25]) = NaN
504s  s = nansum (x, [2, 3])
504s ***** assert (nansum ([]), 0)
504s ***** assert (nansum (NaN), 0)
504s ***** assert (nansum (NaN(3)), [0, 0, 0])
504s ***** assert (nansum ([2 4 NaN 7]), 13)
504s ***** assert (nansum ([2 4 NaN Inf]), Inf)
504s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
504s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
504s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
504s ***** test
504s  x = magic(3);
504s  x([1 6:9]) = NaN;
504s  assert (nansum (x), [7, 6, 0])
504s  assert (nansum (x, 2), [1; 8; 4])
504s ***** test
504s  x = reshape(1:24, [2, 4, 3]);
504s  x([5:6, 20]) = NaN;
504s  assert (nansum (x, "all"), 269)
504s ***** test
504s  x = reshape(1:24,[2, 4, 3]);
504s  x([5:6, 20]) = NaN;
504s  assert (squeeze (nansum (x, [1, 2])), [25; 100; 144])
504s  assert (nansum (x, [2, 3]), [139; 130])
504s ***** error <nansum: X must be numeric.> nansum ({3})
504s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 0)
504s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
504s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
504s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
504s  nansum (ones (3, 3, 3), [2, 2.5])
504s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
504s  nansum (ones (3, 3, 3), [-1, 2])
504s ***** error <nansum: VECDIM must contain non-repeating positive integers.> ...
504s  nansum (ones (3, 3, 3), [2, 2, 3])
504s 18 tests, 18 passed, 0 known failure, 0 skipped
504s [inst/regression_ftest.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/regression_ftest.m
504s ***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
504s ***** error<Invalid call to regression_ftest.  Correct usage> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
504s ***** error<regression_ftest: Y must contain finite real numbers.> ...
504s  regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
504s ***** error<regression_ftest: Y must contain finite real numbers.> ...
504s  regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
504s ***** error<regression_ftest: Y must contain finite real numbers.> ...
504s  regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
504s ***** error<regression_ftest: X must contain finite real numbers.> ...
504s  regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
504s ***** error<regression_ftest: X must contain finite real numbers.> ...
504s  regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
504s ***** error<regression_ftest: X must contain finite real numbers.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
504s ***** error<regression_ftest: invalid value for alpha.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
504s ***** error<regression_ftest: invalid value for alpha.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
504s ***** error<regression_ftest: invalid value for alpha.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
504s ***** error<regression_ftest: invalid value for alpha.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
504s ***** error<regression_ftest: invalid Name argument.> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
504s ***** error<regression_ftest: Y must be a vector of length> ...
504s  regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
504s ***** error<regression_ftest: Y must be a vector of length> ...
504s  regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
504s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
504s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
504s ***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
504s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
504s 18 tests, 18 passed, 0 known failure, 0 skipped
504s [inst/cholcov.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cholcov.m
504s ***** demo
504s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
504s  T = cholcov (C1)
504s  C2 = T'*T
504s ***** test
504s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
504s  T = cholcov (C1);
504s  assert (C1, T'*T, 1e-15 * ones (size (C1)));
504s 1 test, 1 passed, 0 known failure, 0 skipped
504s [inst/dist_obj/PiecewiseLinearDistribution.m]
504s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/PiecewiseLinearDistribution.m
504s ***** demo
504s  ## Generate a data set of 5000 random samples from a Beta distribution with
504s  ## parameters a = 2 and b = 5 scaled to [0,10].
504s  ## Compute empirical CDF, subsample, create PiecewiseLinearDistribution,
504s  ## and plot the PDF superimposed on a histogram of the data.
504s 
504s  randg ("seed", 2);
504s  data = betarnd (2, 5, 5000, 1) * 10;
504s  [f, x] = ecdf (data);
504s  f = f(1:5:end);
504s  x = x(1:5:end);
504s  pd = PiecewiseLinearDistribution (x, f);
504s  [counts, centers] = hist (data, 50);
504s  bin_width = centers(2) - centers(1);
504s  bar (centers, counts / (sum (counts) * bin_width), 1);
504s  hold on
504s  vals = min (data):0.1:max (data);
504s  y = pdf (pd, vals);
504s  plot (vals, y, "-r", "LineWidth", 2)
504s  hold off
504s  title ("Piecewise Linear approximation to scaled Beta(2,5) data")
504s  legend ("Histogram", "Piecewise PDF")
504s ***** shared pd, t
504s  load patients
504s  [f, x] = ecdf (Weight);
504s  f = f(1:5:end);
504s  x = x(1:5:end);
504s  pd = PiecewiseLinearDistribution (x, f);
504s  t = truncate (pd, 130, 180);
504s ***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
504s ***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
504s ***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
504s ***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
504s ***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
504s ***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
504s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
504s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
504s ***** assert (iqr (pd), 50.0833, 1e-4);
504s ***** assert (iqr (t), 36.8077, 1e-4);
504s ***** assert (mean (pd), 153.61, 1e-10);
504s ***** assert (mean (t), 152.311, 1e-3);
512s ***** assert (median (pd), 142, 1e-10);
512s ***** assert (median (t), 141.9462, 1e-4);
512s ***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0004], 6e-3);
512s ***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
512s ***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
512s ***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
512s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
512s ***** assert (any (random (t, 1000, 1) < 130), false);
512s ***** assert (any (random (t, 1000, 1) > 180), false);
512s ***** assert (std (pd), 26.5196, 1e-4);
512s ***** assert (std (t), 18.2941, 1e-4);
526s ***** assert (var (pd), 703.2879, 1e-4);
526s ***** assert (var (t), 334.6757, 1e-4);
541s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, i], [0, 1])
541s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, Inf], [0, 1])
541s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
541s  PiecewiseLinearDistribution (["a", "c"], [0, 1])
541s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
541s  PiecewiseLinearDistribution ([NaN, 1], [0, 1])
541s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, 1], [0, i])
541s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, 1], [0, Inf])
541s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, 1], ["a", "c"])
541s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
541s  PiecewiseLinearDistribution ([0, 1], [NaN, 1])
541s ***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
541s  PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
541s ***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
541s  PiecewiseLinearDistribution ([0], [1])
541s ***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
541s  PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
541s ***** error <cdf: invalid argument for upper tail.> ...
541s  cdf (PiecewiseLinearDistribution, 2, "uper")
541s ***** error <cdf: invalid argument for upper tail.> ...
541s  cdf (PiecewiseLinearDistribution, 2, 3)
541s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
541s  plot (PiecewiseLinearDistribution, "Parent")
541s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
541s  plot (PiecewiseLinearDistribution, "PlotType", 12)
541s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
541s  plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
541s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
541s  plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
541s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
541s  plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
541s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
541s  plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
541s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
541s  plot (PiecewiseLinearDistribution, "Discrete", {true})
541s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
541s  plot (PiecewiseLinearDistribution, "Parent", 12)
541s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
541s  plot (PiecewiseLinearDistribution, "Parent", "hax")
541s ***** error <plot: invalid NAME for optional argument.> ...
541s  plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
541s ***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
541s  plot (PiecewiseLinearDistribution, "PlotType", "probability")
541s ***** error <truncate: missing input argument.> ...
541s  truncate (PiecewiseLinearDistribution)
541s ***** error <truncate: missing input argument.> ...
541s  truncate (PiecewiseLinearDistribution, 2)
541s ***** error <truncate: invalid lower upper limits.> ...
541s  truncate (PiecewiseLinearDistribution, 4, 2)
541s ***** shared pd
541s  pd = PiecewiseLinearDistribution ();
541s  pd(2) = PiecewiseLinearDistribution ();
541s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
541s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
541s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
541s ***** error <mean: requires a scalar probability distribution.> mean (pd)
541s ***** error <median: requires a scalar probability distribution.> median (pd)
541s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
541s ***** error <plot: requires a scalar probability distribution.> plot (pd)
541s ***** error <random: requires a scalar probability distribution.> random (pd)
541s ***** error <std: requires a scalar probability distribution.> std (pd)
541s ***** error <truncate: requires a scalar probability distribution.> ...
541s  truncate (pd, 2, 4)
541s ***** error <var: requires a scalar probability distribution.> var (pd)
541s 63 tests, 63 passed, 0 known failure, 0 skipped
541s [inst/dist_obj/InverseGaussianDistribution.m]
541s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/InverseGaussianDistribution.m
541s ***** shared pd, t
541s  pd = InverseGaussianDistribution (1, 1);
541s  t = truncate (pd, 2, 4);
541s ***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
541s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
541s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
541s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
541s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
541s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
541s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
541s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
541s ***** assert (iqr (pd), 0.8643, 1e-4);
541s ***** assert (iqr (t), 0.8222, 1e-4);
541s ***** assert (mean (pd), 1);
541s ***** assert (mean (t), 2.6953, 1e-4);
541s ***** assert (median (pd), 0.6758, 1e-4);
541s ***** assert (median (t), 2.5716, 1e-4);
541s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
541s ***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
541s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
541s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
541s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
541s ***** assert (any (random (t, 1000, 1) < 2), false);
541s ***** assert (any (random (t, 1000, 1) > 4), false);
541s ***** assert (std (pd), 1);
541s ***** assert (std (t), 0.5332, 1e-4);
541s ***** assert (var (pd), 1);
541s ***** assert (var (t), 0.2843, 1e-4);
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution(0, 1)
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution(Inf, 1)
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution(i, 1)
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution("a", 1)
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution([1, 2], 1)
541s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
541s  InverseGaussianDistribution(NaN, 1)
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, 0)
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, -1)
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, Inf)
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, i)
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, "a")
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, [1, 2])
541s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
541s  InverseGaussianDistribution(1, NaN)
541s ***** error <cdf: invalid argument for upper tail.> ...
541s  cdf (InverseGaussianDistribution, 2, "uper")
541s ***** error <cdf: invalid argument for upper tail.> ...
541s  cdf (InverseGaussianDistribution, 2, 3)
541s ***** shared x
541s  x = invgrnd (1, 1, [1, 100]);
542s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha")
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", "")
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
542s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
542s           "alpha", {0.05})
542s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), ...
542s           "parameter", {"mu", "lambda", "param"})
542s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
542s           "parameter", {"mu", "lambda", "param"})
542s ***** error <paramci: unknown distribution parameter.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
542s ***** error <paramci: unknown distribution parameter.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
542s           "parameter", "param")
542s ***** error <paramci: invalid NAME for optional argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
542s ***** error <paramci: invalid NAME for optional argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
542s ***** error <paramci: invalid NAME for optional argument.> ...
542s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
542s           "parameter", "mu", "NAME", "value")
542s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
542s  plot (InverseGaussianDistribution, "Parent")
542s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
542s  plot (InverseGaussianDistribution, "PlotType", 12)
542s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
542s  plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
542s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
542s  plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
542s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
542s  plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
542s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
542s  plot (InverseGaussianDistribution, "Discrete", [1, 0])
542s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
542s  plot (InverseGaussianDistribution, "Discrete", {true})
542s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
542s  plot (InverseGaussianDistribution, "Parent", 12)
542s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
542s  plot (InverseGaussianDistribution, "Parent", "hax")
542s ***** error <plot: invalid NAME for optional argument.> ...
542s  plot (InverseGaussianDistribution, "invalidNAME", "pdf")
542s ***** error <plot: no fitted DATA to plot a probability plot.> ...
542s  plot (InverseGaussianDistribution, "PlotType", "probability")
542s ***** error <proflik: no fitted data available.> ...
542s  proflik (InverseGaussianDistribution, 2)
542s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 3)
542s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
542s  proflik (InverseGaussianDistribution.fit (x), [1, 2])
542s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
542s  proflik (InverseGaussianDistribution.fit (x), {1})
542s ***** error <proflik: SETPARAM must be a numeric vector.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
542s ***** error <proflik: missing VALUE for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display")
542s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
542s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
542s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
542s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
542s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
542s ***** error <proflik: invalid NAME for optional arguments.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
542s ***** error <proflik: invalid optional argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
542s ***** error <proflik: invalid optional argument.> ...
542s  proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
542s ***** error <truncate: missing input argument.> ...
542s  truncate (InverseGaussianDistribution)
542s ***** error <truncate: missing input argument.> ...
542s  truncate (InverseGaussianDistribution, 2)
542s ***** error <truncate: invalid lower upper limits.> ...
542s  truncate (InverseGaussianDistribution, 4, 2)
542s ***** shared pd
542s  pd = InverseGaussianDistribution(1, 1);
542s  pd(2) = InverseGaussianDistribution(1, 3);
542s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
542s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
542s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
542s ***** error <mean: requires a scalar probability distribution.> mean (pd)
542s ***** error <median: requires a scalar probability distribution.> median (pd)
542s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
542s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
542s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
542s ***** error <plot: requires a scalar probability distribution.> plot (pd)
542s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
542s ***** error <random: requires a scalar probability distribution.> random (pd)
542s ***** error <std: requires a scalar probability distribution.> std (pd)
542s ***** error <truncate: requires a scalar probability distribution.> ...
542s  truncate (pd, 2, 4)
542s ***** error <var: requires a scalar probability distribution.> var (pd)
542s 96 tests, 96 passed, 0 known failure, 0 skipped
542s [inst/dist_obj/BinomialDistribution.m]
542s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/BinomialDistribution.m
542s ***** shared pd, t, t_inf
542s  pd = BinomialDistribution (5, 0.5);
542s  t = truncate (pd, 2, 4);
542s  t_inf = truncate (pd, 2, Inf);
542s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
542s ***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
542s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
542s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
542s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
542s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
542s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
542s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
542s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
542s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
542s ***** assert (iqr (pd), 1);
542s ***** assert (iqr (t), 1);
542s ***** assert (mean (pd), 2.5, 1e-10);
542s ***** assert (mean (t), 2.8, 1e-10);
542s ***** assert (mean (t_inf), 2.8846, 1e-4);
542s ***** assert (median (pd), 2.5);
542s ***** assert (median (t), 3);
542s ***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
542s ***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
542s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
542s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
542s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
542s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
542s ***** assert (any (random (t, 1000, 1) < 2), false);
542s ***** assert (any (random (t, 1000, 1) > 4), false);
542s ***** assert (std (pd), 1.1180, 1e-4);
542s ***** assert (std (t), 0.7483, 1e-4);
542s ***** assert (std (t_inf), 0.8470, 1e-4);
542s ***** assert (var (pd), 1.2500, 1e-4);
542s ***** assert (var (t), 0.5600, 1e-4);
542s ***** assert (var (t_inf), 0.7175, 1e-4);
542s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
542s  BinomialDistribution(Inf, 0.5)
542s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
542s  BinomialDistribution(i, 0.5)
542s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
542s  BinomialDistribution("a", 0.5)
543s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
543s  BinomialDistribution([1, 2], 0.5)
543s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
543s  BinomialDistribution(NaN, 0.5)
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, 1.01)
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, -0.01)
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, Inf)
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, i)
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, "a")
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, [1, 2])
543s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
543s  BinomialDistribution(1, NaN)
543s ***** error <cdf: invalid argument for upper tail.> ...
543s  cdf (BinomialDistribution, 2, "uper")
543s ***** error <cdf: invalid argument for upper tail.> ...
543s  cdf (BinomialDistribution, 2, 3)
543s ***** shared x
543s  rand ("seed", 2);
543s  x = binornd (5, 0.5, [1, 100]);
543s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha")
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", "")
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
543s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
543s           "alpha", {0.05})
543s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), ...
543s           "parameter", {"N", "p", "param"})
543s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
543s           "parameter", {"N", "p", "param"})
543s ***** error <paramci: unknown distribution parameter.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
543s ***** error <paramci: unknown distribution parameter.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
543s ***** error <paramci: unknown distribution parameter.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
543s           "parameter", "param")
543s ***** error <paramci: invalid NAME for optional argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
543s ***** error <paramci: invalid NAME for optional argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
543s           "NAME", "value")
543s ***** error <paramci: invalid NAME for optional argument.> ...
543s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
543s           "parameter", "p", "NAME", "value")
543s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
543s  plot (BinomialDistribution, "Parent")
543s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
543s  plot (BinomialDistribution, "PlotType", 12)
543s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
543s  plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
543s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
543s  plot (BinomialDistribution, "PlotType", "pdfcdf")
543s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
543s  plot (BinomialDistribution, "Discrete", "pdfcdf")
543s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
543s  plot (BinomialDistribution, "Discrete", [1, 0])
543s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
543s  plot (BinomialDistribution, "Discrete", {true})
543s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
543s  plot (BinomialDistribution, "Parent", 12)
543s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
543s  plot (BinomialDistribution, "Parent", "hax")
543s ***** error <plot: invalid NAME for optional argument.> ...
543s  plot (BinomialDistribution, "invalidNAME", "pdf")
543s ***** error <plot: no fitted DATA to plot a probability plot.> ...
543s  plot (BinomialDistribution, "PlotType", "probability")
543s ***** error <proflik: no fitted data available.> ...
543s  proflik (BinomialDistribution, 2)
543s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 3)
543s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
543s  proflik (BinomialDistribution.fit (x, 6), [1, 2])
543s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
543s  proflik (BinomialDistribution.fit (x, 6), {1})
543s ***** error <proflik: SETPARAM must be a numeric vector.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
543s ***** error <proflik: missing VALUE for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display")
543s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
543s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
543s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
543s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
543s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
543s ***** error <proflik: invalid NAME for optional arguments.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
543s ***** error <proflik: invalid optional argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
543s ***** error <proflik: invalid optional argument.> ...
543s  proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
543s ***** error <truncate: missing input argument.> ...
543s  truncate (BinomialDistribution)
543s ***** error <truncate: missing input argument.> ...
543s  truncate (BinomialDistribution, 2)
543s ***** error <truncate: invalid lower upper limits.> ...
543s  truncate (BinomialDistribution, 4, 2)
543s ***** shared pd
543s  pd = BinomialDistribution(1, 0.5);
543s  pd(2) = BinomialDistribution(1, 0.6);
543s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
543s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
543s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
543s ***** error <mean: requires a scalar probability distribution.> mean (pd)
543s ***** error <median: requires a scalar probability distribution.> median (pd)
543s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
543s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
543s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
543s ***** error <plot: requires a scalar probability distribution.> plot (pd)
543s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
543s ***** error <random: requires a scalar probability distribution.> random (pd)
543s ***** error <std: requires a scalar probability distribution.> std (pd)
543s ***** error <truncate: requires a scalar probability distribution.> ...
543s  truncate (pd, 2, 4)
543s ***** error <var: requires a scalar probability distribution.> var (pd)
543s 102 tests, 102 passed, 0 known failure, 0 skipped
543s [inst/dist_obj/HalfNormalDistribution.m]
543s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/HalfNormalDistribution.m
543s ***** shared pd, t
543s  pd = HalfNormalDistribution (0, 1);
543s  t = truncate (pd, 2, 4);
543s ***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
543s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
543s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
543s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
543s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
543s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
543s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
543s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
543s ***** assert (iqr (pd), 0.8317, 1e-4);
543s ***** assert (iqr (t), 0.4111, 1e-4);
543s ***** assert (mean (pd), 0.7979, 1e-4);
543s ***** assert (mean (t), 2.3706, 1e-4);
543s ***** assert (median (pd), 0.6745, 1e-4);
543s ***** assert (median (t), 2.2771, 1e-4);
543s ***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
543s ***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
543s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
543s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
543s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
543s ***** assert (any (random (t, 1000, 1) < 2), false);
543s ***** assert (any (random (t, 1000, 1) > 4), false);
543s ***** assert (std (pd), 0.6028, 1e-4);
543s ***** assert (std (t), 0.3310, 1e-4);
543s ***** assert (var (pd), 0.3634, 1e-4);
543s ***** assert (var (t), 0.1096, 1e-4);
543s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
543s  HalfNormalDistribution(Inf, 1)
543s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
543s  HalfNormalDistribution(i, 1)
543s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
543s  HalfNormalDistribution("a", 1)
543s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
543s  HalfNormalDistribution([1, 2], 1)
543s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
543s  HalfNormalDistribution(NaN, 1)
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, 0)
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, -1)
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, Inf)
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, i)
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, "a")
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, [1, 2])
543s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
543s  HalfNormalDistribution(1, NaN)
543s ***** error <cdf: invalid argument for upper tail.> ...
543s  cdf (HalfNormalDistribution, 2, "uper")
543s ***** error <cdf: invalid argument for upper tail.> ...
543s  cdf (HalfNormalDistribution, 2, 3)
543s ***** shared x
543s  x = hnrnd (1, 1, [1, 100]);
543s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
543s  paramci (HalfNormalDistribution.fit (x, 1), "alpha")
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
544s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
544s           "alpha", {0.05})
544s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), ...
544s           "parameter", {"mu", "sigma", "param"})
544s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
544s           "parameter", {"mu", "sigma", "param"})
544s ***** error <paramci: unknown distribution parameter.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
544s ***** error <paramci: unknown distribution parameter.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
544s           "parameter", "param")
544s ***** error <paramci: invalid NAME for optional argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
544s ***** error <paramci: invalid NAME for optional argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
544s           "NAME", "value")
544s ***** error <paramci: invalid NAME for optional argument.> ...
544s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
544s           "parameter", "sigma", "NAME", "value")
544s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
544s  plot (HalfNormalDistribution, "Parent")
544s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
544s  plot (HalfNormalDistribution, "PlotType", 12)
544s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
544s  plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
544s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
544s  plot (HalfNormalDistribution, "PlotType", "pdfcdf")
544s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
544s  plot (HalfNormalDistribution, "Discrete", "pdfcdf")
544s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
544s  plot (HalfNormalDistribution, "Discrete", [1, 0])
544s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
544s  plot (HalfNormalDistribution, "Discrete", {true})
544s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
544s  plot (HalfNormalDistribution, "Parent", 12)
544s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
544s  plot (HalfNormalDistribution, "Parent", "hax")
544s ***** error <plot: invalid NAME for optional argument.> ...
544s  plot (HalfNormalDistribution, "invalidNAME", "pdf")
544s ***** error <plot: no fitted DATA to plot a probability plot.> ...
544s  plot (HalfNormalDistribution, "PlotType", "probability")
544s ***** error <proflik: no fitted data available.> ...
544s  proflik (HalfNormalDistribution, 2)
544s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
544s  proflik (HalfNormalDistribution.fit (x, 1), 3)
545s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
545s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), {1})
545s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 1)
545s ***** error <proflik: SETPARAM must be a numeric vector.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
545s ***** error <proflik: missing VALUE for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
545s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
545s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
545s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
545s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
545s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
545s ***** error <proflik: invalid NAME for optional arguments.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
545s ***** error <proflik: invalid optional argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
545s ***** error <proflik: invalid optional argument.> ...
545s  proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
545s           "Display", "on")
545s ***** error <truncate: missing input argument.> ...
545s  truncate (HalfNormalDistribution)
545s ***** error <truncate: missing input argument.> ...
545s  truncate (HalfNormalDistribution, 2)
545s ***** error <truncate: invalid lower upper limits.> ...
545s  truncate (HalfNormalDistribution, 4, 2)
545s ***** shared pd
545s  pd = HalfNormalDistribution(1, 1);
545s  pd(2) = HalfNormalDistribution(1, 3);
545s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
545s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
545s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
545s ***** error <mean: requires a scalar probability distribution.> mean (pd)
545s ***** error <median: requires a scalar probability distribution.> median (pd)
545s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
545s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
545s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
545s ***** error <plot: requires a scalar probability distribution.> plot (pd)
545s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
545s ***** error <random: requires a scalar probability distribution.> random (pd)
545s ***** error <std: requires a scalar probability distribution.> std (pd)
545s ***** error <truncate: requires a scalar probability distribution.> ...
545s  truncate (pd, 2, 4)
545s ***** error <var: requires a scalar probability distribution.> var (pd)
545s 96 tests, 96 passed, 0 known failure, 0 skipped
545s [inst/dist_obj/LoguniformDistribution.m]
545s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/LoguniformDistribution.m
545s ***** demo
545s  ## Generate a data set of 5000 random samples from a Log-uniform distribution with
545s  ## parameters Lower = 1 and Upper = 10.  Plot a PDF of the distribution superimposed
545s  ## on a histogram of the data.
545s 
545s  pd_fixed = makedist ("Loguniform", "Lower", 1, "Upper", 10);
545s  rand ("seed", 2);
545s  data = random (pd_fixed, 5000, 1);
545s  plot (pd_fixed)
545s  hold on
545s  hist (data, 50)
545s  hold off
545s  msg = "Log-uniform distribution with Lower = %0.2f and Upper = %0.2f";
545s  title (sprintf (msg, pd_fixed.Lower, pd_fixed.Upper))
545s ***** shared pd, t
545s  pd = LoguniformDistribution (1, 4);
545s  t = truncate (pd, 2, 4);
545s ***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
545s ***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
545s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
545s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
545s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
546s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
546s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
546s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
546s ***** assert (iqr (pd), 1.4142, 1e-4);
546s ***** assert (iqr (t), 0.9852, 1e-4);
546s ***** assert (mean (pd), 2.1640, 1e-4);
546s ***** assert (mean (t), 2.8854, 1e-4);
546s ***** assert (median (pd), 2);
546s ***** assert (median (t), 2.8284, 1e-4);
546s ***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
546s ***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
546s ***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
546s ***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
546s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
546s ***** assert (any (random (pd, 1000, 1) < 1), false);
546s ***** assert (any (random (pd, 1000, 1) > 4), false);
546s ***** assert (any (random (t, 1000, 1) < 2), false);
546s ***** assert (any (random (t, 1000, 1) > 4), false);
546s ***** assert (std (pd), 0.8527, 1e-4);
546s ***** assert (std (t), 0.5751, 1e-4);
546s ***** assert (var (pd), 0.7270, 1e-4);
546s ***** assert (var (t), 0.3307, 1e-4);
546s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
546s  LoguniformDistribution (i, 1)
546s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
546s  LoguniformDistribution (Inf, 1)
546s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
546s  LoguniformDistribution ([1, 2], 1)
546s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
546s  LoguniformDistribution ("a", 1)
546s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
546s  LoguniformDistribution (NaN, 1)
546s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
546s  LoguniformDistribution (1, i)
546s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
546s  LoguniformDistribution (1, Inf)
546s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
546s  LoguniformDistribution (1, [1, 2])
546s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
546s  LoguniformDistribution (1, "a")
546s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
546s  LoguniformDistribution (1, NaN)
546s ***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
546s  LoguniformDistribution (2, 1)
546s ***** error <cdf: invalid argument for upper tail.> ...
546s  cdf (LoguniformDistribution, 2, "uper")
546s ***** error <cdf: invalid argument for upper tail.> ...
546s  cdf (LoguniformDistribution, 2, 3)
546s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
546s  plot (LoguniformDistribution, "Parent")
546s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
546s  plot (LoguniformDistribution, "PlotType", 12)
546s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
546s  plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
546s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
546s  plot (LoguniformDistribution, "PlotType", "pdfcdf")
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (LoguniformDistribution, "Discrete", "pdfcdf")
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (LoguniformDistribution, "Discrete", [1, 0])
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (LoguniformDistribution, "Discrete", {true})
546s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
546s  plot (LoguniformDistribution, "Parent", 12)
546s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
546s  plot (LoguniformDistribution, "Parent", "hax")
546s ***** error <plot: invalid NAME for optional argument.> ...
546s  plot (LoguniformDistribution, "invalidNAME", "pdf")
546s ***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
546s  plot (LoguniformDistribution, "PlotType", "probability")
546s ***** error <truncate: missing input argument.> ...
546s  truncate (LoguniformDistribution)
546s ***** error <truncate: missing input argument.> ...
546s  truncate (LoguniformDistribution, 2)
546s ***** error <truncate: invalid lower upper limits.> ...
546s  truncate (LoguniformDistribution, 4, 2)
546s ***** shared pd
546s  pd = LoguniformDistribution(1, 4);
546s  pd(2) = LoguniformDistribution(2, 5);
546s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
546s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
546s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
546s ***** error <mean: requires a scalar probability distribution.> mean (pd)
546s ***** error <median: requires a scalar probability distribution.> median (pd)
546s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
546s ***** error <plot: requires a scalar probability distribution.> plot (pd)
546s ***** error <random: requires a scalar probability distribution.> random (pd)
546s ***** error <std: requires a scalar probability distribution.> std (pd)
546s ***** error <truncate: requires a scalar probability distribution.> ...
546s  truncate (pd, 2, 4)
546s ***** error <var: requires a scalar probability distribution.> var (pd)
546s 65 tests, 65 passed, 0 known failure, 0 skipped
546s [inst/dist_obj/ExponentialDistribution.m]
546s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/ExponentialDistribution.m
546s ***** shared pd, t
546s  pd = ExponentialDistribution (1);
546s  t = truncate (pd, 2, 4);
546s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
546s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
546s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
546s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
546s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
546s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
546s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
546s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
546s ***** assert (iqr (pd), 1.0986, 1e-4);
546s ***** assert (iqr (t), 0.8020, 1e-4);
546s ***** assert (mean (pd), 1);
546s ***** assert (mean (t), 2.6870, 1e-4);
546s ***** assert (median (pd), 0.6931, 1e-4);
546s ***** assert (median (t), 2.5662, 1e-4);
546s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
546s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
546s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
546s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
546s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
546s ***** assert (any (random (t, 1000, 1) < 2), false);
546s ***** assert (any (random (t, 1000, 1) > 4), false);
546s ***** assert (std (pd), 1);
546s ***** assert (std (t), 0.5253, 1e-4);
546s ***** assert (var (pd), 1);
546s ***** assert (var (t), 0.2759, 1e-4);
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution(0)
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution(-1)
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution(Inf)
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution(i)
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution("a")
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution([1, 2])
546s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
546s  ExponentialDistribution(NaN)
546s ***** error <cdf: invalid argument for upper tail.> ...
546s  cdf (ExponentialDistribution, 2, "uper")
546s ***** error <cdf: invalid argument for upper tail.> ...
546s  cdf (ExponentialDistribution, 2, 3)
546s ***** shared x
546s  x = exprnd (1, [100, 1]);
546s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha")
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 0)
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 1)
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", "")
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
546s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
546s           "alpha", {0.05})
546s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
546s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
546s           "parameter", {"mu", "param"})
546s ***** error <paramci: unknown distribution parameter.> ...
546s  paramci (ExponentialDistribution.fit (x), "parameter", "param")
546s ***** error <paramci: unknown distribution parameter.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
546s ***** error <paramci: invalid NAME for optional argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "NAME", "value")
546s ***** error <paramci: invalid NAME for optional argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
546s ***** error <paramci: invalid NAME for optional argument.> ...
546s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
546s           "parameter", "mu", "NAME", "value")
546s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
546s  plot (ExponentialDistribution, "Parent")
546s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
546s  plot (ExponentialDistribution, "PlotType", 12)
546s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
546s  plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
546s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
546s  plot (ExponentialDistribution, "PlotType", "pdfcdf")
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (ExponentialDistribution, "Discrete", "pdfcdf")
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (ExponentialDistribution, "Discrete", [1, 0])
546s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
546s  plot (ExponentialDistribution, "Discrete", {true})
546s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
546s  plot (ExponentialDistribution, "Parent", 12)
546s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
546s  plot (ExponentialDistribution, "Parent", "hax")
546s ***** error <plot: invalid NAME for optional argument.> ...
546s  plot (ExponentialDistribution, "invalidNAME", "pdf")
546s ***** error <plot: no fitted DATA to plot a probability plot.> ...
546s  plot (ExponentialDistribution, "PlotType", "probability")
546s ***** error <proflik: no fitted data available.> ...
546s  proflik (ExponentialDistribution, 2)
546s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
546s  proflik (ExponentialDistribution.fit (x), 3)
546s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
546s  proflik (ExponentialDistribution.fit (x), [1, 2])
546s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
546s  proflik (ExponentialDistribution.fit (x), {1})
547s ***** error <proflik: SETPARAM must be a numeric vector.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, ones (2))
547s ***** error <proflik: missing VALUE for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display")
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
547s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
547s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
547s ***** error <proflik: invalid NAME for optional arguments.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
547s ***** error <proflik: invalid optional argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
547s ***** error <proflik: invalid optional argument.> ...
547s  proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
547s ***** error <truncate: missing input argument.> ...
547s  truncate (ExponentialDistribution)
547s ***** error <truncate: missing input argument.> ...
547s  truncate (ExponentialDistribution, 2)
547s ***** error <truncate: invalid lower upper limits.> ...
547s  truncate (ExponentialDistribution, 4, 2)
547s ***** shared pd
547s  pd = ExponentialDistribution(1);
547s  pd(2) = ExponentialDistribution(3);
547s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
547s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
547s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
547s ***** error <mean: requires a scalar probability distribution.> mean (pd)
547s ***** error <median: requires a scalar probability distribution.> median (pd)
547s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
547s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
547s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
547s ***** error <plot: requires a scalar probability distribution.> plot (pd)
547s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
547s ***** error <random: requires a scalar probability distribution.> random (pd)
547s ***** error <std: requires a scalar probability distribution.> std (pd)
547s ***** error <truncate: requires a scalar probability distribution.> ...
547s  truncate (pd, 2, 4)
547s ***** error <var: requires a scalar probability distribution.> var (pd)
547s 90 tests, 90 passed, 0 known failure, 0 skipped
547s [inst/dist_obj/PoissonDistribution.m]
547s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/PoissonDistribution.m
547s ***** demo
547s  ## Generate a data set of 5000 random samples from a Poisson distribution with
547s  ## parameter lambda = 5.  Fit a Poisson distribution to this data and plot
547s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
547s 
547s  pd_fixed = makedist ("Poisson", "lambda", 5)
547s  rand ("seed", 2);
547s  data = random (pd_fixed, 5000, 1);
547s  pd_fitted = fitdist (data, "Poisson")
547s  plot (pd_fitted)
547s  msg = "Fitted Poisson distribution with lambda = %0.2f";
547s  title (sprintf (msg, pd_fitted.lambda))
547s ***** shared pd, t, t_inf
547s  pd = PoissonDistribution;
547s  t = truncate (pd, 2, 4);
547s  t_inf = truncate (pd, 2, Inf);
547s ***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
547s ***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
547s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
547s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
547s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
547s ***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
547s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
547s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
547s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
547s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
547s ***** assert (iqr (pd), 2);
547s ***** assert (iqr (t), 1);
547s ***** assert (mean (pd), 1);
547s ***** assert (mean (t), 2.3529, 1e-4);
547s ***** assert (mean (t_inf), 2.3922, 1e-4);
547s ***** assert (median (pd), 1);
547s ***** assert (median (t), 2);
547s ***** assert (median (t_inf), 2);
547s ***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
547s ***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
547s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
547s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
547s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
547s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
547s ***** assert (any (random (t, 1000, 1) < 2), false);
547s ***** assert (any (random (t, 1000, 1) > 4), false);
547s ***** assert (std (pd), 1);
547s ***** assert (std (t), 0.5882, 1e-4);
547s ***** assert (std (t_inf), 0.6738, 1e-4);
547s ***** assert (var (pd), 1);
547s ***** assert (var (t), 0.3460, 1e-4);
547s ***** assert (var (t_inf), 0.4540, 1e-4);
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution(0)
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution(-1)
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution(Inf)
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution(i)
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution("a")
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution([1, 2])
547s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
547s  PoissonDistribution(NaN)
547s ***** error <cdf: invalid argument for upper tail.> ...
547s  cdf (PoissonDistribution, 2, "uper")
547s ***** error <cdf: invalid argument for upper tail.> ...
547s  cdf (PoissonDistribution, 2, 3)
547s ***** shared x
547s  x = poissrnd (1, [1, 100]);
547s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha")
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 0)
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 1)
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", "")
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", {0.05})
547s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
547s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
547s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
547s           "parameter", {"lambda", "param"})
547s ***** error <paramci: unknown distribution parameter.> ...
547s  paramci (PoissonDistribution.fit (x), "parameter", "param")
547s ***** error <paramci: unknown distribution parameter.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
547s ***** error <paramci: invalid NAME for optional argument.> ...
547s  paramci (PoissonDistribution.fit (x), "NAME", "value")
547s ***** error <paramci: invalid NAME for optional argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
547s ***** error <paramci: invalid NAME for optional argument.> ...
547s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
547s           "parameter", "lambda", "NAME", "value")
547s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
547s  plot (PoissonDistribution, "Parent")
547s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
547s  plot (PoissonDistribution, "PlotType", 12)
547s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
547s  plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
547s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
547s  plot (PoissonDistribution, "PlotType", "pdfcdf")
547s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
547s  plot (PoissonDistribution, "Discrete", "pdfcdf")
547s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
547s  plot (PoissonDistribution, "Discrete", [1, 0])
547s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
547s  plot (PoissonDistribution, "Discrete", {true})
547s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
547s  plot (PoissonDistribution, "Parent", 12)
547s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
547s  plot (PoissonDistribution, "Parent", "hax")
547s ***** error <plot: invalid NAME for optional argument.> ...
547s  plot (PoissonDistribution, "invalidNAME", "pdf")
547s ***** error <plot: no fitted DATA to plot a probability plot.> ...
547s  plot (PoissonDistribution, "PlotType", "probability")
547s ***** error <proflik: no fitted data available.> ...
547s  proflik (PoissonDistribution, 2)
547s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
547s  proflik (PoissonDistribution.fit (x), 3)
547s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
547s  proflik (PoissonDistribution.fit (x), [1, 2])
547s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
547s  proflik (PoissonDistribution.fit (x), {1})
547s ***** error <proflik: SETPARAM must be a numeric vector.> ...
547s  proflik (PoissonDistribution.fit (x), 1, ones (2))
547s ***** error <proflik: missing VALUE for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display")
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display", 1)
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display", {1})
547s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
547s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
547s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
547s ***** error <proflik: invalid NAME for optional arguments.> ...
547s  proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
547s ***** error <proflik: invalid optional argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
547s ***** error <proflik: invalid optional argument.> ...
547s  proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
547s ***** error <truncate: missing input argument.> ...
547s  truncate (PoissonDistribution)
547s ***** error <truncate: missing input argument.> ...
547s  truncate (PoissonDistribution, 2)
547s ***** error <truncate: invalid lower upper limits.> ...
547s  truncate (PoissonDistribution, 4, 2)
547s ***** shared pd
547s  pd = PoissonDistribution(1);
547s  pd(2) = PoissonDistribution(3);
547s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
547s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
547s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
547s ***** error <mean: requires a scalar probability distribution.> mean (pd)
547s ***** error <median: requires a scalar probability distribution.> median (pd)
547s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
547s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
547s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
547s ***** error <plot: requires a scalar probability distribution.> plot (pd)
547s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
547s ***** error <random: requires a scalar probability distribution.> random (pd)
547s ***** error <std: requires a scalar probability distribution.> std (pd)
547s ***** error <truncate: requires a scalar probability distribution.> ...
547s  truncate (pd, 2, 4)
547s ***** error <var: requires a scalar probability distribution.> var (pd)
547s 97 tests, 97 passed, 0 known failure, 0 skipped
547s [inst/dist_obj/LoglogisticDistribution.m]
547s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/LoglogisticDistribution.m
547s ***** demo
547s  ## Generate a data set of 5000 random samples from a Log-logistic
547s  ## distribution with parameters mu = 0 and sigma = 1.  Fit a Log-logistic
547s  ## distribution to this data and plot a PDF of the fitted distribution
547s  ## superimposed on a histogram of the data.
547s 
547s  pd_fixed = makedist ("Loglogistic", "mu", 0, "sigma", 1)
547s  rand ("seed", 2);
547s  data = random (pd_fixed, 5000, 1);
547s  pd_fitted = fitdist (data, "Loglogistic")
547s  plot (pd_fitted)
547s  msg = "Fitted Log-logistic distribution with mu = %0.2f and sigma = %0.2f";
547s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
547s ***** shared pd, t
547s  pd = LoglogisticDistribution;
547s  t = truncate (pd, 2, 4);
547s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
547s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
547s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
547s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
547s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
547s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
547s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
547s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
547s ***** assert (iqr (pd), 2.6667, 1e-4);
547s ***** assert (iqr (t), 0.9524, 1e-4);
547s ***** assert (mean (pd), Inf);
547s ***** assert (mean (t), 2.8312, 1e-4);
547s ***** assert (median (pd), 1, 1e-4);
547s ***** assert (median (t), 2.75, 1e-4);
547s ***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
547s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
547s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
547s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
547s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
547s ***** assert (any (random (t, 1000, 1) < 2), false);
547s ***** assert (any (random (t, 1000, 1) > 4), false);
547s ***** assert (std (pd), Inf);
547s ***** assert (std (t), 0.5674, 1e-4);
547s ***** assert (var (pd), Inf);
547s ***** assert (var (t), 0.3220, 1e-4);
547s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
547s  LoglogisticDistribution(Inf, 1)
547s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
547s  LoglogisticDistribution(i, 1)
547s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
547s  LoglogisticDistribution("a", 1)
547s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
547s  LoglogisticDistribution([1, 2], 1)
548s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
548s  LoglogisticDistribution(NaN, 1)
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, 0)
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, -1)
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, Inf)
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, i)
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, "a")
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, [1, 2])
548s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
548s  LoglogisticDistribution(1, NaN)
548s ***** error <cdf: invalid argument for upper tail.> ...
548s  cdf (LoglogisticDistribution, 2, "uper")
548s ***** error <cdf: invalid argument for upper tail.> ...
548s  cdf (LoglogisticDistribution, 2, 3)
548s ***** shared x
548s  x = loglrnd (1, 1, [1, 100]);
548s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha")
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 0)
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 1)
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", "")
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
548s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
548s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
548s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
548s           "parameter", {"mu", "sigma", "param"})
548s ***** error <paramci: unknown distribution parameter.> ...
548s  paramci (LoglogisticDistribution.fit (x), "parameter", "param")
548s ***** error <paramci: unknown distribution parameter.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
548s ***** error <paramci: invalid NAME for optional argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "NAME", "value")
548s ***** error <paramci: invalid NAME for optional argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
548s ***** error <paramci: invalid NAME for optional argument.> ...
548s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
548s           "parameter", "mu", "NAME", "value")
549s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
549s  plot (LoglogisticDistribution, "Parent")
549s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
549s  plot (LoglogisticDistribution, "PlotType", 12)
549s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
549s  plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
549s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
549s  plot (LoglogisticDistribution, "PlotType", "pdfcdf")
549s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
549s  plot (LoglogisticDistribution, "Discrete", "pdfcdf")
549s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
549s  plot (LoglogisticDistribution, "Discrete", [1, 0])
549s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
549s  plot (LoglogisticDistribution, "Discrete", {true})
549s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
549s  plot (LoglogisticDistribution, "Parent", 12)
549s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
549s  plot (LoglogisticDistribution, "Parent", "hax")
549s ***** error <plot: invalid NAME for optional argument.> ...
549s  plot (LoglogisticDistribution, "invalidNAME", "pdf")
549s ***** error <plot: no fitted DATA to plot a probability plot.> ...
549s  plot (LoglogisticDistribution, "PlotType", "probability")
549s ***** error <proflik: no fitted data available.> ...
549s  proflik (LoglogisticDistribution, 2)
549s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
549s  proflik (LoglogisticDistribution.fit (x), 3)
549s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
549s  proflik (LoglogisticDistribution.fit (x), [1, 2])
549s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
549s  proflik (LoglogisticDistribution.fit (x), {1})
549s ***** error <proflik: SETPARAM must be a numeric vector.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, ones (2))
549s ***** error <proflik: missing VALUE for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display")
549s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
549s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
549s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
549s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
549s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
549s ***** error <proflik: invalid NAME for optional arguments.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
549s ***** error <proflik: invalid optional argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
549s ***** error <proflik: invalid optional argument.> ...
549s  proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
550s ***** error <truncate: missing input argument.> ...
550s  truncate (LoglogisticDistribution)
550s ***** error <truncate: missing input argument.> ...
550s  truncate (LoglogisticDistribution, 2)
550s ***** error <truncate: invalid lower upper limits.> ...
550s  truncate (LoglogisticDistribution, 4, 2)
550s ***** shared pd
550s  pd = LoglogisticDistribution(1, 1);
550s  pd(2) = LoglogisticDistribution(1, 3);
550s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
550s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
550s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
550s ***** error <mean: requires a scalar probability distribution.> mean (pd)
550s ***** error <median: requires a scalar probability distribution.> median (pd)
550s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
550s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
550s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
550s ***** error <plot: requires a scalar probability distribution.> plot (pd)
550s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
550s ***** error <random: requires a scalar probability distribution.> random (pd)
550s ***** error <std: requires a scalar probability distribution.> std (pd)
550s ***** error <truncate: requires a scalar probability distribution.> ...
550s  truncate (pd, 2, 4)
550s ***** error <var: requires a scalar probability distribution.> var (pd)
550s 95 tests, 95 passed, 0 known failure, 0 skipped
550s [inst/dist_obj/RicianDistribution.m]
550s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/RicianDistribution.m
550s ***** demo
550s  ## Generate a data set of 5000 random samples from a Rician distribution with
550s  ## parameters s = 2 and sigma = 1. Fit a Rician distribution to this data and
550s  ## plot a PDF of the fitted distribution superimposed on a histogram of the data.
550s 
550s  pd_fixed = makedist ("Rician", "s", 2, "sigma", 1)
550s  rand ("seed", 2);
550s  data = random (pd_fixed, 5000, 1);
550s  pd_fitted = fitdist (data, "Rician")
550s  plot (pd_fitted)
550s  msg = "Fitted Rician distribution with s = %0.2f and sigma = %0.2f";
550s  title (sprintf (msg, pd_fitted.s, pd_fitted.sigma))
550s ***** shared pd, t
550s  pd = RicianDistribution;
550s  t = truncate (pd, 2, 4);
550s ***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
550s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
550s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
550s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
550s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
550s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
550s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
550s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
550s ***** assert (iqr (pd), 1.0890, 1e-4);
550s ***** assert (iqr (t), 0.5928, 1e-4);
550s ***** assert (mean (pd), 1.5486, 1e-4);
550s ***** assert (mean (t), 2.5380, 1e-4);
550s ***** assert (median (pd), 1.4755, 1e-4);
551s ***** assert (median (t), 2.4341, 1e-4);
551s ***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
551s ***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
551s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
551s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
551s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
551s ***** assert (any (random (t, 1000, 1) < 2), false);
551s ***** assert (any (random (t, 1000, 1) > 4), false);
551s ***** assert (std (pd), 0.7758, 1e-4);
551s ***** assert (std (t), 0.4294, 1e-4);
551s ***** assert (var (pd), 0.6019, 1e-4);
551s ***** assert (var (t), 0.1844, 1e-4);
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution(-eps, 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution(-1, 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution(Inf, 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution(i, 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution("a", 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution([1, 2], 1)
551s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
551s  RicianDistribution(NaN, 1)
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, 0)
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, -1)
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, Inf)
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, i)
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, "a")
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, [1, 2])
551s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
551s  RicianDistribution(1, NaN)
551s ***** error <cdf: invalid argument for upper tail.> ...
551s  cdf (RicianDistribution, 2, "uper")
551s ***** error <cdf: invalid argument for upper tail.> ...
551s  cdf (RicianDistribution, 2, 3)
551s ***** shared x
551s  x = gevrnd (1, 1, 1, [1, 100]);
551s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
551s  paramci (RicianDistribution.fit (x), "alpha")
551s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
551s  paramci (RicianDistribution.fit (x), "alpha", 0)
551s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
551s  paramci (RicianDistribution.fit (x), "alpha", 1)
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", "")
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", {0.05})
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
556s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
556s  paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
556s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
556s           "parameter", {"s", "sigma", "param"})
556s ***** error <paramci: unknown distribution parameter.> ...
556s  paramci (RicianDistribution.fit (x), "parameter", "param")
556s ***** error <paramci: unknown distribution parameter.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
556s ***** error <paramci: invalid NAME for optional argument.> ...
556s  paramci (RicianDistribution.fit (x), "NAME", "value")
556s ***** error <paramci: invalid NAME for optional argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
556s ***** error <paramci: invalid NAME for optional argument.> ...
556s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
556s           "NAME", "value")
556s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
556s  plot (RicianDistribution, "Parent")
556s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
556s  plot (RicianDistribution, "PlotType", 12)
556s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
556s  plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
556s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
556s  plot (RicianDistribution, "PlotType", "pdfcdf")
556s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
556s  plot (RicianDistribution, "Discrete", "pdfcdf")
556s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
556s  plot (RicianDistribution, "Discrete", [1, 0])
556s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
556s  plot (RicianDistribution, "Discrete", {true})
556s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
556s  plot (RicianDistribution, "Parent", 12)
556s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
556s  plot (RicianDistribution, "Parent", "hax")
556s ***** error <plot: invalid NAME for optional argument.> ...
556s  plot (RicianDistribution, "invalidNAME", "pdf")
556s ***** error <plot: no fitted DATA to plot a probability plot.> ...
556s  plot (RicianDistribution, "PlotType", "probability")
556s ***** error <proflik: no fitted data available.> ...
556s  proflik (RicianDistribution, 2)
556s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
556s  proflik (RicianDistribution.fit (x), 3)
556s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
556s  proflik (RicianDistribution.fit (x), [1, 2])
556s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
556s  proflik (RicianDistribution.fit (x), {1})
556s ***** error <proflik: SETPARAM must be a numeric vector.> ...
556s  proflik (RicianDistribution.fit (x), 1, ones (2))
556s ***** error <proflik: missing VALUE for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display")
556s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display", 1)
556s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display", {1})
556s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
556s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
556s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
556s ***** error <proflik: invalid NAME for optional arguments.> ...
556s  proflik (RicianDistribution.fit (x), 1, "NAME", "on")
556s ***** error <proflik: invalid optional argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
556s ***** error <proflik: invalid optional argument.> ...
556s  proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
556s ***** error <truncate: missing input argument.> ...
556s  truncate (RicianDistribution)
556s ***** error <truncate: missing input argument.> ...
556s  truncate (RicianDistribution, 2)
556s ***** error <truncate: invalid lower upper limits.> ...
556s  truncate (RicianDistribution, 4, 2)
556s ***** shared pd
556s  pd = RicianDistribution(1, 1);
556s  pd(2) = RicianDistribution(1, 3);
556s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
556s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
556s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
556s ***** error <mean: requires a scalar probability distribution.> mean (pd)
556s ***** error <median: requires a scalar probability distribution.> median (pd)
556s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
556s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
556s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
556s ***** error <plot: requires a scalar probability distribution.> plot (pd)
556s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
556s ***** error <random: requires a scalar probability distribution.> random (pd)
556s ***** error <std: requires a scalar probability distribution.> std (pd)
556s ***** error <truncate: requires a scalar probability distribution.> ...
556s  truncate (pd, 2, 4)
556s ***** error <var: requires a scalar probability distribution.> var (pd)
556s 97 tests, 97 passed, 0 known failure, 0 skipped
556s [inst/dist_obj/tLocationScaleDistribution.m]
556s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/tLocationScaleDistribution.m
556s ***** demo
556s  ## Generate a data set of 5000 random samples from a t Location-Scale distribution
556s  ## with parameters mu = 0, sigma = 1, and nu = 5. Fit a t Location-Scale
556s  ## distribution to this data and plot a PDF of the fitted distribution
556s  ## superimposed on a histogram of the data.
556s 
556s  pd_fixed = makedist ("tLocationScale", "mu", 0, "sigma", 1, "nu", 5);
556s  rand ("seed", 2);
556s  data = random (pd_fixed, 5000, 1);
556s  pd_fitted = fitdist (data, "tLocationScale");
556s  plot (pd_fitted);
556s  msg = "Fitted t Location-Scale distribution with mu = %0.2f, sigma = %0.2f, nu = %0.2f";
556s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma, pd_fitted.nu));
556s ***** shared pd, t
556s  pd = tLocationScaleDistribution;
556s  t = truncate (pd, 2, 4);
556s ***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
556s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
556s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
556s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
556s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
556s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
556s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
556s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
556s ***** assert (iqr (pd), 1.4534, 1e-4);
556s ***** assert (iqr (t), 0.7139, 1e-4);
556s ***** assert (mean (pd), 0, eps);
556s ***** assert (mean (t), 2.6099, 1e-4);
556s ***** assert (median (pd), 0, eps);
556s ***** assert (median (t), 2.4758, 1e-4);
556s ***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
556s ***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
556s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
556s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
556s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
556s ***** assert (any (random (t, 1000, 1) < 2), false);
556s ***** assert (any (random (t, 1000, 1) > 4), false);
556s ***** assert (std (pd), 1.2910, 1e-4);
556s ***** assert (std (t), 0.4989, 1e-4);
556s ***** assert (var (pd), 1.6667, 1e-4);
556s ***** assert (var (t), 0.2489, 1e-4);
556s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
556s  tLocationScaleDistribution(i, 1, 1)
556s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
556s  tLocationScaleDistribution(Inf, 1, 1)
556s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
556s  tLocationScaleDistribution([1, 2], 1, 1)
556s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
556s  tLocationScaleDistribution("a", 1, 1)
556s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
556s  tLocationScaleDistribution(NaN, 1, 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 0, 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, -1, 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, Inf, 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, i, 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, "a", 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, [1, 2], 1)
556s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, NaN, 1)
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, 0)
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, -1)
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, Inf)
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, i)
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, "a")
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, [1, 2])
556s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
556s  tLocationScaleDistribution(0, 1, NaN)
556s ***** error <cdf: invalid argument for upper tail.> ...
556s  cdf (tLocationScaleDistribution, 2, "uper")
556s ***** error <cdf: invalid argument for upper tail.> ...
556s  cdf (tLocationScaleDistribution, 2, 3)
556s ***** shared x
556s  x = tlsrnd (0, 1, 1, [1, 100]);
556s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha")
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", "")
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
556s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
556s           "alpha", {0.05})
556s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), ...
556s           "parameter", {"mu", "sigma", "nu", "param"})
556s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
556s           "parameter", {"mu", "sigma", "nu", "param"})
556s ***** error <paramci: unknown distribution parameter.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
556s ***** error <paramci: unknown distribution parameter.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
556s           "parameter", "param")
556s ***** error <paramci: invalid NAME for optional argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
556s ***** error <paramci: invalid NAME for optional argument.> ...
556s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
557s ***** error <paramci: invalid NAME for optional argument.> ...
557s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
557s           "parameter", "mu", "NAME", "value")
557s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
557s  plot (tLocationScaleDistribution, "Parent")
557s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
557s  plot (tLocationScaleDistribution, "PlotType", 12)
557s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
557s  plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
557s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
557s  plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
557s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
557s  plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
557s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
557s  plot (tLocationScaleDistribution, "Discrete", [1, 0])
557s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
557s  plot (tLocationScaleDistribution, "Discrete", {true})
557s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
557s  plot (tLocationScaleDistribution, "Parent", 12)
557s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
557s  plot (tLocationScaleDistribution, "Parent", "hax")
557s ***** error <plot: invalid NAME for optional argument.> ...
557s  plot (tLocationScaleDistribution, "invalidNAME", "pdf")
557s ***** error <plot: no fitted DATA to plot a probability plot.> ...
557s  plot (tLocationScaleDistribution, "PlotType", "probability")
557s ***** error <proflik: no fitted data available.> ...
557s  proflik (tLocationScaleDistribution, 2)
557s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
557s  proflik (tLocationScaleDistribution.fit (x), 4)
557s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
557s  proflik (tLocationScaleDistribution.fit (x), [1, 2])
557s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
557s  proflik (tLocationScaleDistribution.fit (x), {1})
557s ***** error <proflik: SETPARAM must be a numeric vector.> ...
557s  proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
557s ***** error <proflik: missing VALUE for 'Display' argument.> ...
557s  proflik (tLocationScaleDistribution.fit (x), 1, "Display")
558s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
558s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
558s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
558s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
558s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
558s ***** error <proflik: invalid NAME for optional arguments.> ...
558s  proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
559s ***** error <proflik: invalid optional argument.> ...
559s  proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
559s ***** error <proflik: invalid optional argument.> ...
559s  proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
559s ***** error <truncate: missing input argument.> ...
559s  truncate (tLocationScaleDistribution)
559s ***** error <truncate: missing input argument.> ...
559s  truncate (tLocationScaleDistribution, 2)
559s ***** error <truncate: invalid lower upper limits.> ...
559s  truncate (tLocationScaleDistribution, 4, 2)
559s ***** shared pd
559s  pd = tLocationScaleDistribution (0, 1, 1);
559s  pd(2) = tLocationScaleDistribution (0, 1, 3);
559s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
559s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
559s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
559s ***** error <mean: requires a scalar probability distribution.> mean (pd)
559s ***** error <median: requires a scalar probability distribution.> median (pd)
559s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
559s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
559s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
559s ***** error <plot: requires a scalar probability distribution.> plot (pd)
559s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
559s ***** error <random: requires a scalar probability distribution.> random (pd)
559s ***** error <std: requires a scalar probability distribution.> std (pd)
559s ***** error <truncate: requires a scalar probability distribution.> ...
559s  truncate (pd, 2, 4)
559s ***** error <var: requires a scalar probability distribution.> var (pd)
559s 102 tests, 102 passed, 0 known failure, 0 skipped
559s [inst/dist_obj/BurrDistribution.m]
559s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/BurrDistribution.m
559s ***** demo
559s  ## Generate a data set of 5000 random samples from a Burr type XII
559s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
559s  ## XII distribution to this data and plot a PDF of the fitted distribution
559s  ## superimposed on a histogram of the data
559s 
559s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
559s  rand ("seed", 21);
559s  data = random (pd, 5000, 1);
559s  pd = fitdist (data, "Burr")
559s  plot (pd)
559s  msg = strcat (["Fitted Burr type XII distribution with"], ...
559s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
559s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
559s ***** shared pd, t
559s  pd = BurrDistribution;
559s  t = truncate (pd, 2, 4);
559s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
559s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
559s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
559s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
559s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
559s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
559s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
559s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
559s ***** assert (iqr (pd), 2.6667, 1e-4);
559s ***** assert (iqr (t), 0.9524, 1e-4);
559s ***** assert (mean (pd), Inf);
559s ***** assert (mean (t), 2.8312, 1e-4);
559s ***** assert (median (pd), 1, 1e-4);
559s ***** assert (median (t), 2.75, 1e-4);
559s ***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
559s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
559s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
559s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
559s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
559s ***** assert (any (random (t, 1000, 1) < 2), false);
559s ***** assert (any (random (t, 1000, 1) > 4), false);
559s ***** assert (std (pd), Inf);
559s ***** assert (std (t), 0.5674, 1e-4);
559s ***** assert (var (pd), Inf);
559s ***** assert (var (t), 0.3220, 1e-4);
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution(0, 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution(-1, 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution(Inf, 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution(i, 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution("a", 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution([1, 2], 1, 1)
559s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
559s  BurrDistribution(NaN, 1, 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, 0, 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, -1, 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, Inf, 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, i, 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, "a", 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, [1, 2], 1)
559s ***** error <BurrDistribution: C must be a positive real scalar.> ...
559s  BurrDistribution(1, NaN, 1)
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, 0)
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, -1)
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, Inf)
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, i)
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, "a")
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, [1, 2])
559s ***** error <BurrDistribution: K must be a positive real scalar.> ...
559s  BurrDistribution(1, 1, NaN)
559s ***** error <cdf: invalid argument for upper tail.> ...
559s  cdf (BurrDistribution, 2, "uper")
559s ***** error <cdf: invalid argument for upper tail.> ...
559s  cdf (BurrDistribution, 2, 3)
559s ***** shared x
559s  rand ("seed", 4);
559s  x = burrrnd (1, 1, 1, [1, 100]);
559s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
559s  paramci (BurrDistribution.fit (x), "alpha")
559s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
559s  paramci (BurrDistribution.fit (x), "alpha", 0)
559s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
559s  paramci (BurrDistribution.fit (x), "alpha", 1)
560s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
560s  paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
560s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
560s  paramci (BurrDistribution.fit (x), "alpha", "")
560s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
560s  paramci (BurrDistribution.fit (x), "alpha", {0.05})
560s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
560s  paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
560s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
560s  paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
560s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
560s  paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
560s           "parameter", {"alpha", "c", "k", "param"})
560s ***** error <paramci: unknown distribution parameter.> ...
560s  paramci (BurrDistribution.fit (x), "parameter", "param")
561s ***** error <paramci: unknown distribution parameter.> ...
561s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
561s ***** error <paramci: invalid NAME for optional argument.> ...
561s  paramci (BurrDistribution.fit (x), "NAME", "value")
561s ***** error <paramci: invalid NAME for optional argument.> ...
561s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
561s ***** error <paramci: invalid NAME for optional argument.> ...
561s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
561s           "NAME", "value")
561s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
561s  plot (BurrDistribution, "Parent")
561s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
561s  plot (BurrDistribution, "PlotType", 12)
561s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
561s  plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
561s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
561s  plot (BurrDistribution, "PlotType", "pdfcdf")
561s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
561s  plot (BurrDistribution, "Discrete", "pdfcdf")
561s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
561s  plot (BurrDistribution, "Discrete", [1, 0])
561s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
561s  plot (BurrDistribution, "Discrete", {true})
561s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
561s  plot (BurrDistribution, "Parent", 12)
561s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
561s  plot (BurrDistribution, "Parent", "hax")
561s ***** error <plot: invalid NAME for optional argument.> ...
561s  plot (BurrDistribution, "invalidNAME", "pdf")
561s ***** error <plot: no fitted DATA to plot a probability plot.> ...
561s  plot (BurrDistribution, "PlotType", "probability")
561s ***** error <proflik: no fitted data available.> ...
561s  proflik (BurrDistribution, 2)
561s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
561s  proflik (BurrDistribution.fit (x), 4)
561s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
561s  proflik (BurrDistribution.fit (x), [1, 2])
561s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
561s  proflik (BurrDistribution.fit (x), {1})
562s ***** error <proflik: SETPARAM must be a numeric vector.> ...
562s  proflik (BurrDistribution.fit (x), 1, ones (2))
562s ***** error <proflik: missing VALUE for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display")
562s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display", 1)
562s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display", {1})
562s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
562s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
562s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
562s  proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
563s ***** error <proflik: invalid NAME for optional arguments.> ...
563s  proflik (BurrDistribution.fit (x), 1, "NAME", "on")
563s ***** error <proflik: invalid optional argument.> ...
563s  proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
563s ***** error <proflik: invalid optional argument.> ...
563s  proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
563s ***** error <truncate: missing input argument.> ...
563s  truncate (BurrDistribution)
563s ***** error <truncate: missing input argument.> ...
563s  truncate (BurrDistribution, 2)
563s ***** error <truncate: invalid lower upper limits.> ...
563s  truncate (BurrDistribution, 4, 2)
563s ***** shared pd
563s  pd = BurrDistribution(1, 1, 1);
563s  pd(2) = BurrDistribution(1, 3, 1);
563s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
563s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
563s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
563s ***** error <mean: requires a scalar probability distribution.> mean (pd)
563s ***** error <median: requires a scalar probability distribution.> median (pd)
563s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
563s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
563s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
563s ***** error <plot: requires a scalar probability distribution.> plot (pd)
563s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
563s ***** error <random: requires a scalar probability distribution.> random (pd)
563s ***** error <std: requires a scalar probability distribution.> std (pd)
563s ***** error <truncate: requires a scalar probability distribution.> ...
563s  truncate (pd, 2, 4)
563s ***** error <var: requires a scalar probability distribution.> var (pd)
563s 104 tests, 104 passed, 0 known failure, 0 skipped
563s [inst/dist_obj/NormalDistribution.m]
563s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/NormalDistribution.m
563s ***** demo
563s  ## Generate a data set of 5000 random samples from a Normal distribution with
563s  ## parameters mu = 0 and sigma = 1.  Fit a Normal distribution to this data and plot
563s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
563s 
563s  pd_fixed = makedist ("Normal", "mu", 0, "sigma", 1)
563s  randn ("seed", 2);
563s  data = random (pd_fixed, 5000, 1);
563s  pd_fitted = fitdist (data, "Normal")
563s  plot (pd_fitted)
563s  msg = "Fitted Normal distribution with mu = %0.2f and sigma = %0.2f";
563s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
563s ***** shared pd, t
563s  pd = NormalDistribution;
563s  t = truncate (pd, -2, 2);
563s ***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
563s ***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
563s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
563s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
563s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
563s ***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
563s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
563s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
563s ***** assert (iqr (pd), 1.3490, 1e-4);
563s ***** assert (iqr (t), 1.2782, 1e-4);
563s ***** assert (mean (pd), 0);
563s ***** assert (mean (t), 0, 3e-16);
563s ***** assert (median (pd), 0);
563s ***** assert (median (t), 0, 3e-16);
563s ***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
563s ***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
563s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
563s ***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
563s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
563s ***** assert (any (random (t, 1000, 1) < -2), false);
563s ***** assert (any (random (t, 1000, 1) > 2), false);
563s ***** assert (std (pd), 1);
563s ***** assert (std (t), 0.8796, 1e-4);
563s ***** assert (var (pd), 1);
563s ***** assert (var (t), 0.7737, 1e-4);
563s ***** error <NormalDistribution: MU must be a real scalar.> ...
563s  NormalDistribution(Inf, 1)
563s ***** error <NormalDistribution: MU must be a real scalar.> ...
563s  NormalDistribution(i, 1)
563s ***** error <NormalDistribution: MU must be a real scalar.> ...
563s  NormalDistribution("a", 1)
563s ***** error <NormalDistribution: MU must be a real scalar.> ...
563s  NormalDistribution([1, 2], 1)
563s ***** error <NormalDistribution: MU must be a real scalar.> ...
563s  NormalDistribution(NaN, 1)
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, 0)
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, -1)
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, Inf)
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, i)
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, "a")
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, [1, 2])
563s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
563s  NormalDistribution(1, NaN)
563s ***** error <cdf: invalid argument for upper tail.> ...
563s  cdf (NormalDistribution, 2, "uper")
563s ***** error <cdf: invalid argument for upper tail.> ...
563s  cdf (NormalDistribution, 2, 3)
563s ***** shared x
563s  x = normrnd (1, 1, [1, 100]);
563s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
563s  paramci (NormalDistribution.fit (x), "alpha")
563s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
563s  paramci (NormalDistribution.fit (x), "alpha", 0)
564s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
564s  paramci (NormalDistribution.fit (x), "alpha", 1)
564s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
564s  paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
564s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
564s  paramci (NormalDistribution.fit (x), "alpha", "")
564s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
564s  paramci (NormalDistribution.fit (x), "alpha", {0.05})
564s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
564s  paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
564s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
564s  paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
564s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
564s  paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
564s           "parameter", {"mu", "sigma", "param"})
565s ***** error <paramci: unknown distribution parameter.> ...
565s  paramci (NormalDistribution.fit (x), "parameter", "param")
565s ***** error <paramci: unknown distribution parameter.> ...
565s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
565s ***** error <paramci: invalid NAME for optional argument.> ...
565s  paramci (NormalDistribution.fit (x), "NAME", "value")
565s ***** error <paramci: invalid NAME for optional argument.> ...
565s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
565s ***** error <paramci: invalid NAME for optional argument.> ...
565s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
565s           "NAME", "value")
565s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
565s  plot (NormalDistribution, "Parent")
565s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
565s  plot (NormalDistribution, "PlotType", 12)
565s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
565s  plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
565s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
565s  plot (NormalDistribution, "PlotType", "pdfcdf")
565s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
565s  plot (NormalDistribution, "Discrete", "pdfcdf")
565s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
565s  plot (NormalDistribution, "Discrete", [1, 0])
565s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
565s  plot (NormalDistribution, "Discrete", {true})
565s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
565s  plot (NormalDistribution, "Parent", 12)
565s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
565s  plot (NormalDistribution, "Parent", "hax")
565s ***** error <plot: invalid NAME for optional argument.> ...
565s  plot (NormalDistribution, "invalidNAME", "pdf")
565s ***** error <plot: no fitted DATA to plot a probability plot.> ...
565s  plot (NormalDistribution, "PlotType", "probability")
565s ***** error <proflik: no fitted data available.> ...
565s  proflik (NormalDistribution, 2)
565s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
565s  proflik (NormalDistribution.fit (x), 3)
565s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
565s  proflik (NormalDistribution.fit (x), [1, 2])
566s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
566s  proflik (NormalDistribution.fit (x), {1})
566s ***** error <proflik: SETPARAM must be a numeric vector.> ...
566s  proflik (NormalDistribution.fit (x), 1, ones (2))
566s ***** error <proflik: missing VALUE for 'Display' argument.> ...
566s  proflik (NormalDistribution.fit (x), 1, "Display")
566s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
566s  proflik (NormalDistribution.fit (x), 1, "Display", 1)
566s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
566s  proflik (NormalDistribution.fit (x), 1, "Display", {1})
566s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
566s  proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
566s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
566s  proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
567s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
567s  proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
567s ***** error <proflik: invalid NAME for optional arguments.> ...
567s  proflik (NormalDistribution.fit (x), 1, "NAME", "on")
567s ***** error <proflik: invalid optional argument.> ...
567s  proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
567s ***** error <proflik: invalid optional argument.> ...
567s  proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
567s ***** error <truncate: missing input argument.> ...
567s  truncate (NormalDistribution)
567s ***** error <truncate: missing input argument.> ...
567s  truncate (NormalDistribution, 2)
567s ***** error <truncate: invalid lower upper limits.> ...
567s  truncate (NormalDistribution, 4, 2)
567s ***** shared pd
567s  pd = NormalDistribution(1, 1);
567s  pd(2) = NormalDistribution(1, 3);
567s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
567s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
567s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
567s ***** error <mean: requires a scalar probability distribution.> mean (pd)
567s ***** error <median: requires a scalar probability distribution.> median (pd)
567s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
567s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
567s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
567s ***** error <plot: requires a scalar probability distribution.> plot (pd)
567s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
567s ***** error <random: requires a scalar probability distribution.> random (pd)
567s ***** error <std: requires a scalar probability distribution.> std (pd)
567s ***** error <truncate: requires a scalar probability distribution.> ...
567s  truncate (pd, 2, 4)
567s ***** error <var: requires a scalar probability distribution.> var (pd)
567s 95 tests, 95 passed, 0 known failure, 0 skipped
567s [inst/dist_obj/UniformDistribution.m]
567s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/UniformDistribution.m
567s ***** demo
567s  ## Generate a data set of 5000 random samples from a Uniform distribution with
567s  ## parameters Lower = 0 and Upper = 10. Create a Uniform distribution with these
567s  ## parameters and plot its PDF superimposed on a histogram of the data.
567s 
567s  pd = makedist ("Uniform", "Lower", 0, "Upper", 10);
567s  rand ("seed", 21);
567s  data = random (pd, 5000, 1);
567s  
567s  x = linspace (pd.Lower - 1, pd.Upper + 1, 500);
567s  y = pdf (pd, x);
567s  plot (x, y, 'r-', 'LineWidth', 2);
567s  hold on;
567s  
567s  [counts, centers] = hist (data, 50);
567s  bin_width = centers(2) - centers(1);
567s  normalized_counts = counts / (sum (counts) * bin_width);
567s  bar (centers, normalized_counts, 1);
567s  
567s  msg = "Uniform distribution with Lower = %0.2f and Upper = %0.2f";
567s  title (sprintf (msg, pd.Lower, pd.Upper));
567s  legend ("PDF", "Histogram", "location", "northeast");
567s  
567s  hold off;
567s ***** shared pd, t
567s  pd = UniformDistribution (0, 5);
567s  t = truncate (pd, 2, 4);
567s ***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
567s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
567s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
567s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
567s ***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
567s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
567s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
567s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
567s ***** assert (iqr (pd), 2.5, 1e-14);
567s ***** assert (iqr (t), 1, 1e-14);
567s ***** assert (mean (pd), 2.5, 1e-14);
567s ***** assert (mean (t), 3, 1e-14);
567s ***** assert (median (pd), 2.5, 1e-14);
567s ***** assert (median (t), 3, 1e-14);
567s ***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
567s ***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
567s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
567s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
567s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
567s ***** assert (any (random (t, 1000, 1) < 2), false);
567s ***** assert (any (random (t, 1000, 1) > 4), false);
567s ***** assert (std (pd), 1.4434, 1e-4);
567s ***** assert (std (t), 0.5774, 1e-4);
567s ***** assert (var (pd), 2.0833, 1e-4);
567s ***** assert (var (t), 0.3333, 1e-4);
567s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
567s  UniformDistribution (i, 1)
567s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
567s  UniformDistribution (Inf, 1)
567s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
567s  UniformDistribution ([1, 2], 1)
567s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
567s  UniformDistribution ("a", 1)
567s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
567s  UniformDistribution (NaN, 1)
567s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
567s  UniformDistribution (1, i)
567s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
567s  UniformDistribution (1, Inf)
567s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
567s  UniformDistribution (1, [1, 2])
567s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
567s  UniformDistribution (1, "a")
567s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
567s  UniformDistribution (1, NaN)
567s ***** error <UniformDistribution: LOWER must be less than UPPER.> ...
567s  UniformDistribution (2, 1)
567s ***** error <cdf: invalid argument for upper tail.> ...
567s  cdf (UniformDistribution, 2, "uper")
567s ***** error <cdf: invalid argument for upper tail.> ...
567s  cdf (UniformDistribution, 2, 3)
567s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
567s  plot (UniformDistribution, "Parent")
567s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
567s  plot (UniformDistribution, "PlotType", 12)
567s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
567s  plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
567s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
567s  plot (UniformDistribution, "PlotType", "pdfcdf")
567s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
567s  plot (UniformDistribution, "Discrete", "pdfcdf")
567s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
567s  plot (UniformDistribution, "Discrete", [1, 0])
567s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
567s  plot (UniformDistribution, "Discrete", {true})
567s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
567s  plot (UniformDistribution, "Parent", 12)
567s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
567s  plot (UniformDistribution, "Parent", "hax")
567s ***** error <plot: invalid NAME for optional argument.> ...
567s  plot (UniformDistribution, "invalidNAME", "pdf")
567s ***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
567s  plot (UniformDistribution, "PlotType", "probability")
567s ***** error <truncate: missing input argument.> ...
567s  truncate (UniformDistribution)
567s ***** error <truncate: missing input argument.> ...
567s  truncate (UniformDistribution, 2)
567s ***** error <truncate: invalid lower upper limits.> ...
567s  truncate (UniformDistribution, 4, 2)
567s ***** shared pd
567s  pd = UniformDistribution (0, 1);
567s  pd(2) = UniformDistribution (0, 2);
567s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
567s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
567s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
567s ***** error <mean: requires a scalar probability distribution.> mean (pd)
567s ***** error <median: requires a scalar probability distribution.> median (pd)
567s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
567s ***** error <plot: requires a scalar probability distribution.> plot (pd)
567s ***** error <random: requires a scalar probability distribution.> random (pd)
567s ***** error <std: requires a scalar probability distribution.> std (pd)
567s ***** error <truncate: requires a scalar probability distribution.> ...
567s  truncate (pd, 2, 4)
567s ***** error <var: requires a scalar probability distribution.> var (pd)
567s 63 tests, 63 passed, 0 known failure, 0 skipped
567s [inst/dist_obj/MultinomialDistribution.m]
567s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/MultinomialDistribution.m
567s ***** demo
567s  ## Generate a data set of 5000 random samples from a Multinomial distribution
567s  ## with parameters Probabilities = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1].  Create
567s  ## the distribution and plot the PDF superimposed on a histogram of the data.
567s 
567s  probs = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1];
567s  pd = makedist ("Multinomial", "Probabilities", probs);
567s  rand ("seed", 2);
567s  data = random (pd, 5000, 1);
567s  hist (data, length (probs));
567s  hold on
567s  x = 1:length (probs);
567s  y = pdf (pd, x) * 5000;
567s  stem (x, y, "r", "LineWidth", 2);
567s  hold off
567s  msg = "Multinomial distribution with Probabilities = [%s]";
567s  probs_str = num2str (probs, "%0.1f ");
567s  title (sprintf (msg, probs_str))
567s ***** shared pd, t
567s  pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
567s  t = truncate (pd, 2, 4);
567s ***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
567s ***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
567s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
567s ***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
567s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
567s ***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
567s ***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
567s ***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
567s ***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
567s ***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
567s ***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
567s ***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
567s ***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
567s ***** assert (iqr (pd), 2);
567s ***** assert (iqr (t), 2);
567s ***** assert (mean (pd), 3.3, 1e-14);
567s ***** assert (mean (t), 3, eps);
567s ***** assert (median (pd), 3);
567s ***** assert (median (t), 3);
567s ***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
567s ***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
567s  [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
567s ***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
567s  [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
567s ***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
567s  [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
567s ***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
567s ***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
567s ***** assert (std (pd), 1.4177, 1e-4);
567s ***** assert (std (t), 0.7559, 1e-4);
567s ***** assert (var (pd), 2.0100, 1e-4);
567s ***** assert (var (t), 0.5714, 1e-4);
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution(0)
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution(-1)
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution(Inf)
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution(i)
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution("a")
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution([1, 2])
568s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
568s  MultinomialDistribution(NaN)
568s ***** error <cdf: invalid argument for upper tail.> ...
568s  cdf (MultinomialDistribution, 2, "uper")
568s ***** error <cdf: invalid argument for upper tail.> ...
568s  cdf (MultinomialDistribution, 2, 3)
568s ***** error <cdf: X must be real.> ...
568s  cdf (MultinomialDistribution, i)
568s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
568s  plot (MultinomialDistribution, "Parent")
568s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
568s  plot (MultinomialDistribution, "PlotType", 12)
568s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
568s  plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
568s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
568s  plot (MultinomialDistribution, "PlotType", "pdfcdf")
568s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
568s  plot (MultinomialDistribution, "Discrete", "pdfcdf")
568s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
568s  plot (MultinomialDistribution, "Discrete", [1, 0])
568s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
568s  plot (MultinomialDistribution, "Discrete", {true})
568s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
568s  plot (MultinomialDistribution, "Parent", 12)
568s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
568s  plot (MultinomialDistribution, "Parent", "hax")
568s ***** error <plot: invalid NAME for optional argument.> ...
568s  plot (MultinomialDistribution, "invalidNAME", "pdf")
568s ***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
568s  plot (MultinomialDistribution, "PlotType", "probability")
568s ***** error <truncate: is_nan input argument.> ...
568s  truncate (MultinomialDistribution)
568s ***** error <truncate: is_nan input argument.> ...
568s  truncate (MultinomialDistribution, 2)
568s ***** error <truncate: invalid lower upper limits.> ...
568s  truncate (MultinomialDistribution, 4, 2)
568s ***** shared pd
568s  pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
568s  pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
568s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
568s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
568s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
568s ***** error <mean: requires a scalar probability distribution.> mean (pd)
568s ***** error <median: requires a scalar probability distribution.> median (pd)
568s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
568s ***** error <plot: requires a scalar probability distribution.> plot (pd)
568s ***** error <random: requires a scalar probability distribution.> random (pd)
568s ***** error <std: requires a scalar probability distribution.> std (pd)
568s ***** error <truncate: requires a scalar probability distribution.> ...
568s  truncate (pd, 2, 4)
568s ***** error <var: requires a scalar probability distribution.> var (pd)
568s 64 tests, 64 passed, 0 known failure, 0 skipped
568s [inst/dist_obj/LognormalDistribution.m]
568s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/LognormalDistribution.m
568s ***** demo
568s  ## Generate a data set of 5000 random samples from a Lognormal distribution with
568s  ## parameters mu = 0 and sigma = 1.  Fit a Lognormal distribution to this data and plot
568s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
568s 
568s  pd_fixed = makedist ("Lognormal", "mu", 0, "sigma", 1)
568s  randn ("seed", 2);
568s  data = random (pd_fixed, 5000, 1);
568s  pd_fitted = fitdist (data, "Lognormal")
568s  plot (pd_fitted)
568s  msg = "Fitted Lognormal distribution with mu = %0.2f and sigma = %0.2f";
568s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
568s ***** shared pd, t
568s  pd = LognormalDistribution;
568s  t = truncate (pd, 2, 4);
568s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
568s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
568s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
568s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
568s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
568s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
568s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
568s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
568s ***** assert (iqr (pd), 1.4536, 1e-4);
568s ***** assert (iqr (t), 0.8989, 1e-4);
568s ***** assert (mean (pd), 1.6487, 1e-4);
568s ***** assert (mean (t), 2.7692, 1e-4);
568s ***** assert (median (pd), 1, 1e-4);
568s ***** assert (median (t), 2.6653, 1e-4);
568s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
568s ***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
568s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
568s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
568s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
568s ***** assert (any (random (t, 1000, 1) < 2), false);
568s ***** assert (any (random (t, 1000, 1) > 4), false);
568s ***** assert (std (pd), 2.1612, 1e-4);
568s ***** assert (std (t), 0.5540, 1e-4);
568s ***** assert (var (pd), 4.6708, 1e-4);
568s ***** assert (var (t), 0.3069, 1e-4);
568s ***** error <LognormalDistribution: MU must be a real scalar.> ...
568s  LognormalDistribution(Inf, 1)
568s ***** error <LognormalDistribution: MU must be a real scalar.> ...
568s  LognormalDistribution(i, 1)
568s ***** error <LognormalDistribution: MU must be a real scalar.> ...
568s  LognormalDistribution("a", 1)
568s ***** error <LognormalDistribution: MU must be a real scalar.> ...
568s  LognormalDistribution([1, 2], 1)
568s ***** error <LognormalDistribution: MU must be a real scalar.> ...
568s  LognormalDistribution(NaN, 1)
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, 0)
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, -1)
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, Inf)
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, i)
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, "a")
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, [1, 2])
568s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
568s  LognormalDistribution(1, NaN)
568s ***** error <cdf: invalid argument for upper tail.> ...
568s  cdf (LognormalDistribution, 2, "uper")
568s ***** error <cdf: invalid argument for upper tail.> ...
568s  cdf (LognormalDistribution, 2, 3)
568s ***** shared x
568s  randn ("seed", 1);
568s  x = lognrnd (1, 1, [1, 100]);
568s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha")
568s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha", 0)
568s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha", 1)
568s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
568s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha", "")
568s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
568s  paramci (LognormalDistribution.fit (x), "alpha", {0.05})
569s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
569s  paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
569s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
569s  paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
569s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
569s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
569s           "parameter", {"mu", "sigma", "param"})
569s ***** error <paramci: unknown distribution parameter.> ...
569s  paramci (LognormalDistribution.fit (x), "parameter", "param")
569s ***** error <paramci: unknown distribution parameter.> ...
569s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
569s ***** error <paramci: invalid NAME for optional argument.> ...
569s  paramci (LognormalDistribution.fit (x), "NAME", "value")
569s ***** error <paramci: invalid NAME for optional argument.> ...
569s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
570s ***** error <paramci: invalid NAME for optional argument.> ...
570s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
570s           "NAME", "value")
570s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
570s  plot (LognormalDistribution, "Parent")
570s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
570s  plot (LognormalDistribution, "PlotType", 12)
570s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
570s  plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
570s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
570s  plot (LognormalDistribution, "PlotType", "pdfcdf")
570s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
570s  plot (LognormalDistribution, "Discrete", "pdfcdf")
570s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
570s  plot (LognormalDistribution, "Discrete", [1, 0])
570s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
570s  plot (LognormalDistribution, "Discrete", {true})
570s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
570s  plot (LognormalDistribution, "Parent", 12)
570s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
570s  plot (LognormalDistribution, "Parent", "hax")
570s ***** error <plot: invalid NAME for optional argument.> ...
570s  plot (LognormalDistribution, "invalidNAME", "pdf")
570s ***** error <plot: no fitted DATA to plot a probability plot.> ...
570s  plot (LognormalDistribution, "PlotType", "probability")
570s ***** error <proflik: no fitted data available.> ...
570s  proflik (LognormalDistribution, 2)
570s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
570s  proflik (LognormalDistribution.fit (x), 3)
570s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
570s  proflik (LognormalDistribution.fit (x), [1, 2])
570s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
570s  proflik (LognormalDistribution.fit (x), {1})
570s ***** error <proflik: SETPARAM must be a numeric vector.> ...
570s  proflik (LognormalDistribution.fit (x), 1, ones (2))
570s ***** error <proflik: missing VALUE for 'Display' argument.> ...
570s  proflik (LognormalDistribution.fit (x), 1, "Display")
571s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "Display", 1)
571s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "Display", {1})
571s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
571s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
571s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
571s ***** error <proflik: invalid NAME for optional arguments.> ...
571s  proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
571s ***** error <proflik: invalid optional argument.> ...
571s  proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
572s ***** error <proflik: invalid optional argument.> ...
572s  proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
572s ***** error <truncate: missing input argument.> ...
572s  truncate (LognormalDistribution)
572s ***** error <truncate: missing input argument.> ...
572s  truncate (LognormalDistribution, 2)
572s ***** error <truncate: invalid lower upper limits.> ...
572s  truncate (LognormalDistribution, 4, 2)
572s ***** shared pd
572s  pd = LognormalDistribution(1, 1);
572s  pd(2) = LognormalDistribution(1, 3);
572s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
572s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
572s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
572s ***** error <mean: requires a scalar probability distribution.> mean (pd)
572s ***** error <median: requires a scalar probability distribution.> median (pd)
572s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
572s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
572s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
572s ***** error <plot: requires a scalar probability distribution.> plot (pd)
572s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
572s ***** error <random: requires a scalar probability distribution.> random (pd)
572s ***** error <std: requires a scalar probability distribution.> std (pd)
572s ***** error <truncate: requires a scalar probability distribution.> ...
572s  truncate (pd, 2, 4)
572s ***** error <var: requires a scalar probability distribution.> var (pd)
572s 95 tests, 95 passed, 0 known failure, 0 skipped
572s [inst/dist_obj/LogisticDistribution.m]
572s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/LogisticDistribution.m
572s ***** shared pd, t
572s  pd = LogisticDistribution (0, 1);
572s  t = truncate (pd, 2, 4);
572s ***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
572s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
572s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
572s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
572s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
572s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
572s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
572s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
572s ***** assert (iqr (pd), 2.1972, 1e-4);
572s ***** assert (iqr (t), 0.8286, 1e-4);
572s ***** assert (mean (pd), 0, 1e-4);
572s ***** assert (mean (t), 2.7193, 1e-4);
572s ***** assert (median (pd), 0);
572s ***** assert (median (t), 2.6085, 1e-4);
572s ***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
572s ***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
572s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
572s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
572s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
572s ***** assert (any (random (t, 1000, 1) < 2), false);
572s ***** assert (any (random (t, 1000, 1) > 4), false);
572s ***** assert (std (pd), 1.8138, 1e-4);
572s ***** assert (std (t), 0.5320, 1e-4);
572s ***** assert (var (pd), 3.2899, 1e-4);
572s ***** assert (var (t), 0.2830, 1e-4);
572s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
572s  LogisticDistribution(Inf, 1)
572s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
572s  LogisticDistribution(i, 1)
572s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
572s  LogisticDistribution("a", 1)
572s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
572s  LogisticDistribution([1, 2], 1)
572s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
572s  LogisticDistribution(NaN, 1)
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, 0)
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, -1)
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, Inf)
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, i)
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, "a")
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, [1, 2])
572s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
572s  LogisticDistribution(1, NaN)
572s ***** error <cdf: invalid argument for upper tail.> ...
572s  cdf (LogisticDistribution, 2, "uper")
572s ***** error <cdf: invalid argument for upper tail.> ...
572s  cdf (LogisticDistribution, 2, 3)
572s ***** shared x
572s  x = logirnd (1, 1, [1, 100]);
572s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha")
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", 0)
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", 1)
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", "")
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", {0.05})
572s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
572s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
572s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
572s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
572s           "parameter", {"mu", "sigma", "param"})
573s ***** error <paramci: unknown distribution parameter.> ...
573s  paramci (LogisticDistribution.fit (x), "parameter", "param")
573s ***** error <paramci: unknown distribution parameter.> ...
573s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
573s ***** error <paramci: invalid NAME for optional argument.> ...
573s  paramci (LogisticDistribution.fit (x), "NAME", "value")
573s ***** error <paramci: invalid NAME for optional argument.> ...
573s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
573s ***** error <paramci: invalid NAME for optional argument.> ...
573s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
573s           "NAME", "value")
573s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
573s  plot (LogisticDistribution, "Parent")
573s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
573s  plot (LogisticDistribution, "PlotType", 12)
573s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
573s  plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
573s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
573s  plot (LogisticDistribution, "PlotType", "pdfcdf")
573s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
573s  plot (LogisticDistribution, "Discrete", "pdfcdf")
573s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
573s  plot (LogisticDistribution, "Discrete", [1, 0])
573s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
573s  plot (LogisticDistribution, "Discrete", {true})
573s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
573s  plot (LogisticDistribution, "Parent", 12)
573s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
573s  plot (LogisticDistribution, "Parent", "hax")
573s ***** error <plot: invalid NAME for optional argument.> ...
573s  plot (LogisticDistribution, "invalidNAME", "pdf")
573s ***** error <plot: no fitted DATA to plot a probability plot.> ...
573s  plot (LogisticDistribution, "PlotType", "probability")
573s ***** error <proflik: no fitted data available.> ...
573s  proflik (LogisticDistribution, 2)
573s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
573s  proflik (LogisticDistribution.fit (x), 3)
573s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
573s  proflik (LogisticDistribution.fit (x), [1, 2])
573s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
573s  proflik (LogisticDistribution.fit (x), {1})
573s ***** error <proflik: SETPARAM must be a numeric vector.> ...
573s  proflik (LogisticDistribution.fit (x), 1, ones (2))
573s ***** error <proflik: missing VALUE for 'Display' argument.> ...
573s  proflik (LogisticDistribution.fit (x), 1, "Display")
573s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
573s  proflik (LogisticDistribution.fit (x), 1, "Display", 1)
573s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
573s  proflik (LogisticDistribution.fit (x), 1, "Display", {1})
573s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
573s  proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
573s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
573s  proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
574s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
574s  proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
574s ***** error <proflik: invalid NAME for optional arguments.> ...
574s  proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
574s ***** error <proflik: invalid optional argument.> ...
574s  proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
574s ***** error <proflik: invalid optional argument.> ...
574s  proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
574s ***** error <truncate: missing input argument.> ...
574s  truncate (LogisticDistribution)
574s ***** error <truncate: missing input argument.> ...
574s  truncate (LogisticDistribution, 2)
574s ***** error <truncate: invalid lower upper limits.> ...
574s  truncate (LogisticDistribution, 4, 2)
574s ***** shared pd
574s  pd = LogisticDistribution(1, 1);
574s  pd(2) = LogisticDistribution(1, 3);
574s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
574s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
574s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
574s ***** error <mean: requires a scalar probability distribution.> mean (pd)
574s ***** error <median: requires a scalar probability distribution.> median (pd)
574s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
574s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
574s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
574s ***** error <plot: requires a scalar probability distribution.> plot (pd)
574s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
574s ***** error <random: requires a scalar probability distribution.> random (pd)
574s ***** error <std: requires a scalar probability distribution.> std (pd)
574s ***** error <truncate: requires a scalar probability distribution.> ...
574s  truncate (pd, 2, 4)
574s ***** error <var: requires a scalar probability distribution.> var (pd)
574s 95 tests, 95 passed, 0 known failure, 0 skipped
574s [inst/dist_obj/RayleighDistribution.m]
574s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/RayleighDistribution.m
574s ***** demo
574s  ## Generate a data set of 5000 random samples from a Rayleigh distribution with
574s  ## parameter sigma = 2.  Fit a Rayleigh distribution to this data and plot
574s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
574s 
574s  pd_fixed = makedist ("Rayleigh", "sigma", 2)
574s  rand ("seed", 2);
574s  data = random (pd_fixed, 5000, 1);
574s  pd_fitted = fitdist (data, "Rayleigh")
574s  plot (pd_fitted)
574s  msg = "Fitted Rayleigh distribution with sigma = %0.2f";
574s  title (sprintf (msg, pd_fitted.sigma))
574s ***** shared pd, t
574s  pd = RayleighDistribution;
574s  t = truncate (pd, 2, 4);
574s ***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
574s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
574s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
574s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
574s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
574s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
574s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
574s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
574s ***** assert (iqr (pd), 0.9066, 1e-4);
574s ***** assert (iqr (t), 0.4609, 1e-4);
574s ***** assert (mean (pd), 1.2533, 1e-4);
574s ***** assert (mean (t), 2.4169, 1e-4);
574s ***** assert (median (pd), 1.1774, 1e-4);
574s ***** assert (median (t), 2.3198, 1e-4);
574s ***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
574s ***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
574s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
574s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
574s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
574s ***** assert (any (random (t, 1000, 1) < 2), false);
574s ***** assert (any (random (t, 1000, 1) > 4), false);
574s ***** assert (std (pd), 0.6551, 1e-4);
574s ***** assert (std (t), 0.3591, 1e-4);
574s ***** assert (var (pd), 0.4292, 1e-4);
574s ***** assert (var (t), 0.1290, 1e-4);
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution(0)
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution(-1)
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution(Inf)
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution(i)
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution("a")
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution([1, 2])
574s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
574s  RayleighDistribution(NaN)
574s ***** error <cdf: invalid argument for upper tail.> ...
574s  cdf (RayleighDistribution, 2, "uper")
574s ***** error <cdf: invalid argument for upper tail.> ...
574s  cdf (RayleighDistribution, 2, 3)
574s ***** shared x
574s  x = raylrnd (1, [1, 100]);
574s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha")
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", 0)
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", 1)
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", "")
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", {0.05})
574s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
574s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
574s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
574s           "parameter", {"sigma", "param"})
574s ***** error <paramci: unknown distribution parameter.> ...
574s  paramci (RayleighDistribution.fit (x), "parameter", "param")
574s ***** error <paramci: unknown distribution parameter.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
574s ***** error <paramci: invalid NAME for optional argument.> ...
574s  paramci (RayleighDistribution.fit (x), "NAME", "value")
574s ***** error <paramci: invalid NAME for optional argument.> ...
574s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
575s ***** error <paramci: invalid NAME for optional argument.> ...
575s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
575s           "parameter", "sigma", "NAME", "value")
575s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
575s  plot (RayleighDistribution, "Parent")
575s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
575s  plot (RayleighDistribution, "PlotType", 12)
575s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
575s  plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
575s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
575s  plot (RayleighDistribution, "PlotType", "pdfcdf")
575s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
575s  plot (RayleighDistribution, "Discrete", "pdfcdf")
575s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
575s  plot (RayleighDistribution, "Discrete", [1, 0])
575s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
575s  plot (RayleighDistribution, "Discrete", {true})
575s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
575s  plot (RayleighDistribution, "Parent", 12)
575s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
575s  plot (RayleighDistribution, "Parent", "hax")
575s ***** error <plot: invalid NAME for optional argument.> ...
575s  plot (RayleighDistribution, "invalidNAME", "pdf")
575s ***** error <plot: no fitted DATA to plot a probability plot.> ...
575s  plot (RayleighDistribution, "PlotType", "probability")
575s ***** error <proflik: no fitted data available.> ...
575s  proflik (RayleighDistribution, 2)
575s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
575s  proflik (RayleighDistribution.fit (x), 3)
575s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
575s  proflik (RayleighDistribution.fit (x), [1, 2])
575s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
575s  proflik (RayleighDistribution.fit (x), {1})
575s ***** error <proflik: SETPARAM must be a numeric vector.> ...
575s  proflik (RayleighDistribution.fit (x), 1, ones (2))
575s ***** error <proflik: missing VALUE for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display")
575s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display", 1)
575s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display", {1})
575s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
575s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
575s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
575s ***** error <proflik: invalid NAME for optional arguments.> ...
575s  proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
575s ***** error <proflik: invalid optional argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
575s ***** error <proflik: invalid optional argument.> ...
575s  proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
575s ***** error <truncate: missing input argument.> ...
575s  truncate (RayleighDistribution)
575s ***** error <truncate: missing input argument.> ...
575s  truncate (RayleighDistribution, 2)
575s ***** error <truncate: invalid lower upper limits.> ...
575s  truncate (RayleighDistribution, 4, 2)
575s ***** shared pd
575s  pd = RayleighDistribution(1);
575s  pd(2) = RayleighDistribution(3);
575s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
575s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
575s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
575s ***** error <mean: requires a scalar probability distribution.> mean (pd)
575s ***** error <median: requires a scalar probability distribution.> median (pd)
575s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
575s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
575s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
575s ***** error <plot: requires a scalar probability distribution.> plot (pd)
575s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
575s ***** error <random: requires a scalar probability distribution.> random (pd)
575s ***** error <std: requires a scalar probability distribution.> std (pd)
575s ***** error <truncate: requires a scalar probability distribution.> ...
575s  truncate (pd, 2, 4)
575s ***** error <var: requires a scalar probability distribution.> var (pd)
575s 90 tests, 90 passed, 0 known failure, 0 skipped
575s [inst/dist_obj/BetaDistribution.m]
575s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/BetaDistribution.m
575s ***** demo
575s  ## Generate a data set of 5000 random samples from a Beta distribution with
575s  ## parameters a = 2 and b = 5.  Fit a Beta distribution to this data and plot
575s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
575s 
575s  pd_fixed = makedist ("Beta", "a", 2, "b", 5)
575s  randg ("seed", 2);
575s  data = random (pd_fixed, 5000, 1);
575s  pd_fitted = fitdist (data, "Beta")
575s  plot (pd_fitted)
575s  msg = "Fitted Beta distribution with a = %0.2f and b = %0.2f";
575s  title (sprintf (msg, pd_fitted.a, pd_fitted.b))
575s ***** shared pd, t
575s  pd = BetaDistribution;
575s  t = truncate (pd, 0.2, 0.8);
575s ***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
575s ***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
575s ***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
575s ***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
575s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
575s ***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
575s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
575s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
575s ***** assert (iqr (pd), 0.5, 1e-4);
575s ***** assert (iqr (t), 0.3, 1e-4);
575s ***** assert (mean (pd), 0.5);
575s ***** assert (mean (t), 0.5, 1e-6);
575s ***** assert (median (pd), 0.5);
575s ***** assert (median (t), 0.5, 1e-6);
575s ***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
575s ***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
575s ***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
575s ***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
575s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
575s ***** assert (any (random (t, 1000, 1) < 0.2), false);
575s ***** assert (any (random (t, 1000, 1) > 0.8), false);
575s ***** assert (std (pd), 0.2887, 1e-4);
575s ***** assert (std (t), 0.1732, 1e-4);
579s ***** assert (var (pd), 0.0833, 1e-4);
579s ***** assert (var (t), 0.0300, 1e-4);
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution(0, 1)
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution(Inf, 1)
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution(i, 1)
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution("a", 1)
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution([1, 2], 1)
579s ***** error <BetaDistribution: A must be a positive real scalar.> ...
579s  BetaDistribution(NaN, 1)
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, 0)
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, -1)
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, Inf)
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, i)
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, "a")
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, [1, 2])
579s ***** error <BetaDistribution: B must be a positive real scalar.> ...
579s  BetaDistribution(1, NaN)
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (BetaDistribution, 2, "uper")
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (BetaDistribution, 2, 3)
579s ***** shared x
579s  randg ("seed", 1);
579s  x = betarnd (1, 1, [100, 1]);
579s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
579s  paramci (BetaDistribution.fit (x), "alpha")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 0)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 1)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", "")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", {0.05})
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
579s           "parameter", {"a", "b", "param"})
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (BetaDistribution.fit (x), "parameter", "param")
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (BetaDistribution.fit (x), "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
579s           "NAME", "value")
579s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
579s  plot (BetaDistribution, "Parent")
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (BetaDistribution, "PlotType", 12)
579s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
579s  plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (BetaDistribution, "PlotType", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (BetaDistribution, "Discrete", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (BetaDistribution, "Discrete", [1, 0])
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (BetaDistribution, "Discrete", {true})
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (BetaDistribution, "Parent", 12)
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (BetaDistribution, "Parent", "hax")
579s ***** error <plot: invalid NAME for optional argument.> ...
579s  plot (BetaDistribution, "invalidNAME", "pdf")
579s ***** error <plot: no fitted DATA to plot a probability plot.> ...
579s  plot (BetaDistribution, "PlotType", "probability")
579s ***** error <proflik: no fitted data available.> ...
579s  proflik (BetaDistribution, 2)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (BetaDistribution.fit (x), 3)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (BetaDistribution.fit (x), [1, 2])
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (BetaDistribution.fit (x), {1})
579s ***** error <proflik: SETPARAM must be a numeric vector.> ...
579s  proflik (BetaDistribution.fit (x), 1, ones (2))
579s ***** error <proflik: missing VALUE for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display")
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display", 1)
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display", {1})
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
579s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
579s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
579s ***** error <proflik: invalid NAME for optional arguments.> ...
579s  proflik (BetaDistribution.fit (x), 1, "NAME", "on")
579s ***** error <proflik: invalid optional argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
579s ***** error <proflik: invalid optional argument.> ...
579s  proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
579s ***** error <truncate: missing input argument.> ...
579s  truncate (BetaDistribution)
579s ***** error <truncate: missing input argument.> ...
579s  truncate (BetaDistribution, 2)
579s ***** error <truncate: invalid lower upper limits.> ...
579s  truncate (BetaDistribution, 4, 2)
579s ***** shared pd
579s  pd = BetaDistribution(1, 1);
579s  pd(2) = BetaDistribution(1, 3);
579s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
579s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
579s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
579s ***** error <mean: requires a scalar probability distribution.> mean (pd)
579s ***** error <median: requires a scalar probability distribution.> median (pd)
579s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
579s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
579s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
579s ***** error <plot: requires a scalar probability distribution.> plot (pd)
579s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
579s ***** error <random: requires a scalar probability distribution.> random (pd)
579s ***** error <std: requires a scalar probability distribution.> std (pd)
579s ***** error <truncate: requires a scalar probability distribution.> ...
579s  truncate (pd, 2, 4)
579s ***** error <var: requires a scalar probability distribution.> var (pd)
579s 96 tests, 96 passed, 0 known failure, 0 skipped
579s [inst/dist_obj/WeibullDistribution.m]
579s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/WeibullDistribution.m
579s ***** demo
579s  ## Generate a data set of 5000 random samples from a Weibull distribution with
579s  ## parameters lambda = 1 and k = 2.  Fit a Weibull distribution to this data and plot
579s  ## a PDF of the fitted distribution superimposed on a histogram of a data.
579s 
579s  pd_fixed = makedist ("Weibull", "lambda", 1, "k", 2)
579s  rand ("seed", 2);
579s  data = random (pd_fixed, 5000, 1);
579s  pd_fitted = fitdist (data, "Weibull")
579s  plot (pd_fitted)
579s  msg = "Fitted Weibull distribution with lambda = %0.2f and k = %0.2f";
579s  title (sprintf (msg, pd_fitted.lambda, pd_fitted.k))
579s ***** shared pd, t
579s  pd = WeibullDistribution;
579s  t = truncate (pd, 2, 4);
579s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
579s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
579s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
579s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
579s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
579s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
579s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
579s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
579s ***** assert (iqr (pd), 1.0986, 1e-4);
579s ***** assert (iqr (t), 0.8020, 1e-4);
579s ***** assert (mean (pd), 1, 1e-14);
579s ***** assert (mean (t), 2.6870, 1e-4);
579s ***** assert (median (pd), 0.6931, 1e-4);
579s ***** assert (median (t), 2.5662, 1e-4);
579s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
579s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
579s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
579s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
579s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
579s ***** assert (any (random (t, 1000, 1) < 2), false);
579s ***** assert (any (random (t, 1000, 1) > 4), false);
579s ***** assert (std (pd), 1, 1e-14);
579s ***** assert (std (t), 0.5253, 1e-4);
579s ***** assert (var (pd), 1, 1e-14);
579s ***** assert (var (t), 0.2759, 1e-4);
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution(0, 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution(-1, 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution(Inf, 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution(i, 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution("a", 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution([1, 2], 1)
579s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
579s  WeibullDistribution(NaN, 1)
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, 0)
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, -1)
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, Inf)
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, i)
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, "a")
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, [1, 2])
579s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
579s  WeibullDistribution(1, NaN)
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (WeibullDistribution, 2, "uper")
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (WeibullDistribution, 2, 3)
579s ***** shared x
579s  x = wblrnd (1, 1, [1, 100]);
579s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 0)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 1)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", "")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", {0.05})
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
579s           "parameter", {"lambda", "k", "param"})
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (WeibullDistribution.fit (x), "parameter", "param")
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (WeibullDistribution.fit (x), "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
579s           "NAME", "value")
579s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
579s  plot (WeibullDistribution, "Parent")
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (WeibullDistribution, "PlotType", 12)
579s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
579s  plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (WeibullDistribution, "PlotType", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (WeibullDistribution, "Discrete", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (WeibullDistribution, "Discrete", [1, 0])
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (WeibullDistribution, "Discrete", {true})
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (WeibullDistribution, "Parent", 12)
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (WeibullDistribution, "Parent", "hax")
579s ***** error <plot: invalid NAME for optional argument.> ...
579s  plot (WeibullDistribution, "invalidNAME", "pdf")
579s ***** error <plot: no fitted DATA to plot a probability plot.> ...
579s  plot (WeibullDistribution, "PlotType", "probability")
579s ***** error <proflik: no fitted data available.> ...
579s  proflik (WeibullDistribution, 2)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (WeibullDistribution.fit (x), 3)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (WeibullDistribution.fit (x), [1, 2])
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (WeibullDistribution.fit (x), {1})
579s ***** error <proflik: SETPARAM must be a numeric vector.> ...
579s  proflik (WeibullDistribution.fit (x), 1, ones (2))
579s ***** error <proflik: missing VALUE for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display")
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display", 1)
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display", {1})
579s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
579s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
579s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
579s ***** error <proflik: invalid NAME for optional arguments.> ...
579s  proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
579s ***** error <proflik: invalid optional argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
579s ***** error <proflik: invalid optional argument.> ...
579s  proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
579s ***** error <truncate: missing input argument.> ...
579s  truncate (WeibullDistribution)
579s ***** error <truncate: missing input argument.> ...
579s  truncate (WeibullDistribution, 2)
579s ***** error <truncate: invalid lower upper limits.> ...
579s  truncate (WeibullDistribution, 4, 2)
579s ***** shared pd
579s  pd = WeibullDistribution(1, 1);
579s  pd(2) = WeibullDistribution(1, 3);
579s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
579s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
579s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
579s ***** error <mean: requires a scalar probability distribution.> mean (pd)
579s ***** error <median: requires a scalar probability distribution.> median (pd)
579s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
579s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
579s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
579s ***** error <plot: requires a scalar probability distribution.> plot (pd)
579s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
579s ***** error <random: requires a scalar probability distribution.> random (pd)
579s ***** error <std: requires a scalar probability distribution.> std (pd)
579s ***** error <truncate: requires a scalar probability distribution.> ...
579s  truncate (pd, 2, 4)
579s ***** error <var: requires a scalar probability distribution.> var (pd)
579s 97 tests, 97 passed, 0 known failure, 0 skipped
579s [inst/dist_obj/GeneralizedExtremeValueDistribution.m]
579s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/GeneralizedExtremeValueDistribution.m
579s ***** shared pd, t
579s  pd = GeneralizedExtremeValueDistribution;
579s  t = truncate (pd, 2, 4);
579s ***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
579s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
579s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
579s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
579s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
579s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
579s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
579s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
579s ***** assert (iqr (pd), 1.5725, 1e-4);
579s ***** assert (iqr (t), 0.8164, 1e-4);
579s ***** assert (mean (pd), 0.5772, 1e-4);
579s ***** assert (mean (t), 2.7043, 1e-4);
579s ***** assert (median (pd), 0.3665, 1e-4);
579s ***** assert (median (t), 2.5887, 1e-4);
579s ***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
579s ***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
579s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
579s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
579s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
579s ***** assert (any (random (t, 1000, 1) < 2), false);
579s ***** assert (any (random (t, 1000, 1) > 4), false);
579s ***** assert (std (pd), 1.2825, 1e-4);
579s ***** assert (std (t), 0.5289, 1e-4);
579s ***** assert (var (pd), 1.6449, 1e-4);
579s ***** assert (var (t), 0.2798, 1e-4);
579s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(Inf, 1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(i, 1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution("a", 1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution([1, 2], 1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(NaN, 1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 0, 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, -1, 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, Inf, 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, i, 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, "a", 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, [1, 2], 1)
579s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, NaN, 1)
579s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 1, Inf)
579s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 1, i)
579s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 1, "a")
579s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 1, [1, 2])
579s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
579s  GeneralizedExtremeValueDistribution(1, 1, NaN)
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (GeneralizedExtremeValueDistribution, 2, "uper")
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (GeneralizedExtremeValueDistribution, 2, 3)
579s ***** shared x
579s  x = gevrnd (1, 1, 1, [1, 100]);
579s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
579s           "parameter", "sigma", "alpha", {0.05})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
579s           "parameter", {"k", "sigma", "mu", "param"})
580s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
580s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
580s           "parameter", {"k", "sigma", "mu", "param"})
580s ***** error <paramci: unknown distribution parameter.> ...
580s  paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
580s ***** error <paramci: unknown distribution parameter.> ...
580s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
580s           "parameter", "param")
580s ***** error <paramci: invalid NAME for optional argument.> ...
580s  paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
580s ***** error <paramci: invalid NAME for optional argument.> ...
580s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
580s           "NAME", "value")
581s ***** error <paramci: invalid NAME for optional argument.> ...
581s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
581s           "parameter", "sigma", "NAME", "value")
581s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Parent")
581s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
581s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
581s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
581s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
581s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
581s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
581s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Parent", 12)
581s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
581s ***** error <plot: invalid NAME for optional argument.> ...
581s  plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
581s ***** error <plot: no fitted DATA to plot a probability plot.> ...
581s  plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
581s ***** error <proflik: no fitted data available.> ...
581s  proflik (GeneralizedExtremeValueDistribution, 2)
581s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
581s  proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
581s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
581s  proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
581s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
581s  proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
582s ***** error <proflik: SETPARAM must be a numeric vector.> ...
582s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
582s ***** error <proflik: missing VALUE for 'Display' argument.> ...
582s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
582s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
582s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
582s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
582s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
582s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
582s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
583s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
583s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
583s           "Display", ["on"; "on"])
583s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
583s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
583s ***** error <proflik: invalid NAME for optional arguments.> ...
583s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
583s ***** error <proflik: invalid optional argument.> ...
583s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
584s ***** error <proflik: invalid optional argument.> ...
584s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
584s           "Display", "on")
584s ***** error <truncate: missing input argument.> ...
584s  truncate (GeneralizedExtremeValueDistribution)
584s ***** error <truncate: missing input argument.> ...
584s  truncate (GeneralizedExtremeValueDistribution, 2)
584s ***** error <truncate: invalid lower upper limits.> ...
584s  truncate (GeneralizedExtremeValueDistribution, 4, 2)
584s ***** shared pd
584s  pd = GeneralizedExtremeValueDistribution(1, 1, 1);
584s  pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
584s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
584s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
584s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
584s ***** error <mean: requires a scalar probability distribution.> mean (pd)
584s ***** error <median: requires a scalar probability distribution.> median (pd)
584s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
584s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
584s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
584s ***** error <plot: requires a scalar probability distribution.> plot (pd)
584s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
584s ***** error <random: requires a scalar probability distribution.> random (pd)
584s ***** error <std: requires a scalar probability distribution.> std (pd)
584s ***** error <truncate: requires a scalar probability distribution.> ...
584s  truncate (pd, 2, 4)
584s ***** error <var: requires a scalar probability distribution.> var (pd)
584s 100 tests, 100 passed, 0 known failure, 0 skipped
584s [inst/dist_obj/GammaDistribution.m]
584s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/GammaDistribution.m
584s ***** shared pd, t
584s  pd = GammaDistribution (1, 1);
584s  t = truncate (pd, 2, 4);
584s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
584s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
584s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
584s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
584s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
584s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
584s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
584s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
584s ***** assert (iqr (pd), 1.0986, 1e-4);
584s ***** assert (iqr (t), 0.8020, 1e-4);
584s ***** assert (mean (pd), 1);
584s ***** assert (mean (t), 2.6870, 1e-4);
584s ***** assert (median (pd), 0.6931, 1e-4);
584s ***** assert (median (t), 2.5662, 1e-4);
584s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
584s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
584s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
584s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
584s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
584s ***** assert (any (random (t, 1000, 1) < 2), false);
584s ***** assert (any (random (t, 1000, 1) > 4), false);
584s ***** assert (std (pd), 1);
584s ***** assert (std (t), 0.5253, 1e-4);
584s ***** assert (var (pd), 1);
584s ***** assert (var (t), 0.2759, 1e-4);
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution(0, 1)
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution(Inf, 1)
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution(i, 1)
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution("a", 1)
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution([1, 2], 1)
584s ***** error <GammaDistribution: A must be a positive real scalar.> ...
584s  GammaDistribution(NaN, 1)
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, 0)
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, -1)
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, Inf)
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, i)
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, "a")
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, [1, 2])
584s ***** error <GammaDistribution: B must be a positive real scalar.> ...
584s  GammaDistribution(1, NaN)
584s ***** error <cdf: invalid argument for upper tail.> ...
584s  cdf (GammaDistribution, 2, "uper")
584s ***** error <cdf: invalid argument for upper tail.> ...
584s  cdf (GammaDistribution, 2, 3)
584s ***** shared x
584s  x = gamrnd (1, 1, [100, 1]);
584s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
584s  paramci (GammaDistribution.fit (x), "alpha")
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", 0)
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", 1)
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", "")
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", {0.05})
584s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
584s  paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
584s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
584s  paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
584s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
584s           "parameter", {"a", "b", "param"})
584s ***** error <paramci: unknown distribution parameter.> ...
584s  paramci (GammaDistribution.fit (x), "parameter", "param")
584s ***** error <paramci: unknown distribution parameter.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
584s ***** error <paramci: invalid NAME for optional argument.> ...
584s  paramci (GammaDistribution.fit (x), "NAME", "value")
584s ***** error <paramci: invalid NAME for optional argument.> ...
584s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
585s ***** error <paramci: invalid NAME for optional argument.> ...
585s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
585s           "NAME", "value")
585s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
585s  plot (GammaDistribution, "Parent")
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (GammaDistribution, "PlotType", 12)
585s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
585s  plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (GammaDistribution, "PlotType", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (GammaDistribution, "Discrete", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (GammaDistribution, "Discrete", [1, 0])
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (GammaDistribution, "Discrete", {true})
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (GammaDistribution, "Parent", 12)
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (GammaDistribution, "Parent", "hax")
585s ***** error <plot: invalid NAME for optional argument.> ...
585s  plot (GammaDistribution, "invalidNAME", "pdf")
585s ***** error <plot: no fitted DATA to plot a probability plot.> ...
585s  plot (GammaDistribution, "PlotType", "probability")
585s ***** error <proflik: no fitted data available.> ...
585s  proflik (GammaDistribution, 2)
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (GammaDistribution.fit (x), 3)
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (GammaDistribution.fit (x), [1, 2])
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (GammaDistribution.fit (x), {1})
585s ***** error <proflik: SETPARAM must be a numeric vector.> ...
585s  proflik (GammaDistribution.fit (x), 1, ones (2))
585s ***** error <proflik: missing VALUE for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display")
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display", 1)
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display", {1})
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
585s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
585s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
585s ***** error <proflik: invalid NAME for optional arguments.> ...
585s  proflik (GammaDistribution.fit (x), 1, "NAME", "on")
585s ***** error <proflik: invalid optional argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
585s ***** error <proflik: invalid optional argument.> ...
585s  proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
585s ***** error <truncate: missing input argument.> ...
585s  truncate (GammaDistribution)
585s ***** error <truncate: missing input argument.> ...
585s  truncate (GammaDistribution, 2)
585s ***** error <truncate: invalid lower upper limits.> ...
585s  truncate (GammaDistribution, 4, 2)
585s ***** shared pd
585s  pd = GammaDistribution(1, 1);
585s  pd(2) = GammaDistribution(1, 3);
585s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
585s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
585s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
585s ***** error <mean: requires a scalar probability distribution.> mean (pd)
585s ***** error <median: requires a scalar probability distribution.> median (pd)
585s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
585s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
585s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
585s ***** error <plot: requires a scalar probability distribution.> plot (pd)
585s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
585s ***** error <random: requires a scalar probability distribution.> random (pd)
585s ***** error <std: requires a scalar probability distribution.> std (pd)
585s ***** error <truncate: requires a scalar probability distribution.> ...
585s  truncate (pd, 2, 4)
585s ***** error <var: requires a scalar probability distribution.> var (pd)
585s 96 tests, 96 passed, 0 known failure, 0 skipped
585s [inst/dist_obj/TriangularDistribution.m]
585s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/TriangularDistribution.m
585s ***** demo
585s  ## Generate a data set of 5000 random samples from a Triangular distribution
585s  ## with parameters A = 0, B = 1, C = 2. Fit a Triangular distribution to
585s  ## this data and plot a PDF of the fitted distribution superimposed on a
585s  ## histogram of the data.
585s 
585s  pd_fixed = makedist ("Triangular", "A", 0, "B", 1, "C", 2);
585s  rand ("seed", 2);
585s  data = random (pd_fixed, 5000, 1);
585s  A = min (data);
585s  C = mean (data);
585s  B = max (data);
585s 
585s  [counts, centers] = hist (data, 50);
585s  bin_width = centers(2) - centers(1);
585s  normalized_counts = counts / (sum (counts) * bin_width);
585s  bar (centers, normalized_counts, 1);
585s  hold on;
585s 
585s  x = linspace (A, B, 100);
585s  y = (2 * (x - A) / (C - A) .* (x <= C)) + (2 * (B - x) / (B - C) .* (x > C));
585s 
585s  plot (x, y, 'r-', 'LineWidth', 2);
585s 
585s  msg = sprintf ("Fitted Triangular distribution with A = %0.2f, C = %0.2f, B = %0.2f", A, C, B);
585s  title (msg);
585s 
585s  hold off;
585s ***** shared pd, t
585s  pd = TriangularDistribution (0, 3, 5);
585s  t = truncate (pd, 2, 4);
585s ***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
585s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
585s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
585s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
585s ***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
585s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
585s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
585s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
585s ***** assert (iqr (pd), 1.4824, 1e-4);
585s ***** assert (iqr (t), 0.8678, 1e-4);
585s ***** assert (mean (pd), 2.6667, 1e-4);
585s ***** assert (mean (t), 2.9649, 1e-4);
585s ***** assert (median (pd), 2.7386, 1e-4);
585s ***** assert (median (t), 2.9580, 1e-4);
585s ***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
585s ***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
585s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
585s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
585s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
585s ***** assert (any (random (t, 1000, 1) < 2), false);
585s ***** assert (any (random (t, 1000, 1) > 4), false);
585s ***** assert (std (pd), 1.0274, 1e-4);
585s ***** assert (std (t), 0.5369, 1e-4);
585s ***** assert (var (pd), 1.0556, 1e-4);
585s ***** assert (var (t), 0.2882, 1e-4);
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution (i, 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution (Inf, 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution ([1, 2], 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution ("a", 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution (NaN, 1, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, i, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, Inf, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, [1, 2], 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, "a", 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, NaN, 2)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, i)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, Inf)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, [1, 2])
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, "a")
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, NaN)
585s ***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
585s  TriangularDistribution (1, 1, 1)
585s ***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
585s  TriangularDistribution (1, 0.5, 2)
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (TriangularDistribution, 2, "uper")
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (TriangularDistribution, 2, 3)
585s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
585s  plot (TriangularDistribution, "Parent")
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (TriangularDistribution, "PlotType", 12)
585s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
585s  plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (TriangularDistribution, "PlotType", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", [1, 0])
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", {true})
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (TriangularDistribution, "Parent", 12)
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (TriangularDistribution, "Parent", "hax")
585s ***** error <plot: invalid NAME for optional argument.> ...
585s  plot (TriangularDistribution, "invalidNAME", "pdf")
585s ***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
585s  plot (TriangularDistribution, "PlotType", "probability")
585s ***** error <truncate: missing input argument.> ...
585s  truncate (TriangularDistribution)
585s ***** error <truncate: missing input argument.> ...
585s  truncate (TriangularDistribution, 2)
585s ***** error <truncate: invalid lower upper limits.> ...
585s  truncate (TriangularDistribution, 4, 2)
585s ***** shared pd
585s  pd = TriangularDistribution (0, 1, 2);
585s  pd(2) = TriangularDistribution (0, 1, 2);
585s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
585s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
585s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
585s ***** error <mean: requires a scalar probability distribution.> mean (pd)
585s ***** error <median: requires a scalar probability distribution.> median (pd)
585s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
585s ***** error <plot: requires a scalar probability distribution.> plot (pd)
585s ***** error <random: requires a scalar probability distribution.> random (pd)
585s ***** error <std: requires a scalar probability distribution.> std (pd)
585s ***** error <truncate: requires a scalar probability distribution.> ...
585s  truncate (pd, 2, 4)
585s ***** error <var: requires a scalar probability distribution.> var (pd)
585s 69 tests, 69 passed, 0 known failure, 0 skipped
585s [inst/dist_obj/BirnbaumSaundersDistribution.m]
585s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/BirnbaumSaundersDistribution.m
585s ***** demo
585s  ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
585s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
585s  ## distribution to this data and plot a PDF of the fitted distribution
585s  ## superimposed on a histogram of the data.
585s 
585s  pd_fixed = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
585s  randg ("seed", 21);
585s  data = random (pd_fixed, 5000, 1);
585s  pd_fitted = fitdist (data, "BirnbaumSaunders")
585s  plot (pd_fitted)
585s  msg = "Fitted Birnbaum-Saunders distribution with beta = %0.2f and gamma = %0.2f";
585s  title (sprintf (msg, pd_fitted.beta, pd_fitted.gamma))
585s ***** shared pd, t
585s  pd = BirnbaumSaundersDistribution;
585s  t = truncate (pd, 2, 4);
585s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
585s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
585s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
585s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
585s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
585s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
585s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
585s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
585s ***** assert (iqr (pd), 1.4236, 1e-4);
585s ***** assert (iqr (t), 0.8968, 1e-4);
585s ***** assert (mean (pd), 1.5, eps);
585s ***** assert (mean (t), 2.7723, 1e-4);
585s ***** assert (median (pd), 1, 1e-4);
585s ***** assert (median (t), 2.6711, 1e-4);
585s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
585s ***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
585s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
585s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
585s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
585s ***** assert (any (random (t, 1000, 1) < 2), false);
585s ***** assert (any (random (t, 1000, 1) > 4), false);
585s ***** assert (std (pd), 1.5, eps);
585s ***** assert (std (t), 0.5528, 1e-4);
585s ***** assert (var (pd), 2.25, eps);
585s ***** assert (var (t), 0.3056, 1e-4);
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(0, 1)
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(Inf, 1)
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(i, 1)
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution("beta", 1)
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution([1, 2], 1)
585s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(NaN, 1)
585s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(1, 0)
585s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(1, -1)
585s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(1, Inf)
585s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(1, i)
585s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
585s  BirnbaumSaundersDistribution(1, "beta")
586s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
586s  BirnbaumSaundersDistribution(1, [1, 2])
586s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
586s  BirnbaumSaundersDistribution(1, NaN)
586s ***** error <cdf: invalid argument for upper tail.> ...
586s  cdf (BirnbaumSaundersDistribution, 2, "uper")
586s ***** error <cdf: invalid argument for upper tail.> ...
586s  cdf (BirnbaumSaundersDistribution, 2, 3)
586s ***** shared x
586s  rand ("seed", 5);
586s  x = bisarnd (1, 1, [100, 1]);
586s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
586s           "beta", "alpha", {0.05})
586s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), ...
586s           "parameter", {"beta", "gamma", "param"})
586s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
586s           "parameter", {"beta", "gamma", "param"})
586s ***** error <paramci: unknown distribution parameter.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
586s ***** error <paramci: unknown distribution parameter.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
586s           "parameter", "param")
586s ***** error <paramci: invalid NAME for optional argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
586s ***** error <paramci: invalid NAME for optional argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
586s           "NAME", "value")
586s ***** error <paramci: invalid NAME for optional argument.> ...
586s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
586s           "parameter", "beta", "NAME", "value")
587s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
587s  plot (BirnbaumSaundersDistribution, "Parent")
587s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "PlotType", 12)
587s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
587s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
587s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
587s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
587s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "Discrete", {true})
587s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "Parent", 12)
587s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
587s  plot (BirnbaumSaundersDistribution, "Parent", "hax")
587s ***** error <plot: invalid NAME for optional argument.> ...
587s  plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
587s ***** error <plot: no fitted DATA to plot a probability plot.> ...
587s  plot (BirnbaumSaundersDistribution, "PlotType", "probability")
587s ***** error <proflik: no fitted data available.> ...
587s  proflik (BirnbaumSaundersDistribution, 2)
587s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 3)
587s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
587s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), {1})
587s ***** error <proflik: SETPARAM must be a numeric vector.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
587s ***** error <proflik: missing VALUE for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
587s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
587s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
587s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
587s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
587s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
587s ***** error <proflik: invalid NAME for optional arguments.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
587s ***** error <proflik: invalid optional argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
587s ***** error <proflik: invalid optional argument.> ...
587s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
588s ***** error <truncate: missing input argument.> ...
588s  truncate (BirnbaumSaundersDistribution)
588s ***** error <truncate: missing input argument.> ...
588s  truncate (BirnbaumSaundersDistribution, 2)
588s ***** error <truncate: invalid lower upper limits.> ...
588s  truncate (BirnbaumSaundersDistribution, 4, 2)
588s ***** shared pd
588s  pd = BirnbaumSaundersDistribution(1, 1);
588s  pd(2) = BirnbaumSaundersDistribution(1, 3);
588s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
588s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
588s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
588s ***** error <mean: requires a scalar probability distribution.> mean (pd)
588s ***** error <median: requires a scalar probability distribution.> median (pd)
588s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
588s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
588s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
588s ***** error <plot: requires a scalar probability distribution.> plot (pd)
588s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
588s ***** error <random: requires a scalar probability distribution.> random (pd)
588s ***** error <std: requires a scalar probability distribution.> std (pd)
588s ***** error <truncate: requires a scalar probability distribution.> ...
588s  truncate (pd, 2, 4)
588s ***** error <var: requires a scalar probability distribution.> var (pd)
588s 96 tests, 96 passed, 0 known failure, 0 skipped
588s [inst/dist_obj/ExtremeValueDistribution.m]
588s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/ExtremeValueDistribution.m
588s ***** shared pd, t
588s  pd = ExtremeValueDistribution (0, 1);
588s  t = truncate (pd, 2, 4);
588s ***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
588s ***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
588s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
588s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
588s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
588s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
588s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
588s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
588s ***** assert (iqr (pd), 1.5725, 1e-4);
588s ***** assert (iqr (t), 0.1338, 1e-4);
588s ***** assert (mean (pd), -0.5772, 1e-4);
588s ***** assert (mean (t), 2.1206, 1e-4);
588s ***** assert (median (pd), -0.3665, 1e-4);
588s ***** assert (median (t), 2.0897, 1e-4);
588s ***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
588s ***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
588s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
588s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
588s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
588s ***** assert (any (random (t, 1000, 1) < 2), false);
588s ***** assert (any (random (t, 1000, 1) > 4), false);
588s ***** assert (std (pd), 1.2825, 1e-4);
588s ***** assert (std (t), 0.1091, 1e-4);
588s ***** assert (var (pd), 1.6449, 1e-4);
588s ***** assert (var (t), 0.0119, 1e-4);
588s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
588s  ExtremeValueDistribution(Inf, 1)
588s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
588s  ExtremeValueDistribution(i, 1)
588s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
588s  ExtremeValueDistribution("a", 1)
588s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
588s  ExtremeValueDistribution([1, 2], 1)
588s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
588s  ExtremeValueDistribution(NaN, 1)
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, 0)
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, -1)
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, Inf)
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, i)
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, "a")
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, [1, 2])
588s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
588s  ExtremeValueDistribution(1, NaN)
588s ***** error <cdf: invalid argument for upper tail.> ...
588s  cdf (ExtremeValueDistribution, 2, "uper")
588s ***** error <cdf: invalid argument for upper tail.> ...
588s  cdf (ExtremeValueDistribution, 2, 3)
588s ***** shared x
588s  rand ("seed", 1);
588s  x = evrnd (1, 1, [1000, 1]);
588s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
588s  paramci (ExtremeValueDistribution.fit (x), "alpha")
588s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
588s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
589s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
589s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
589s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", "")
589s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
589s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), ...
589s           "parameter", "mu", "alpha", {0.05})
589s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), ...
589s           "parameter", {"mu", "sigma", "param"})
589s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
589s           "parameter", {"mu", "sigma", "param"})
589s ***** error <paramci: unknown distribution parameter.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
589s ***** error <paramci: unknown distribution parameter.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
589s           "parameter", "param")
589s ***** error <paramci: invalid NAME for optional argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
589s ***** error <paramci: invalid NAME for optional argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
589s ***** error <paramci: invalid NAME for optional argument.> ...
589s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
589s           "parameter", "mu", "NAME", "value")
589s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
589s  plot (ExtremeValueDistribution, "Parent")
589s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
589s  plot (ExtremeValueDistribution, "PlotType", 12)
589s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
589s  plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
589s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
589s  plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
589s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
589s  plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
589s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
589s  plot (ExtremeValueDistribution, "Discrete", [1, 0])
589s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
589s  plot (ExtremeValueDistribution, "Discrete", {true})
589s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
589s  plot (ExtremeValueDistribution, "Parent", 12)
589s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
589s  plot (ExtremeValueDistribution, "Parent", "hax")
589s ***** error <plot: invalid NAME for optional argument.> ...
589s  plot (ExtremeValueDistribution, "invalidNAME", "pdf")
589s ***** error <plot: no fitted DATA to plot a probability plot.> ...
589s  plot (ExtremeValueDistribution, "PlotType", "probability")
589s ***** error <proflik: no fitted data available.> ...
589s  proflik (ExtremeValueDistribution, 2)
589s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
589s  proflik (ExtremeValueDistribution.fit (x), 3)
589s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (ExtremeValueDistribution.fit (x), [1, 2])
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (ExtremeValueDistribution.fit (x), {1})
601s ***** error <proflik: SETPARAM must be a numeric vector.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
601s ***** error <proflik: missing VALUE for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display")
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
601s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
601s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
601s ***** error <proflik: invalid NAME for optional arguments.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
601s ***** error <truncate: missing input argument.> ...
601s  truncate (ExtremeValueDistribution)
601s ***** error <truncate: missing input argument.> ...
601s  truncate (ExtremeValueDistribution, 2)
601s ***** error <truncate: invalid lower upper limits.> ...
601s  truncate (ExtremeValueDistribution, 4, 2)
601s ***** shared pd
601s  pd = ExtremeValueDistribution(1, 1);
601s  pd(2) = ExtremeValueDistribution(1, 3);
601s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
601s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
601s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
601s ***** error <mean: requires a scalar probability distribution.> mean (pd)
601s ***** error <median: requires a scalar probability distribution.> median (pd)
601s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
601s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
601s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
601s ***** error <plot: requires a scalar probability distribution.> plot (pd)
601s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
601s ***** error <random: requires a scalar probability distribution.> random (pd)
601s ***** error <std: requires a scalar probability distribution.> std (pd)
601s ***** error <truncate: requires a scalar probability distribution.> ...
601s  truncate (pd, 2, 4)
601s ***** error <var: requires a scalar probability distribution.> var (pd)
601s 95 tests, 95 passed, 0 known failure, 0 skipped
601s [inst/dist_obj/NakagamiDistribution.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/NakagamiDistribution.m
601s ***** demo
601s  ## Generate a data set of 5000 random samples from a Nakagami distribution with
601s  ## parameters mu = 1 and omega = 1.  Fit a Nakagami distribution to this data and plot
601s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
601s 
601s  pd_fixed = makedist ("Nakagami", "mu", 1, "omega", 1)
601s  rand ("seed", 2);
601s  data = random (pd_fixed, 5000, 1);
601s  pd_fitted = fitdist (data, "Nakagami")
601s  plot (pd_fitted)
601s  msg = "Fitted Nakagami distribution with mu = %0.2f and omega = %0.2f";
601s  title (sprintf (msg, pd_fitted.mu, pd_fitted.omega))
601s ***** shared pd, t
601s  pd = NakagamiDistribution;
601s  t = truncate (pd, 2, 4);
601s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
601s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
601s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
601s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
601s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
601s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
601s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
601s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
601s ***** assert (iqr (pd), 0.6411, 1e-4);
601s ***** assert (iqr (t), 0.2502, 1e-4);
601s ***** assert (mean (pd), 0.8862, 1e-4);
601s ***** assert (mean (t), 2.2263, 1e-4);
601s ***** assert (median (pd), 0.8326, 1e-4);
601s ***** assert (median (t), 2.1664, 1e-4);
601s ***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
601s ***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
601s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
601s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
601s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
601s ***** assert (any (random (t, 1000, 1) < 2), false);
601s ***** assert (any (random (t, 1000, 1) > 4), false);
601s ***** assert (std (pd), 0.4633, 1e-4);
601s ***** assert (std (t), 0.2083, 1e-4);
601s ***** assert (var (pd), 0.2146, 1e-4);
601s ***** assert (var (t), 0.0434, 1e-4);
601s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
601s  NakagamiDistribution(Inf, 1)
601s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
601s  NakagamiDistribution(i, 1)
601s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
601s  NakagamiDistribution("a", 1)
601s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
601s  NakagamiDistribution([1, 2], 1)
601s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
601s  NakagamiDistribution(NaN, 1)
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, 0)
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, -1)
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, Inf)
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, i)
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, "a")
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, [1, 2])
601s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
601s  NakagamiDistribution(1, NaN)
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (NakagamiDistribution, 2, "uper")
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (NakagamiDistribution, 2, 3)
601s ***** shared x
601s  x = nakarnd (1, 0.5, [1, 100]);
601s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 0)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 1)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", "")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
601s           "parameter", {"mu", "omega", "param"})
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (NakagamiDistribution.fit (x), "parameter", "param")
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
601s           "NAME", "value")
601s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
601s  plot (NakagamiDistribution, "Parent")
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (NakagamiDistribution, "PlotType", 12)
601s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
601s  plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (NakagamiDistribution, "PlotType", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NakagamiDistribution, "Discrete", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NakagamiDistribution, "Discrete", [1, 0])
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NakagamiDistribution, "Discrete", {true})
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (NakagamiDistribution, "Parent", 12)
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (NakagamiDistribution, "Parent", "hax")
601s ***** error <plot: invalid NAME for optional argument.> ...
601s  plot (NakagamiDistribution, "invalidNAME", "pdf")
601s ***** error <plot: no fitted DATA to plot a probability plot.> ...
601s  plot (NakagamiDistribution, "PlotType", "probability")
601s ***** error <proflik: no fitted data available.> ...
601s  proflik (NakagamiDistribution, 2)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NakagamiDistribution.fit (x), 3)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NakagamiDistribution.fit (x), [1, 2])
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NakagamiDistribution.fit (x), {1})
601s ***** error <proflik: SETPARAM must be a numeric vector.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, ones (2))
601s ***** error <proflik: missing VALUE for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display")
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
601s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
601s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
601s ***** error <proflik: invalid NAME for optional arguments.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
601s ***** error <truncate: missing input argument.> ...
601s  truncate (NakagamiDistribution)
601s ***** error <truncate: missing input argument.> ...
601s  truncate (NakagamiDistribution, 2)
601s ***** error <truncate: invalid lower upper limits.> ...
601s  truncate (NakagamiDistribution, 4, 2)
601s ***** shared pd
601s  pd = NakagamiDistribution(1, 0.5);
601s  pd(2) = NakagamiDistribution(1, 0.6);
601s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
601s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
601s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
601s ***** error <mean: requires a scalar probability distribution.> mean (pd)
601s ***** error <median: requires a scalar probability distribution.> median (pd)
601s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
601s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
601s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
601s ***** error <plot: requires a scalar probability distribution.> plot (pd)
601s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
601s ***** error <random: requires a scalar probability distribution.> random (pd)
601s ***** error <std: requires a scalar probability distribution.> std (pd)
601s ***** error <truncate: requires a scalar probability distribution.> ...
601s  truncate (pd, 2, 4)
601s ***** error <var: requires a scalar probability distribution.> var (pd)
601s 95 tests, 95 passed, 0 known failure, 0 skipped
601s [inst/dist_obj/NegativeBinomialDistribution.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/NegativeBinomialDistribution.m
601s ***** demo
601s  ## Generate a data set of 5000 random samples from a Negative Binomial
601s  ## distribution with parameters R = 5 and P = 0.5.  Fit a Negative Binomial
601s  ## distribution to this data and plot a PDF of the fitted distribution
601s  ## superimposed on a histogram of the data.
601s 
601s  pd_fixed = makedist ("NegativeBinomial", "R", 5, "P", 0.5)
601s  rand ("seed", 2);
601s  data = random (pd_fixed, 5000, 1);
601s  pd_fitted = fitdist (data, "NegativeBinomial")
601s  plot (pd_fitted)
601s  msg = "Fitted Negative Binomial distribution with R = %0.2f and P = %0.2f";
601s  title (sprintf (msg, pd_fitted.R, pd_fitted.P))
601s ***** shared pd, t, t_inf
601s  pd = NegativeBinomialDistribution (5, 0.5);
601s  t = truncate (pd, 2, 4);
601s  t_inf = truncate (pd, 2, Inf);
601s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
601s ***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
601s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
601s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
601s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
601s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
601s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
601s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
601s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
601s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
601s ***** assert (iqr (pd), 4);
601s ***** assert (iqr (t), 2);
601s ***** assert (mean (pd), 5);
601s ***** assert (mean (t), 3.0500, 1e-4);
601s ***** assert (mean (t_inf), 5.5263, 1e-4);
601s ***** assert (median (pd), 4);
601s ***** assert (median (t), 3);
601s ***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
601s ***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
601s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
601s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
601s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
601s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
601s ***** assert (any (random (t, 1000, 1) < 2), false);
601s ***** assert (any (random (t, 1000, 1) > 4), false);
601s ***** assert (std (pd), 3.1623, 1e-4);
601s ***** assert (std (t), 0.8047, 1e-4);
601s ***** assert (std (t_inf), 2.9445, 1e-4);
601s ***** assert (var (pd), 10);
601s ***** assert (var (t), 0.6475, 1e-4);
601s ***** assert (var (t_inf), 8.6704, 1e-4);
601s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
601s  NegativeBinomialDistribution(Inf, 1)
601s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
601s  NegativeBinomialDistribution(i, 1)
601s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
601s  NegativeBinomialDistribution("a", 1)
601s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
601s  NegativeBinomialDistribution([1, 2], 1)
601s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
601s  NegativeBinomialDistribution(NaN, 1)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, 0)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, -1)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, Inf)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, i)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, "a")
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, [1, 2])
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, NaN)
601s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
601s  NegativeBinomialDistribution(1, 1.2)
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (NegativeBinomialDistribution, 2, "uper")
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (NegativeBinomialDistribution, 2, 3)
601s ***** shared x
601s  x = nbinrnd (1, 0.5, [1, 100]);
601s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
601s           "alpha", {0.05})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), ...
601s           "parameter", {"R", "P", "param"})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
601s           "parameter", {"R", "P", "param"})
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
601s           "parameter", "param")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
601s           "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
601s           "parameter", "R", "NAME", "value")
601s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
601s  plot (NegativeBinomialDistribution, "Parent")
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (NegativeBinomialDistribution, "PlotType", 12)
601s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
601s  plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NegativeBinomialDistribution, "Discrete", [1, 0])
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (NegativeBinomialDistribution, "Discrete", {true})
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (NegativeBinomialDistribution, "Parent", 12)
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (NegativeBinomialDistribution, "Parent", "hax")
601s ***** error <plot: invalid NAME for optional argument.> ...
601s  plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
601s ***** error <plot: no fitted DATA to plot a probability plot.> ...
601s  plot (NegativeBinomialDistribution, "PlotType", "probability")
601s ***** error <proflik: no fitted data available.> ...
601s  proflik (NegativeBinomialDistribution, 2)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 3)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), [1, 2])
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), {1})
601s ***** error <proflik: SETPARAM must be a numeric vector.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
601s ***** error <proflik: missing VALUE for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
601s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
601s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
601s ***** error <proflik: invalid NAME for optional arguments.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
601s ***** error <truncate: missing input argument.> ...
601s  truncate (NegativeBinomialDistribution)
601s ***** error <truncate: missing input argument.> ...
601s  truncate (NegativeBinomialDistribution, 2)
601s ***** error <truncate: invalid lower upper limits.> ...
601s  truncate (NegativeBinomialDistribution, 4, 2)
601s ***** shared pd
601s  pd = NegativeBinomialDistribution(1, 0.5);
601s  pd(2) = NegativeBinomialDistribution(1, 0.6);
601s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
601s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
601s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
601s ***** error <mean: requires a scalar probability distribution.> mean (pd)
601s ***** error <median: requires a scalar probability distribution.> median (pd)
601s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
601s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
601s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
601s ***** error <plot: requires a scalar probability distribution.> plot (pd)
601s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
601s ***** error <random: requires a scalar probability distribution.> random (pd)
601s ***** error <std: requires a scalar probability distribution.> std (pd)
601s ***** error <truncate: requires a scalar probability distribution.> ...
601s  truncate (pd, 2, 4)
601s ***** error <var: requires a scalar probability distribution.> var (pd)
601s 102 tests, 102 passed, 0 known failure, 0 skipped
601s [inst/dist_obj/GeneralizedParetoDistribution.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_obj/GeneralizedParetoDistribution.m
601s ***** shared pd, t
601s  pd = GeneralizedParetoDistribution (1, 1, 1);
601s  t = truncate (pd, 2, 4);
601s ***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
601s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
601s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
601s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
601s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
601s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
601s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
601s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
601s ***** assert (iqr (pd), 2.6667, 1e-4);
601s ***** assert (iqr (t), 0.9143, 1e-4);
601s ***** assert (mean (pd), Inf);
601s ***** assert (mean (t), 2.7726, 1e-4);
601s ***** assert (median (pd), 2);
601s ***** assert (median (t), 2.6667, 1e-4);
601s ***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
601s ***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
601s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
601s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
601s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
601s ***** assert (any (random (t, 1000, 1) < 2), false);
601s ***** assert (any (random (t, 1000, 1) > 4), false);
601s ***** assert (std (pd), Inf);
601s ***** assert (std (t), 0.5592, 1e-4);
601s ***** assert (var (pd), Inf);
601s ***** assert (var (t), 0.3128, 1e-4);
601s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
601s  GeneralizedParetoDistribution(Inf, 1, 1)
601s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
601s  GeneralizedParetoDistribution(i, 1, 1)
601s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
601s  GeneralizedParetoDistribution("a", 1, 1)
601s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
601s  GeneralizedParetoDistribution([1, 2], 1, 1)
601s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
601s  GeneralizedParetoDistribution(NaN, 1, 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, 0, 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, -1, 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, Inf, 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, i, 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, "a", 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, [1, 2], 1)
601s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
601s  GeneralizedParetoDistribution(1, NaN, 1)
601s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
601s  GeneralizedParetoDistribution(1, 1, Inf)
601s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
601s  GeneralizedParetoDistribution(1, 1, i)
601s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
601s  GeneralizedParetoDistribution(1, 1, "a")
601s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
601s  GeneralizedParetoDistribution(1, 1, [1, 2])
601s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
601s  GeneralizedParetoDistribution(1, 1, NaN)
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (GeneralizedParetoDistribution, 2, "uper")
601s ***** error <cdf: invalid argument for upper tail.> ...
601s  cdf (GeneralizedParetoDistribution, 2, 3)
601s ***** shared x
601s  x = gprnd (1, 1, 1, [1, 100]);
601s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
601s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
601s           "parameter", "sigma", "alpha", {0.05})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
601s           "parameter", {"k", "sigma", "param"})
601s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
601s           "parameter", {"k", "sigma", "param"})
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
601s ***** error <paramci: unknown distribution parameter.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
601s           "parameter", "param")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
601s           "NAME", "value")
601s ***** error <paramci: invalid NAME for optional argument.> ...
601s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
601s           "parameter", "sigma", "NAME", "value")
601s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
601s  plot (GeneralizedParetoDistribution, "Parent")
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (GeneralizedParetoDistribution, "PlotType", 12)
601s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
601s  plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
601s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
601s  plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
601s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
601s  plot (GeneralizedParetoDistribution, "Discrete", {true})
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (GeneralizedParetoDistribution, "Parent", 12)
601s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
601s  plot (GeneralizedParetoDistribution, "Parent", "hax")
601s ***** error <plot: invalid NAME for optional argument.> ...
601s  plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
601s ***** error <plot: no fitted DATA to plot a probability plot.> ...
601s  plot (GeneralizedParetoDistribution, "PlotType", "probability")
601s ***** error <proflik: no fitted data available.> ...
601s  proflik (GeneralizedParetoDistribution, 2)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
601s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
601s ***** error <proflik: SETPARAM must be a numeric vector.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
601s ***** error <proflik: missing VALUE for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
601s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
601s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
601s           "Display", ["on"; "on"])
601s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
601s ***** error <proflik: invalid NAME for optional arguments.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
601s ***** error <proflik: invalid optional argument.> ...
601s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
601s           "Display", "on")
601s ***** error <truncate: missing input argument.> ...
601s  truncate (GeneralizedParetoDistribution)
601s ***** error <truncate: missing input argument.> ...
601s  truncate (GeneralizedParetoDistribution, 2)
601s ***** error <truncate: invalid lower upper limits.> ...
601s  truncate (GeneralizedParetoDistribution, 4, 2)
601s ***** shared pd
601s  pd = GeneralizedParetoDistribution(1, 1, 1);
601s  pd(2) = GeneralizedParetoDistribution(1, 3, 1);
601s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
601s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
601s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
601s ***** error <mean: requires a scalar probability distribution.> mean (pd)
601s ***** error <median: requires a scalar probability distribution.> median (pd)
601s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
601s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
601s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
601s ***** error <plot: requires a scalar probability distribution.> plot (pd)
601s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
601s ***** error <random: requires a scalar probability distribution.> random (pd)
601s ***** error <std: requires a scalar probability distribution.> std (pd)
601s ***** error <truncate: requires a scalar probability distribution.> ...
601s  truncate (pd, 2, 4)
601s ***** error <var: requires a scalar probability distribution.> var (pd)
601s 100 tests, 100 passed, 0 known failure, 0 skipped
601s [inst/vartestn.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/vartestn.m
601s ***** demo
601s  ## Test the null hypothesis that the variances are equal across the five
601s  ## columns of data in the students’ exam grades matrix, grades.
601s 
601s  load examgrades
601s  vartestn (grades)
601s ***** demo
601s  ## Test the null hypothesis that the variances in miles per gallon (MPG) are
601s  ## equal across different model years.
601s 
601s  load carsmall
601s  vartestn (MPG, Model_Year)
601s ***** demo
601s  ## Use Levene’s test to test the null hypothesis that the variances in miles
601s  ## per gallon (MPG) are equal across different model years.
601s 
601s  load carsmall
601s  p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
601s ***** demo
601s  ## Test the null hypothesis that the variances are equal across the five
601s  ## columns of data in the students’ exam grades matrix, grades, using the
601s  ## Brown-Forsythe test.  Suppress the display of the summary table of
601s  ## statistics and the box plot.
601s 
601s  load examgrades
601s  [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
601s ***** error<vartestn: too few input arguments.> vartestn ();
601s ***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
601s ***** error<vartestn: if X is a vector then a group vector is required.> ...
601s  vartestn ([1, 2, 3, 4, 5, 6, 7]);
601s ***** error<vartestn: if X is a vector then a group vector is required.> ...
601s  vartestn ([1, 2, 3, 4, 5, 6, 7], []);
601s ***** error<vartestn: if X is a vector then a group vector is required.> ...
601s  vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
601s ***** error<vartestn: if X is a vector then a group vector is required.> ...
601s  vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
601s ***** error<vartestn: invalid value for display.> ...
601s  vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
601s ***** error<vartestn: invalid value for display.> ...
601s  vartestn (ones (50,3), "Display", "some");
601s ***** error<vartestn: invalid value for testtype.> ...
601s  vartestn (ones (50,3), "Display", "off", "testtype", "some");
601s ***** error<vartestn: optional arguments must be in name/value pairs.> ...
601s  vartestn (ones (50,3), [], "som");
601s ***** error<vartestn: invalid name for optional arguments.> ...
601s  vartestn (ones (50,3), [], "some", "some");
601s ***** error<vartestn: columns in X and GROUP length do not match.> ...
601s  vartestn (ones (50,3), [1, 2], "Display", "off");
601s ***** test
601s  load examgrades
601s  [p, stat] = vartestn (grades, "Display", "off");
601s  assert (p, 7.908647337018238e-08, 1e-14);
601s  assert (stat.chisqstat, 38.7332, 1e-4);
601s  assert (stat.df, 4);
601s ***** test
601s  load examgrades
601s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
601s  assert (p, 9.523239714592791e-07, 1e-14);
601s  assert (stat.fstat, 8.5953, 1e-4);
601s  assert (stat.df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
601s  assert (p, 7.219514351897161e-07, 1e-14);
601s  assert (stat.fstat, 8.7503, 1e-4);
601s  assert (stat.df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
601s  assert (p, 1.312093241723211e-06, 1e-14);
601s  assert (stat.fstat, 8.4160, 1e-4);
601s  assert (stat.df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
601s  assert (p, 8.235660885480556e-07, 1e-14);
601s  assert (stat.fstat, 8.6766, 1e-4);
601s  assert (stat.df, [4, 595]);
601s 17 tests, 17 passed, 0 known failure, 0 skipped
601s [inst/nanmean.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/nanmean.m
601s ***** demo
601s  ## Find the column means for a matrix with missing values.,
601s 
601s  x = magic (3);
601s  x([1, 4, 7:9]) = NaN
601s  y = nanmean (x)
601s ***** demo
601s  ## Find the row means for a matrix with missing values.,
601s 
601s  x = magic (3);
601s  x([1, 4, 7:9]) = NaN
601s  y = nanmean (x, 2)
601s ***** demo
601s  ## Find the mean of all the values in a multidimensional array
601s  ## with missing values.
601s 
601s  x = reshape (1:30, [2, 5, 3]);
601s  x([10:12, 25]) = NaN
601s  y = nanmean (x, "all")
601s ***** demo
601s  ## Find the mean of a multidimensional array with missing values over
601s  ## multiple dimensions.
601s 
601s  x = reshape (1:30, [2, 5, 3]);
601s  x([10:12, 25]) = NaN
601s  y = nanmean (x, [2, 3])
601s ***** assert (nanmean ([]), NaN)
601s ***** assert (nanmean (NaN), NaN)
601s ***** assert (nanmean (NaN(3)), [NaN, NaN, NaN])
601s ***** assert (nanmean ([3 2 NaN 7]), 4)
601s ***** assert (nanmean ([2 4 NaN Inf]), Inf)
601s ***** assert (nanmean ([1 NaN 3; NaN 4 6; 7 8 NaN]), [4 6 4.5])
601s ***** assert (nanmean ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [2; 5.5; 7.5])
601s ***** assert (nanmean (uint8 ([2 4 1 7])), 3.5)
601s ***** test
601s  x = magic(3);
601s  x([1 6:9]) = NaN;
601s  assert (nanmean (x), [3.5, 3, NaN])
601s  assert (nanmean (x, 2), [1; 4; 4])
601s ***** test
601s  x = reshape(1:24, [2, 4, 3]);
601s  x([5:6, 20]) = NaN;
601s  assert (nanmean (x, "all"), 269/21)
601s ***** test
601s  x = reshape(1:24,[2, 4, 3]);
601s  x([5:6, 20]) = NaN;
601s  assert (squeeze (nanmean (x, [1, 2])), [25/6; 100/8; 144/7])
601s  assert (nanmean (x, [2, 3]), [139/11; 13])
601s 11 tests, 11 passed, 0 known failure, 0 skipped
601s [inst/combnk.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/combnk.m
601s ***** demo
601s  c = combnk (1:5, 2);
601s  disp ("All pairs of integers between 1 and 5:");
601s  disp (c);
601s ***** test
601s  c = combnk (1:3, 2);
601s  assert (c, [1, 2; 1, 3; 2, 3]);
601s ***** test
601s  c = combnk (1:3, 6);
601s  assert (isempty (c));
601s ***** test
601s  c = combnk ({1, 2, 3}, 2);
601s  assert (c, {1, 2; 1, 3; 2, 3});
601s ***** test
601s  c = combnk ("hello", 2);
601s  assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
601s 4 tests, 4 passed, 0 known failure, 0 skipped
601s [inst/signrank.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/signrank.m
601s ***** test
601s  load gradespaired.mat
601s  [p, h, stats] = signrank (gradespaired(:,1), ...
601s                            gradespaired(:,2), 'tail', 'left');
601s  assert (p, 0.0047, 1e-4);
601s  assert (h, true);
601s  assert (stats.zval, -2.5982, 1e-4);
601s  assert (stats.signedrank, 2017.5);
601s ***** test
601s  load ('gradespaired.mat');
601s  [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
601s                            'tail', 'left', 'method', 'exact');
601s  assert (p, 0.0045, 1e-4);
601s  assert (h, true);
601s  assert (stats.zval, NaN);
601s  assert (stats.signedrank, 2017.5);
601s ***** test
601s  load mileage
601s  [p, h, stats] = signrank (mileage(:,2), 33);
601s  assert (p, 0.0312, 1e-4);
601s  assert (h, true);
601s  assert (stats.zval, NaN);
601s  assert (stats.signedrank, 21);
601s ***** test
601s  load mileage
601s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
601s  assert (p, 0.0156, 1e-4);
601s  assert (h, true);
601s  assert (stats.zval, NaN);
601s  assert (stats.signedrank, 21);
601s ***** test
601s  load mileage
601s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
601s                            'alpha', 0.01, 'method', 'approximate');
601s  assert (p, 0.0180, 1e-4);
601s  assert (h, false);
601s  assert (stats.zval, 2.0966, 1e-4);
601s  assert (stats.signedrank, 21);
601s ***** error <signrank: X must be a vector.> signrank (ones (2))
601s ***** error <signrank: Y must be either a scalar of a vector.> ...
601s  signrank ([1, 2, 3, 4], ones (2))
601s ***** error <signrank: X and Y vectors have different lengths.> ...
601s  signrank ([1, 2, 3, 4], [1, 2, 3])
601s ***** error <signrank: optional arguments must be in pairs.> ...
601s  signrank ([1, 2, 3, 4], [], 'tail')
601s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
601s  signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
601s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
601s  signrank ([1, 2, 3, 4], [], 'alpha', 0)
601s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
601s  signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
601s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
601s  signrank ([1, 2, 3, 4], [], 'alpha', "a")
601s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
601s  signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
601s ***** error <signrank: 'tail' argument must be a character vector.> ...
601s  signrank ([1, 2, 3, 4], [], 'tail', 0.01)
601s ***** error <signrank: 'tail' argument must be a character vector.> ...
601s  signrank ([1, 2, 3, 4], [], 'tail', {"both"})
601s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
601s  signrank ([1, 2, 3, 4], [], 'tail', "some")
601s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
601s  signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
601s ***** error <signrank: 'method' argument must be a character vector.> ...
601s  signrank ([1, 2, 3, 4], [], 'method', 0.01)
601s ***** error <signrank: 'method' argument must be a character vector.> ...
601s  signrank ([1, 2, 3, 4], [], 'method', {"exact"})
601s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
601s  signrank ([1, 2, 3, 4], [], 'method', "some")
601s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
601s  signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
601s 22 tests, 22 passed, 0 known failure, 0 skipped
601s [inst/bar3h.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/bar3h.m
601s ***** demo
601s  ## Plotting 5 bars in the same series.
601s 
601s  y = [50; 40; 30; 20; 10];
601s  bar3h (y);
601s ***** demo
601s  ## Plotting 5 bars in different groups.
601s 
601s  y = [50, 40, 30, 20, 10];
601s  bar3h (y);
601s ***** demo
601s  ## A 3D bar graph with each series corresponding to a column in y.
601s 
601s  y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
601s  bar3h (y);
601s ***** demo
601s  ## Specify z-axis locations as tick names. z must be a column vector!
601s 
601s  z = [1950, 1960, 1970, 1980, 1990]';
601s  y = [16, 8, 4, 2, 1]';
601s  bar3h (z, y);
601s ***** demo
601s  ## Plot 3 series as a grouped plot without any space between the grouped bars
601s 
601s  y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
601s  bar3h (y, 1, 'grouped');
601s ***** demo
601s  ## Plot a stacked style 3D bar graph
601s 
601s  y = [19, 30, 21, 30; 40, 16, 32, 12];
601s  b = bar3h (y, 0.5, 'stacked');
601s ***** error <bar3h: Z must be numeric.> bar3h ("A")
601s ***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
601s ***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
601s  bar3h ([1,2,3]', ones (2))
601s ***** error <bar3h: WIDTH must be a scalar in the range> ...
601s  bar3h ([1:5], 1.2)
601s ***** error <bar3h: WIDTH must be a scalar in the range> ...
601s  bar3h ([1:5]', ones (5), 1.2)
601s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
601s  bar3h ([1:5]', ones (5), [0.8, 0.7])
601s ***** error <bar3h: missing value for optional argument 'width'.> ...
601s  bar3h (ones (5), 'width')
601s ***** error <bar3h: invalid value for optional argument 'width'.> ...
601s  bar3h (ones (5), 'width', 1.2)
601s ***** error <bar3h: invalid value for optional argument 'width'.> ...
601s  bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
601s ***** error <bar3h: missing value for optional argument 'color'.> ...
601s  bar3h (ones (5), 'color')
601s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
601s  bar3h (ones (5), 'color', [0.8, 0.8])
601s ***** error <bar3h: invalid value for optional argument 'color'.> ...
601s  bar3h (ones (5), 'color', "brown")
601s ***** error <bar3h: invalid value for optional argument 'color'.> ...
601s  bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
601s ***** error <bar3h: missing value for optional argument 'xlabel'.> ...
601s  bar3h (ones (5), 'xlabel')
601s ***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
601s  bar3h (ones (5), 'xlabel', 4)
601s ***** error <bar3h: missing value for optional argument 'zlabel'.> ...
601s  bar3h (ones (5), 'zlabel')
601s ***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
601s  bar3h (ones (5), 'zlabel', 4)
601s ***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
601s ***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
601s  bar3h (ones (5), 'xlabel', {"A", "B", "C"})
601s ***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
601s  bar3h (ones (5), 'zlabel', {"A", "B", "C"})
601s 20 tests, 20 passed, 0 known failure, 0 skipped
601s [inst/levene_test.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/levene_test.m
601s ***** error<levene_test: invalid number of input arguments.> levene_test ()
601s ***** error<levene_test: invalid number of input arguments.> ...
601s  levene_test (1, 2, 3, 4, 5);
601s ***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
601s ***** error<levene_test: GROUP and X mismatch.> ...
601s  levene_test (randn (50, 2), [1, 2, 3]);
601s ***** error<levene_test: GROUP and X mismatch.> ...
601s  levene_test (randn (50, 1), ones (55, 1));
601s ***** error<levene_test: invalid second input argument.> ...
601s  levene_test (randn (50, 1), ones (50, 2));
601s ***** error<levene_test: wrong value for alpha.> ...
601s  levene_test (randn (50, 2), [], 1.2);
601s ***** error<levene_test: GROUP and X mismatch.> ...
601s  levene_test (randn (50, 2), "some_string");
601s ***** error<levene_test: invalid third input argument.> ...
601s  levene_test (randn (50, 2), [], "alpha");
601s ***** error<levene_test: wrong value for alpha.> ...
601s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
601s ***** error<levene_test: invalid third input argument.> ...
601s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
601s ***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
601s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
601s ***** warning<levene_test: GROUP> ...
601s  levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades);
601s  assert (h, 1);
601s  assert (pval, 9.523239714592791e-07, 1e-14);
601s  assert (W, 8.59529, 1e-5);
601s  assert (df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades, [], "quadratic");
601s  assert (h, 1);
601s  assert (pval, 9.523239714592791e-07, 1e-14);
601s  assert (W, 8.59529, 1e-5);
601s  assert (df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades, [], "median");
601s  assert (h, 1);
601s  assert (pval, 1.312093241723211e-06, 1e-14);
601s  assert (W, 8.415969, 1e-6);
601s  assert (df, [4, 595]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades(:,[1:3]));
601s  assert (h, 1);
601s  assert (pval, 0.004349390980463497, 1e-14);
601s  assert (W, 5.52139, 1e-5);
601s  assert (df, [2, 357]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
601s  assert (h, 1);
601s  assert (pval, 0.004355216763951453, 1e-14);
601s  assert (W, 5.52001, 1e-5);
601s  assert (df, [2, 357]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
601s  assert (h, 0);
601s  assert (pval, 0.1807494957440653, 2e-14);
601s  assert (W, 1.80200, 1e-5);
601s  assert (df, [1, 238]);
601s ***** test
601s  load examgrades
601s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
601s  assert (h, 0);
601s  assert (pval, 0.1978225622063785, 2e-14);
601s  assert (W, 1.66768, 1e-5);
601s  assert (df, [1, 238]);
601s 20 tests, 20 passed, 0 known failure, 0 skipped
601s [inst/confusionmat.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/confusionmat.m
601s ***** test
601s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
601s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
601s  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; ...
601s        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];
601s  assert (confusionmat (Yt, Yp), C)
601s 1 test, 1 passed, 0 known failure, 0 skipped
601s [inst/Clustering/cvpartition.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/cvpartition.m
601s ***** test
601s  custom = [1, 1, 1, 2, 2, 2, 1, 2, 3, 2, 3, 3, 2, 1, 3]';
601s  cv = cvpartition ('CustomPartition', custom);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 15);
601s  assert (cv.NumTestSets, 3);
601s  assert (cv.TrainSize, [10, 9, 11]);
601s  assert (cv.TestSize, [5, 6, 4]);
601s  assert (cv.IsCustom, true);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  idx = training (cv, 1);
601s  assert (idx, custom != 1);
601s  idx = test (cv, 1);
601s  assert (idx, custom == 1);
601s  idx = training (cv, 2);
601s  assert (idx, custom != 2);
601s  idx = test (cv, 2);
601s  assert (idx, custom == 2);
601s  idx = training (cv, 3);
601s  assert (idx, custom != 3);
601s  idx = test (cv, 3);
601s  assert (idx, custom == 3);
601s  idx1 = training (cv, 'all');
601s  idx2 = test (cv, 'all');
601s  assert (idx1, ! idx2);
601s ***** test
601s  custom = logical ([1, 1, 1, 0, 0, 0, 1, 0, 1, 1])';
601s  cv = cvpartition ('CustomPartition', custom);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 4);
601s  assert (cv.TestSize, 6);
601s  assert (cv.IsCustom, true);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  idx = training (cv, 1);
601s  assert (idx, custom != 1);
601s  assert (idx, training (cv, 'all'));
601s  idx = test (cv, 1);
601s  assert (idx, custom == 1);
601s  assert (idx, test (cv, 'all'));
601s ***** test
601s  custom = logical ([1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1]);
601s  cv = cvpartition ('CustomPartition', custom);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 4);
601s  assert (cv.NumTestSets, 3);
601s  assert (cv.TrainSize, [2, 3, 3]);
601s  assert (cv.TestSize, [2, 1, 1]);
601s  assert (cv.IsCustom, true);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  idx = training (cv, 1);
601s  assert (idx, custom(:,1) == false);
601s  idx = test (cv, 1);
601s  assert (idx, custom(:,1) == true);
601s  idx = training (cv, 2);
601s  assert (idx, custom(:,2) == false);
601s  idx = test (cv, 2);
601s  assert (idx, custom(:,2) == true);
601s  assert (! custom, training (cv, 'all'));
601s  assert (custom, test (cv, 'all'));
601s ***** test
601s  cv = cvpartition ('CustomPartition', [1:8]);
601s  assert (cv.Type, 'leaveout');
601s  assert (cv.NumObservations, 8);
601s  assert (cv.NumTestSets, 8);
601s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
601s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
601s  assert (cv.IsCustom, true);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 7);
601s  assert (sum (training (cv, 'all')), cv.TrainSize);
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 1);
601s  assert (sum (test (cv, 'all')), cv.TestSize);
601s  assert (! training (cv, 'all'), test (cv, 'all'));
601s ***** test
601s  cv = cvpartition ('CustomPartition', logical (eye (8)));
601s  assert (cv.Type, 'leaveout');
601s  assert (cv.NumObservations, 8);
601s  assert (cv.NumTestSets, 8);
601s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
601s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
601s  assert (cv.IsCustom, true);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 7);
601s  assert (sum (training (cv, 'all')), cv.TrainSize);
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 1);
601s  assert (sum (test (cv, 'all')), cv.TestSize);
601s  assert (! training (cv, 'all'), test (cv, 'all'));
601s ***** test
601s  cv = cvpartition (10, 'resubstitution');
601s  assert (cv.Type, 'resubstitution');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 10);
601s  assert (cv.TestSize, 10);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 10);
601s  assert (training (cv, 'all'), logical (ones (10, 1)));
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 10);
601s  assert (test (cv, 'all'), logical (ones (10, 1)));
601s  assert (test (cv), training (cv));
601s ***** test
601s  cv = cvpartition (10, 'leaveout');
601s  assert (cv.Type, 'leaveout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 10);
601s  assert (cv.TrainSize, ones (1, 10) * 9);
601s  assert (cv.TestSize, ones (1, 10));
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 9);
601s  assert (training (cv, 'all'), ! logical (eye (10)));
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 1);
601s  assert (test (cv, 'all'), logical (eye (10)));
601s  assert (test (cv), ! training (cv));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s ***** test
601s  rand ('seed', 5);  # for reproducibility
601s  cv = cvpartition (10, 'holdout', 0.3);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 7);
601s  assert (cv.TestSize, 3);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 7);
601s  assert (training (cv, 'all'), logical ([1, 0, 1, 1, 0, 1, 1, 1, 0, 1])');
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 3);
601s  assert (test (cv, 'all'), logical ([0, 1, 0, 0, 1, 0, 0, 0, 1, 0])');
601s  assert (test (cv), ! training (cv));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s ***** test
601s  cv = cvpartition (10, 'holdout', 4);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 6);
601s  assert (cv.TestSize, 4);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (class (training (cv, 1)), 'logical');
601s  assert (sum (training (cv, 1)), 6);
601s  assert (class (test (cv, 1)), 'logical');
601s  assert (sum (test (cv, 1)), 4);
601s  assert (test (cv), ! training (cv));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s ***** test
601s  cv = cvpartition (5, 'kfold');
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 5);
601s  assert (cv.NumTestSets, 5);
601s ***** test
601s  cv = cvpartition (20, 'kfold');
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 20);
601s  assert (cv.NumTestSets, 10);
601s ***** test
601s  cv = cvpartition (10, 'kfold', 5);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 5);
601s  assert (cv.TrainSize, [8, 8, 8, 8, 8]);
601s  assert (cv.TestSize, [2, 2, 2, 2, 2]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [10, 5]);
601s ***** test
601s  grpvar = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 5, 5];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 5, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 5);
601s  assert (cv.TrainSize, [10, 10, 10, 8, 10]);
601s  assert (cv.TestSize, [2, 2, 2, 4, 2]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 5]);
601s  assert (sum (test (cv, 'all')), [2, 2, 2, 4, 2]);
601s  assert (sum (training (cv, 'all')), [10, 10, 10, 8, 10]);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 3, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 3);
601s  assert (cv.TrainSize, [9, 10, 5]);
601s  assert (cv.TestSize, [3, 2, 7]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 3]);
601s  assert (sum (test (cv, 'all')), [3, 2, 7]);
601s  assert (sum (training (cv, 'all')), [9, 10, 5]);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [6, 6]);
601s  assert (cv.TestSize, [6, 6]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 2]);
601s  assert (sum (test (cv, 'all')), [6, 6]);
601s  assert (sum (training (cv, 'all')), [6, 6]);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, NaN, 2, 3, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 11);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [6, 5]);
601s  assert (cv.TestSize, [5, 6]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [11, 2]);
601s  assert (sum (test (cv, 'all')), [5, 6]);
601s  assert (sum (training (cv, 'all')), [6, 5]);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [5, 7]);
601s  assert (cv.TestSize, [7, 5]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 2]);
601s  assert (sum (test (cv, 'all')), [7, 5]);
601s  assert (sum (training (cv, 'all')), [5, 7]);
601s  assert (test (cv, 1)', grpvar == 2);
601s  assert (test (cv, 2)', grpvar != 2);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [7, 5]);
601s  assert (cv.TestSize, [5, 7]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 2]);
601s  assert (sum (test (cv, 'all')), [5, 7]);
601s  assert (sum (training (cv, 'all')), [7, 5]);
601s  assert (test (cv, 1)', grpvar == 2);
601s  assert (test (cv, 2)', grpvar != 2);
601s ***** test
601s  grpvar = [1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3];
601s  rand ('seed', 5);
601s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 12);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [7, 5]);
601s  assert (cv.TestSize, [5, 7]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, true);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (size (test (cv, 'all')), [12, 2]);
601s  assert (sum (test (cv, 'all')), [5, 7]);
601s  assert (sum (training (cv, 'all')), [7, 5]);
601s  assert (test (cv, 1)', grpvar == 3);
601s  assert (test (cv, 2)', grpvar != 3);
601s ***** test
601s  rand ('seed', 5);
601s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 3);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 7);
601s  assert (cv.TestSize, 3);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, true);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv), logical ([0, 0, 0, 0, 1, 0, 1, 0, 0, 1])');
601s ***** test
601s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 4);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 6);
601s  assert (cv.TestSize, 4);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, true);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (sum (test (cv)(1:5)), 2);
601s  assert (sum (test (cv)(6:10)), 2);
601s ***** test
601s  grpvar = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2];
601s  rand ('seed', 5);
601s  cv = cvpartition (grpvar, 'holdout', 4, 'Stratify', false);
601s  assert (cv.Type, 'holdout');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 1);
601s  assert (cv.TrainSize, 6);
601s  assert (cv.TestSize, 4);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (sum (test (cv)(1:5)), 3);
601s  assert (sum (test (cv)(6:10)), 1);
601s ***** test
601s  cv = cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [5, 5]);
601s  assert (cv.TestSize, [5, 5]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, true);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (sum (test (cv, 1)(1:5)), 3);
601s  assert (sum (test (cv, 2)(1:5)), 2);
601s  assert (sum (test (cv, 1)(6:10)), 2);
601s  assert (sum (test (cv, 2)(6:10)), 3);
601s ***** test
601s  grpvar = [1 1 1 1 1 2 2 2 2 1];
601s  rand ('seed', 5);
601s  cv = cvpartition (grpvar, 'kfold', 2, 'Stratify', false);
601s  assert (cv.Type, 'kfold');
601s  assert (cv.NumObservations, 10);
601s  assert (cv.NumTestSets, 2);
601s  assert (cv.TrainSize, [5, 5]);
601s  assert (cv.TestSize, [5, 5]);
601s  assert (cv.IsCustom, false);
601s  assert (cv.IsGrouped, false);
601s  assert (cv.IsStratified, false);
601s  assert (test (cv, 1), ! training (cv, 1));
601s  assert (test (cv, 'all'), ! training (cv, 'all'));
601s  assert (sum (test (cv, 1)(1:5)), 4);
601s  assert (sum (test (cv, 2)(1:5)), 1);
601s  assert (sum (test (cv, 1)(6:10)), 1);
601s  assert (sum (test (cv, 2)(6:10)), 4);
601s ***** error <cvpartition: too few input arguments.> cvpartition (2)
601s ***** error <cvpartition: too many input arguments.> cvpartition (1, 2, 3, 4, 5, 6)
601s ***** error <cvpartition: TESTSETS must be numeric of logical.> ...
601s  cvpartition ("CustomPartition", 'a')
601s ***** error <cvpartition: TESTSETS must be a numeric vector.> ...
601s  cvpartition ("CustomPartition", [2, 3; 2, 3])
601s ***** error <cvpartition: TESTSETS must be a logical vector or matrix.> ...
601s  cvpartition ("CustomPartition", false (3, 3, 3))
601s ***** error <cvpartition: each observation in TESTSETS must be exactly one in each row.> ...
601s  cvpartition ("CustomPartition", [false, true; true, true; true, false])
601s ***** error <cvpartition: a logical matrix in TESTSETS must not have more columns that rows.> ...
601s  cvpartition ("CustomPartition", false (3, 5))
601s ***** error <cvpartition: X must be a scalar positive integer value.> ...
601s  cvpartition (-20, "LeaveOut")
601s ***** error <cvpartition: X must be a scalar positive integer value.> ...
601s  cvpartition (20.5, "LeaveOut")
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition (20, "HoldOut", [0.2, 0.3])
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition (20, "HoldOut", 'a')
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition (20, "HoldOut", 0)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition (20, "HoldOut", -0.1)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition (20, "HoldOut", 21)
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition (20, "kfold", [2, 3])
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition (20, "kfold", 'a')
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
601s  cvpartition (20, "kfold", 2.5)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
601s  cvpartition (20, "kfold", 21)
601s ***** error <cvpartition: invalid optional paired argument for 'GroupingVariables'.> ...
601s  cvpartition (10, "kfold", 3, "Group")
601s ***** error <cvpartition: missing value for optional paired argument 'GroupingVariables'.> ...
601s  cvpartition (10, "kfold", 3, "GroupingVariables")
601s ***** error <cvpartition: invalid value for optional paired argument 'GroupingVariables'.> ...
601s  cvpartition (10, "kfold", 3, "GroupingVariables", ones (3, 3, 3))
601s ***** error <cvpartition: grouping variable does not match the number of observations.> ...
601s  cvpartition (10, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'})
601s ***** warning <cvpartition: number of folds K in greater that the groups in 'GroupingVariables'.> ...
601s  cvpartition (5, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'});
601s ***** error <cvpartition: invalid optional paired argument.> ...
601s  cvpartition (20, "some")
601s ***** error <cvpartition: invalid optional paired argument for stratification.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "strat")
601s ***** error <cvpartition: missing value for optional paired argument 'stratify'.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify")
601s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", [true, true])
601s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", 'no')
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a')
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a', "stratify", true)
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3])
601s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3], "stratify", true)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0, "stratify", true)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1, "stratify", true)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2, "stratify", false)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6)
601s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
601s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6, "stratify", false)
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a')
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a', "stratify", true)
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3])
601s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3], "stratify", false)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0, "stratify", true)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5, "stratify", true)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5)
601s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
601s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5, "stratify", true)
601s ***** error <cvpartition: invalid optional paired argument.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "leaveout")
601s ***** error <cvpartition: invalid optional paired argument.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "resubstitution")
601s ***** error <cvpartition: invalid optional paired argument.> ...
601s  cvpartition ([1, 1, 1, 2, 2], "some")
601s ***** error <cvpartition: invalid first input argument.> ...
601s  cvpartition ({1, 1; 2, 2}, "kfold")
601s ***** error <cvpartition.repartition: cannot repartition a custom partition.> ...
601s  repartition (cvpartition ('CustomPartition', [1,1,2,2,3,3]))
601s ***** error <cvpartition.repartition: 'legacy' flag does not apply to stratified or grouped 'kfold' partitioned objects.> ...
601s  repartition (cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2, 'Stratify', true), 'legacy')
601s ***** error <cvpartition.repartition: 'legacy' flag is only valid for 'kfold' partitioned objects.> ...
601s  repartition (cvpartition (20, 'Leaveout', 0.2), 'legacy')
601s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
601s  repartition (cvpartition (20, 'Leaveout', 0.2), 'asd')
601s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
601s  repartition (cvpartition (20, 'Leaveout', 0.2), 2+i)
601s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
601s  repartition (cvpartition (20, 'KFold', 5), [34, 56; 2, 3])
601s ***** error <cvpartition.test: too many input arguments.> ...
601s  test (cvpartition (20, "kfold"), 2, 3)
601s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
601s  test (cvpartition (20, "kfold"), 0)
601s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
601s  test (cvpartition (20, "kfold"), 1.5)
601s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
601s  test (cvpartition (20, "kfold"), [1, 1.5])
601s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
601s  test (cvpartition (20, "kfold"), [2, 3; 2, 3])
601s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
601s  test (cvpartition (20, "kfold"), 21)
601s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
601s  test (cvpartition (20, "kfold"), [18, 21])
601s ***** error <cvpartition.training: too many input arguments.> ...
601s  training (cvpartition (20, "kfold"), 2, 3)
601s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
601s  training (cvpartition (20, "kfold"), 0)
601s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
601s  training (cvpartition (20, "kfold"), 1.5)
601s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
601s  training (cvpartition (20, "kfold"), [1, 1.5])
601s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
601s  training (cvpartition (20, "kfold"), [2, 3; 2, 3])
601s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
601s  training (cvpartition (20, "kfold"), 21)
601s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
601s  training (cvpartition (20, "kfold"), [18, 21])
601s 98 tests, 98 passed, 0 known failure, 0 skipped
601s [inst/Clustering/hnswSearcher.m]
601s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/hnswSearcher.m
601s ***** demo
601s  ## Create an hnswSearcher with Euclidean distance
601s  X = [1, 2; 3, 4; 5, 6];
601s  obj = hnswSearcher (X);
601s  ## Find the nearest neighbor to [2, 3]
601s  Y = [2, 3];
601s  [idx, D] = knnsearch (obj, Y, "K", 1);
601s  disp ("Nearest neighbor index:");
601s  disp (idx);
601s  disp ("Distance:");
601s  disp (D);
601s ***** demo
601s  ## Create an hnswSearcher with Minkowski distance (P=3)
601s  X = [0, 0; 1, 0; 2, 0];
601s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
601s  ## Find the nearest neighbor to [1, 0]
601s  Y = [1, 0];
601s  [idx, D] = knnsearch (obj, Y, "K", 1);
601s  disp ("Nearest neighbor index:");
601s  disp (idx);
601s  disp ("Distance:");
601s  disp (D);
601s ***** test
601s  load fisheriris
601s  X = meas;
601s  obj = hnswSearcher (X, "Distance", "chebychev");
601s  Y = X(30:35,:);
601s  [idx, D] = knnsearch (obj, Y, "K", 4);
601s  assert (idx, [[30 31 4 12]; [31 30 10 35]; [32 21 37 28]; [33 47 20 34]; ...
601s                [34 16 33 15]; [35 10 26 2]])
601s  assert (D, [[0 0.1000 0.1000 0.2000]; [0 0.1000 0.1000 0.1000]; [0 0.2000 ...
601s               0.2000 0.2000]; [0 0.3000 0.3000 0.3000]; [0 0.2000 0.3000 ...
601s               0.3000]; [0 0.1000 0.1000 0.1000]], 5e-15)
605s ***** test
605s  load fisheriris
605s  X = meas;
605s  C = cov (X);
605s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
605s  Y = X(120:125,:);
605s  [idx, D] = knnsearch (obj, Y, "K", 2);
605s  assert (idx(1, :), [120 82])
605s  assert (idx(4, :), [123 106])
605s  assert (idx(5, :), [124 127])
605s  assert (idx(6, :), [125 57])
605s  assert (D(1, :), [0 0.7734], 1e-4)
605s  assert (D(4, :), [0 0.8452], 1e-4)
605s  assert (D(5, :), [0 0.4152], 1e-4)
605s  assert (D(6, :), [0 0.7322], 1e-4)
617s ***** test
617s  ## Basic constructor with default Euclidean
617s  X = [1, 2; 3, 4; 5, 6];
617s  obj = hnswSearcher (X);
617s  assert (obj.X, X);
617s  assert (obj.Distance, "euclidean");
617s  assert (isempty (obj.DistParameter));
617s ***** test
617s  ## Minkowski distance with custom P
617s  X = [0, 0; 1, 1; 2, 2];
617s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
617s  assert (obj.Distance, "minkowski");
617s  assert (obj.DistParameter, 3);
617s ***** test
617s  ## Seuclidean distance with custom Scale
617s  X = [1, 2; 3, 4; 5, 6];
617s  S = [1, 2];
617s  obj = hnswSearcher (X, "Distance", "seuclidean", "Scale", S);
617s  assert (obj.Distance, "seuclidean");
617s  assert (obj.DistParameter, S);
617s ***** test
617s  ## Mahalanobis distance with custom Cov
617s  X = [1, 2; 3, 4; 5, 6];
617s  C = [1, 0; 0, 1];
617s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
617s  assert (obj.Distance, "mahalanobis");
617s  assert (obj.DistParameter, C);
617s ***** test
617s  ## knnsearch with Euclidean distance
617s  X = [1, 2; 3, 4; 5, 6];
617s  obj = hnswSearcher (X);
617s  Y = [2, 3];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [2]));
617s  assert (abs (D - sqrt(2)) < 1e-2);
617s ***** test
617s  ## knnsearch with Cityblock distance
617s  X = [0, 0; 1, 1; 2, 2];
617s  obj = hnswSearcher (X, "Distance", "cityblock");
617s  Y = [1, 0];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [1, 2]));
617s  assert (abs (D - 1) < 1e-2);
617s ***** test
617s  ## knnsearch with Chebychev distance
617s  X = [1, 1; 2, 3; 4, 2];
617s  obj = hnswSearcher (X, "Distance", "chebychev");
617s  Y = [2, 2];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [1, 2]));
617s  assert (abs (D - 1) < 1e-2);
617s ***** test
617s  ## knnsearch with Minkowski P=3
617s  X = [0, 0; 1, 0; 2, 0];
617s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
617s  Y = [1, 0];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [2]));
617s  assert (abs (D - 0) < 1e-2);
617s ***** test
617s  ## Diverse dataset with Euclidean
617s  X = [0, 10; 5, 5; 10, 0];
617s  obj = hnswSearcher (X);
617s  Y = [5, 5];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [2]));
617s  assert (abs (D - 0) < 1e-2);
617s ***** test
617s  ## High-dimensional data with Cityblock
617s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
617s  obj = hnswSearcher (X, "Distance", "cityblock");
617s  Y = [4, 5, 6];
617s  [idx, D] = knnsearch (obj, Y, "K", 1);
617s  assert (ismember (idx, [2]));
617s  assert (abs (D - 0) < 1e-2);
617s ***** error<hnswSearcher: too few input arguments.> ...
617s  hnswSearcher ()
617s ***** error<hnswSearcher: Name-Value arguments must be in pairs.> ...
617s  hnswSearcher (ones(3,2), "Distance")
617s ***** error<hnswSearcher: X cannot be empty.> ...
617s  hnswSearcher ([])
617s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
617s  hnswSearcher ("abc")
617s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
617s  hnswSearcher ([1; Inf; 3])
617s ***** error<hnswSearcher: invalid parameter name: 'foo'.> ...
617s  hnswSearcher (ones(3,2), "foo", "bar")
617s ***** error<hnswSearcher: unsupported distance metric 'invalid'.> ...
617s  hnswSearcher (ones(3,2), "Distance", "invalid")
617s ***** error<hnswSearcher: 'Distance' must be a string.> ...
617s  hnswSearcher (ones(3,2), "Distance", 1)
617s ***** error<hnswSearcher: 'P' must be a positive finite scalar.> ...
617s  hnswSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
617s ***** error<hnswSearcher: 'Scale' must be a nonnegative vector matching X columns.> ...
617s  hnswSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
617s ***** error<hnswSearcher: 'Cov' must be a square matrix matching X columns.> ...
617s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
617s ***** error<hnswSearcher: 'Cov' must be symmetric for mahalanobis.> ...
617s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", [1, 2; 3, 4])
617s ***** error<hnswSearcher: 'Cov' must be positive definite for mahalanobis.> ...
617s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
617s ***** error<hnswSearcher: 'MaxNumLinksPerNode' must be a positive integer.> ...
617s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 0)
617s ***** error<hnswSearcher: 'TrainSetSize' must be a positive integer.> ...
617s  hnswSearcher (ones(3,2), "TrainSetSize", -1)
617s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
617s  hnswSearcher (ones(3,2), "TrainSetSize", 4)
617s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
617s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 200, "TrainSetSize", 100)
617s ***** error<hnswSearcher.knnsearch: too few input arguments.> ...
617s  knnsearch (hnswSearcher (ones(3,2)))
617s ***** error<hnswSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K")
617s ***** error<hnswSearcher.knnsearch: Y cannot be empty.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), [])
617s ***** error<hnswSearcher.knnsearch: Y must be a finite numeric matrix.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), "abc")
617s ***** error<hnswSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), ones(3,3))
617s ***** error<hnswSearcher.knnsearch: 'K' must be a positive integer.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K", 0)
617s ***** error<hnswSearcher.knnsearch: invalid parameter name: 'foo'.> ...
617s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "foo", "bar")
617s ***** error<hnswSearcher.subsref: \(\) indexing not supported.> ...
617s  obj = hnswSearcher (ones(3,2)); obj(1)
617s ***** error<hnswSearcher.subsref: {} indexing not supported.> ...
617s  obj = hnswSearcher (ones(3,2)); obj{1}
617s ***** error<hnswSearcher.subsref: unrecognized property: 'invalid'.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.invalid
617s ***** error<hnswSearcher.subsasgn: \(\) indexing not supported.> ...
617s  obj = hnswSearcher (ones(3,2)); obj(1) = 1
617s ***** error<hnswSearcher.subsasgn: {} indexing not supported.> ...
617s  obj = hnswSearcher (ones(3,2)); obj{1} = 1
617s ***** error<hnswSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.X = 1
617s ***** error<hnswSearcher.subsasgn: 'HNSWGraph' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.HNSWGraph = 1
617s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.Distance = "invalid"
617s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.Distance = 1
617s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
617s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
617s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
617s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
617s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
617s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
617s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.DistParameter = 1
617s ***** error<hnswSearcher.subsasgn: 'MaxNumLinksPerNode' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.MaxNumLinksPerNode = 0
617s ***** error<hnswSearcher.subsasgn: 'TrainSetSize' is read-only and cannot be modified.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.TrainSetSize = -1
617s ***** error<hnswSearcher.subsasgn: unrecognized property: 'efSearch'.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.efSearch = 1.5
617s ***** error<hnswSearcher.subsasgn: unrecognized property: 'invalid'.> ...
617s  obj = hnswSearcher (ones(3,2)); obj.invalid = 1
617s 54 tests, 54 passed, 0 known failure, 0 skipped
617s [inst/Clustering/DaviesBouldinEvaluation.m]
617s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/DaviesBouldinEvaluation.m
617s ***** test
617s  load fisheriris
617s  eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
617s  assert (class (eva), "DaviesBouldinEvaluation");
617s 1 test, 1 passed, 0 known failure, 0 skipped
617s [inst/Clustering/ClusterCriterion.m]
617s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/ClusterCriterion.m
617s ***** error <ClusterCriterion: X must be a numeric matrix.> ...
617s  ClusterCriterion ("1", "kmeans", [1:6])
617s ***** error <ClusterCriterion: unknown clustering algorithm 'k'.> ...
617s  ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
617s ***** error <ClusterCriterion: invalid matrix of clustering solutions.> ...
617s  ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
617s ***** error <ClusterCriterion: invalid argument.> ...
617s  ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
617s 4 tests, 4 passed, 0 known failure, 0 skipped
617s [inst/Clustering/CalinskiHarabaszEvaluation.m]
617s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/CalinskiHarabaszEvaluation.m
617s ***** test
617s  load fisheriris
617s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
617s  assert (class (eva), "CalinskiHarabaszEvaluation");
618s 1 test, 1 passed, 0 known failure, 0 skipped
618s [inst/Clustering/GapEvaluation.m]
618s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/GapEvaluation.m
618s ***** test
618s  load fisheriris
618s  eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
618s                      "referencedistribution", "uniform");
618s  assert (class (eva), "GapEvaluation");
624s 1 test, 1 passed, 0 known failure, 0 skipped
624s [inst/Clustering/SilhouetteEvaluation.m]
624s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/SilhouetteEvaluation.m
624s ***** test
624s  load fisheriris
624s  eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
624s  assert (class (eva), "SilhouetteEvaluation");
625s 1 test, 1 passed, 0 known failure, 0 skipped
625s [inst/Clustering/KDTreeSearcher.m]
625s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/KDTreeSearcher.m
625s ***** demo
625s  ## Demo to verify implementation using fisheriris dataset
625s  load fisheriris
625s  numSamples = size (meas, 1);
625s  queryIndices = [1, 23, 46, 63, 109];
625s  dataIndices = ~ismember (1:numSamples, queryIndices);
625s  queryPoints = meas(queryIndices, :);
625s  dataPoints = meas(dataIndices, :);
625s  searchRadius = 0.3;
625s  kdTree = KDTreeSearcher (dataPoints, 'Distance', 'minkowski')
625s  nearestNeighbors = knnsearch (kdTree, queryPoints, "K", 2)
625s  neighborsInRange = rangesearch (kdTree, queryPoints, searchRadius)
625s ***** demo
625s  ## Create a KDTreeSearcher with Euclidean distance
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = KDTreeSearcher (X);
625s  ## Find the nearest neighbor to [2, 3]
625s  Y = [2, 3];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  disp ("Nearest neighbor index:");
625s  disp (idx);
625s  disp ("Distance:");
625s  disp (D);
625s  ## Find all points within radius 2
625s  [idx, D] = rangesearch (obj, Y, 2);
625s  disp ("Indices within radius:");
625s  disp (idx);
625s  disp ("Distances:");
625s  disp (D);
625s ***** demo
625s  ## Create a KDTreeSearcher with Minkowski distance (P=3)
625s  X = [0, 0; 1, 0; 2, 0];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  ## Find the nearest neighbor to [1, 0]
625s  Y = [1, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  disp ("Nearest neighbor index:");
625s  disp (idx);
625s  disp ("Distance:");
625s  disp (D);
625s ***** demo
625s  rng(42);
625s  disp('Demonstrating KDTreeSearcher');
625s 
625s  n = 100;
625s  mu1 = [0.3, 0.3];
625s  mu2 = [0.7, 0.7];
625s  sigma = 0.1;
625s  X1 = mu1 + sigma * randn (n / 2, 2);
625s  X2 = mu2 + sigma * randn (n / 2, 2);
625s  X = [X1; X2];
625s 
625s  obj = KDTreeSearcher(X);
625s 
625s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
625s 
625s  K = 5;
625s  [idx, D] = knnsearch (obj, Y, "K", K);
625s 
625s  disp ('For the first query point:');
625s  disp (['Query point: ', num2str(Y(1,:))]);
625s  disp ('Indices of nearest neighbors:');
625s  disp (idx(1,:));
625s  disp ('Distances:');
625s  disp (D(1,:));
625s 
625s  figure;
625s  scatter (X(:,1), X(:,2), 36, 'b', 'filled'); # Training points
625s  hold on;
625s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled'); # Query points
625s  for i = 1:size (Y, 1)
625s      query = Y(i,:);
625s      neighbors = X(idx(i,:), :);
625s      for j = 1:K
625s          plot ([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
625s      endfor
625s  endfor
625s  hold off;
625s  title ('K Nearest Neighbors with KDTreeSearcher');
625s  xlabel ('X1');
625s  ylabel ('X2');
625s 
625s  r = 0.15;
625s  [idx, D] = rangesearch (obj, Y, r);
625s 
625s  disp ('For the first query point in rangesearch:');
625s  disp (['Query point: ', num2str(Y(1,:))]);
625s  disp ('Indices of points within radius:');
625s  disp (idx{1});
625s  disp ('Distances:');
625s  disp (D{1});
625s 
625s  figure;
625s  scatter (X(:,1), X(:,2), 36, 'b', 'filled');
625s  hold on;
625s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled');
625s  theta = linspace (0, 2 * pi, 100);
625s  for i = 1:size (Y, 1)
625s      center = Y(i,:);
625s      x_circle = center(1) + r * cos (theta);
625s      y_circle = center(2) + r * sin (theta);
625s      plot (x_circle, y_circle, 'g-');
625s      ## Highlight points within radius
625s      if (! isempty (idx{i}))
625s        in_radius = X(idx{i}, :);
625s        scatter (in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
625s      endif
625s  endfor
625s  hold off
625s  title ('Points within Radius with KDTreeSearcher');
625s  xlabel ('X1');
625s  ylabel ('X2');
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X);
625s  Y = X(1:5,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 3);
625s  assert (idx, [[1, 18, 5]; [2, 35, 46]; [3, 48, 4]; [4, 48, 30]; [5, 38, 1]])
625s  assert (D, [[0, 0.1000, 0.1414]; [0,  0.1414,  0.1414]; [0, 0.1414, 0.2449];
625s              [0, 0.1414, 0.1732]; [0, 0.1414, 0.1414]], 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = X(10:15,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 2);
625s  assert (idx, [[10, 35]; [11, 49]; [12, 30]; [13, 2]; [14, 39]; [15, 34]])
625s  assert (D, [[0, 0.1000]; [0, 0.1000]; [0, 0.2080]; [0, 0.1260]; [0, 0.2154];
625s              [0, 0.3503]], 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = X(20:25,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, [20; 21; 22; 23; 24; 25])
625s  assert (D, [0; 0; 0; 0; 0; 0])
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = X(30:35,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 4);
625s  assert (idx, [[30, 31, 4, 12]; [31, 30, 10, 35]; [32, 21, 37, 28];
625s                [33, 20, 34, 47]; [34, 16, 15, 33]; [35, 10, 2, 26]])
625s  assert (D, [[0, 0.1000, 0.1000, 0.2000]; [0, 0.1000, 0.1000, 0.1000];
625s              [0, 0.2000, 0.2000, 0.2000]; [0, 0.3000, 0.3000, 0.3000];
625s              [0, 0.2000, 0.3000, 0.3000]; [0, 0.1000, 0.1000, 0.1000]], 5e-15)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "BucketSize", 20);
625s  Y = X(40:45,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 2);
625s  assert (idx, [[40, 8]; [41, 18]; [42, 9]; [43, 39]; [44, 27]; [45, 47]])
625s  assert (D, [[0, 0.1000]; [0, 0.1414]; [0, 0.6245]; [0, 0.2000]; [0, 0.2236];
625s              [0, 0.3606]], 4.7e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X);
625s  Y = X(50:55,:);
625s  [idx, D] = knnsearch (obj, Y, "K", 3, "IncludeTies", true);
625s  assert (idx, {[50; 8; 40]; [51; 53; 87]; [52; 57; 76]; [53; 51; 87]; [54; ...
625s                 90; 81]; [55; 59; 76]})
625s  assert (D, {[0; 0.1414; 0.1732]; [0; 0.2646; 0.3317]; [0; 0.2646; 0.3162];
625s              [0; 0.2646; 0.2828]; [0; 0.2000; 0.3000]; [0; 0.2449; 0.3162]}, 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X);
625s  Y = X(60:65,:);
625s  [idx, D] = rangesearch (obj, Y, 0.4);
625s  assert (idx, {[60; 90]; [61; 94]; [62; 97; 79; 96; 100; 89; 98; 72]; [63];
625s                [64; 92; 74; 79]; [65]})
625s  assert (D, {[0; 0.3873]; [0; 0.3606];
625s              [0; 0.3000; 0.3317; 0.3606; 0.3606; 0.3742; 0.3873; 0.4000]; [0];
625s              [0; 0.1414; 0.2236; 0.2449]; [0]}, 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = X(70:72,:);
625s  [idx, D] = rangesearch (obj, Y, 1.0);
625s  assert (idx, {[70; 81; 90; 82; 83; 93; 54; 68; 95; 80; 91; 100; 60; 65; ...
625s                 89; 63]; [71; 139; 128; 150; 127; 57; 86; 64; 79; 92; 124];
625s                [72; 100; 98; 83; 93; 97; 75; 68; 62; 89; 95; 74; 56; 90; ...
625s                 79; 92; 96; 64; 63; 65]})
625s  assert (D, {[0; 0.3000; 0.4000; 0.5000; 0.5000; 0.5000; 0.6000; 0.7000; ...
625s               0.7000; 0.7000; 0.8000; 0.8000; 0.9000; 0.9000; 0.9000; 0.9000];
625s              [0; 0.3000; 0.5000; 0.5000; 0.7000; 0.8000; 0.8000; 1.0000; ...
625s               1.0000; 1.0000; 1]; [0; 0.5000; 0.5000; 0.6000; 0.6000; ...
625s               0.7000; 0.7000; 0.8000; 0.8000; 0.8000; 0.8000; 0.8000; ...
625s               0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 1.0000; 1]}, 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = X(80:85,:);
625s  [idx, D] = rangesearch (obj, Y, 0.8);
625s  assert (idx, {[80; 82; 81; 65; 70; 83; 93; 90; 54; 63; 68; 72; 100; 60; ...
625s                 89; 99; 95; 94; 97; 96]; [81; 82; 70; 54; 90; 93; 80; 83; ...
625s                 60; 68; 95; 100; 65; 63; 97; 61; 91; 94; 89; 96; 72; 58; ...
625s                 62; 56]; [82; 81; 70; 80; 54; 90; 93; 83; 68; 60; 65; 63; ...
625s                 100; 95; 94; 61; 58; 97; 89; 72; 96; 91; 99]; [83; 93; ...
625s                 100; 68; 70; 72; 95; 90; 97; 65; 89; 96; 81; 82; 80; 62; ...
625s                 54; 98; 63; 91; 56; 60; 79; 67; 75; 88; 85; 92; 69]; [84; ...
625s                 134; 102; 143; 150; 124; 128; 73; 127; 139; 147; 64; 112; ...
625s                 114; 120; 74; 135; 122; 71; 92; 104; 138; 148; 117; 79; ...
625s                 55; 56; 57; 67; 111; 129; 69; 78; 59; 52; 133; 85; 88; ...
625s                 87]; [85; 67; 56; 97; 95; 89; 96; 91; 100; 62; 71; 122; ...
625s                 79; 60; 107; 90; 139; 93; 68; 86; 83; 92; 64; 150; 102; ...
625s                 143; 74; 114; 70; 128; 84; 54; 72]})
625s  assert (D, {[0; 0.2884; 0.3530; 0.3826; 0.4062; 0.4198; 0.5117; 0.5440; 0.5718;
625s               0.6000; 0.6018; 0.6073; 0.6308; 0.6333; 0.6753; 0.7000; 0.7192;
625s               0.7230; 0.7350; 0.7459];
625s              [0; 0.1260; 0.1442; 0.2571; 0.2571; 0.3530; 0.3530; 0.3826; 0.4344;
625s               0.4344; 0.4642; 0.4747; 0.5217; 0.5217; 0.5896; 0.6009; 0.6082;
625s               0.6316; 0.6316; 0.6611; 0.6664; 0.6993; 0.7417; 0.7507];
625s              [0; 0.1260; 0.2224; 0.2884; 0.3803; 0.3803; 0.4121; 0.4121; 0.4905;
625s               0.5013; 0.5360; 0.5429; 0.5463; 0.5646; 0.5749; 0.5819; 0.6542;
625s               0.6581; 0.6753; 0.6938; 0.7094; 0.7107; 0.7423];
625s              [0; 0.1260; 0.2224; 0.2520; 0.2571; 0.3107; 0.3302; 0.3332; 0.3332;
625s               0.3530; 0.3530; 0.3803; 0.3826; 0.4121; 0.4198; 0.4344; 0.4531;
625s               0.5155; 0.5217; 0.5348; 0.6028; 0.6073; 0.6374; 0.6527; 0.6611;
625s               0.6804; 0.6938; 0.7399; 0.7560];
625s              [0; 0.3072; 0.3271; 0.3271; 0.3302; 0.3503; 0.3530; 0.3530; 0.3530;
625s               0.3958; 0.3979; 0.4327; 0.4626; 0.4642; 0.5027; 0.5066; 0.5130;
625s               0.5155; 0.5440; 0.5440; 0.5518; 0.5848; 0.6009; 0.6073; 0.6082;
625s               0.6316; 0.6471; 0.6746; 0.6753; 0.6797; 0.6804; 0.7047; 0.7192;
625s               0.7218; 0.7405; 0.7405; 0.7719; 0.7725; 0.7786];
625s              [0; 0.2000; 0.3503; 0.3979; 0.4121; 0.4309; 0.4327; 0.4531; 0.4747;
625s               0.5337; 0.5718; 0.5896; 0.6009; 0.6316; 0.6366; 0.6374; 0.6463;
625s               0.6542; 0.6542; 0.6550; 0.6938; 0.7014; 0.7067; 0.7166; 0.7186;
625s               0.7186; 0.7281; 0.7380; 0.7447; 0.7571; 0.7719; 0.7813; 0.7851]}, 5e-5)
625s ***** test
625s  load fisheriris
625s  X = meas;
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = X(90,:);
625s  [idx, D] = rangesearch (obj, Y, 0.7);
625s  assert (idx, {[90; 70; 54; 81; 95; 60; 83; 93; 100; 68; 82; 65; 97; 91; ...
625s                 56; 61; 62; 63; 67; 79; 80; 85; 89; 96; 72; 92; 107]})
625s  assert (D, {[0; 0.2000; 0.2000; 0.2000; 0.2000; 0.3000; 0.3000; 0.3000; ...
625s              0.3000; 0.3000; 0.3000; 0.4000; 0.4000; 0.4000; 0.5000; ...
625s              0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; ...
625s              0.5000; 0.5000; 0.6000; 0.6000; 0.6000]}, 5e-16)
625s ***** test
625s  ## Constructor with single-point dataset
625s  X = [0, 0];
625s  obj = KDTreeSearcher (X);
625s  assert (obj.X, X);
625s  assert (obj.Distance, "euclidean");
625s  assert (isempty (obj.DistParameter));
625s  assert (obj.BucketSize, 50);
625s ***** test
625s  ## Constructor with duplicate points
625s  X = [0, 0; 0, 0; 1, 0];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  assert (obj.X, X);
625s  assert (obj.Distance, "cityblock");
625s ***** test
625s  ## Constructor with 3D data
625s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  assert (obj.X, X);
625s  assert (obj.DistParameter, 3);
625s ***** test
625s  ## knnsearch with grid, K = 1
625s  X = [0, 0; 0, 1; 1, 0; 1, 1];
625s  obj = KDTreeSearcher (X, "Distance", "euclidean");
625s  Y = [0.5, 0.5];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  D_true = pdist2 (X, Y, "euclidean");
625s  assert (D, min (D_true), 1e-10);
625s  assert (any (idx == find (D_true == min (D_true))));
625s ***** test
625s  ## knnsearch with IncludeTies, all points equidistant
625s  X = [0, 0; 0, 1; 1, 0; 1, 1];
625s  obj = KDTreeSearcher (X);
625s  Y = [0.5, 0.5];
625s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
625s  D_true = pdist2 (X, Y, "euclidean");
625s  expected_idx = find (D_true == min (D_true));
625s  assert (sort (idx{1}(:)), sort (expected_idx));
625s  assert (D{1}(:)', repmat (min (D_true), 1, 4), 1e-10);
625s ***** test
625s  ## rangesearch with line dataset
625s  X = [0, 0; 1, 0; 2, 0; 3, 0];
625s  obj = KDTreeSearcher (X);
625s  Y = [1.5, 0];
625s  r = 1;
625s  [idx, D] = rangesearch (obj, Y, r);
625s  D_true = pdist2 (X, Y, "euclidean");
625s  expected_idx = find (D_true <= r);
625s  assert (sort (idx{1}(:)), sort (expected_idx));
625s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
625s ***** test
625s  ## knnsearch with duplicates
625s  X = [0, 0; 0, 0; 1, 0];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = [0, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
625s  assert (sort (idx{1}(:))', [1, 2]);
625s  assert (D{1}', [0, 0], 1e-10);
625s ***** test
625s  ## rangesearch with 3D data
625s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = [0, 0, 0];
625s  r = 1;
625s  [idx, D] = rangesearch (obj, Y, r);
625s  assert (sort (idx{1}(:))', [1, 2, 3]);
625s  assert (D{1}', [0, 1, 1], 1e-10);
625s ***** test
625s  ## knnsearch with P = 2 (Euclidean equivalent)
625s  X = [0, 0; 1, 1];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 2);
625s  Y = [0, 1];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 1);
625s  assert (D, 1, 1e-10);
625s ***** test
625s  ## rangesearch with P = 3
625s  X = [0, 0; 1, 0; 0, 1];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = [0.5, 0.5];
625s  r = 0.8;
625s  [idx, D] = rangesearch (obj, Y, r);
625s  D_true = pdist2 (X, Y, "minkowski", 3);
625s  expected_idx = find (D_true <= r);
625s  assert (sort (idx{1}(:)), sort (expected_idx));
625s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
625s ***** test
625s  ## knnsearch with P = 4, random data
625s  X = rand (5, 2);
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 4);
625s  Y = rand (1, 2);
625s  [idx, D] = knnsearch (obj, Y, "K", 3);
625s  D_true = pdist2 (X, Y, "minkowski", 4);
625s  [sorted_D, sort_idx] = sort (D_true);
625s  assert (idx', sort_idx(1:3));
625s  assert (D', sorted_D(1:3), 1e-10);
625s ***** test
625s  ## knnsearch with all same points
625s  X = [1, 1; 1, 1; 1, 1];
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = [1, 1];
625s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
625s  assert (sort (idx{1}(:))', [1, 2, 3]);
625s  assert (D{1}', [0, 0, 0], 1e-10);
625s ***** test
625s  ## rangesearch with grid
625s  X = [0, 0; 0, 1; 1, 0; 1, 1];
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = [0.5, 0.5];
625s  r = 0.5;
625s  [idx, D] = rangesearch (obj, Y, r);
625s  D_true = pdist2 (X, Y, "chebychev");
625s  expected_idx = find (D_true <= r);
625s  assert (sort (idx{1}(:)), sort (expected_idx));
625s  assert (D{1}, D_true(expected_idx), 1e-10);
625s ***** test
625s  ## Changing Distance and verifying search
625s  X = [0,0; 1,0];
625s  obj = KDTreeSearcher(X, "Distance", "euclidean");
625s  Y = [0,1];
625s  [idx, D] = knnsearch(obj, Y, "K", 1);
625s  assert(D, 1, 1e-10);
625s  obj.Distance = "chebychev";
625s  [idx, D] = knnsearch(obj, Y, "K", 1);
625s  assert(D, 1, 1e-10);
625s ***** test
625s  ## Changing DistParameter for minkowski
625s  X = [0,0; 1,0];
625s  obj = KDTreeSearcher(X, "Distance", "minkowski", "P", 1);
625s  Y = [0,1];
625s  [idx, D] = knnsearch(obj, Y, "K", 1);
625s  assert(D, 1, 1e-10);
625s  obj.DistParameter = 3;
625s  [idx, D] = knnsearch(obj, Y, "K", 1);
625s  assert(D, 1, 1e-10);
625s ***** test
625s  ## Different BucketSize values
625s  X = rand(20,2);
625s  obj1 = KDTreeSearcher(X, "BucketSize", 5);
625s  obj2 = KDTreeSearcher(X, "BucketSize", 15);
625s  Y = rand(1,2);
625s  [idx1, D1] = knnsearch(obj1, Y, "K", 3);
625s  [idx2, D2] = knnsearch(obj2, Y, "K", 3);
625s  assert(idx1, idx2);
625s  assert(D1, D2, 1e-10);
625s ***** test
625s  ## Basic constructor with default Euclidean
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = KDTreeSearcher (X);
625s  assert (obj.X, X);
625s  assert (obj.Distance, "euclidean");
625s  assert (isempty (obj.DistParameter));
625s  assert (obj.BucketSize, 50);
625s ***** test
625s  ## Minkowski distance with custom P
625s  X = [0, 0; 1, 1; 2, 2];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  assert (obj.Distance, "minkowski");
625s  assert (obj.DistParameter, 3);
625s ***** test
625s  ## Cityblock distance
625s  X = [0, 0; 1, 0; 0, 1];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  assert (obj.Distance, "cityblock");
625s  assert (isempty (obj.DistParameter));
625s ***** test
625s  ## Chebychev distance
625s  X = [1, 1; 2, 3; 4, 2];
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  assert (obj.Distance, "chebychev");
625s  assert (isempty (obj.DistParameter));
625s ***** test
625s  ## knnsearch with Euclidean distance
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = KDTreeSearcher (X);
625s  Y = [2, 3];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 1);
625s  assert (D, sqrt(2), 1e-10);
625s ***** test
625s  ## knnsearch with Cityblock distance
625s  X = [0, 0; 1, 1; 2, 2];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = [1, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (ismember (idx, [1, 2]));
625s  assert (D, 1, 1e-10);
625s ***** test
625s  ## knnsearch with Chebychev distance
625s  X = [1, 1; 2, 3; 4, 2];
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = [2, 2];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (ismember (idx, [1, 2]));
625s  assert (D, 1, 1e-10);
625s ***** test
625s  ## knnsearch with Minkowski P=3
625s  X = [0, 0; 1, 0; 2, 0];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = [1, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 2);
625s  assert (D, 0, 1e-10);
625s ***** test
625s  ## knnsearch with IncludeTies
625s  X = [0, 0; 1, 0; 0, 1];
625s  obj = KDTreeSearcher (X);
625s  Y = [0.5, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
625s  assert (iscell (idx));
625s  assert (sort (idx{1}(:))', [1, 2]);
625s  assert (sort (D{1}(:)), [0.5; 0.5], 1e-10);
625s ***** test
625s  ## rangesearch with Euclidean
625s  X = [1, 1; 2, 2; 3, 3];
625s  obj = KDTreeSearcher (X);
625s  Y = [0, 0];
625s  [idx, D] = rangesearch (obj, Y, 2);
625s  assert (idx{1}, [1]);
625s  assert (D{1}, [sqrt(2)], 1e-10);
625s ***** test
625s  ## rangesearch with Cityblock
625s  X = [0, 0; 1, 1; 2, 2];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = [0, 0];
625s  [idx, D] = rangesearch (obj, Y, 1);
625s  assert (idx{1}, [1]);
625s  assert (D{1}, [0], 1e-10);
625s ***** test
625s  ## rangesearch with Chebychev
625s  X = [1, 1; 2, 3; 4, 2];
625s  obj = KDTreeSearcher (X, "Distance", "chebychev");
625s  Y = [2, 2];
625s  [idx, D] = rangesearch (obj, Y, 1);
625s  assert (sort (idx{1}(:))', [1, 2]);
625s  assert (sort (D{1}(:))', [1, 1], 1e-10);
625s ***** test
625s  ## rangesearch with Minkowski P=3
625s  X = [0, 0; 1, 0; 2, 0];
625s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = [1, 0];
625s  [idx, D] = rangesearch (obj, Y, 1);
625s  assert (sort (idx{1}(:))', [1, 2, 3]);
625s  assert (sort (D{1}(:))', [0, 1, 1], 1e-10);
625s ***** test
625s  ## Diverse dataset with Euclidean
625s  X = [0, 10; 5, 5; 10, 0];
625s  obj = KDTreeSearcher (X);
625s  Y = [5, 5];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 2);
625s  assert (D, 0, 1e-10);
625s ***** test
625s  ## High-dimensional data with Cityblock
625s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
625s  obj = KDTreeSearcher (X, "Distance", "cityblock");
625s  Y = [4, 5, 6];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 2);
625s  assert (D, 0, 1e-10);
625s ***** error<KDTreeSearcher: too few input arguments.> ...
625s  KDTreeSearcher ()
625s ***** error<KDTreeSearcher: Name-Value arguments must be in pairs.> ...
625s  KDTreeSearcher (ones(3,2), "Distance")
625s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
625s  KDTreeSearcher ("abc")
625s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
625s  KDTreeSearcher ([1; Inf; 3])
625s ***** error<KDTreeSearcher: invalid parameter name: 'foo'.> ...
625s  KDTreeSearcher (ones(3,2), "foo", "bar")
625s ***** error<KDTreeSearcher: unsupported distance metric 'invalid'.> ...
625s  KDTreeSearcher (ones(3,2), "Distance", "invalid")
625s ***** error<KDTreeSearcher: Distance must be a string.> ...
625s  KDTreeSearcher (ones(3,2), "Distance", 1)
625s ***** error<KDTreeSearcher: P must be a positive finite scalar.> ...
625s  KDTreeSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
625s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
625s  KDTreeSearcher (ones(3,2), "BucketSize", 0)
625s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
625s  KDTreeSearcher(ones(3,2), "BucketSize", -1)
625s ***** error<KDTreeSearcher.knnsearch: too few input arguments.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)))
625s ***** error<KDTreeSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies")
625s ***** error<KDTreeSearcher.knnsearch: Y must be a finite numeric matrix.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), "abc", "K", 1)
625s ***** error<KDTreeSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,3), "K", 1)
625s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 0)
625s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
625s  obj = KDTreeSearcher(ones(3,2)); knnsearch(obj, ones(1,2), "K", Inf)
625s ***** error<KDTreeSearcher.knnsearch: invalid parameter name: 'foo'.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "foo", "bar")
625s ***** error<KDTreeSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies", 1)
625s ***** error<KDTreeSearcher.knnsearch: SortIndices must be a logical scalar.> ...
625s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "SortIndices", 1)
625s ***** error<KDTreeSearcher.rangesearch: too few input arguments.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)))
625s ***** error<KDTreeSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
625s ***** error<KDTreeSearcher.rangesearch: Y must be a finite numeric matrix.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), "abc", 1)
625s ***** error<KDTreeSearcher.rangesearch: number of columns in X and Y must match.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,3), 1)
625s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), -1)
625s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
625s  obj = KDTreeSearcher(ones(3,2)); rangesearch(obj, ones(1,2), Inf)
625s ***** error<KDTreeSearcher.rangesearch: invalid parameter name: 'foo'.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
625s ***** error<KDTreeSearcher.rangesearch: SortIndices must be a logical scalar.> ...
625s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
625s ***** error<KDTreeSearcher.subsref: \(\) indexing not supported.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj(1)
625s ***** error<KDTreeSearcher.subsref: {} indexing not supported.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj{1}
625s ***** error<KDTreeSearcher.subsref: unrecognized property: 'invalid'.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.invalid
625s ***** error<KDTreeSearcher.subsasgn: \(\) indexing not supported.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj(1) = 1
625s ***** error<KDTreeSearcher.subsasgn: {} indexing not supported.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj{1} = 1
625s ***** error<KDTreeSearcher.subsasgn: chained subscripts not allowed.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.X.Y = 1
625s ***** error<KDTreeSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.X = 1
625s ***** error<KDTreeSearcher.subsasgn: 'KDTree' is read-only and cannot be modified.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.KDTree = 1
625s ***** error<KDTreeSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = "invalid"
625s ***** error<KDTreeSearcher.subsasgn: 'Distance' must be a string.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = 1
625s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be a positive finite scalar for Minkowski distance.> ...
625s  obj = KDTreeSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
625s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be empty for this distance metric.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.DistParameter = 1
625s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.BucketSize = 0
625s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
625s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = -1
625s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
625s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = 1.5
625s ***** error<KDTreeSearcher.subsasgn: unrecognized property: 'invalid'.> ...
625s  obj = KDTreeSearcher (ones(3,2)); obj.invalid = 1
625s 84 tests, 84 passed, 0 known failure, 0 skipped
625s [inst/Clustering/ExhaustiveSearcher.m]
625s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/Clustering/ExhaustiveSearcher.m
625s ***** demo
625s  ## Demo to verify implementation using fisheriris dataset
625s  load fisheriris
625s  rng('default');
625s  numSamples = size (meas, 1);
625s  queryIndices = [20, 95, 123, 136, 138];
625s  dataPoints = meas(~ismember (1:numSamples, queryIndices), :);
625s  queryPoints = meas(queryIndices, :);
625s  searchModel = ExhaustiveSearcher (dataPoints, 'Distance', 'mahalanobis')
625s  mahalanobisParam = searchModel.DistParameter
625s  searchRadius = 3;
625s  nearestNeighbors = knnsearch (searchModel, queryPoints, "K", 2)
625s  neighborsInRange = rangesearch (searchModel, queryPoints, searchRadius)
625s ***** demo
625s  ## Create an ExhaustiveSearcher with Euclidean distance
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = ExhaustiveSearcher (X);
625s  ## Find the nearest neighbor to [2, 3]
625s  Y = [2, 3];
625s  [idx, D] = knnsearch (obj, Y);
625s  disp ("Nearest neighbor index:"); disp (idx);
625s  disp ("Distance:"); disp (D);
625s  ## Find all points within radius 2
625s  [idx, D] = rangesearch (obj, Y, 2);
625s  disp ("Indices within radius:"); disp (idx);
625s  disp ("Distances:"); disp (D);
625s ***** demo
625s  ## Create an ExhaustiveSearcher with Minkowski distance (P=1)
625s  X = [0, 0; 1, 0; 0, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
625s  ## Find the 2 nearest neighbors to [0.5, 0.5]
625s  Y = [0.5, 0.5];
625s  [idx, D] = knnsearch (obj, Y, "K", 2);
625s  disp ("Nearest neighbor indices:"); disp (idx);
625s  disp ("Distances:"); disp (D);
625s ***** demo
625s  rng(42);
625s  disp('Demonstrating ExhaustiveSearcher');
625s 
625s  n = 100;
625s  mu1 = [0.3, 0.3];
625s  mu2 = [0.7, 0.7];
625s  sigma = 0.1;
625s  X1 = mu1 + sigma * randn(n/2, 2);
625s  X2 = mu2 + sigma * randn(n/2, 2);
625s  X = [X1; X2];
625s 
625s  obj = ExhaustiveSearcher(X);
625s 
625s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
625s 
625s  K = 5;
625s  [idx, D] = knnsearch(obj, Y, "K", K);
625s 
625s  disp('For the first query point:');
625s  disp(['Query point: ', num2str(Y(1,:))]);
625s  disp('Indices of nearest neighbors:');
625s  disp(idx(1,:));
625s  disp('Distances:');
625s  disp(D(1,:));
625s 
625s  figure;
625s  scatter(X(:,1), X(:,2), 36, 'b', 'filled'); % Training points
625s  hold on;
625s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled'); % Query points
625s  for i = 1:size(Y,1)
625s      query = Y(i,:);
625s      neighbors = X(idx(i,:), :);
625s      for j = 1:K
625s          plot([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
625s      end
625s  end
625s  hold off;
625s  title('K Nearest Neighbors with ExhaustiveSearcher');
625s  xlabel('X1');
625s  ylabel('X2');
625s 
625s  r = 0.15;
625s  [idx, D] = rangesearch(obj, Y, r);
625s 
625s  disp('For the first query point in rangesearch:');
625s  disp(['Query point: ', num2str(Y(1,:))]);
625s  disp('Indices of points within radius:');
625s  disp(idx{1});
625s  disp('Distances:');
625s  disp(D{1});
625s 
625s  figure;
625s  scatter(X(:,1), X(:,2), 36, 'b', 'filled');
625s  hold on;
625s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled');
625s  theta = linspace(0, 2*pi, 100);
625s  for i = 1:size(Y,1)
625s      center = Y(i,:);
625s      x_circle = center(1) + r * cos(theta);
625s      y_circle = center(2) + r * sin(theta);
625s      plot(x_circle, y_circle, 'g-');
625s      % Highlight points within radius
625s      if ~isempty(idx{i})
625s          in_radius = X(idx{i}, :);
625s          scatter(in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
625s      end
625s  end
625s  hold off;
625s  title('Points within Radius with ExhaustiveSearcher');
625s  xlabel('X1');
625s  ylabel('X2');
625s ***** test
625s  ## Basic constructor with default Euclidean
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = ExhaustiveSearcher (X);
625s  assert (obj.X, X)
625s  assert (obj.Distance, "euclidean")
625s  assert (isempty (obj.DistParameter))
625s ***** test
625s  ## Minkowski distance with custom P
625s  X = [1, 2; 3, 4];
625s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
625s  assert (obj.Distance, "minkowski")
625s  assert (obj.DistParameter, 3)
625s ***** test
625s  ## Seuclidean distance with custom Scale
625s  X = [1, 2; 3, 4; 5, 6];
625s  S = [1, 2];
625s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
625s  assert (obj.Distance, "seuclidean")
625s  assert (obj.DistParameter, S)
625s ***** test
625s  ## Mahalanobis distance with custom Cov
625s  X = [1, 2; 3, 4; 5, 6];
625s  C = [1, 0; 0, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
625s  assert (obj.Distance, "mahalanobis")
625s  assert (obj.DistParameter, C)
625s ***** test
625s  ## knnsearch with Euclidean distance
625s  X = [1, 2; 3, 4; 5, 6];
625s  obj = ExhaustiveSearcher (X);
625s  Y = [2, 3];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 1)
625s  assert (D, sqrt(2), 1e-10)
625s ***** test
625s  ## knnsearch with Cityblock distance
625s  X = [0, 0; 1, 1; 2, 2];
625s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
625s  Y = [1, 0];
625s  [idx, D] = knnsearch (obj, Y, "K", 1);
625s  assert (idx, 1)
625s  assert (D, 1, 1e-10)
625s ***** test
625s  ## knnsearch with Chebychev distance
625s  X = [1, 1; 2, 3; 4, 2];
625s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
625s  Y = [2, 2];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 1)
625s  assert (D, 1, 1e-10)
625s ***** test
625s  ## knnsearch with Cosine distance
625s  X = [1, 0; 0, 1; 1, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
625s  Y = [1, 0.5];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 3)
625s  assert (D < 0.1, true)
625s ***** test
625s  ## knnsearch with Minkowski P=1 (Manhattan)
625s  X = [0, 0; 1, 0; 0, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
625s  Y = [0.5, 0.5];
625s  [idx, D] = knnsearch (obj, Y, "K", 2, "IncludeTies", true);
625s  assert (iscell (idx))
625s  assert (idx{1}, [1, 2, 3])
625s  assert (D{1}, [1, 1, 1], 1e-10)
625s ***** test
625s  ## rangesearch with Seuclidean
625s  X = [1, 1; 2, 2; 3, 3];
625s  S = [1, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
625s  Y = [0, 0];
625s  [idx, D] = rangesearch (obj, Y, 2);
625s  assert (idx{1}, [1])
625s  assert (D{1}, [sqrt(2)], 1e-10)
625s ***** test
625s  ## rangesearch with Mahalanobis
625s  X = [1, 1; 2, 2; 3, 3];
625s  C = [1, 0; 0, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
625s  Y = [0, 0];
625s  [idx, D] = rangesearch (obj, Y, 3, "SortIndices", false);
625s  assert (idx{1}, [1, 2])
625s  assert (D{1}, [sqrt(2), sqrt(8)], 1e-10)
625s ***** test
625s  ## rangesearch with Hamming distance
625s  X = [0, 1; 1, 0; 1, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "hamming");
625s  Y = [0, 0];
625s  [idx, D] = rangesearch (obj, Y, 0.5);
625s  assert (idx{1}, [1, 2])
625s  assert (D{1}, [0.5, 0.5], 1e-10)
625s ***** test
625s  ## Custom distance function
625s  X = [1, 2; 3, 4];
625s  custom_dist = @(x, y) sum(abs(x - y));
625s  obj = ExhaustiveSearcher (X, "Distance", custom_dist);
625s  Y = [2, 3];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 1)
625s  assert (D, 2, 1e-10)
625s ***** test
625s  ## Euclidean with high-dimensional data
625s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9; 10, 11, 12];
625s  obj = ExhaustiveSearcher (X);
625s  Y = [5, 6, 7];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 2)
625s  assert (D, sqrt(3), 1e-10)
625s ***** test
625s  ## Minkowski P=3 with scaled data
625s  X = [0, 1; 2, 3; 4, 5] * 10;
625s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
625s  Y = [20, 30];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 2)
625s  assert (D, 0, 1e-10)
625s ***** test
625s  ## Seuclidean with custom scales on diverse data
625s  X = [1, 10; 2, 20; 3, 30];
625s  S = [1, 5];
625s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
625s  Y = [1.5, 15];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 1)
625s  assert (D, sqrt((0.5/1)^2 + (5/5)^2), 1e-10)
625s ***** test
625s  ## Mahalanobis with correlated data
625s  X = [1, 1; 2, 1.5; 3, 2];
625s  C = [1, 0.5; 0.5, 1];
625s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
625s  Y = [2, 1.5];
625s  [idx, D] = knnsearch (obj, Y);
625s  assert (idx, 2)
625s  assert (D, 0, 1e-10)
626s ***** test
626s  ## Cityblock with sparse data
626s  X = [0, 0, 1; 1, 0, 0; 0, 1, 0];
626s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
626s  Y = [0, 0, 0];
626s  [idx, D] = rangesearch (obj, Y, 1);
626s  assert (idx{1}, [1, 2, 3])
626s  assert (D{1}, [1, 1, 1], 1e-10)
626s ***** test
626s  ## Chebychev with extreme values
626s  X = [0, 100; 50, 50; 100, 0];
626s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
626s  Y = [60, 60];
626s  [idx, D] = knnsearch (obj, Y);
626s  assert (idx, 2)
626s  assert (D, 10, 1e-10)
626s ***** test
626s  ## Cosine with normalized data
626s  X = [1, 0; 0, 1; 1/sqrt(2), 1/sqrt(2)];
626s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
626s  Y = [1, 1];
626s  [idx, D] = knnsearch (obj, Y);
626s  assert (idx, 3)
626s  assert (D < 0.1, true)
626s ***** test
626s  ## Correlation with time-series-like data
626s  X = [1, 2, 3; 2, 4, 6; 1, 1, 1];
626s  obj = ExhaustiveSearcher (X, "Distance", "correlation");
626s  Y = [1.5, 3, 4.5];
626s  [idx, D] = knnsearch (obj, Y);
626s  assert (idx, 1)
626s  assert (D < 0.1, true)
626s ***** test
626s  ## Spearman with ranked data
626s  X = [1, 2, 3; 3, 2, 1; 2, 1, 3];
626s  obj = ExhaustiveSearcher (X, "Distance", "spearman");
626s  Y = [1, 2, 3];
626s  [idx, D] = knnsearch (obj, Y);
626s  assert (idx, 1)
626s  assert (D, 0, 1e-10)
626s ***** test
626s  ## Jaccard with binary sparse data
626s  X = [1, 0, 0; 0, 1, 0; 1, 1, 0];
626s  obj = ExhaustiveSearcher (X, "Distance", "jaccard");
626s  Y = [1, 0, 0];
626s  [idx, D] = knnsearch (obj, Y);
626s  assert (idx, 1)
626s  assert (D, 0, 1e-10)
626s ***** test
626s  obj = ExhaustiveSearcher (ones(3,2));
626s  assert (obj.X, ones(3,2))
626s  assert (obj.Distance, "euclidean")
626s  assert (isempty (obj.DistParameter))
626s ***** test
626s  obj = ExhaustiveSearcher (ones(3,2));
626s  obj.Distance = "minkowski";
626s  assert (obj.Distance, "minkowski")
626s ***** test
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski");
626s  obj.DistParameter = 3;
626s  assert (obj.DistParameter, 3)
626s ***** test
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean");
626s  obj.DistParameter = [1, 2];
626s  assert (obj.DistParameter, [1, 2])
626s ***** test
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis");
626s  obj.DistParameter = eye(2);
626s  assert (obj.DistParameter, eye(2))
626s ***** error<ExhaustiveSearcher: too few input arguments.> ...
626s  ExhaustiveSearcher ()
626s ***** error<ExhaustiveSearcher: Name-Value arguments must be in pairs.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance")
626s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
626s  ExhaustiveSearcher ("abc")
626s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
626s  ExhaustiveSearcher ([1; Inf; 3])
626s ***** error<ExhaustiveSearcher: invalid parameter name: 'foo'.> ...
626s  ExhaustiveSearcher (ones(3,2), "foo", "bar")
626s ***** error<ExhaustiveSearcher: unsupported distance metric 'invalid'.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", "invalid")
626s ***** error<ExhaustiveSearcher: invalid distance function handle.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", @(x) x)
626s ***** error<ExhaustiveSearcher: Distance must be a string or function handle.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", 1)
626s ***** error<ExhaustiveSearcher: P must be a positive finite scalar.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
626s ***** error<ExhaustiveSearcher: Scale must be a nonnegative vector matching X columns.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
626s ***** error<ExhaustiveSearcher: Cov must be a square matrix matching X columns.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
626s ***** error<ExhaustiveSearcher: Cov must be positive definite.> ...
626s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
626s ***** error<ExhaustiveSearcher.knnsearch: too few input arguments.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)))
626s ***** error<ExhaustiveSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies")
626s ***** error<ExhaustiveSearcher.knnsearch: Y must be a finite numeric matrix.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), "abc")
626s ***** error<ExhaustiveSearcher.knnsearch: number of columns in X and Y must match.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,3))
626s ***** error<ExhaustiveSearcher.knnsearch: K must be a positive integer.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "K", 0)
626s ***** error<ExhaustiveSearcher.knnsearch: invalid parameter name: 'foo'.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "foo", "bar")
626s ***** error<ExhaustiveSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
626s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies", 1)
626s ***** error<ExhaustiveSearcher.rangesearch: too few input arguments.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)))
626s ***** error<ExhaustiveSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
626s ***** error<ExhaustiveSearcher.rangesearch: Y must be a finite numeric matrix.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), "abc", 1)
626s ***** error<ExhaustiveSearcher.rangesearch: number of columns in X and Y must match.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,3), 1)
626s ***** error<ExhaustiveSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), -1)
626s ***** error<ExhaustiveSearcher.rangesearch: invalid parameter name: 'foo'.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
626s ***** error<ExhaustiveSearcher.rangesearch: SortIndices must be a logical scalar.> ...
626s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
626s ***** error<ExhaustiveSearcher.subsref: \(\) indexing not supported.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj(1)
626s ***** error<ExhaustiveSearcher.subsref: {} indexing not supported.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj{1}
626s ***** error<dynamic structure field names must be strings> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1)
626s ***** error<ExhaustiveSearcher.subsref: unrecognized property: 'invalid'> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid
626s ***** error<ExhaustiveSearcher.subsasgn: \(\) indexing not supported.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj(1) = 1
626s ***** error<ExhaustiveSearcher.subsasgn: {} indexing not supported.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj{1} = 1
626s ***** error<ExhaustiveSearcher.subsasgn: chained subscripts not allowed.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.X.Y = 1
626s ***** error<dynamic structure field names must be strings> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1) = 1
626s ***** error<ExhaustiveSearcher.subsasgn: X is read-only and cannot be modified.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.X = 1
626s ***** error<ExhaustiveSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = "invalid"
626s ***** error<ExhaustiveSearcher.subsasgn: invalid distance function handle.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x) x
626s ***** error<ExhaustiveSearcher.subsasgn: custom distance function output invalid.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x, y) [1; 1]
626s ***** error<ExhaustiveSearcher.subsasgn: Distance must be a string or function handle.> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = 1
626s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a positive finite scalar for minkowski.> ...
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
626s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a nonnegative vector matching X columns.> ...
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
626s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a square matrix matching X columns.> ...
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
626s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be positive definite for mahalanobis.> ...
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
626s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be empty for this distance metric.> ...
626s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "euclidean"); obj.DistParameter = 1
626s ***** error<ExhaustiveSearcher.subsasgn: unrecognized property: 'invalid'> ...
626s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid = 1
626s 73 tests, 73 passed, 0 known failure, 0 skipped
626s [inst/fillmissing.m]
626s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fillmissing.m
626s ***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
626s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
626s ***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
626s ***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
626s ***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
626s ***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
626s ***** test
626s  x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
626s  y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
626s  assert (fillmissing (x, "constant", 99), y);
626s  y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
626s  assert (fillmissing (x, "constant", [94:99]), y);
626s  assert (fillmissing (x, "constant", [94:99]'), y);
626s  assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
626s  assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
626s  assert (fillmissing (x, "constant", [94:99], 1), y);
626s  y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
626s  assert (fillmissing (x, "constant", [96:99], 2), y);
626s  y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
626s  assert (fillmissing (x, "constant", [94:99], 3), y);
626s  y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
626s  assert (fillmissing (x, "constant", [88:99], 99), y);
626s ***** test
626s  x = reshape ([1:24], 4, 3, 2);
626s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  y = x;
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
626s  assert (fillmissing (x, "constant", [94:99], 1), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
626s  assert (fillmissing (x, "constant", [92:99], 2), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
626s  assert (fillmissing (x, "constant", [88:99], 3), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
626s  assert (fillmissing (x, "constant", [76:99], 99), y);
626s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
626s ***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
626s ***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
626s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
626s ***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
626s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
626s ***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
626s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
626s ***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
626s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
626s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
626s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
626s ***** test
626s  x = reshape ([1:24], 3, 4, 2);
626s  y = x;
626s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
626s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
626s  y([8]) = 99;
626s  assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
626s  assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
626s  y = x;
626s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
626s  y([6, 18, 20, 21]) = 99;
626s  assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
626s  y(y == 99) = 88;
626s  assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
626s  assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
626s  assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
626s  y([8]) = 94;
626s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
626s  y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
626s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
626s  y = x;
626s  y(isnan (y)) = 88;
626s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
626s  y = x;
626s  y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
626s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
626s  y = x;
626s  y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
626s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
626s  y = x;
626s  y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
626s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
626s  assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
626s ***** test
626s  x = reshape ([1:24],4,3,2);
626s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  y = x;
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
626s  assert (fillmissing (x, "constant", [94:99], 1), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
626s  assert (fillmissing (x, "constant", [92:99], 2), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
626s  assert (fillmissing (x, "constant", [88:99], 3), y);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
626s  assert (fillmissing (x, "constant", [76:99], 99), y);
626s ***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
626s ***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
626s ***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
626s ***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
626s ***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
626s ***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
626s ***** test
626s  x = reshape ([1:24], 4, 3, 2);
626s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  y = x;
626s  y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
626s  assert (fillmissing (x, "next", 1), y);
626s  y = x;
626s  y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
626s  assert (fillmissing (x, "next", 2), y);
626s  y = x;
626s  y([1, 6, 9, 12]) = [13, 18, 21, 24];
626s  assert (fillmissing (x, "next", 3), y);
626s  assert (fillmissing (x, "next", 99), x);
626s  y = x;
626s  y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
626s  assert (fillmissing (x, "previous", 1), y);
626s  y = x;
626s  y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
626s  assert (fillmissing (x, "previous", 2), y);
626s  y = x;
626s  y([14, 16, 22, 23]) = [2, 4, 10, 11];
626s  assert (fillmissing (x, "previous", 3), y);
626s  assert (fillmissing (x, "previous", 99), x);
626s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
626s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
626s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
626s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
626s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
626s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
626s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
626s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
626s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
626s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
626s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
626s ***** test
626s  x = reshape ([1:24], 3, 4, 2);
626s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
626s  y = x;
626s  y([5, 6, 8, 18]) = [4, 4, 0, 17];
626s  assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
626s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
626s  y = x;
626s  y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
626s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
626s  y = x;
626s  y([16, 19, 21]) = [4, 7, 9];
626s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
626s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
626s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
626s  y = x;
626s  y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
626s  assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
626s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
626s  y = x;
626s  y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
626s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
626s  y = x;
626s  y([2, 5]) = [14, 17];
626s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
626s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
626s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
626s ***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
626s ***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
626s ***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
626s ***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
626s ***** test
626s  x = reshape ([1:24], 4, 3, 2);
626s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  y = x;
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
626s  assert (fillmissing (x, "nearest", 1), y);
626s  y = x;
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
626s  assert (fillmissing (x, "nearest", 2), y);
626s  y = x;
626s  y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
626s  assert (fillmissing (x, "nearest", 3), y);
626s  assert (fillmissing (x, "nearest", 99), x);
626s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
626s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
626s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
626s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
626s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
626s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
626s ***** test
626s  x = reshape ([1:24], 3, 4, 2);
626s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
626s  y = x;
626s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
626s  assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
626s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
626s  y = x;
626s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
626s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
626s  y = x;
626s  y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
626s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
626s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
626s ***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
626s ***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
626s ***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
626s ***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
626s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
626s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
626s ***** test
626s  x = reshape ([1:24], 4, 3, 2);
626s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
626s  y = reshape ([1:24], 4, 3, 2);
626s  y([1, 9, 14, 19, 22, 23]) = NaN;
626s  assert (fillmissing (x, "linear", 2), y);
626s  y = reshape ([1:24], 4, 3, 2);
626s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
626s  assert (fillmissing (x, "linear", 3), y);
626s  assert (fillmissing (x, "linear", 99), x);
626s ***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
626s ***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
626s ***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
626s ***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
626s ***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
626s ***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
626s ***** test
626s  x = reshape ([1:24], 3, 4, 2);
626s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
626s  y = x;
626s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
626s  y(8) = 8;
626s  assert (fillmissing (x, "linear", "endvalues", 0), y);
626s  assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
626s  y = x;
626s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
626s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
626s  assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
626s  y = x;
626s  y(isnan(y)) = 0;
626s  assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
626s  assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
626s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
626s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
626s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
626s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
626s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
626s ***** test
626s  x = reshape ([1:24], 3, 4, 2);
626s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
626s  y = x;
626s  y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
626s  assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
626s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
626s  assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
626s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
626s  assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
627s ***** test <60965>
627s  x = reshape ([1:24], 3, 4, 2);
627s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
627s  y = x;
627s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
627s  assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
627s !!!!! known bug: https://octave.org/testfailure/?60965
627s interp1: invalid METHOD 'makima'
627s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
627s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
627s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
627s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
627s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
627s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
627s ***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
627s ***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
627s ***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
627s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
627s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
627s ***** 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])
627s ***** test
627s  x = reshape ([1:24], 3, 4, 2);
627s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
627s  y = x;
627s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
627s  assert (fillmissing (x, "movmean", 3), y);
627s  assert (fillmissing (x, "movmean", [1, 1]), y);
627s  assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
627s  y = x;
627s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
627s  assert (fillmissing (x, "movmean", 3, 2), y);
627s  assert (fillmissing (x, "movmean", [1, 1], 2), y);
627s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
627s  y([1, 18]) = NaN;
627s  y(6) = 9;
627s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
627s  y = x;
627s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
627s  y(8) = 8;
627s  assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
627s  y = x;
627s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
627s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
627s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
627s ***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
627s ***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
627s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
627s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
627s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
627s ***** 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])
627s ***** test
627s  x = reshape ([1:24], 3, 4, 2);
627s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
627s  y = x;
627s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
627s  assert (fillmissing (x, "movmedian", 3), y);
627s  assert (fillmissing (x, "movmedian", [1, 1]), y);
627s  assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
627s  y = x;
627s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
627s  assert (fillmissing (x, "movmedian", 3, 2), y);
627s  assert (fillmissing (x, "movmedian", [1, 1], 2), y);
627s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
627s  y([1,18]) = NaN;
627s  y(6) = 9;
627s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
627s  y = x;
627s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
627s  y(8) = 8;
627s  assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
627s  y = x;
627s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
627s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
627s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
627s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
627s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
627s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
627s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
627s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
627s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
627s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
627s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
627s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
627s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
627s ***** function A = testfcn (x, y, z)
627s   if (isempty (y))
627s     A = z;
627s   elseif (numel (y) == 1)
627s     A = repelem (x(1), numel(z));
627s   else
627s     A = interp1 (y, x, z, "linear", "extrap");
627s   endif
627s ***** endfunction
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
627s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
627s ***** 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]
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
627s ***** 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]'
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
627s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
627s ***** 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)
627s ***** 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]
627s ***** test
627s ***** function A = testfcn (x, y, z)
627s   if (isempty (y))
627s     A = z;
627s   elseif (numel (y) == 1)
627s     A = repelem (x(1), numel(z));
627s   else
627s     A = interp1 (y, x, z, "linear", "extrap");
627s   endif
627s ***** endfunction
627s  x = reshape ([1:24], 3, 4, 2);
627s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
627s  y = x;
627s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
627s  assert (fillmissing (x, @testfcn, 3), y);
627s  assert (fillmissing (x, @testfcn, [1, 1]), y);
627s  assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
627s  y= x;
627s  y(isnan (x)) = 99;
627s  y(8) = 8;
627s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
627s  y = x;
627s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
627s  assert (fillmissing (x, @testfcn, 3, 2), y);
627s  assert (fillmissing (x, @testfcn, [1, 1], 2), y);
627s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
627s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
627s  y(1) = NaN;
627s  y([6, 18, 21]) = [9, 24, 24];
627s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
627s  y = x;
627s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
627s  y(8) = 8;
627s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
627s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
627s  y(8) = 99;
627s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
627s  y([6, 18, 20, 21]) = 99;
627s  assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
627s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
627s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
627s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
627s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
627s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
627s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
627s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
627s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
627s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
627s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
627s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
627s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
627s ***** 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])
627s ***** test
627s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
627s  assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
627s  y = x;
627s  y([4, 7, 12]) = 0;
627s  assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
627s  assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
627s  y = x;
627s  y([5, 7, 12]) = 0;
627s  assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
627s  y = x;
627s  y([4, 5, 7]) = 0;
627s  assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
627s ***** test
627s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
627s  [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
627s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
627s  [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
627s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
627s  [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
627s  assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
627s  [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
627s  assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
627s ***** test
627s  x = [NaN, 2, 3];
627s  [~, idx] = fillmissing (x, "previous");
627s  assert (idx, logical ([0, 0, 0]));
627s  [~, idx] = fillmissing (x, "movmean", 1);
627s  assert (idx, logical ([0, 0, 0]));
627s  x = [1:3; 4:6; 7:9];
627s  x([2, 4, 7, 9]) = NaN;
627s  [~, idx] = fillmissing (x, "linear");
627s  assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
627s  [~, idx] = fillmissing (x, "movmean", 2);
627s  assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
627s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
627s  assert (A, [1, 2, 3, 3, NaN]);
627s  assert (idx, logical ([0, 0, 0, 1, 0]));
627s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
627s  assert (A, [1, 2, 3, 3, NaN]);
627s  assert (idx, logical ([0, 0, 0, 1, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
627s  assert (A, [1, 2, 3, 3, NaN]);
627s  assert (idx, logical ([0, 0, 0, 1, 0]));
627s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
627s  assert (A, [1, 2, 1, 4, 1]);
627s  assert (idx, logical ([0, 1, 0, 1, 0]));
627s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
627s  assert (A, [1, NaN, 1, NaN, 1]);
627s  assert (idx, logical ([0, 0, 0, 0, 0]));
627s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
627s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
627s ***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
627s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
627s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
627s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
627s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
627s ***** test
627s  [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
627s  assert (idx, logical ([0, 0, 0, 0, 0]));
627s  [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
627s  assert (idx, logical ([0, 1, 1, 1, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
627s  assert (A, [1, 2, 2, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 1, 0, 0]));
627s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
627s  assert (A, [1, 1, 1, 1, 1]);
627s  assert (idx, logical ([0, 1, 0, 1, 1]));
627s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
627s  assert (A, [1, NaN, 1, NaN, NaN]);
627s  assert (idx, logical ([0, 0, 0, 0, 0]));
627s ***** test
627s  [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
627s  assert (A, [1, 3, 5]);
627s  assert (idx, logical ([0, 1, 0]));
627s ***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
627s ***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
627s ***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
627s ***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
627s ***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
627s ***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
627s ***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
627s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
627s ***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
627s ***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
627s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
627s ***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
627s ***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
627s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
627s ***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
627s ***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
627s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
627s ***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
627s ***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
628s ***** test
628s  [A, idx] = fillmissing (" a b c", "constant", " ");
628s  assert (A, " a b c");
628s  assert (idx, logical ([0, 0, 0, 0, 0, 0]));
628s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
628s  assert (A, {"foo", "", "bar", ""});
628s  assert (idx, logical ([0, 0, 0, 0]));
628s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
628s  assert (A, {"foo", "", "bar", ""});
628s  assert (idx, logical ([0, 0, 0, 0]));
628s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
628s  assert (A, "afaoaoa");
628s  assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
628s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
628s  assert (A, " f o o ");
628s  assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
628s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
628s  assert (A, {"a", "foo", "a"});
628s  assert (idx, logical ([1, 0, 1]));
628s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
628s  assert (A, {"", "foo", ""});
628s  assert (idx, logical ([0, 0, 0]));
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
628s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
628s ***** 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]))
628s ***** 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]))
628s ***** test
628s  x = logical ([1, 0, 1, 0, 1]);
628s  [~, idx] = fillmissing (x, "constant", true);
628s  assert (idx, logical ([0, 0, 0, 0, 0]));
628s  [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([0, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 0]));
628s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
628s  assert (idx, logical ([0, 0, 0, 0, 0]))
628s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]))
628s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([0, 0, 1, 0, 1]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
628s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
628s ***** 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]))
628s ***** 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]))
628s ***** test
628s  x = int32 ([1, 2, 3, 4, 5]);
628s  [~, idx] = fillmissing (x, "constant", 0);
628s  assert (idx, logical ([0, 0, 0, 0, 0]));
628s  [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
628s  assert (idx, logical ([0, 0, 1, 0, 0]));
628s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([0, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 0]));
628s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
628s  assert (idx, logical ([0, 0, 0, 0, 0]));
628s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([1, 0, 1, 0, 1]));
628s  [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
628s  assert (idx, logical ([0, 0, 1, 0, 1]));
628s ***** test
628s  [A, idx] = fillmissing ([struct, struct], "constant", 1);
628s  assert (A, [struct, struct])
628s  assert (idx, [false, false])
628s ***** error <Invalid call> fillmissing ()
628s ***** error <Invalid call> fillmissing (1)
628s ***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
628s ***** error <second input must be a> fillmissing (1, 2)
628s ***** error <unknown fill method 'foo'> fillmissing (1, "foo")
628s ***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
628s ***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
628s ***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
628s ***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
628s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
628s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
628s ***** error <'constant' method must be followed by> fillmissing (1, "constant")
628s ***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
628s ***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
628s ***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
628s ***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
628s ***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
628s ***** error <moving window method must be followed by> fillmissing (1, "movmean")
628s ***** error <moving window method must be followed by> fillmissing (1, "movmedian")
628s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
628s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
628s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
628s ***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
628s ***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
628s ***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
628s ***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
628s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
628s ***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
628s ***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
628s ***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
628s ***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
628s ***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
628s ***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
628s ***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
628s ***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
628s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
628s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
628s ***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
628s ***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
628s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
628s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
628s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
628s ***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
628s ***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
628s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
628s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
628s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
628s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
628s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
628s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
628s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
628s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
628s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
628s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
628s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
628s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
628s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
628s ***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
628s ***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
628s ***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
628s ***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
628s ***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
628s 380 tests, 379 passed, 0 known failure, 1 skipped
628s [inst/regress.m]
628s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/regress.m
628s ***** test
628s  % Longley data from the NIST Statistical Reference Dataset
628s  Z = [  60323    83.0   234289   2356     1590    107608  1947
628s         61122    88.5   259426   2325     1456    108632  1948
628s         60171    88.2   258054   3682     1616    109773  1949
628s         61187    89.5   284599   3351     1650    110929  1950
628s         63221    96.2   328975   2099     3099    112075  1951
628s         63639    98.1   346999   1932     3594    113270  1952
628s         64989    99.0   365385   1870     3547    115094  1953
628s         63761   100.0   363112   3578     3350    116219  1954
628s         66019   101.2   397469   2904     3048    117388  1955
628s         67857   104.6   419180   2822     2857    118734  1956
628s         68169   108.4   442769   2936     2798    120445  1957
628s         66513   110.8   444546   4681     2637    121950  1958
628s         68655   112.6   482704   3813     2552    123366  1959
628s         69564   114.2   502601   3931     2514    125368  1960
628s         69331   115.7   518173   4806     2572    127852  1961
628s         70551   116.9   554894   4007     2827    130081  1962 ];
628s  % Results certified by NIST using 500 digit arithmetic
628s  % b and standard error in b
628s  V = [  -3482258.63459582         890420.383607373
628s          15.0618722713733         84.9149257747669
628s         -0.358191792925910E-01    0.334910077722432E-01
628s         -2.02022980381683         0.488399681651699
628s         -1.03322686717359         0.214274163161675
628s         -0.511041056535807E-01    0.226073200069370
628s          1829.15146461355         455.478499142212 ];
628s  Rsq = 0.995479004577296;
628s  F = 330.285339234588;
628s  y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
628s  alpha = 0.05;
628s  [b, bint, r, rint, stats] = regress (y, X, alpha);
628s  assert(b,V(:,1),4e-6);
628s  assert(stats(1),Rsq,1e-12);
628s  assert(stats(2),F,3e-8);
628s  assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
628s warning: matrix singular to machine precision, rcond = 3.50566e-20
628s warning: called from
628s     regress at line 131 column 5
628s     __test__ at line 33 column 3
628s     test at line 685 column 11
628s     /tmp/tmp.vpN5xdrKMr at line 1374 column 2
628s 
628s 1 test, 1 passed, 0 known failure, 0 skipped
628s [inst/cl_multinom.m]
628s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cl_multinom.m
628s ***** demo
628s  CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
628s ***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
628s ***** error cl_multinom (1, 2, 3, 4, 5);
628s ***** error<cl_multinom: argument method must be a string.> ...
628s  cl_multinom (1, 2, 3, 4);
628s ***** error<cl_multinom: unknown calculation type.> ...
628s  cl_multinom (1, 2, 3, "some string");
628s 4 tests, 4 passed, 0 known failure, 0 skipped
628s [inst/linkage.m]
628s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/linkage.m
628s ***** shared x, t
628s  x = reshape (mod (magic (6),5), [], 3);
628s  t = 1e-6;
628s ***** assert (cond (linkage (pdist (x))),                   34.119045, t);
628s ***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
628s ***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
628s ***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
628s  lastwarn(); # Clear last warning before the test
628s ***** warning <cluster distances> linkage (pdist (x), "centroid");
628s ***** test
628s  warning off Octave:clustering
628s  assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
628s  warning on Octave:clustering
628s ***** warning <cluster distances> linkage (pdist (x), "median");
628s ***** test
628s  warning off Octave:clustering
628s  assert (cond (linkage (pdist (x), "median")),        27.683325, t);
628s  warning on Octave:clustering
628s ***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
628s ***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
628s ***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
628s ***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
628s 12 tests, 12 passed, 0 known failure, 0 skipped
628s [inst/fitlm.m]
628s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitlm.m
628s ***** demo
628s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
628s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
628s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
628s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
628s         25.694 ]';
628s  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]';
628s 
628s  [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
628s ***** demo
628s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
628s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
628s  brands = {'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'};
628s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
628s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
628s 
628s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
628s                           "CategoricalVars",[1,2],"display","on");
628s ***** test
628s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
628s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
628s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
628s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
628s         25.694 ]';
628s  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]';
628s  [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
628s  [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
628s  [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
628s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
628s  [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
628s  assert (TAB{2,2}, 10, 1e-04);
628s  assert (TAB{3,2}, 7.99999999999999, 1e-09);
628s  assert (TAB{4,2}, 8.99999999999999, 1e-09);
628s  assert (TAB{5,2}, 11.0001428571429, 1e-09);
628s  assert (TAB{6,2}, 19.0001111111111, 1e-09);
628s  assert (TAB{2,3}, 1.01775379540949, 1e-09);
628s  assert (TAB{3,3}, 1.64107868458008, 1e-09);
628s  assert (TAB{4,3}, 1.43932122062479, 1e-09);
628s  assert (TAB{5,3}, 1.48983900477565, 1e-09);
628s  assert (TAB{6,3}, 1.3987687997822, 1e-09);
628s  assert (TAB{2,6}, 9.82555903510687, 1e-09);
628s  assert (TAB{3,6}, 4.87484242844031, 1e-09);
628s  assert (TAB{4,6}, 6.25294748040552, 1e-09);
628s  assert (TAB{5,6}, 7.38344399756088, 1e-09);
628s  assert (TAB{6,6}, 13.5834536158296, 1e-09);
628s  assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
628s  assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
628s  assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
628s  assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
628s ***** test
628s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
628s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
628s  brands = bsxfun (@times, ones(6,1), [1,2,3]);
628s  popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));
628s 
628s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
628s                           "categoricalvars",[1,2],"display","off");
628s  assert (TAB{2,2}, 5.66666666666667, 1e-09);
628s  assert (TAB{3,2}, -1.33333333333333, 1e-09);
628s  assert (TAB{4,2}, -2.16666666666667, 1e-09);
628s  assert (TAB{5,2}, 1.16666666666667, 1e-09);
628s  assert (TAB{6,2}, -0.333333333333334, 1e-09);
628s  assert (TAB{7,2}, -0.166666666666667, 1e-09);
628s  assert (TAB{2,3}, 0.215165741455965, 1e-09);
628s  assert (TAB{3,3}, 0.304290309725089, 1e-09);
628s  assert (TAB{4,3}, 0.304290309725089, 1e-09);
628s  assert (TAB{5,3}, 0.304290309725089, 1e-09);
628s  assert (TAB{6,3}, 0.43033148291193, 1e-09);
628s  assert (TAB{7,3}, 0.43033148291193, 1e-09);
628s  assert (TAB{2,6}, 26.3362867542108, 1e-09);
628s  assert (TAB{3,6}, -4.38178046004138, 1e-09);
628s  assert (TAB{4,6}, -7.12039324756724, 1e-09);
628s  assert (TAB{5,6}, 3.83405790253621, 1e-09);
628s  assert (TAB{6,6}, -0.774596669241495, 1e-09);
628s  assert (TAB{7,6}, -0.387298334620748, 1e-09);
628s  assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
628s  assert (TAB{3,7}, 0.000893505495903642, 1e-09);
628s  assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
628s  assert (TAB{5,7}, 0.00237798044119407, 1e-09);
628s  assert (TAB{6,7}, 0.453570536021938, 1e-09);
628s  assert (TAB{7,7}, 0.705316781644046, 1e-09);
628s  ## Test with string ids for categorical variables
628s  brands = {'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'; ...
628s            'Gourmet', 'National', 'Generic'};
628s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
628s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
628s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
628s                           "categoricalvars",[1,2],"display","off");
628s ***** test
628s  load carsmall
628s  X = [Weight,Horsepower,Acceleration];
628s  [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
628s  [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
628s  assert (TAB{2,2}, 47.9767628118615, 1e-09);
628s  assert (TAB{3,2}, -0.00654155878851796, 1e-09);
628s  assert (TAB{4,2}, -0.0429433065881864, 1e-09);
628s  assert (TAB{5,2}, -0.0115826516894871, 1e-09);
628s  assert (TAB{2,3}, 3.87851641748551, 1e-09);
628s  assert (TAB{3,3}, 0.00112741016370336, 1e-09);
628s  assert (TAB{4,3}, 0.0243130608813806, 1e-09);
628s  assert (TAB{5,3}, 0.193325043113178, 1e-09);
628s  assert (TAB{2,6}, 12.369874881944, 1e-09);
628s  assert (TAB{3,6}, -5.80228828790225, 1e-09);
628s  assert (TAB{4,6}, -1.76626492228599, 1e-09);
628s  assert (TAB{5,6}, -0.0599128364487485, 1e-09);
628s  assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
628s  assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
628s  assert (TAB{4,7}, 0.0807803098213114, 1e-09);
628s  assert (TAB{5,7}, 0.952359384151778, 1e-09);
628s 3 tests, 3 passed, 0 known failure, 0 skipped
628s [inst/cmdscale.m]
628s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cmdscale.m
628s ***** shared m, n, X, D
628s  m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
628s ***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
628s ***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
629s 2 tests, 2 passed, 0 known failure, 0 skipped
629s [inst/fitcknn.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitcknn.m
629s ***** demo
629s  ## Train a k-nearest neighbor classifier for k = 10
629s  ## and plot the decision boundaries.
629s 
629s  load fisheriris
629s  idx = ! strcmp (species, "setosa");
629s  X = meas(idx,3:4);
629s  Y = cast (strcmpi (species(idx), "virginica"), "double");
629s  obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
629s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
629s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
629s  [x1G, x2G] = meshgrid (x1, x2);
629s  XGrid = [x1G(:), x2G(:)];
629s  pred = predict (obj, XGrid);
629s  gidx = logical (pred);
629s 
629s  figure
629s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
629s  hold on
629s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
629s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
629s  xlabel ("Petal length (cm)");
629s  ylabel ("Petal width (cm)");
629s  title ("5-Nearest Neighbor Classifier Decision Boundary");
629s  legend ({"Versicolor Region", "Virginica Region", ...
629s          "Sampled Versicolor", "Sampled Virginica"}, ...
629s          "location", "northwest")
629s  axis tight
629s  hold off
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y);
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y, "NSMethod", "exhaustive");
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  k = 10;
629s  a = fitcknn (x, y, "NumNeighbors" ,k);
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = ones (4, 11);
629s  y = ["a"; "a"; "b"; "b"];
629s  k = 10;
629s  a = fitcknn (x, y, "NumNeighbors" ,k);
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  k = 10;
629s  a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  k = 10;
629s  a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  weights = ones (4,1);
629s  a = fitcknn (x, y, "Standardize", 1);
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.Standardize}, {true})
629s  assert ({a.Sigma}, {std(x, [], 1)})
629s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  weights = ones (4,1);
629s  a = fitcknn (x, y, "Standardize", false);
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.Standardize}, {false})
629s  assert ({a.Sigma}, {[]})
629s  assert ({a.Mu}, {[]})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  s = ones (1, 3);
629s  a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.DistParameter}, {s})
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
629s  assert (class (a), "ClassificationKNN");
629s  assert (a.DistParameter, 5)
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
629s                     "NSMethod", "exhaustive");
629s  assert (class (a), "ClassificationKNN");
629s  assert (a.DistParameter, 5)
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
629s  assert (class (a), "ClassificationKNN");
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
629s  assert ({a.BucketSize}, {20})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y, "IncludeTies", true);
629s  assert (class (a), "ClassificationKNN");
629s  assert (a.IncludeTies, true);
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y);
629s  assert (class (a), "ClassificationKNN");
629s  assert (a.IncludeTies, false);
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  a = fitcknn (x, y);
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Prior, [0.5; 0.5])
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  prior = [0.5; 0.5];
629s  a = fitcknn (x, y, "Prior", "empirical");
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Prior, prior)
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "a"; "b"];
629s  prior = [0.75; 0.25];
629s  a = fitcknn (x, y, "Prior", "empirical");
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Prior, prior)
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "a"; "b"];
629s  prior = [0.5; 0.5];
629s  a = fitcknn (x, y, "Prior", "uniform");
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Prior, prior)
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  cost = eye (2);
629s  a = fitcknn (x, y, "Cost", cost);
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Cost, [1, 0; 0, 1])
629s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  cost = eye (2);
629s  a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
629s  assert (class (a), "ClassificationKNN")
629s  assert (a.Cost, [1, 0; 0, 1])
629s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
629s  assert ({a.BucketSize}, {50})
629s ***** test
629s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
629s  y = ["a"; "a"; "b"; "b"];
629s  rand ("seed", 23);
629s  a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
629s  assert (class (a), "ClassificationPartitionedModel");
629s  assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
629s  assert (a.ModelParameters.NSMethod, "exhaustive")
629s  assert (a.ModelParameters.Distance, "euclidean")
629s  assert ({a.Trained{1}.BucketSize}, {50})
629s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
629s warning: called from
629s     cvpartition at line 764 column 19
629s     crossval at line 1701 column 9
629s     fitcknn at line 354 column 7
629s     __test__ at line 6 column 2
629s     test at line 685 column 11
629s     /tmp/tmp.vpN5xdrKMr at line 1414 column 2
629s 
629s ***** error<fitcknn: too few arguments.> fitcknn ()
629s ***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
629s ***** error<fitcknn: Name-Value arguments must be in pairs.>
629s  fitcknn (ones (4,2), ones (4, 1), "K")
629s ***** error<fitcknn: number of rows in X and Y must be equal.>
629s  fitcknn (ones (4,2), ones (3, 1))
629s ***** error<fitcknn: number of rows in X and Y must be equal.>
629s  fitcknn (ones (4,2), ones (3, 1), "K", 2)
629s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
629s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
629s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
629s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
629s ***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
629s  fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
629s 29 tests, 29 passed, 0 known failure, 0 skipped
629s [inst/ttest2.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ttest2.m
629s ***** test
629s  a = 1:5;
629s  b = 6:10;
629s  b(5) = NaN;
629s  [h,p,ci,stats] = ttest2 (a,b);
629s  assert (h, 1);
629s  assert (p, 0.002535996080258229, 1e-14);
629s  assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
629s  assert (stats.tstat, -4.582575694955839, 1e-14);
629s  assert (stats.df, 7);
629s  assert (stats.sd, 1.4638501094228, 1e-13);
629s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
629s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
629s 3 tests, 3 passed, 0 known failure, 0 skipped
629s [inst/grp2idx.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/grp2idx.m
629s ***** test
629s  in = [true false false true];
629s  out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
629s  assert (nthargout (1:3, @grp2idx, in), out)
629s  assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
629s ***** test
629s  assert (nthargout (1:3, @grp2idx, [false, true]),
629s          {[1; 2] {"0"; "1"} [false; true]});
629s  assert (nthargout (1:3, @grp2idx, [true, false]),
629s          {[1; 2] {"1"; "0"} [true; false]});
629s ***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
629s         {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
629s ***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
629s         {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
629s ***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
629s         {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
629s          [1; -3; -2; 2; -1; 3]});
629s ***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
629s         {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
629s ***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
629s         {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
629s ***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
629s         {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
629s ***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
629s         {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
629s ***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
629s         {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
629s 10 tests, 10 passed, 0 known failure, 0 skipped
629s [inst/ztest.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ztest.m
629s ***** error<ztest: too few input arguments.> ztest ();
629s ***** error<ztest: invalid value for standard deviation.> ...
629s  ztest ([1, 2, 3, 4], 2, -0.5);
629s ***** error<ztest: invalid VALUE for alpha.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
629s ***** error<ztest: invalid VALUE for alpha.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
629s ***** error<ztest: invalid VALUE for alpha.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
629s ***** error<ztest: invalid VALUE for tail.>  ...
629s  ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
629s ***** error<ztest: invalid VALUE for tail.>  ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
629s ***** error<ztest: invalid VALUE for operating dimension.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
629s ***** error<ztest: invalid VALUE for operating dimension.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
629s ***** error<ztest: invalid NAME for optional arguments.> ...
629s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
629s ***** test
629s  load carsmall
629s  [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
629s  assert (h, 0);
629s  assert (pval, 1, 1e-14);
629s  assert (ci, [22.094; 25.343], 1e-3);
629s ***** test
629s  load carsmall
629s  [h, pval, ci] = ztest (MPG, 26, 8);
629s  assert (h, 1);
629s  assert (pval, 0.00568359158544743, 1e-14);
629s  assert (ci, [22.101; 25.335], 1e-3);
629s ***** test
629s  load carsmall
629s  [h, pval, ci] = ztest (MPG, 26, 4);
629s  assert (h, 1);
629s  assert (pval, 3.184168011941316e-08, 1e-14);
629s  assert (ci, [22.909; 24.527], 1e-3);
629s ***** test
629s  x = normrnd (10, 2, 100, 1);
629s  [h, pval, ci] = ztest (x, 10, 2, "tail", "right");
629s  assert (isnan (pval), false);
629s  assert (pval >= 0 && pval <= 1, true);
629s ***** test
629s  x = normrnd (10, 2, 100, 1);
629s  [h, pval, ci] = ztest (x, 10, 2, "tail", "left");
629s  assert (isnan (pval), false);
629s  assert (pval >= 0 && pval <= 1, true);
629s ***** test
629s  load fisheriris;
629s  x = meas(:,1);
629s  m = 5.8;
629s  sigma = 0.8;
629s  [h, pval, ci] = ztest (x, m, sigma, "tail", "right");
629s  assert (h, 0)
629s  assert (pval, 0.2535, 1e-4)
629s  assert (ci, [5.7359; Inf], 1e-5)
629s ***** test
629s  load fisheriris;
629s  x = meas(:,1);
629s  m = 5.8;
629s  sigma = 0.8;
629s  [h, pval, ci] = ztest (x, m, sigma, "tail", "left");
629s  assert (h, 0)
629s  assert (pval, 0.7465, 1e-4)
629s  assert (ci, [-Inf; 5.9508], 1e-4)    
629s 17 tests, 17 passed, 0 known failure, 0 skipped
629s [inst/adtest.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/adtest.m
629s ***** error<Invalid call to adtest.  Correct usage is:> adtest ();
629s ***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
629s ***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
629s ***** error<adtest: invalid distribution family in char string.> ...
629s  adtest (ones (20,1), "Distribution", "normal");
629s ***** error<adtest: invalid distribution family in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"normal", 5, 3});
629s ***** error<adtest: invalid distribution parameters in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"norm", 5});
629s ***** error<adtest: invalid distribution parameters in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"exp", 5, 4});
629s ***** error<adtest: invalid distribution parameters in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"ev", 5});
629s ***** error<adtest: invalid distribution parameters in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
629s ***** error<adtest: invalid distribution parameters in cell array.> ...
629s  adtest (rand (20,1), "Distribution", {"Weibull", 5});
629s ***** error<adtest: invalid distribution option.> ...
629s  adtest (rand (20,1), "Distribution", 35);
629s ***** error<adtest: invalid Name argument.> ...
629s  adtest (rand (20,1), "Name", "norm");
629s ***** error<adtest: invalid Name argument.> ...
629s  adtest (rand (20,1), "Name", {"norm", 75, 10});
629s ***** error<adtest: asymptotic option is not valid for the composite> ...
629s  adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
629s ***** error<adtest: asymptotic option is not valid for the composite> ...
629s  adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
629s ***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
629s  adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
629s          "Asymptotic", true);
629s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
629s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
629s                                  "Alpha", 0.000000001);
629s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
629s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
629s                                  "Alpha", 0.999999999);
629s ***** error<adtest: not enough data for composite testing.> ...
629s  adtest (10);
629s ***** warning<adtest: out of range min p-value:> ...
629s  randn ("seed", 34);
629s  adtest (ones (20,1), "Alpha", 0.000001);
629s ***** warning<adtest: alpha not within the lookup table.> ...
629s  randn ("seed", 34);
629s  adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
629s ***** warning<adtest: alpha not within the lookup table.> ...
629s  randn ("seed", 34);
629s  adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
629s ***** test
629s  load examgrades
629s  x = grades(:,1);
629s  [h, pval, adstat, cv] = adtest (x);
629s  assert (h, false);
629s  assert (pval, 0.1854, 1e-4);
629s  assert (adstat, 0.5194, 1e-4);
629s  assert (cv, 0.7470, 1e-4);
629s ***** test
629s  load examgrades
629s  x = grades(:,1);
629s  [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
629s  assert (h, false);
629s  assert (pval, 0.071363, 1e-6);
629s ***** test
629s  load examgrades
629s  x = grades(:,1);
629s  [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
629s  assert (h, false);
629s  assert (pval, 0.4687, 1e-4);
629s 25 tests, 25 passed, 0 known failure, 0 skipped
629s [inst/logit.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/logit.m
629s ***** test
629s  p = [0.01:0.01:0.99];
629s  assert (logit (p), log (p ./ (1-p)), 25*eps);
629s ***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
629s ***** error logit ()
629s ***** error logit (1, 2)
629s 4 tests, 4 passed, 0 known failure, 0 skipped
629s [inst/bartlett_test.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/bartlett_test.m
629s ***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
629s ***** error<bartlett_test: invalid number of input arguments.> ...
629s  bartlett_test (1, 2, 3, 4);
629s ***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
629s ***** error<bartlett_test: GROUP and X mismatch.> ...
629s  bartlett_test (randn (50, 2), [1, 2, 3]);
629s ***** error<bartlett_test: GROUP and X mismatch.> ...
629s  bartlett_test (randn (50, 1), ones (55, 1));
629s ***** error<bartlett_test: invalid second input argument.> ...
629s  bartlett_test (randn (50, 1), ones (50, 2));
629s ***** error<bartlett_test: wrong value for alpha.> ...
629s  bartlett_test (randn (50, 2), [], 1.2);
629s ***** error<bartlett_test: wrong value for alpha.> ...
629s  bartlett_test (randn (50, 2), [], "alpha");
629s ***** error<bartlett_test: wrong value for alpha.> ...
629s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
629s ***** error<bartlett_test: wrong value for alpha.> ...
629s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
629s ***** warning<bartlett_test: GROUP> ...
629s  bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
629s ***** test
629s  load examgrades
629s  [h, pval, chisq, df] = bartlett_test (grades);
629s  assert (h, 1);
629s  assert (pval, 7.908647337018238e-08, 1e-14);
629s  assert (chisq, 38.73324, 1e-5);
629s  assert (df, 4);
629s ***** test
629s  load examgrades
629s  [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
629s  assert (h, 1);
629s  assert (pval, 0.01172, 1e-5);
629s  assert (chisq, 8.89274, 1e-5);
629s  assert (df, 2);
629s ***** test
629s  load examgrades
629s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
629s  assert (h, 0);
629s  assert (pval, 0.88118, 1e-5);
629s  assert (chisq, 0.02234, 1e-5);
629s  assert (df, 1);
629s ***** test
629s  load examgrades
629s  grades = [grades; nan(10, 5)];
629s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
629s  assert (h, 0);
629s  assert (pval, 0.88118, 1e-5);
629s  assert (chisq, 0.02234, 1e-5);
629s  assert (df, 1);
629s ***** test
629s  load examgrades
629s  [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
629s  assert (h, 0);
629s  assert (pval, 0.01791, 1e-5);
629s  assert (chisq, 5.60486, 1e-5);
629s  assert (df, 1);
629s 16 tests, 16 passed, 0 known failure, 0 skipped
629s [inst/mahal.m]
629s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/mahal.m
629s ***** error mahal ()
629s ***** error mahal (1, 2, 3)
629s ***** error mahal ("A", "B")
629s ***** error <must be numeric> mahal ([1, 2], ["A", "B"])
629s ***** error mahal (ones (2, 2, 2))
629s ***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
629s ***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
629s ***** test
629s  X = [1 0; 0 1; 1 1; 0 0];
629s  assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
629s  assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
629s ***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
629s 9 tests, 9 passed, 0 known failure, 0 skipped
629s [inst/hmmviterbi.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hmmviterbi.m
630s ***** test
630s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
630s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
630s  transprob = [0.8, 0.2; 0.4, 0.6];
630s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
630s  vpath = hmmviterbi (sequence, transprob, outprob);
630s  expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
630s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
630s  assert (vpath, expected);
630s ***** test
630s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
630s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
630s  transprob = [0.8, 0.2; 0.4, 0.6];
630s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
630s  symbols = {"A", "B", "C"};
630s  statenames = {"One", "Two"};
630s  vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
630s                                                    "statenames", statenames);
630s  expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
630s              "One", "One", "One", "One", "One", "One", "One", "Two", ...
630s              "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
630s  assert (vpath, expected);
630s 2 tests, 2 passed, 0 known failure, 0 skipped
630s [inst/grpstats.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/grpstats.m
630s ***** demo
630s  load carsmall;
630s  [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
630s  n = length(m);
630s  errorbar((1:n)',m,p(:,2)-m);
630s  set (gca, "xtick", 1:n, "xticklabel", g);
630s  title ("95% prediction intervals for mean weight by year");
630s ***** demo
630s  load carsmall;
630s  [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
630s                      {"mean", "meanci", "gname"}, 0.05)
630s  [c,r] = size (m);
630s  errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
630s  set (gca, "xtick", 1:c, "xticklabel", g);
630s  title ("95% prediction intervals for mean weight by year");
630s ***** test
630s  load carsmall
630s  means = grpstats (Acceleration, Origin);
630s  assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
630s ***** test
630s  load carsmall
630s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
630s  assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
630s  assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
630s ***** test
630s  load carsmall
630s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
630s  assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
630s ***** test
630s  load carsmall
630s  [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
630s                      {"mean", "meanci", "gname"}, 0.05);
630s  assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
630s                  [1e-14, 2e-14, 1e-14]');
630s ***** test
630s  [mC, g] = grpstats ([], []);
630s  assert (isempty (mC), true);
630s  assert (isempty (g), true);
630s ***** error<grpstats: X must be a vector or 2d matrix.> ...
630s  grpstats (ones (3, 3, 3));
630s ***** error<grpstats: samples in X and GROUPS mismatch.> ...
630s  grpstats ([], {'A'; 'B'; 'A'; 'B'})
630s ***** error<grpstats: missing VALUE for optional 'alpha' parameter.> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha");
630s ***** error<grpstats: invalid NAME for optional 'alpha' parameter.> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "somename", -0.1);
630s ***** error<grpstats: invalid fourth input argument.> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", {2, 3}, -0.1);
630s ***** error<grpstats: 'alpha' must be a real scalar in the range \(0,1\).> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha", -0.1);
630s ***** error<grpstats: inconsistent number of output arguments.> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, {'mean', 'sum'});
630s ***** error<grpstats: inconsistent number of output arguments.> ...
630s  [q, w] = grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'});
630s ***** error<grpstats: wrong whichstats option.> ...
630s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "whatever");
630s 14 tests, 14 passed, 0 known failure, 0 skipped
630s [inst/fitgmdist.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitgmdist.m
630s ***** demo
630s  ## Generate a two-cluster problem
630s  C1 = randn (100, 2) + 2;
630s  C2 = randn (100, 2) - 2;
630s  data = [C1; C2];
630s 
630s  ## Perform clustering
630s  GMModel = fitgmdist (data, 2);
630s 
630s  ## Plot the result
630s  figure
630s  [heights, bins] = hist3([C1; C2]);
630s  [xx, yy] = meshgrid(bins{1}, bins{2});
630s  bbins = [xx(:), yy(:)];
630s  contour (reshape (GMModel.pdf (bbins), size (heights)));
630s ***** demo
630s  Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
630s  nbOrientations = 2;
630s  initial_orientations = [38.0; 18.0];
630s  initial_weights = ones (1, nbOrientations) / nbOrientations;
630s  initial_Sigma = 10 * ones (1, 1, nbOrientations);
630s  start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
630s                  "ComponentProportion", initial_weights);
630s  GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
630s                             "RegularizationValue", 0.0001)
630s ***** test
630s  load fisheriris
630s  classes = unique (species);
630s  [~, score] = pca (meas, "NumComponents", 2);
630s  options.MaxIter = 1000;
630s  options.TolFun = 1e-6;
630s  options.Display = "off";
630s  GMModel = fitgmdist (score, 2, "Options", options);
630s  assert (isa (GMModel, "gmdistribution"), true);
630s  assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
630s 1 test, 1 passed, 0 known failure, 0 skipped
630s [inst/probit.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/probit.m
630s ***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
630s ***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
630s ***** error probit ()
630s ***** error probit (1, 2)
630s 4 tests, 4 passed, 0 known failure, 0 skipped
630s [inst/ecdf.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ecdf.m
630s ***** demo
630s  y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
630s  d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
630s  t = min (y, d);            ## we observe the minimum of these times
630s  censored = (y > d);        ## we also observe whether the subject failed
630s 
630s  ## Calculate and plot the empirical cdf and confidence bounds
630s  [f, x, flo, fup] = ecdf (t, "censoring", censored);
630s  stairs (x, f);
630s  hold on;
630s  stairs (x, flo, "r:"); stairs (x, fup, "r:");
630s 
630s  ## Superimpose a plot of the known true cdf
630s  xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
630s  hold off;
630s ***** demo
630s  R = wblrnd (100, 2, 100, 1);
630s  ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
630s  hold on
630s  x = 1:1:250;
630s  wblsurv = 1 - cdf ("weibull", x, 100, 2);
630s  plot (x, wblsurv, "g-", "LineWidth", 2)
630s  legend ("Empirical survivor function", "Lower confidence bound", ...
630s          "Upper confidence bound", "Weibull survivor function", ...
630s          "Location", "northeast");
630s  hold off
630s ***** error ecdf ();
630s ***** error ecdf (randi (15,2));
630s ***** error ecdf ([3,2,4,3+2i,5]);
630s ***** error kstest ([2,3,4,5,6],"tail");
630s ***** error kstest ([2,3,4,5,6],"tail", "whatever");
630s ***** error kstest ([2,3,4,5,6],"function", "");
630s ***** error kstest ([2,3,4,5,6],"badoption", 0.51);
630s ***** error kstest ([2,3,4,5,6],"tail", 0);
630s ***** error kstest ([2,3,4,5,6],"alpha", 0);
630s ***** error kstest ([2,3,4,5,6],"alpha", NaN);
630s ***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
630s ***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
630s ***** test
630s  hf = figure ("visible", "off");
630s  unwind_protect
630s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
630s    [F, x, Flo, Fup] = ecdf (x);
630s    F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
630s    assert (F, F_out, ones (10,1) * 1e-4);
630s    x_out = [0 0 2 3 4 5 6 7 8 9]';
630s    assert (x, x_out);
630s    Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
630s    assert (Flo, Flo_out, ones (10,1) * 1e-4);
630s    Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
630s    assert (Fup, Fup_out, ones (10,1) * 1e-4);
630s  unwind_protect_cleanup
630s    close (hf);
630s  end_unwind_protect
630s ***** test
630s  hf = figure ("visible", "off");
630s  unwind_protect
630s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
630s    ecdf (x);
630s  unwind_protect_cleanup
630s    close (hf);
630s  end_unwind_protect
630s 14 tests, 14 passed, 0 known failure, 0 skipped
630s [inst/stepwisefit.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/stepwisefit.m
630s ***** test
630s  % Sample data from Draper and Smith (n = 13, k = 4)
630s  X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
630s      26 29 56 31 52 55 71 31 54 47 40 66 68; ...
630s      6 15 8 8 6 9 17 22 18 4 23 9 8; ...
630s      60 52 20 47 33 22 6 44 22 26 34 12 12]';
630s  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]';
630s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
630s  assert(X_use, [4 1])
630s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
630s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
630s  assert(X_use, [4 1])
630s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
630s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
630s  assert(X_use, [4 1])
630s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
630s 1 test, 1 passed, 0 known failure, 0 skipped
630s [inst/fitcsvm.m]
630s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitcsvm.m
630s ***** demo
630s  ## Use a subset of Fisher's iris data set
630s 
630s  load fisheriris
630s  inds = ! strcmp (species, 'setosa');
630s  X = meas(inds, [3,4]);
630s  Y = species(inds);
630s 
630s  ## Train a linear SVM classifier
630s  SVMModel = fitcsvm (X, Y)
630s 
630s  ## Plot a scatter diagram of the data and circle the support vectors.
630s  sv = SVMModel.SupportVectors;
630s  figure
630s  gscatter (X(:,1), X(:,2), Y)
630s  hold on
630s  plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
630s  legend ('versicolor', 'virginica', 'Support Vector')
630s  hold off
630s ***** test
630s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
630s  y = {"a"; "a"; "b"; "b"};
630s  a = fitcsvm (x, y);
630s  assert (class (a), "ClassificationSVM");
630s  assert ({a.X, a.Y}, {x, y})
630s  assert (a.NumObservations, 4)
630s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
630s  assert (a.ModelParameters.SVMtype, "c_svc")
630s  assert (a.ClassNames, {"a"; "b"})
630s ***** test
630s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
630s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
630s  a = fitcsvm (x, y);
630s  assert (class (a), "ClassificationSVM");
630s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
630s  assert (a.ModelParameters.BoxConstraint, 1)
630s  assert (a.ModelParameters.KernelOffset, 0)
630s  assert (a.ClassNames, [1; -1])
630s ***** test
630s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
630s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
630s  a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
630s  "KernelOffset", 2);
630s  assert (class (a), "ClassificationSVM");
630s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
630s  assert (a.ModelParameters.BoxConstraint, 2)
630s  assert (a.ModelParameters.KernelOffset, 2)
630s  assert (isempty (a.Alpha), true)
630s  assert (isempty (a.Beta), false)
630s ***** test
630s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
630s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
630s  a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
630s  assert (class (a), "ClassificationSVM");
630s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
630s  assert (a.ModelParameters.PolynomialOrder, 3)
630s  assert (isempty (a.Alpha), true)
630s  assert (isempty (a.Beta), false)
630s ***** test
630s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
630s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
630s  a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
630s  assert (class (a), "ClassificationSVM");
630s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
630s  assert (a.ModelParameters.PolynomialOrder, 3)
630s  assert (isempty (a.Alpha), false)
630s  assert (isempty (a.Beta), true)
630s ***** test
630s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
630s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
630s  a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
630s  assert (class (a), "ClassificationPartitionedModel");
630s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
630s  assert (a.ModelParameters.PolynomialOrder, 3)
630s  assert (isempty (a.Trained{1}.Alpha), false)
630s  assert (isempty (a.Trained{1}.Beta), true)
630s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
630s warning: called from
630s     cvpartition at line 764 column 19
630s     crossval at line 1300 column 9
630s     fitcsvm at line 284 column 7
630s     __test__ at line 5 column 2
630s     test at line 685 column 11
630s     /tmp/tmp.vpN5xdrKMr at line 1526 column 2
630s 
631s ***** error<fitcsvm: too few arguments.> fitcsvm ()
631s ***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
631s ***** error<fitcsvm: Name-Value arguments must be in pairs.>
631s  fitcsvm (ones (4,2), ones (4, 1), 'KFold')
631s ***** error<fitcsvm: number of rows in X and Y must be equal.>
631s  fitcsvm (ones (4,2), ones (3, 1))
631s ***** error<fitcsvm: number of rows in X and Y must be equal.>
631s  fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
631s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
631s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
631s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
631s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
631s ***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
631s  fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
631s 14 tests, 14 passed, 0 known failure, 0 skipped
631s [inst/multcompare.m]
631s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/multcompare.m
631s ***** demo
631s 
631s  ## Demonstration using balanced one-way ANOVA from anova1
631s 
631s  x = ones (50, 4) .* [-2, 0, 1, 5];
631s  randn ("seed", 1);    # for reproducibility
631s  x = x + normrnd (0, 2, 50, 4);
631s  groups = {"A", "B", "C", "D"};
631s  [p, tbl, stats] = anova1 (x, groups, "off");
631s  multcompare (stats);
631s ***** demo
631s 
631s  ## Demonstration using unbalanced one-way ANOVA example from anovan
631s 
631s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
631s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
631s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
631s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
631s         25.694 ]';
631s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
631s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
631s 
631s  [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
631s                                   "ControlGroup", 1, "display", "on")
631s 
631s ***** demo
631s 
631s  ## Demonstration using factorial ANCOVA example from anovan
631s 
631s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
631s  97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
631s  81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
631s  84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
631s  100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
631s  91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
631s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
631s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
631s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
631s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
631s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
631s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
631s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
631s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
631s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
631s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
631s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
631s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
631s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
631s  58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
631s  75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
631s 
631s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
631s                             [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
631s                             "sstype", "h", "display", "off", "contrasts", ...
631s                             {"simple","poly",""});
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
631s                                   "display", "on")
631s 
631s ***** demo
631s 
631s  ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
631s  ## squares to account for heteroskedasticity.
631s 
631s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
631s       2, 2, 2, 2, 2, 2, 2, 2, ...
631s       3, 3, 3, 3, 3, 3, 3, 3]';
631s 
631s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
631s       10, 25, 66, 43, 47, 56,  6, 39, ...
631s       11, 39, 26, 35, 25, 14, 24, 17]';
631s 
631s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
631s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
631s  b = polyfit (fitted, abs (STATS.resid), 1);
631s  v = polyval (b, fitted);  # Variance as a function of the fitted values
631s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
631s  [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
631s ***** demo
631s 
631s  ## Demonstration of p-value adjustments to control the false discovery rate
631s  ## Data from Westfall (1997) JASA. 92(437):299-306
631s 
631s  p = [.005708; .023544; .024193; .044895; ...
631s        .048805; .221227; .395867; .693051; .775755];
631s 
631s  padj = multcompare(p,'ctype','fdr')
631s ***** test
631s 
631s  ## Tests using unbalanced one-way ANOVA example from anovan and anova1
631s 
631s  ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"
631s 
631s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
631s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
631s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
631s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
631s         25.694 ]';
631s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
631s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
631s 
631s  [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
631s                                   "display", "off");
631s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
631s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
631s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
631s  assert (C(5,6), 0.546591417210693, 1e-09);
631s  assert (C(6,6), 0.0845897945254446, 1e-09);
631s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
631s  assert (C(8,6), 0.188873478781067, 1e-09);
631s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
631s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
631s  assert (M(1,1), 10, 1e-09);
631s  assert (M(2,1), 18, 1e-09);
631s  assert (M(3,1), 19, 1e-09);
631s  assert (M(4,1), 21.0001428571429, 1e-09);
631s  assert (M(5,1), 29.0001111111111, 1e-09);
631s  assert (M(1,2), 1.0177537954095, 1e-09);
631s  assert (M(2,2), 1.28736803631001, 1e-09);
631s  assert (M(3,2), 1.0177537954095, 1e-09);
631s  assert (M(4,2), 1.0880245732889, 1e-09);
631s  assert (M(5,2), 0.959547480416536, 1e-09);
631s 
631s  ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
631s                                   "display", "off");
631s  assert (C(1,6), 4.08303457454140e-05, 1e-09);
631s  assert (C(2,6), 1.04587348240817e-06, 1e-09);
631s  assert (C(3,6), 1.06397381604573e-07, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
631s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
631s  assert (C(6,6), 1.05737243156806e-01, 1e-09);
631s  assert (C(7,6), 2.36859139493832e-07, 1e-09);
631s  assert (C(8,6), 2.09859420867852e-01, 1e-09);
631s  assert (C(9,6), 1.36324670121399e-07, 1e-09);
631s  assert (C(10,6), 7.40712246958735e-06, 1e-09);
631s 
631s  ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
631s                                   "display", "off");
631s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
631s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
631s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
631s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
631s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
631s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
631s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
631s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
631s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
631s 
631s  ## Compare "holm" adjusted p-values to those obtained using p.adjust in R
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
631s                                   "display", "off");
631s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
631s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
631s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
631s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
631s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
631s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
631s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
631s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
631s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
631s 
631s  ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
631s                                   "display", "off");
631s  assert (C(1,6), 0.00108105386141085, 1e-09);
631s  assert (C(2,6), 2.7779386789517e-05, 1e-09);
631s  assert (C(3,6), 1.3599854038198e-06, 1e-09);
631s  assert (C(4,6), 7.58830197867751e-13, 1e-09);
631s  assert (C(5,6), 0.984039948220281, 1e-09);
631s  assert (C(6,6), 0.539077018557706, 1e-09);
631s  assert (C(7,6), 5.59475764460574e-06, 1e-09);
631s  assert (C(8,6), 0.771173490574105, 1e-09);
631s  assert (C(9,6), 2.52838425729905e-06, 1e-09);
631s  assert (C(10,6), 0.000200719143889168, 1e-09);
631s 
631s  ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R
631s 
631s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
631s                                   "display", "off");
631s  assert (C(1,6), 2.85812420217898e-04, 1e-09);
631s  assert (C(2,6), 5.22936741204085e-06, 1e-09);
631s  assert (C(3,6), 2.12794763209146e-07, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
631s  assert (C(5,6), 1.00000000000000e+00, 1e-09);
631s  assert (C(6,6), 8.45897945254446e-01, 1e-09);
631s  assert (C(7,6), 9.47436557975328e-07, 1e-09);
631s  assert (C(8,6), 1.00000000000000e+00, 1e-09);
631s  assert (C(9,6), 4.08974010364197e-07, 1e-09);
631s  assert (C(10,6), 4.44427348175241e-05, 1e-09);
631s 
631s  ## Test for anova1 ("equal")- comparison of results from Matlab
631s 
631s  [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
631s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
631s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
631s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
631s  assert (C(5,6), 0.546591417210693, 1e-09);
631s  assert (C(6,6), 0.0845897945254446, 1e-09);
631s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
631s  assert (C(8,6), 0.188873478781067, 1e-09);
631s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
631s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
631s  assert (M(1,1), 10, 1e-09);
631s  assert (M(2,1), 18, 1e-09);
631s  assert (M(3,1), 19, 1e-09);
631s  assert (M(4,1), 21.0001428571429, 1e-09);
631s  assert (M(5,1), 29.0001111111111, 1e-09);
631s  assert (M(1,2), 1.0177537954095, 1e-09);
631s  assert (M(2,2), 1.28736803631001, 1e-09);
631s  assert (M(3,2), 1.0177537954095, 1e-09);
631s  assert (M(4,2), 1.0880245732889, 1e-09);
631s  assert (M(5,2), 0.959547480416536, 1e-09);
631s 
631s  ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
631s  [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
631s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 0.001247025266382, 1e-09);
631s  assert (C(2,6), 0.000018037115146, 1e-09);
631s  assert (C(3,6), 0.000002974595187, 1e-09);
631s  assert (C(4,6), 0.000000000786046, 1e-09);
631s  assert (C(5,6), 0.5693192886650109, 1e-09);
631s  assert (C(6,6), 0.110501699029776, 1e-09);
631s  assert (C(7,6), 0.000131226488700, 1e-09);
631s  assert (C(8,6), 0.1912101409715992, 1e-09);
631s  assert (C(9,6), 0.000005385256394, 1e-09);
631s  assert (C(10,6), 0.000074089106171, 1e-09);
631s ***** test
631s 
631s  ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
631s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
631s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
631s  [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
631s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
631s                                   "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 1.49311100811177e-05, 1e-09);
631s  assert (C(2,6), 2.20506904243535e-07, 1e-09);
631s  assert (C(3,6), 0.00449897860490058, 1e-09);
631s  assert (M(1,1), 6.25, 1e-09);
631s  assert (M(2,1), 4.75, 1e-09);
631s  assert (M(3,1), 4, 1e-09);
631s  assert (M(1,2), 0.152145154862547, 1e-09);
631s  assert (M(2,2), 0.152145154862547, 1e-09);
631s  assert (M(3,2), 0.152145154862547, 1e-09);
631s ***** test
631s 
631s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
631s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
631s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
631s  [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
631s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
631s                                   "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 0.000020799832702, 1e-09);
631s  assert (C(2,6), 0.000000035812410, 1e-09);
631s  assert (C(3,6), 0.003038942449215, 1e-09);
631s ***** test
631s 
631s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
631s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
631s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
631s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
631s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
631s  [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
631s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
631s                                   "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 0.261031111511073, 1e-09);
631s  assert (C(2,6), 0.065879755907745, 1e-09);
631s  assert (C(3,6), 0.241874613529270, 1e-09);
631s ***** shared visibility_setting
631s  visibility_setting = get (0, "DefaultFigureVisible");
631s ***** test
631s  set (0, "DefaultFigureVisible", "off");
631s 
631s  ## Test for kruskalwallis - comparison with results from MATLAB
631s  data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
631s          4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
631s  group = [1:3] .* ones (10,3);
631s  [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
631s  C = multcompare (STATS, "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 0.000163089828959986, 1e-09);
631s  assert (C(2,6), 0.630298044801257, 1e-09);
631s  assert (C(3,6), 0.00100567660695682, 1e-09);
631s  C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
631s  assert (C(1,6), 0.000489269486879958, 1e-09);
631s  assert (C(2,6), 1, 1e-09);
631s  assert (C(3,6), 0.00301702982087047, 1e-09);
631s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
631s  assert (C(1,6), 0.000819054880289573, 1e-09);
631s  assert (C(2,6), 0.890628039849261, 1e-09);
631s  assert (C(3,6), 0.00447816059021654, 1e-09);
631s  set (0, "DefaultFigureVisible", visibility_setting);
631s ***** test
631s  set (0, "DefaultFigureVisible", "off");
631s  ## Test for friedman - comparison with results from MATLAB
631s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
631s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
631s  [P, ATAB, STATS] = friedman (popcorn, 3, "off");
631s  C = multcompare(STATS, "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 0.227424558028569, 1e-09);
631s  assert (C(2,6), 0.0327204848315735, 1e-09);
631s  assert (C(3,6), 0.353160353315988, 1e-09);
631s  C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
631s  assert (C(1,6), 0.682273674085708, 1e-09);
631s  assert (C(2,6), 0.0981614544947206, 1e-09);
631s  assert (C(3,6), 1, 1e-09);
631s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
631s  assert (C(1,6), 0.482657360384373, 1e-09);
631s  assert (C(2,6), 0.102266573027672, 1e-09);
631s  assert (C(3,6), 0.649836502233148, 1e-09);
631s  set (0, "DefaultFigureVisible", visibility_setting);
631s ***** test
631s  set (0, "DefaultFigureVisible", "off");
631s  ## Test for fitlm - same comparisons as for first anovan example
631s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
631s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
631s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
631s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
631s         25.694 ]';
631s  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]';
631s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
631s                       "contrasts","simple");
631s  [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
631s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
631s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
631s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
631s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
631s  assert (C(5,6), 0.546591417210693, 1e-09);
631s  assert (C(6,6), 0.0845897945254446, 1e-09);
631s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
631s  assert (C(8,6), 0.188873478781067, 1e-09);
631s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
631s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
631s  assert (M(1,1), 10, 1e-09);
631s  assert (M(2,1), 18, 1e-09);
631s  assert (M(3,1), 19, 1e-09);
631s  assert (M(4,1), 21.0001428571429, 1e-09);
631s  assert (M(5,1), 29.0001111111111, 1e-09);
631s  assert (M(1,2), 1.0177537954095, 1e-09);
631s  assert (M(2,2), 1.28736803631001, 1e-09);
631s  assert (M(3,2), 1.0177537954095, 1e-09);
631s  assert (M(4,2), 1.0880245732889, 1e-09);
631s  assert (M(5,2), 0.959547480416536, 1e-09);
631s  set (0, "DefaultFigureVisible", visibility_setting);
631s ***** test
631s  ## Test p-value adjustments compared to R stats package function p.adjust
631s  ## Data from Westfall (1997) JASA. 92(437):299-306
631s  p = [.005708; .023544; .024193; .044895; ...
631s        .048805; .221227; .395867; .693051; .775755];
631s  padj = multcompare (p);
631s  assert (padj(1), 0.051372, 1e-06);
631s  assert (padj(2), 0.188352, 1e-06);
631s  assert (padj(3), 0.188352, 1e-06);
631s  assert (padj(4), 0.269370, 1e-06);
631s  assert (padj(5), 0.269370, 1e-06);
631s  assert (padj(6), 0.884908, 1e-06);
631s  assert (padj(7), 1.000000, 1e-06);
631s  assert (padj(8), 1.000000, 1e-06);
631s  assert (padj(9), 1.000000, 1e-06);
631s  padj = multcompare(p,'ctype','holm');
631s  assert (padj(1), 0.051372, 1e-06);
631s  assert (padj(2), 0.188352, 1e-06);
631s  assert (padj(3), 0.188352, 1e-06);
631s  assert (padj(4), 0.269370, 1e-06);
631s  assert (padj(5), 0.269370, 1e-06);
631s  assert (padj(6), 0.884908, 1e-06);
631s  assert (padj(7), 1.000000, 1e-06);
631s  assert (padj(8), 1.000000, 1e-06);
631s  assert (padj(9), 1.000000, 1e-06);
631s  padj = multcompare(p,'ctype','hochberg');
631s  assert (padj(1), 0.051372, 1e-06);
631s  assert (padj(2), 0.169351, 1e-06);
631s  assert (padj(3), 0.169351, 1e-06);
631s  assert (padj(4), 0.244025, 1e-06);
631s  assert (padj(5), 0.244025, 1e-06);
631s  assert (padj(6), 0.775755, 1e-06);
631s  assert (padj(7), 0.775755, 1e-06);
631s  assert (padj(8), 0.775755, 1e-06);
631s  assert (padj(9), 0.775755, 1e-06);
631s  padj = multcompare(p,'ctype','fdr');
631s  assert (padj(1), 0.0513720, 1e-07);
631s  assert (padj(2), 0.0725790, 1e-07);
631s  assert (padj(3), 0.0725790, 1e-07);
631s  assert (padj(4), 0.0878490, 1e-07);
631s  assert (padj(5), 0.0878490, 1e-07);
631s  assert (padj(6), 0.3318405, 1e-07);
631s  assert (padj(7), 0.5089719, 1e-07);
631s  assert (padj(8), 0.7757550, 1e-07);
631s  assert (padj(9), 0.7757550, 1e-07);
631s 8 tests, 8 passed, 0 known failure, 0 skipped
631s [inst/ff2n.m]
631s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ff2n.m
631s ***** error ff2n ();
631s ***** error ff2n (2, 5);
631s ***** error ff2n (2.5);
631s ***** error ff2n (0);
631s ***** error ff2n (-3);
631s ***** error ff2n (3+2i);
631s ***** error ff2n (Inf);
631s ***** error ff2n (NaN);
631s ***** test
631s  A = ff2n (3);
631s  assert (A, [0, 0, 0; 0, 0, 1; 0, 1, 0; 0, 1, 1; ...
631s              1, 0, 0; 1, 0, 1; 1, 1, 0; 1, 1, 1]);
631s ***** test
631s  A = ff2n (2);
631s  assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
631s 10 tests, 10 passed, 0 known failure, 0 skipped
631s [inst/friedman.m]
631s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/friedman.m
631s ***** demo
631s  load popcorn;
631s  friedman (popcorn, 3);
631s ***** demo
631s  load popcorn;
631s  [p, atab] = friedman (popcorn, 3, "off");
631s  disp (p);
631s ***** test
631s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
631s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
631s  [p, atab] = friedman (popcorn, 3, "off");
631s  assert (p, 0.001028853354594794, 1e-14);
631s  assert (atab{2,2}, 99.75, 1e-14);
631s  assert (atab{2,3}, 2, 0);
631s  assert (atab{2,4}, 49.875, 1e-14);
631s  assert (atab{2,5}, 13.75862068965517, 1e-14);
631s  assert (atab{2,6}, 0.001028853354594794, 1e-14);
631s  assert (atab{3,2}, 0.08333333333333215, 1e-14);
631s  assert (atab{3,4}, 0.04166666666666607, 1e-14);
631s  assert (atab{4,3}, 12, 0);
631s ***** test
631s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
631s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
631s  [p, atab, stats] = friedman (popcorn, 3, "off");
631s  assert (atab{5,2}, 116, 0);
631s  assert (atab{5,3}, 17, 0);
631s  assert (stats.source, "friedman");
631s  assert (stats.n, 2);
631s  assert (stats.meanranks, [8, 4.75, 2.25], 0);
631s  assert (stats.sigma, 2.692582403567252, 1e-14);
631s 2 tests, 2 passed, 0 known failure, 0 skipped
631s [inst/optimalleaforder.m]
631s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/optimalleaforder.m
631s ***** demo
631s  randn ("seed", 5)  # for reproducibility
631s  X = randn (10, 2);
631s  D = pdist (X);
631s  tree = linkage(D, 'average');
631s  optimalleaforder (tree, D, 'Transformation', 'linear')
631s ***** error optimalleaforder ()
631s ***** error optimalleaforder (1)
631s ***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
631s ***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
631s ***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
631s ***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
631s ***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
631s 7 tests, 7 passed, 0 known failure, 0 skipped
631s [inst/dist_fun/geornd.m]
631s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/geornd.m
631s ***** assert (size (geornd (0.5)), [1, 1])
631s ***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
631s ***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
631s ***** assert (size (geornd (0.5, 3)), [3, 3])
631s ***** assert (size (geornd (0.5, [4 1])), [4, 1])
631s ***** assert (size (geornd (0.5, 4, 1)), [4, 1])
631s ***** assert (class (geornd (0.5)), "double")
631s ***** assert (class (geornd (single (0.5))), "single")
632s ***** assert (class (geornd (single ([0.5 0.5]))), "single")
632s ***** assert (class (geornd (single (0))), "single")
632s ***** assert (class (geornd (single (1))), "single")
632s ***** error<geornd: function called with too few input arguments.> geornd ()
632s ***** error<geornd: PS must not be complex.> geornd (i)
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (1, -1)
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (1, 1.2)
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (1, ones (2))
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (1, [2 -1 2])
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (1, [2 0 2.5])
632s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  geornd (ones (2), ones (2))
632s ***** error<geornd: dimensions must be non-negative integers.> ...
632s  geornd (1, 2, -1, 5)
632s ***** error<geornd: dimensions must be non-negative integers.> ...
632s  geornd (1, 2, 1.5, 5)
632s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
632s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
632s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
632s 24 tests, 24 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/evrnd.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/evrnd.m
632s ***** assert (size (evrnd (1, 1)), [1 1])
632s ***** assert (size (evrnd (1, ones (2,1))), [2, 1])
632s ***** assert (size (evrnd (1, ones (2,2))), [2, 2])
632s ***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
632s ***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
632s ***** assert (size (evrnd (1, 1, 3)), [3, 3])
632s ***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
632s ***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
632s ***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
632s ***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
632s ***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
632s ***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
632s ***** assert (class (evrnd (1, 1)), "double")
632s ***** assert (class (evrnd (1, single (1))), "single")
632s ***** assert (class (evrnd (1, single ([1, 1]))), "single")
632s ***** assert (class (evrnd (single (1), 1)), "single")
632s ***** assert (class (evrnd (single ([1, 1]), 1)), "single")
632s ***** error<evrnd: function called with too few input arguments.> evrnd ()
632s ***** error<evrnd: function called with too few input arguments.> evrnd (1)
632s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
632s  evrnd (ones (3), ones (2))
632s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
632s  evrnd (ones (2), ones (3))
632s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
632s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
632s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  evrnd (1, 2, -1)
632s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  evrnd (1, 2, 1.2)
632s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  evrnd (1, 2, ones (2))
632s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  evrnd (1, 2, [2 -1 2])
632s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  evrnd (1, 2, [2 0 2.5])
632s ***** error<evrnd: dimensions must be non-negative integers.> ...
632s  evrnd (1, 2, 2, -1, 5)
632s ***** error<evrnd: dimensions must be non-negative integers.> ...
632s  evrnd (1, 2, 2, 1.5, 5)
632s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
632s  evrnd (2, ones (2), 3)
632s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
632s  evrnd (2, ones (2), [3, 2])
632s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
632s  evrnd (2, ones (2), 3, 2)
632s 33 tests, 33 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/exppdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/exppdf.m
632s ***** demo
632s  ## Plot various PDFs from the exponential distribution
632s  x = 0:0.01:5;
632s  y1 = exppdf (x, 2/3);
632s  y2 = exppdf (x, 1.0);
632s  y3 = exppdf (x, 2.0);
632s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
632s  grid on
632s  ylim ([0, 1.5])
632s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
632s  title ("Exponential PDF")
632s  xlabel ("values in x")
632s  ylabel ("density")
632s ***** shared x,y
632s  x = [-1 0 0.5 1 Inf];
632s  y = gampdf (x, 1, 2);
632s ***** assert (exppdf (x, 2*ones (1,5)), y)
632s ***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
632s ***** assert (exppdf ([x, NaN], 2), [y, NaN])
632s ***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
632s ***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
632s ***** error<exppdf: function called with too few input arguments.> exppdf ()
632s ***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
632s ***** error<exppdf: X and MU must be of common size or scalars.> ...
632s  exppdf (ones (3), ones (2))
632s ***** error<exppdf: X and MU must be of common size or scalars.> ...
632s  exppdf (ones (2), ones (3))
632s ***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
632s ***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
632s 11 tests, 11 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/laplacernd.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/laplacernd.m
632s ***** assert (size (laplacernd (1, 1)), [1 1])
632s ***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
632s ***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
632s ***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
632s ***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
632s ***** assert (size (laplacernd (1, 1, 3)), [3, 3])
632s ***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
632s ***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
632s ***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
632s ***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
632s ***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
632s ***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
632s ***** assert (class (laplacernd (1, 1)), "double")
632s ***** assert (class (laplacernd (1, single (1))), "single")
632s ***** assert (class (laplacernd (1, single ([1, 1]))), "single")
632s ***** assert (class (laplacernd (single (1), 1)), "single")
632s ***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
632s ***** error<laplacernd: function called with too few input arguments.> laplacernd ()
632s ***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
632s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
632s  laplacernd (ones (3), ones (2))
632s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
632s  laplacernd (ones (2), ones (3))
632s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
632s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
632s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  laplacernd (1, 2, -1)
632s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  laplacernd (1, 2, 1.2)
632s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  laplacernd (1, 2, ones (2))
632s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  laplacernd (1, 2, [2 -1 2])
632s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  laplacernd (1, 2, [2 0 2.5])
632s ***** error<laplacernd: dimensions must be non-negative integers.> ...
632s  laplacernd (1, 2, 2, -1, 5)
632s ***** error<laplacernd: dimensions must be non-negative integers.> ...
632s  laplacernd (1, 2, 2, 1.5, 5)
632s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
632s  laplacernd (2, ones (2), 3)
632s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
632s  laplacernd (2, ones (2), [3, 2])
632s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
632s  laplacernd (2, ones (2), 3, 2)
632s 33 tests, 33 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/copulacdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/copulacdf.m
632s ***** test
632s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
632s  theta = [1; 2];
632s  p = copulacdf ("Clayton", x, theta);
632s  expected_p = [0.1395; 0.1767];
632s  assert (p, expected_p, 0.001);
632s ***** test
632s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
632s  p = copulacdf ("Gumbel", x, 2);
632s  expected_p = [0.1464; 0.1464];
632s  assert (p, expected_p, 0.001);
632s ***** test
632s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
632s  theta = [1; 2];
632s  p = copulacdf ("Frank", x, theta);
632s  expected_p = [0.0699; 0.0930];
632s  assert (p, expected_p, 0.001);
632s ***** test
632s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
632s  theta = [0.3; 0.7];
632s  p = copulacdf ("AMH", x, theta);
632s  expected_p = [0.0629; 0.0959];
632s  assert (p, expected_p, 0.001);
632s ***** test
632s  x = [0.2:0.2:0.6; 0.2:0.1:0.4];
632s  theta = [0.2, 0.1, 0.1, 0.05];
632s  p = copulacdf ("FGM", x, theta);
632s  expected_p = [0.0558; 0.0293];
632s  assert (p, expected_p, 0.001);
632s 5 tests, 5 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/lognpdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/lognpdf.m
632s ***** demo
632s  ## Plot various PDFs from the log-normal distribution
632s  x = 0:0.01:5;
632s  y1 = lognpdf (x, 0, 1);
632s  y2 = lognpdf (x, 0, 0.5);
632s  y3 = lognpdf (x, 0, 0.25);
632s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
632s  grid on
632s  ylim ([0, 2])
632s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
632s           "location", "northeast")
632s  title ("Log-normal PDF")
632s  xlabel ("values in x")
632s  ylabel ("density")
632s ***** shared x, y
632s  x = [-1 0 e Inf];
632s  y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
632s ***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
632s ***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
632s ***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
632s ***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
632s ***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
632s ***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
632s ***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
632s ***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
632s ***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
632s ***** error lognpdf ()
632s ***** error lognpdf (1,2,3,4)
632s ***** error lognpdf (ones (3), ones (2), ones (2))
632s ***** error lognpdf (ones (2), ones (3), ones (2))
632s ***** error lognpdf (ones (2), ones (2), ones (3))
632s ***** error lognpdf (i, 2, 2)
632s ***** error lognpdf (2, i, 2)
632s ***** error lognpdf (2, 2, i)
632s 17 tests, 17 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/invgpdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/invgpdf.m
632s ***** demo
632s  ## Plot various PDFs from the inverse Gaussian distribution
632s  x = 0:0.001:3;
632s  y1 = invgpdf (x, 1, 0.2);
632s  y2 = invgpdf (x, 1, 1);
632s  y3 = invgpdf (x, 1, 3);
632s  y4 = invgpdf (x, 3, 0.2);
632s  y5 = invgpdf (x, 3, 1);
632s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
632s  grid on
632s  xlim ([0, 3])
632s  ylim ([0, 3])
632s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
632s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
632s  title ("Inverse Gaussian PDF")
632s  xlabel ("values in x")
632s  ylabel ("density")
632s ***** shared x, y
632s  x = [-Inf, -1, 0, 1/2, 1, Inf];
632s  y = [0, 0, 0, 0.8788, 0.3989, 0];
632s ***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
632s ***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
632s ***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
632s ***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
632s ***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
632s ***** error<invgpdf: function called with too few input arguments.> invgpdf ()
632s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
632s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
632s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
632s  invgpdf (1, ones (2), ones (3))
632s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
632s  invgpdf (ones (2), 1, ones (3))
632s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
632s  invgpdf (ones (2), ones (3), 1)
632s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
632s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
632s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
632s 14 tests, 14 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/fpdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/fpdf.m
632s ***** demo
632s  ## Plot various PDFs from the F distribution
632s  x = 0.01:0.01:4;
632s  y1 = fpdf (x, 1, 1);
632s  y2 = fpdf (x, 2, 1);
632s  y3 = fpdf (x, 5, 2);
632s  y4 = fpdf (x, 10, 1);
632s  y5 = fpdf (x, 100, 100);
632s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
632s  grid on
632s  ylim ([0, 2.5])
632s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
632s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
632s           "df1 = 100, df2 = 100"}, "location", "northeast")
632s  title ("F PDF")
632s  xlabel ("values in x")
632s  ylabel ("density")
632s ***** shared x, y
632s  x = [-1, 0, 0.5, 1, 2];
632s  y = [0, 0, 4/9, 1/4, 1/9];
632s ***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
632s ***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
632s ***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
632s ***** assert (fpdf (x, [0, NaN, Inf, 2, 2], 2), [NaN, NaN, 0.5413, y(4:5)], 1e-4)
632s ***** assert (fpdf (x, 2, [0, NaN, Inf, 2, 2]), [NaN, NaN, 0.6065, y(4:5)], 1e-4)
632s ***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
632s ***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
632s  rand ("seed", 1234);    # for reproducibility
632s  xr = rand (10,1);
632s  xr = xr(x > 0.1 & x < 0.9);
632s  yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
632s  assert (fpdf (xr, 1, 2), yr, 5*eps);
632s ***** test
632s  yy = fpdf (2, 4, Inf);
632s  assert (yy, 0.1465, 1e-4)
632s ***** test
632s  yy = fpdf (2, 4, 1000000000000000);
632s  assert (yy, 0.1465, 1e-4)
632s ***** test
632s  yy = fpdf (2, Inf, 4);
632s  assert (yy, 0.1839, 1e-4)
632s ***** test
632s  yy = fpdf (2, 10000000000000000, 4);
632s  assert (yy, 0.1839, 1e-4)
632s ***** test
632s  yy = fpdf (2, Inf, Inf);
632s  assert (yy, 0)
632s ***** test
632s  yy = fpdf (NaN, Inf, Inf);
632s  assert (yy, NaN)
632s ***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
632s ***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
632s ***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
632s ***** error<fpdf: function called with too few input arguments.> fpdf ()
632s ***** error<fpdf: function called with too few input arguments.> fpdf (1)
632s ***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
632s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
632s  fpdf (ones (3), ones (2), ones (2))
632s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
632s  fpdf (ones (2), ones (3), ones (2))
632s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
632s  fpdf (ones (2), ones (2), ones (3))
632s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
632s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
632s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
632s 25 tests, 25 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/laplacecdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/laplacecdf.m
632s ***** demo
632s  ## Plot various CDFs from the Laplace distribution
632s  x = -10:0.01:10;
632s  p1 = laplacecdf (x, 0, 1);
632s  p2 = laplacecdf (x, 0, 2);
632s  p3 = laplacecdf (x, 0, 4);
632s  p4 = laplacecdf (x, -5, 4);
632s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
632s  grid on
632s  xlim ([-10, 10])
632s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
632s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
632s  title ("Laplace CDF")
632s  xlabel ("values in x")
632s  ylabel ("probability")
632s ***** shared x, y
632s  x = [-Inf, -log(2), 0, log(2), Inf];
632s  y = [0, 1/4, 1/2, 3/4, 1];
632s ***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
632s ***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
632s ***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
632s ***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
632s ***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
632s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
632s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
632s ***** error<laplacecdf: function called with too few input arguments.> ...
632s  laplacecdf (1, 2)
632s ***** error<laplacecdf: function called with too many inputs> ...
632s  laplacecdf (1, 2, 3, 4, 5)
632s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
632s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
632s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
632s  laplacecdf (ones (3), ones (2), ones (2))
632s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
632s  laplacecdf (ones (2), ones (3), ones (2))
632s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
632s  laplacecdf (ones (2), ones (2), ones (3))
632s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
632s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
632s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
632s 17 tests, 17 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/nctcdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nctcdf.m
632s ***** demo
632s  ## Plot various CDFs from the noncentral Τ distribution
632s  x = -5:0.01:5;
632s  p1 = nctcdf (x, 1, 0);
632s  p2 = nctcdf (x, 4, 0);
632s  p3 = nctcdf (x, 1, 2);
632s  p4 = nctcdf (x, 4, 2);
632s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
632s  grid on
632s  xlim ([-5, 5])
632s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
632s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
632s  title ("Noncentral Τ CDF")
632s  xlabel ("values in x")
632s  ylabel ("probability")
632s ***** demo
632s  ## Compare the noncentral T CDF with MU = 1 to the T CDF
632s  ## with the same number of degrees of freedom (10).
632s 
632s  x = -5:0.1:5;
632s  p1 = nctcdf (x, 10, 1);
632s  p2 = tcdf (x, 10);
632s  plot (x, p1, "-", x, p2, "-")
632s  grid on
632s  xlim ([-5, 5])
632s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
632s  title ("Noncentral T vs T CDFs")
632s  xlabel ("values in x")
632s  ylabel ("probability")
632s ***** test
632s  x = -2:0.1:2;
632s  p = nctcdf (x, 10, 1);
632s  assert (p(1), 0.003302485766631558, 1e-14);
632s  assert (p(2), 0.004084668193532631, 1e-14);
632s  assert (p(3), 0.005052800319478737, 1e-14);
632s  assert (p(41), 0.8076115625303751, 1e-14);
632s ***** test
632s  p = nctcdf (12, 10, 3);
632s  assert (p, 0.9997719343243797, 1e-14);
632s ***** test
632s  p = nctcdf (2, 3, 2);
632s  assert (p, 0.4430757822176028, 1e-14);
632s ***** test
632s  p = nctcdf (2, 3, 2, "upper");
632s  assert (p, 0.5569242177823971, 1e-14);
632s ***** test
632s  p = nctcdf ([3, 6], 3, 2, "upper");
632s  assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
632s ***** error<nctcdf: function called with too few input arguments.> nctcdf ()
632s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
632s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
632s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
632s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
632s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
632s  nctcdf (ones (3), ones (2), ones (2))
632s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
632s  nctcdf (ones (2), ones (3), ones (2))
632s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
632s  nctcdf (ones (2), ones (2), ones (3))
632s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
632s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
632s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
632s 16 tests, 16 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/nakarnd.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nakarnd.m
632s ***** assert (size (nakarnd (1, 1)), [1 1])
632s ***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
632s ***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
632s ***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
632s ***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
632s ***** assert (size (nakarnd (1, 1, 3)), [3, 3])
632s ***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
632s ***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
632s ***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
632s ***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
632s ***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
632s ***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
632s ***** assert (class (nakarnd (1, 1)), "double")
632s ***** assert (class (nakarnd (1, single (1))), "single")
632s ***** assert (class (nakarnd (1, single ([1, 1]))), "single")
632s ***** assert (class (nakarnd (single (1), 1)), "single")
632s ***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
632s ***** error<nakarnd: function called with too few input arguments.> nakarnd ()
632s ***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
632s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
632s  nakarnd (ones (3), ones (2))
632s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
632s  nakarnd (ones (2), ones (3))
632s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
632s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
632s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  nakarnd (1, 2, -1)
632s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  nakarnd (1, 2, 1.2)
632s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  nakarnd (1, 2, ones (2))
632s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  nakarnd (1, 2, [2 -1 2])
632s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
632s  nakarnd (1, 2, [2 0 2.5])
632s ***** error<nakarnd: dimensions must be non-negative integers.> ...
632s  nakarnd (1, 2, 2, -1, 5)
632s ***** error<nakarnd: dimensions must be non-negative integers.> ...
632s  nakarnd (1, 2, 2, 1.5, 5)
632s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
632s  nakarnd (2, ones (2), 3)
632s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
632s  nakarnd (2, ones (2), [3, 2])
632s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
632s  nakarnd (2, ones (2), 3, 2)
632s 33 tests, 33 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/unidpdf.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unidpdf.m
632s ***** demo
632s  ## Plot various PDFs from the discrete uniform distribution
632s  x = 0:10;
632s  y1 = unidpdf (x, 5);
632s  y2 = unidpdf (x, 9);
632s  plot (x, y1, "*b", x, y2, "*g")
632s  grid on
632s  xlim ([0, 10])
632s  ylim ([0, 0.25])
632s  legend ({"N = 5", "N = 9"}, "location", "northeast")
632s  title ("Discrete uniform PDF")
632s  xlabel ("values in x")
632s  ylabel ("density")
632s ***** shared x, y
632s  x = [-1 0 1 2 10 11];
632s  y = [0 0 0.1 0.1 0.1 0];
632s ***** assert (unidpdf (x, 10*ones (1,6)), y)
632s ***** assert (unidpdf (x, 10), y)
632s ***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
632s ***** assert (unidpdf ([x, NaN], 10), [y, NaN])
632s ***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
632s ***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
632s ***** error<unidpdf: function called with too few input arguments.> unidpdf ()
632s ***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
632s ***** error<unidpdf: X and N must be of common size or scalars.> ...
632s  unidpdf (ones (3), ones (2))
632s ***** error<unidpdf: X and N must be of common size or scalars.> ...
632s  unidpdf (ones (2), ones (3))
632s ***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
632s ***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
632s 12 tests, 12 passed, 0 known failure, 0 skipped
632s [inst/dist_fun/nakainv.m]
632s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nakainv.m
632s ***** demo
632s  ## Plot various iCDFs from the Nakagami distribution
632s  p = 0.001:0.001:0.999;
632s  x1 = nakainv (p, 0.5, 1);
632s  x2 = nakainv (p, 1, 1);
632s  x3 = nakainv (p, 1, 2);
632s  x4 = nakainv (p, 1, 3);
632s  x5 = nakainv (p, 2, 1);
632s  x6 = nakainv (p, 2, 2);
632s  x7 = nakainv (p, 5, 1);
632s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
632s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
632s  grid on
632s  ylim ([0, 3])
632s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
632s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
632s           "μ = 5, ω = 1"}, "location", "northwest")
632s  title ("Nakagami iCDF")
632s  xlabel ("probability")
632s  ylabel ("values in x")
632s ***** shared p, y
632s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
632s  y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
632s ***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
632s ***** assert (nakainv (p, 1, 1), y, eps)
632s ***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
632s ***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
632s ***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
632s ***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
632s ***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
632s ***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
632s ***** error<nakainv: function called with too few input arguments.> nakainv ()
632s ***** error<nakainv: function called with too few input arguments.> nakainv (1)
632s ***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
632s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
632s  nakainv (ones (3), ones (2), ones(2))
632s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
632s  nakainv (ones (2), ones (3), ones(2))
632s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
632s  nakainv (ones (2), ones (2), ones(3))
632s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
632s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
632s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
633s 17 tests, 17 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/betainv.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/betainv.m
633s ***** demo
633s  ## Plot various iCDFs from the Beta distribution
633s  p = 0.001:0.001:0.999;
633s  x1 = betainv (p, 0.5, 0.5);
633s  x2 = betainv (p, 5, 1);
633s  x3 = betainv (p, 1, 3);
633s  x4 = betainv (p, 2, 2);
633s  x5 = betainv (p, 2, 5);
633s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
633s  grid on
633s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
633s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
633s  title ("Beta iCDF")
633s  xlabel ("probability")
633s  ylabel ("values in x")
633s ***** shared p
633s  p = [-1 0 0.75 1 2];
633s ***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
633s ***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
633s ***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
633s ***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
633s ***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
633s ***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
633s ***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
633s ***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
633s ***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
633s ***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
633s ***** error<betainv: function called with too few input arguments.> betainv ()
633s ***** error<betainv: function called with too few input arguments.> betainv (1)
633s ***** error<betainv: function called with too few input arguments.> betainv (1,2)
633s ***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
633s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
633s  betainv (ones (3), ones (2), ones (2))
633s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
633s  betainv (ones (2), ones (3), ones (2))
633s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
633s  betainv (ones (2), ones (2), ones (3))
633s ***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
633s ***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
633s ***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
633s 20 tests, 20 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/wishrnd.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wishrnd.m
633s ***** assert(size (wishrnd (1,2)), [1, 1]);
633s ***** assert(size (wishrnd (1,2,[])), [1, 1]);
633s ***** assert(size (wishrnd (1,2,1)), [1, 1]);
633s ***** assert(size (wishrnd ([],2,1)), [1, 1]);
633s ***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
633s ***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
633s ***** error wishrnd ()
633s ***** error wishrnd (1)
633s ***** error wishrnd ([1; 1], 2)
633s 9 tests, 9 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/nbincdf.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nbincdf.m
633s ***** demo
633s  ## Plot various CDFs from the negative binomial distribution
633s  x = 0:50;
633s  p1 = nbincdf (x, 2, 0.15);
633s  p2 = nbincdf (x, 5, 0.2);
633s  p3 = nbincdf (x, 4, 0.4);
633s  p4 = nbincdf (x, 10, 0.3);
633s  plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
633s  grid on
633s  xlim ([0, 40])
633s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
633s           "r = 10, ps = 0.3"}, "location", "southeast")
633s  title ("Negative binomial CDF")
633s  xlabel ("values in x (number of failures)")
633s  ylabel ("probability")
633s ***** shared x, y
633s  x = [-1 0 1 2 Inf];
633s  y = [0 1/2 3/4 7/8 1];
633s ***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
633s ***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
633s ***** assert (nbincdf (x, ones (1,5), 0.5), y)
633s ***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
633s ***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
633s  [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
633s ***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
633s ***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
633s ***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
633s ***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
633s ***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
633s ***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
633s ***** error<nbincdf: function called with too few input arguments.> nbincdf ()
633s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
633s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
633s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
633s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
633s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
633s  nbincdf (ones (3), ones (2), ones (2))
633s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
633s  nbincdf (ones (2), ones (3), ones (2))
633s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
633s  nbincdf (ones (2), ones (2), ones (3))
633s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
633s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
633s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
633s 22 tests, 22 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/chi2rnd.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/chi2rnd.m
633s ***** assert (size (chi2rnd (2)), [1, 1])
633s ***** assert (size (chi2rnd (ones (2,1))), [2, 1])
633s ***** assert (size (chi2rnd (ones (2,2))), [2, 2])
633s ***** assert (size (chi2rnd (1, 3)), [3, 3])
633s ***** assert (size (chi2rnd (1, [4 1])), [4, 1])
633s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
633s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
633s ***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
633s ***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
633s ***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
633s ***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
633s ***** assert (class (chi2rnd (2)), "double")
633s ***** assert (class (chi2rnd (single (2))), "single")
633s ***** assert (class (chi2rnd (single ([2 2]))), "single")
633s ***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
633s ***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (1, -1)
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (1, 1.2)
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (1, ones (2))
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (1, [2 -1 2])
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (1, [2 0 2.5])
633s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
633s  chi2rnd (ones (2), ones (2))
633s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
633s  chi2rnd (1, 2, -1, 5)
633s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
633s  chi2rnd (1, 2, 1.5, 5)
633s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
633s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
633s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
633s 27 tests, 27 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/hygecdf.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hygecdf.m
633s ***** demo
633s  ## Plot various CDFs from the hypergeometric distribution
633s  x = 0:60;
633s  p1 = hygecdf (x, 500, 50, 100);
633s  p2 = hygecdf (x, 500, 60, 200);
633s  p3 = hygecdf (x, 500, 70, 300);
633s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
633s  grid on
633s  xlim ([0, 60])
633s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
633s           "m = 500, k = 70, n = 300"}, "location", "southeast")
633s  title ("Hypergeometric CDF")
633s  xlabel ("values in x (number of successes)")
633s  ylabel ("probability")
633s ***** shared x, y
633s  x = [-1 0 1 2 3];
633s  y = [0 1/6 5/6 1 1];
633s ***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
633s ***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
633s ***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
633s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
633s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
633s  [y(5) NaN NaN NaN y(1)], 5*eps)
633s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
633s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
633s  [y(5) NaN NaN NaN y(1)], 5*eps)
633s ***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
633s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
633s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
633s  [y(5) NaN NaN NaN y(1)], 5*eps)
633s ***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
633s ***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
633s ***** test
633s  p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
633s  assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
633s ***** test
633s  p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
633s  assert (p, [1, 34/90, 0, 0, 0], 10*eps);
633s ***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
633s ***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
633s  eps ("single"))
633s ***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
633s  eps ("single"))
633s ***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
633s  eps ("single"))
633s ***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
633s  eps ("single"))
633s ***** error<hygecdf: function called with too few input arguments.> hygecdf ()
633s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
633s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
633s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
633s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
633s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
633s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
633s  hygecdf (ones (2), ones (3), 1, 1)
633s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
633s  hygecdf (1, ones (2), ones (3), 1)
633s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
633s  hygecdf (1, 1, ones (2), ones (3))
633s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
633s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
633s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
633s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
633s 32 tests, 32 passed, 0 known failure, 0 skipped
633s [inst/dist_fun/riceinv.m]
633s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/riceinv.m
633s ***** demo
633s  ## Plot various iCDFs from the Rician distribution
633s  p = 0.001:0.001:0.999;
633s  x1 = riceinv (p, 0, 1);
633s  x2 = riceinv (p, 0.5, 1);
633s  x3 = riceinv (p, 1, 1);
633s  x4 = riceinv (p, 2, 1);
633s  x5 = riceinv (p, 4, 1);
633s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
633s  grid on
633s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
633s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
633s  title ("Rician iCDF")
633s  xlabel ("probability")
633s  ylabel ("values in x")
633s ***** shared p
633s  p = [-1 0 0.75 1 2];
633s ***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
633s ***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
633s ***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
633s ***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
633s ***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
633s ***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
633s ***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
633s ***** assert (riceinv (single ([p, NaN]), 1, 2), ...
633s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
633s ***** assert (riceinv ([p, NaN], single (1), 2), ...
633s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
633s ***** assert (riceinv ([p, NaN], 1, single (2)), ...
633s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
634s ***** error<riceinv: function called with too few input arguments.> riceinv ()
634s ***** error<riceinv: function called with too few input arguments.> riceinv (1)
634s ***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
634s ***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
634s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
634s  riceinv (ones (3), ones (2), ones (2))
634s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
634s  riceinv (ones (2), ones (3), ones (2))
634s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
634s  riceinv (ones (2), ones (2), ones (3))
634s ***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
634s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
634s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
634s 20 tests, 20 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/fcdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/fcdf.m
634s ***** demo
634s  ## Plot various CDFs from the F distribution
634s  x = 0.01:0.01:4;
634s  p1 = fcdf (x, 1, 2);
634s  p2 = fcdf (x, 2, 1);
634s  p3 = fcdf (x, 5, 2);
634s  p4 = fcdf (x, 10, 1);
634s  p5 = fcdf (x, 100, 100);
634s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
634s  grid on
634s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
634s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
634s           "df1 = 100, df2 = 100"}, "location", "southeast")
634s  title ("F CDF")
634s  xlabel ("values in x")
634s  ylabel ("probability")
634s ***** shared x, y
634s  x = [-1, 0, 0.5, 1, 2, Inf];
634s  y = [0, 0, 1/3, 1/2, 2/3, 1];
634s ***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
634s ***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
634s ***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
634s ***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
634s ***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
634s ***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
634s ***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
634s ***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
634s ***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
634s ***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
634s ***** error<fcdf: function called with too few input arguments.> fcdf ()
634s ***** error<fcdf: function called with too few input arguments.> fcdf (1)
634s ***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
634s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
634s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
634s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
634s  fcdf (ones (3), ones (2), ones (2))
634s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
634s  fcdf (ones (2), ones (3), ones (2))
634s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
634s  fcdf (ones (2), ones (2), ones (3))
634s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
634s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
634s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
634s 21 tests, 21 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/tpdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tpdf.m
634s ***** demo
634s  ## Plot various PDFs from the Student's T distribution
634s  x = -5:0.01:5;
634s  y1 = tpdf (x, 1);
634s  y2 = tpdf (x, 2);
634s  y3 = tpdf (x, 5);
634s  y4 = tpdf (x, Inf);
634s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
634s  grid on
634s  xlim ([-5, 5])
634s  ylim ([0, 0.41])
634s  legend ({"df = 1", "df = 2", ...
634s           "df = 5", 'df = \infty'}, "location", "northeast")
634s  title ("Student's T PDF")
634s  xlabel ("values in x")
634s  ylabel ("density")
634s ***** test
634s  x = rand (10,1);
634s  y = 1./(pi * (1 + x.^2));
634s  assert (tpdf (x, 1), y, 5*eps);
634s ***** shared x, y
634s  x = [-Inf 0 0.5 1 Inf];
634s  y = 1./(pi * (1 + x.^2));
634s ***** assert (tpdf (x, ones (1,5)), y, eps)
634s ***** assert (tpdf (x, 1), y, eps)
634s ***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
634s ***** assert (tpdf (x, Inf), normpdf (x))
634s ***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
634s ***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
634s ***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
634s ***** error<tpdf: function called with too few input arguments.> tpdf ()
634s ***** error<tpdf: function called with too few input arguments.> tpdf (1)
634s ***** error<tpdf: X and DF must be of common size or scalars.> ...
634s  tpdf (ones (3), ones (2))
634s ***** error<tpdf: X and DF must be of common size or scalars.> ...
634s  tpdf (ones (2), ones (3))
634s ***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
634s ***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
634s 14 tests, 14 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/geopdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/geopdf.m
634s ***** demo
634s  ## Plot various PDFs from the geometric distribution
634s  x = 0:10;
634s  y1 = geopdf (x, 0.2);
634s  y2 = geopdf (x, 0.5);
634s  y3 = geopdf (x, 0.7);
634s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
634s  grid on
634s  ylim ([0, 0.8])
634s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
634s  title ("Geometric PDF")
634s  xlabel ("values in x (number of failures)")
634s  ylabel ("density")
634s ***** shared x, y
634s  x = [-1 0 1 Inf];
634s  y = [0, 1/2, 1/4, NaN];
634s ***** assert (geopdf (x, 0.5*ones (1,4)), y)
634s ***** assert (geopdf (x, 0.5), y)
634s ***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
634s ***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
634s ***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
634s ***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
634s ***** error geopdf ()
634s ***** error geopdf (1)
634s ***** error geopdf (1,2,3)
634s ***** error geopdf (ones (3), ones (2))
634s ***** error geopdf (ones (2), ones (3))
634s ***** error geopdf (i, 2)
634s ***** error geopdf (2, i)
634s 13 tests, 13 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/loglrnd.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/loglrnd.m
634s ***** assert (size (loglrnd (1, 1)), [1 1])
634s ***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
634s ***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
634s ***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
634s ***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
634s ***** assert (size (loglrnd (1, 1, 3)), [3, 3])
634s ***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
634s ***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
634s ***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
634s ***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
634s ***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
634s ***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
634s ***** assert (class (loglrnd (1, 1)), "double")
634s ***** assert (class (loglrnd (1, single (1))), "single")
634s ***** assert (class (loglrnd (1, single ([1, 1]))), "single")
634s ***** assert (class (loglrnd (single (1), 1)), "single")
634s ***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
634s ***** error<loglrnd: function called with too few input arguments.> loglrnd ()
634s ***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
634s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
634s  loglrnd (ones (3), ones (2))
634s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
634s  loglrnd (ones (2), ones (3))
634s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
634s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
634s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
634s  loglrnd (1, 2, -1)
634s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
634s  loglrnd (1, 2, 1.2)
634s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
634s  loglrnd (1, 2, ones (2))
634s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
634s  loglrnd (1, 2, [2 -1 2])
634s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
634s  loglrnd (1, 2, [2 0 2.5])
634s ***** error<loglrnd: dimensions must be non-negative integers.> ...
634s  loglrnd (1, 2, 2, -1, 5)
634s ***** error<loglrnd: dimensions must be non-negative integers.> ...
634s  loglrnd (1, 2, 2, 1.5, 5)
634s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
634s  loglrnd (2, ones (2), 3)
634s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
634s  loglrnd (2, ones (2), [3, 2])
634s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
634s  loglrnd (2, ones (2), 3, 2)
634s 33 tests, 33 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/bisarnd.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bisarnd.m
634s ***** assert (size (bisarnd (1, 1)), [1 1])
634s ***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
634s ***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
634s ***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
634s ***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
634s ***** assert (size (bisarnd (1, 1, 3)), [3, 3])
634s ***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
634s ***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
634s ***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
634s ***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
634s ***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
634s ***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
634s ***** assert (class (bisarnd (1, 1)), "double")
634s ***** assert (class (bisarnd (1, single (1))), "single")
634s ***** assert (class (bisarnd (1, single ([1, 1]))), "single")
634s ***** assert (class (bisarnd (single (1), 1)), "single")
634s ***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
634s ***** error<bisarnd: function called with too few input arguments.> bisarnd ()
634s ***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
634s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
634s  bisarnd (ones (3), ones (2))
634s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
634s  bisarnd (ones (2), ones (3))
634s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
634s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
634s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  bisarnd (1, 2, -1)
634s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  bisarnd (1, 2, 1.2)
634s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  bisarnd (1, 2, ones (2))
634s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  bisarnd (1, 2, [2 -1 2])
634s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  bisarnd (1, 2, [2 0 2.5])
634s ***** error<bisarnd: dimensions must be non-negative integers.> ...
634s  bisarnd (1, 2, 2, -1, 5)
634s ***** error<bisarnd: dimensions must be non-negative integers.> ...
634s  bisarnd (1, 2, 2, 1.5, 5)
634s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
634s  bisarnd (2, ones (2), 3)
634s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
634s  bisarnd (2, ones (2), [3, 2])
634s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
634s  bisarnd (2, ones (2), 3, 2)
634s 33 tests, 33 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/bvtcdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bvtcdf.m
634s ***** test
634s  x = [1, 2];
634s  rho = [1, 0.5; 0.5, 1];
634s  df = 4;
634s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
634s ***** test
634s  x = [3, 2;2, 4;1, 5];
634s  rho = [1, 0.5; 0.5, 1];
634s  df = 4;
634s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
634s 2 tests, 2 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/poissinv.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/poissinv.m
634s ***** demo
634s  ## Plot various iCDFs from the Poisson distribution
634s  p = 0.001:0.001:0.999;
634s  x1 = poissinv (p, 13);
634s  x2 = poissinv (p, 4);
634s  x3 = poissinv (p, 10);
634s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
634s  grid on
634s  ylim ([0, 20])
634s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
634s  title ("Poisson iCDF")
634s  xlabel ("probability")
634s  ylabel ("values in x (number of occurences)")
634s ***** shared p
634s  p = [-1 0 0.5 1 2];
634s ***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
634s ***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
634s ***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
634s ***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
634s ***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
634s ***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
634s ***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
634s ***** error<poissinv: function called with too few input arguments.> poissinv ()
634s ***** error<poissinv: function called with too few input arguments.> poissinv (1)
634s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
634s  poissinv (ones (3), ones (2))
634s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
634s  poissinv (ones (2), ones (3))
634s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
634s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
634s 13 tests, 13 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/ncfcdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncfcdf.m
634s ***** demo
634s  ## Plot various CDFs from the noncentral F distribution
634s  x = 0:0.01:5;
634s  p1 = ncfcdf (x, 2, 5, 1);
634s  p2 = ncfcdf (x, 2, 5, 2);
634s  p3 = ncfcdf (x, 5, 10, 1);
634s  p4 = ncfcdf (x, 10, 20, 10);
634s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
634s  grid on
634s  xlim ([0, 5])
634s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
634s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
634s          "location", "southeast")
634s  title ("Noncentral F CDF")
634s  xlabel ("values in x")
634s  ylabel ("probability")
634s ***** demo
634s  ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
634s  ## same number of numerator and denominator degrees of freedom (5, 20)
634s 
634s  x = 0.01:0.1:10.01;
634s  p1 = ncfcdf (x, 5, 20, 10);
634s  p2 = fcdf (x, 5, 20);
634s  plot (x, p1, "-", x, p2, "-");
634s  grid on
634s  xlim ([0, 10])
634s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
634s  title ("Noncentral F vs F CDFs")
634s  xlabel ("values in x")
634s  ylabel ("probability")
634s ***** test
634s  x = -2:0.1:2;
634s  p = ncfcdf (x, 10, 1, 3);
634s  assert (p([1:21]), zeros (1, 21), 1e-76);
634s  assert (p(22), 0.004530737275319753, 1e-14);
634s  assert (p(30), 0.255842099135669, 1e-14);
634s  assert (p(41), 0.4379890998457305, 1e-14);
634s ***** test
634s  p = ncfcdf (12, 10, 3, 2);
634s  assert (p, 0.9582287900447416, 1e-14);
634s ***** test
634s  p = ncfcdf (2, 3, 2, 1);
634s  assert (p, 0.5731985522994989, 1e-14);
634s ***** test
634s  p = ncfcdf (2, 3, 2, 1, "upper");
634s  assert (p, 0.4268014477004823, 1e-14);
634s ***** test
634s  p = ncfcdf ([3, 6], 3, 2, 5, "upper");
634s  assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
634s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
634s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
634s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
634s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
634s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
634s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
634s  ncfcdf (ones (3), ones (2), ones (2), ones (2))
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
634s  ncfcdf (ones (2), ones (3), ones (2), ones (2))
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
634s  ncfcdf (ones (2), ones (2), ones (3), ones (2))
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
634s  ncfcdf (ones (2), ones (2), ones (2), ones (3))
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
634s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
634s 19 tests, 19 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/gaminv.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gaminv.m
634s ***** demo
634s  ## Plot various iCDFs from the Gamma distribution
634s  p = 0.001:0.001:0.999;
634s  x1 = gaminv (p, 1, 2);
634s  x2 = gaminv (p, 2, 2);
634s  x3 = gaminv (p, 3, 2);
634s  x4 = gaminv (p, 5, 1);
634s  x5 = gaminv (p, 9, 0.5);
634s  x6 = gaminv (p, 7.5, 1);
634s  x7 = gaminv (p, 0.5, 1);
634s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
634s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
634s  ylim ([0, 20])
634s  grid on
634s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
634s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
634s           "α = 0.5, β = 1"}, "location", "northwest")
634s  title ("Gamma iCDF")
634s  xlabel ("probability")
634s  ylabel ("x")
634s ***** shared p
634s  p = [-1 0 0.63212055882855778 1 2];
634s ***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
634s ***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
634s ***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
634s ***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
634s ***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
634s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
634s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
634s ***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
634s ***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
634s ***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
634s ***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
634s ***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
634s ***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
634s ***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
634s  eps ("single"))
634s ***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
634s  eps ("single"))
634s ***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
634s  eps ("single"))
634s ***** error<gaminv: function called with too few input arguments.> gaminv ()
634s ***** error<gaminv: function called with too few input arguments.> gaminv (1)
634s ***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
634s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
634s  gaminv (ones (3), ones (2), ones (2))
634s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
634s  gaminv (ones (2), ones (3), ones (2))
634s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
634s  gaminv (ones (2), ones (2), ones (3))
634s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
634s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
634s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
634s 25 tests, 25 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/burrcdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/burrcdf.m
634s ***** demo
634s  ## Plot various CDFs from the Burr type XII distribution
634s  x = 0.001:0.001:5;
634s  p1 = burrcdf (x, 1, 1, 1);
634s  p2 = burrcdf (x, 1, 1, 2);
634s  p3 = burrcdf (x, 1, 1, 3);
634s  p4 = burrcdf (x, 1, 2, 1);
634s  p5 = burrcdf (x, 1, 3, 1);
634s  p6 = burrcdf (x, 1, 0.5, 2);
634s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
634s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
634s  grid on
634s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
634s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
634s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
634s          "location", "southeast")
634s  title ("Burr type XII CDF")
634s  xlabel ("values in x")
634s  ylabel ("probability")
634s ***** shared x, y
634s  x = [-1, 0, 1, 2, Inf];
634s  y = [0, 0, 1/2, 2/3, 1];
634s ***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
634s ***** assert (burrcdf (x, 1, 1, 1), y, eps)
634s ***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
634s ***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
634s ***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
634s ***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
634s ***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
634s ***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
634s ***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
634s ***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
634s ***** error<burrcdf: function called with too few input arguments.> burrcdf ()
634s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
634s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
634s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
634s ***** error<burrcdf: function called with too many inputs> ...
634s  burrcdf (1, 2, 3, 4, 5, 6)
634s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
634s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
634s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
634s  burrcdf (ones (3), ones (2), ones(2), ones(2))
634s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
634s  burrcdf (ones (2), ones (3), ones(2), ones(2))
634s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
634s  burrcdf (ones (2), ones (2), ones(3), ones(2))
634s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
634s  burrcdf (ones (2), ones (2), ones(2), ones(3))
634s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
634s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
634s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
634s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
634s 25 tests, 25 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/cauchyrnd.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/cauchyrnd.m
634s ***** assert (size (cauchyrnd (1, 1)), [1 1])
634s ***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
634s ***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
634s ***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
634s ***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
634s ***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
634s ***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
634s ***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
634s ***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
634s ***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
634s ***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
634s ***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
634s ***** assert (class (cauchyrnd (1, 1)), "double")
634s ***** assert (class (cauchyrnd (1, single (1))), "single")
634s ***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
634s ***** assert (class (cauchyrnd (single (1), 1)), "single")
634s ***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
634s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
634s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
634s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
634s  cauchyrnd (ones (3), ones (2))
634s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
634s  cauchyrnd (ones (2), ones (3))
634s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
634s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
634s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  cauchyrnd (1, 2, -1)
634s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  cauchyrnd (1, 2, 1.2)
634s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  cauchyrnd (1, 2, ones (2))
634s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  cauchyrnd (1, 2, [2 -1 2])
634s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
634s  cauchyrnd (1, 2, [2 0 2.5])
634s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
634s  cauchyrnd (1, 2, 2, -1, 5)
634s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
634s  cauchyrnd (1, 2, 2, 1.5, 5)
634s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
634s  cauchyrnd (2, ones (2), 3)
634s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
634s  cauchyrnd (2, ones (2), [3, 2])
634s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
634s  cauchyrnd (2, ones (2), 3, 2)
634s 33 tests, 33 passed, 0 known failure, 0 skipped
634s [inst/dist_fun/wblpdf.m]
634s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wblpdf.m
634s ***** demo
634s  ## Plot various PDFs from the Weibul distribution
634s  x = 0:0.001:2.5;
634s  y1 = wblpdf (x, 1, 0.5);
634s  y2 = wblpdf (x, 1, 1);
634s  y3 = wblpdf (x, 1, 1.5);
634s  y4 = wblpdf (x, 1, 5);
634s  plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
634s  grid on
634s  ylim ([0, 2.5])
634s  legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
634s           "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
634s  title ("Weibul PDF")
634s  xlabel ("values in x")
634s  ylabel ("density")
634s ***** shared x,y
634s  x = [-1 0 0.5 1 Inf];
634s  y = [0, exp(-x(2:4)), NaN];
634s ***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
634s ***** assert (wblpdf (x, 1, ones (1,5)), y)
634s ***** assert (wblpdf (x, ones (1,5), 1), y)
634s ***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
634s ***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
634s ***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
634s ***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
634s ***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
635s ***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
635s ***** error wblpdf ()
635s ***** error wblpdf (1,2,3,4)
635s ***** error wblpdf (ones (3), ones (2), ones (2))
635s ***** error wblpdf (ones (2), ones (3), ones (2))
635s ***** error wblpdf (ones (2), ones (2), ones (3))
635s ***** error wblpdf (i, 2, 2)
635s ***** error wblpdf (2, i, 2)
635s ***** error wblpdf (2, 2, i)
635s 17 tests, 17 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/wishpdf.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wishpdf.m
635s ***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
635s ***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
635s ***** 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);
635s ***** error wishpdf ()
635s ***** error wishpdf (1, 2)
635s ***** error wishpdf (1, 2, 0)
635s ***** error wishpdf (1, 2)
635s 7 tests, 7 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/evinv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/evinv.m
635s ***** demo
635s  ## Plot various iCDFs from the extreme value distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = evinv (p, 0.5, 2);
635s  x2 = evinv (p, 1.0, 2);
635s  x3 = evinv (p, 1.5, 3);
635s  x4 = evinv (p, 3.0, 4);
635s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
635s  grid on
635s  ylim ([-10, 10])
635s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
635s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
635s  title ("Extreme value iCDF")
635s  xlabel ("probability")
635s  ylabel ("values in x")
635s ***** shared p, x
635s  p = [0, 0.05, 0.5 0.95];
635s  x = [-Inf, -2.9702, -0.3665, 1.0972];
635s ***** assert (evinv (p), x, 1e-4)
635s ***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
635s ***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
635s ***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
635s ***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
635s ***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
635s ***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
635s ***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
635s ***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
635s ***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
635s ***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
635s ***** error<evinv: invalid number of input arguments.> evinv ()
635s ***** error evinv (1,2,3,4,5,6)
635s ***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
635s  evinv (ones (3), ones (2), ones (2))
635s ***** error<evinv: invalid size of covariance matrix.> ...
635s  [p, plo, pup] = evinv (2, 3, 4, [1, 2])
635s ***** error<evinv: covariance matrix is required for confidence bounds.> ...
635s  [p, plo, pup] = evinv (1, 2, 3)
635s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
635s  evinv (1, 2, 3, [1, 0; 0, 1], 0)
635s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
635s  evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
635s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
635s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
635s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
635s ***** error<evinv: bad covariance matrix.> ...
635s  [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
635s 22 tests, 22 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/tinv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tinv.m
635s ***** demo
635s  ## Plot various iCDFs from the Student's T distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = tinv (p, 1);
635s  x2 = tinv (p, 2);
635s  x3 = tinv (p, 5);
635s  x4 = tinv (p, Inf);
635s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
635s  grid on
635s  xlim ([0, 1])
635s  ylim ([-5, 5])
635s  legend ({"df = 1", "df = 2", ...
635s           "df = 5", 'df = \infty'}, "location", "northwest")
635s  title ("Student's T iCDF")
635s  xlabel ("probability")
635s  ylabel ("values in x")
635s ***** shared p
635s  p = [-1 0 0.5 1 2];
635s ***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
635s ***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
635s ***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
635s ***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
635s ***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
635s ***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
635s ***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
635s ***** error<tinv: function called with too few input arguments.> tinv ()
635s ***** error<tinv: function called with too few input arguments.> tinv (1)
635s ***** error<tinv: P and DF must be of common size or scalars.> ...
635s  tinv (ones (3), ones (2))
635s ***** error<tinv: P and DF must be of common size or scalars.> ...
635s  tinv (ones (2), ones (3))
635s ***** error<tinv: P and DF must not be complex.> tinv (i, 2)
635s ***** error<tinv: P and DF must not be complex.> tinv (2, i)
635s 13 tests, 13 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/logirnd.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logirnd.m
635s ***** assert (size (logirnd (1, 1)), [1 1])
635s ***** assert (size (logirnd (1, ones (2,1))), [2, 1])
635s ***** assert (size (logirnd (1, ones (2,2))), [2, 2])
635s ***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
635s ***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
635s ***** assert (size (logirnd (1, 1, 3)), [3, 3])
635s ***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
635s ***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
635s ***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
635s ***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
635s ***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
635s ***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
635s ***** assert (class (logirnd (1, 1)), "double")
635s ***** assert (class (logirnd (1, single (1))), "single")
635s ***** assert (class (logirnd (1, single ([1, 1]))), "single")
635s ***** assert (class (logirnd (single (1), 1)), "single")
635s ***** assert (class (logirnd (single ([1, 1]), 1)), "single")
635s ***** error<logirnd: function called with too few input arguments.> logirnd ()
635s ***** error<logirnd: function called with too few input arguments.> logirnd (1)
635s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
635s  logirnd (ones (3), ones (2))
635s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
635s  logirnd (ones (2), ones (3))
635s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
635s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
635s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
635s  logirnd (1, 2, -1)
635s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
635s  logirnd (1, 2, 1.2)
635s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
635s  logirnd (1, 2, ones (2))
635s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
635s  logirnd (1, 2, [2 -1 2])
635s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
635s  logirnd (1, 2, [2 0 2.5])
635s ***** error<logirnd: dimensions must be non-negative integers.> ...
635s  logirnd (1, 2, 2, -1, 5)
635s ***** error<logirnd: dimensions must be non-negative integers.> ...
635s  logirnd (1, 2, 2, 1.5, 5)
635s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
635s  logirnd (2, ones (2), 3)
635s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
635s  logirnd (2, ones (2), [3, 2])
635s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
635s  logirnd (2, ones (2), 3, 2)
635s 33 tests, 33 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/cauchycdf.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/cauchycdf.m
635s ***** demo
635s  ## Plot various CDFs from the Cauchy distribution
635s  x = -5:0.01:5;
635s  p1 = cauchycdf (x, 0, 0.5);
635s  p2 = cauchycdf (x, 0, 1);
635s  p3 = cauchycdf (x, 0, 2);
635s  p4 = cauchycdf (x, -2, 1);
635s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
635s  grid on
635s  xlim ([-5, 5])
635s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
635s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
635s  title ("Cauchy CDF")
635s  xlabel ("values in x")
635s  ylabel ("probability")
635s ***** shared x, y
635s  x = [-1 0 0.5 1 2];
635s  y = 1/pi * atan ((x-1) / 2) + 1/2;
635s ***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
635s ***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
635s ***** assert (cauchycdf (x, ones (1,5), 2), y)
635s ***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
635s ***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
635s ***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
635s ***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
635s ***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
635s ***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
635s ***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
635s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
635s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
635s ***** error<cauchycdf: function called with too few input arguments.> ...
635s  cauchycdf (1, 2)
635s ***** error<cauchycdf: function called with too many inputs> ...
635s  cauchycdf (1, 2, 3, 4, 5)
635s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
635s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
635s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
635s  cauchycdf (ones (3), ones (2), ones (2))
635s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
635s  cauchycdf (ones (2), ones (3), ones (2))
635s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
635s  cauchycdf (ones (2), ones (2), ones (3))
635s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
635s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
635s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
635s 22 tests, 22 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/chi2inv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/chi2inv.m
635s ***** demo
635s  ## Plot various iCDFs from the chi-squared distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = chi2inv (p, 1);
635s  x2 = chi2inv (p, 2);
635s  x3 = chi2inv (p, 3);
635s  x4 = chi2inv (p, 4);
635s  x5 = chi2inv (p, 6);
635s  x6 = chi2inv (p, 9);
635s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
635s        p, x4, "-c", p, x5, "-m", p, x6, "-y")
635s  grid on
635s  ylim ([0, 8])
635s  legend ({"df = 1", "df = 2", "df = 3", ...
635s           "df = 4", "df = 6", "df = 9"}, "location", "northwest")
635s  title ("Chi-squared iCDF")
635s  xlabel ("probability")
635s  ylabel ("values in x")
635s ***** shared p
635s  p = [-1 0 0.3934693402873666 1 2];
635s ***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
635s ***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
635s ***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
635s ***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
635s ***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
635s ***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
635s ***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
635s ***** error<chi2inv: function called with too few input arguments.> chi2inv ()
635s ***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
635s ***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
635s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
635s  chi2inv (ones (3), ones (2))
635s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
635s  chi2inv (ones (2), ones (3))
635s ***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
635s ***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
635s 14 tests, 14 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/evpdf.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/evpdf.m
635s ***** demo
635s  ## Plot various PDFs from the Extreme value distribution
635s  x = -10:0.001:10;
635s  y1 = evpdf (x, 0.5, 2);
635s  y2 = evpdf (x, 1.0, 2);
635s  y3 = evpdf (x, 1.5, 3);
635s  y4 = evpdf (x, 3.0, 4);
635s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
635s  grid on
635s  ylim ([0, 0.2])
635s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
635s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
635s  title ("Extreme value PDF")
635s  xlabel ("values in x")
635s  ylabel ("density")
635s ***** shared x, y0, y1
635s  x = [-5, 0, 1, 2, 3];
635s  y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
635s  y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
635s ***** assert (evpdf (x), y0, 1e-4)
635s ***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
635s ***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
635s ***** error<evpdf: function called with too few input arguments.> evpdf ()
635s ***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
635s  evpdf (ones (3), ones (2), ones (2))
635s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
635s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
635s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
635s 8 tests, 8 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/geoinv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/geoinv.m
635s ***** demo
635s  ## Plot various iCDFs from the geometric distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = geoinv (p, 0.2);
635s  x2 = geoinv (p, 0.5);
635s  x3 = geoinv (p, 0.7);
635s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
635s  grid on
635s  ylim ([0, 10])
635s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
635s  title ("Geometric iCDF")
635s  xlabel ("probability")
635s  ylabel ("values in x (number of failures)")
635s ***** shared p
635s  p = [-1 0 0.75 1 2];
635s ***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
635s ***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
635s ***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
635s ***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
635s ***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
635s ***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
635s ***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
635s ***** error<geoinv: function called with too few input arguments.> geoinv ()
635s ***** error<geoinv: function called with too few input arguments.> geoinv (1)
635s ***** error<geoinv: P and PS must be of common size or scalars.> ...
635s  geoinv (ones (3), ones (2))
635s ***** error<geoinv: P and PS must be of common size or scalars.> ...
635s  geoinv (ones (2), ones (3))
635s ***** error<geoinv: P and PS must not be complex.> ...
635s  geoinv (i, 2)
635s ***** error<geoinv: P and PS must not be complex.> ...
635s  geoinv (2, i)
635s 13 tests, 13 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/vmpdf.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/vmpdf.m
635s ***** demo
635s  ## Plot various PDFs from the von Mises distribution
635s  x1 = [-pi:0.1:pi];
635s  y1 = vmpdf (x1, 0, 0.5);
635s  y2 = vmpdf (x1, 0, 1);
635s  y3 = vmpdf (x1, 0, 2);
635s  y4 = vmpdf (x1, 0, 4);
635s  plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
635s  grid on
635s  xlim ([-pi, pi])
635s  ylim ([0, 0.8])
635s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
635s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
635s  title ("Von Mises PDF")
635s  xlabel ("values in x")
635s  ylabel ("density")
635s ***** shared x, y0, y1
635s  x = [-pi:pi/2:pi];
635s  y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
635s  y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
635s ***** assert (vmpdf (x, 0, 1), y0, 1e-5)
635s ***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
635s ***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
635s ***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
635s ***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
635s ***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
635s ***** error<vmpdf: function called with too few input arguments.> vmpdf ()
635s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
635s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
635s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
635s  vmpdf (ones (3), ones (2), ones (2))
635s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
635s  vmpdf (ones (2), ones (3), ones (2))
635s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
635s  vmpdf (ones (2), ones (2), ones (3))
635s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
635s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
635s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
635s 15 tests, 15 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/wblinv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wblinv.m
635s ***** demo
635s  ## Plot various iCDFs from the Weibull distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = wblinv (p, 1, 0.5);
635s  x2 = wblinv (p, 1, 1);
635s  x3 = wblinv (p, 1, 1.5);
635s  x4 = wblinv (p, 1, 5);
635s  plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
635s  ylim ([0, 2.5])
635s  grid on
635s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
635s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
635s  title ("Weibull iCDF")
635s  xlabel ("probability")
635s  ylabel ("x")
635s ***** shared p
635s  p = [-1 0 0.63212055882855778 1 2];
635s ***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
635s ***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
635s ***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
635s ***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
635s ***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
635s ***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
635s ***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
635s ***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
635s ***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
635s ***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
635s ***** error<wblinv: invalid number of input arguments.> wblinv ()
635s ***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
635s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
635s  wblinv (ones (3), ones (2), ones (2))
635s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
635s  wblinv (ones (2), ones (3), ones (2))
635s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
635s  wblinv (ones (2), ones (2), ones (3))
635s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
635s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
635s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
635s 18 tests, 18 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/tlsinv.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tlsinv.m
635s ***** demo
635s  ## Plot various iCDFs from the location-scale Student's T distribution
635s  p = 0.001:0.001:0.999;
635s  x1 = tlsinv (p, 0, 1, 1);
635s  x2 = tlsinv (p, 0, 2, 2);
635s  x3 = tlsinv (p, 3, 2, 5);
635s  x4 = tlsinv (p, -1, 3, Inf);
635s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
635s  grid on
635s  xlim ([0, 1])
635s  ylim ([-8, 8])
635s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
635s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
635s          "location", "southeast")
635s  title ("Location-scale Student's T iCDF")
635s  xlabel ("probability")
635s  ylabel ("values in x")
635s ***** shared p
635s  p = [-1 0 0.5 1 2];
635s ***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
635s ***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
635s ***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
635s ***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
635s ***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
635s ***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
635s ***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
635s ***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
635s ***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
635s ***** error<tlsinv: function called with too few input arguments.> tlsinv ()
635s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
635s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
635s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
635s  tlsinv (ones (3), ones (2), 1, 1)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
635s  tlsinv (ones (2), 1, ones (3), 1)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
635s  tlsinv (ones (2), 1, 1, ones (3))
635s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
635s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
635s 20 tests, 20 passed, 0 known failure, 0 skipped
635s [inst/dist_fun/nctpdf.m]
635s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nctpdf.m
635s ***** demo
635s  ## Plot various PDFs from the noncentral T distribution
635s  x = -5:0.01:10;
635s  y1 = nctpdf (x, 1, 0);
635s  y2 = nctpdf (x, 4, 0);
635s  y3 = nctpdf (x, 1, 2);
635s  y4 = nctpdf (x, 4, 2);
635s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
635s  grid on
635s  xlim ([-5, 10])
635s  ylim ([0, 0.4])
635s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
635s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
635s  title ("Noncentral T PDF")
635s  xlabel ("values in x")
635s  ylabel ("density")
635s ***** demo
635s  ## Compare the noncentral T PDF with MU = 1 to the T PDF
635s  ## with the same number of degrees of freedom (10).
635s 
635s  x = -5:0.1:5;
635s  y1 = nctpdf (x, 10, 1);
635s  y2 = tpdf (x, 10);
635s  plot (x, y1, "-", x, y2, "-");
635s  grid on
635s  xlim ([-5, 5])
635s  ylim ([0, 0.4])
635s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
635s  title ("Noncentral T vs T PDFs")
635s  xlabel ("values in x")
635s  ylabel ("density")
635s ***** shared x1, df, mu
635s  x1 = [-Inf, 2, NaN, 4, Inf];
635s  df = [2, 0, -1, 1, 4];
635s  mu = [1, NaN, 3, -1, 2];
635s ***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
635s ***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
635s ***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
635s ***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
635s ***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
636s ***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
636s                              0.0146500727180389, 0.3082302682110299], 1e-14);
636s ***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
636s                              0.00401787561306999, 0.0972086534042828], 1e-14);
636s ***** error<nctpdf: function called with too few input arguments.> nctpdf ()
636s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
636s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
636s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
636s  nctpdf (ones (3), ones (2), ones (2))
636s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
636s  nctpdf (ones (2), ones (3), ones (2))
636s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
636s  nctpdf (ones (2), ones (2), ones (3))
636s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
636s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
636s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
636s 16 tests, 16 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/raylrnd.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/raylrnd.m
636s ***** assert (size (raylrnd (2)), [1, 1])
636s ***** assert (size (raylrnd (ones (2,1))), [2, 1])
636s ***** assert (size (raylrnd (ones (2,2))), [2, 2])
636s ***** assert (size (raylrnd (1, 3)), [3, 3])
636s ***** assert (size (raylrnd (1, [4 1])), [4, 1])
636s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
636s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
636s ***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
636s ***** assert (size (raylrnd (1, 0, 1)), [0, 1])
636s ***** assert (size (raylrnd (1, 1, 0)), [1, 0])
636s ***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
636s ***** assert (raylrnd (0, 1, 1), NaN)
636s ***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
636s ***** assert (class (raylrnd (2)), "double")
636s ***** assert (class (raylrnd (single (2))), "single")
636s ***** assert (class (raylrnd (single ([2 2]))), "single")
636s ***** error<raylrnd: function called with too few input arguments.> raylrnd ()
636s ***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (1, -1)
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (1, 1.2)
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (1, ones (2))
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (1, [2 -1 2])
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (1, [2 0 2.5])
636s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  raylrnd (ones (2), ones (2))
636s ***** error<raylrnd: dimensions must be non-negative integers.> ...
636s  raylrnd (1, 2, -1, 5)
636s ***** error<raylrnd: dimensions must be non-negative integers.> ...
636s  raylrnd (1, 2, 1.5, 5)
636s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
636s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
636s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
636s 29 tests, 29 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/trnd.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/trnd.m
636s ***** assert (size (trnd (2)), [1, 1])
636s ***** assert (size (trnd (ones (2,1))), [2, 1])
636s ***** assert (size (trnd (ones (2,2))), [2, 2])
636s ***** assert (size (trnd (1, 3)), [3, 3])
636s ***** assert (size (trnd (1, [4 1])), [4, 1])
636s ***** assert (size (trnd (1, 4, 1)), [4, 1])
636s ***** assert (size (trnd (1, 4, 1)), [4, 1])
636s ***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
636s ***** assert (size (trnd (1, 0, 1)), [0, 1])
636s ***** assert (size (trnd (1, 1, 0)), [1, 0])
636s ***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
636s ***** assert (trnd (0, 1, 1), NaN)
636s ***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
636s ***** assert (class (trnd (2)), "double")
636s ***** assert (class (trnd (single (2))), "single")
636s ***** assert (class (trnd (single ([2 2]))), "single")
636s ***** error<trnd: function called with too few input arguments.> trnd ()
636s ***** error<trnd: DF must not be complex.> trnd (i)
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (1, -1)
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (1, 1.2)
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (1, ones (2))
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (1, [2 -1 2])
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (1, [2 0 2.5])
636s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  trnd (ones (2), ones (2))
636s ***** error<trnd: dimensions must be non-negative integers.> ...
636s  trnd (1, 2, -1, 5)
636s ***** error<trnd: dimensions must be non-negative integers.> ...
636s  trnd (1, 2, 1.5, 5)
636s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
636s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
636s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
636s 29 tests, 29 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/burrinv.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/burrinv.m
636s ***** demo
636s  ## Plot various iCDFs from the Burr type XII distribution
636s  p = 0.001:0.001:0.999;
636s  x1 = burrinv (p, 1, 1, 1);
636s  x2 = burrinv (p, 1, 1, 2);
636s  x3 = burrinv (p, 1, 1, 3);
636s  x4 = burrinv (p, 1, 2, 1);
636s  x5 = burrinv (p, 1, 3, 1);
636s  x6 = burrinv (p, 1, 0.5, 2);
636s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
636s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
636s  grid on
636s  ylim ([0, 5])
636s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
636s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
636s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
636s          "location", "northwest")
636s  title ("Burr type XII iCDF")
636s  xlabel ("probability")
636s  ylabel ("values in x")
636s ***** shared p, y
636s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
636s  y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
636s ***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
636s ***** assert (burrinv (p, 1, 1, 1), y, eps)
636s ***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
636s ***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
636s ***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
636s ***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
636s ***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
636s ***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
636s ***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
636s ***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
636s ***** error<burrinv: function called with too few input arguments.> burrinv ()
636s ***** error<burrinv: function called with too few input arguments.> burrinv (1)
636s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
636s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
636s ***** error<burrinv: function called with too many inputs> ...
636s  burrinv (1, 2, 3, 4, 5)
636s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
636s  burrinv (ones (3), ones (2), ones(2), ones(2))
636s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
636s  burrinv (ones (2), ones (3), ones(2), ones(2))
636s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
636s  burrinv (ones (2), ones (2), ones(3), ones(2))
636s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
636s  burrinv (ones (2), ones (2), ones(2), ones(3))
636s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
636s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
636s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
636s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
636s 23 tests, 23 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/hygeinv.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hygeinv.m
636s ***** demo
636s  ## Plot various iCDFs from the hypergeometric distribution
636s  p = 0.001:0.001:0.999;
636s  x1 = hygeinv (p, 500, 50, 100);
636s  x2 = hygeinv (p, 500, 60, 200);
636s  x3 = hygeinv (p, 500, 70, 300);
636s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
636s  grid on
636s  ylim ([0, 60])
636s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
636s           "m = 500, k = 70, n = 300"}, "location", "northwest")
636s  title ("Hypergeometric iCDF")
636s  xlabel ("probability")
636s  ylabel ("values in p (number of successes)")
636s ***** shared p
636s  p = [-1 0 0.5 1 2];
636s ***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
636s ***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
636s ***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
636s ***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
636s ***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
636s ***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
636s ***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
636s ***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
636s ***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
636s ***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
636s ***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
636s ***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
636s ***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
636s ***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
636s ***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
636s ***** error<hygeinv: function called with too few input arguments.> hygeinv ()
636s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
636s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
636s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
636s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
636s  hygeinv (ones (2), ones (3), 1, 1)
636s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
636s  hygeinv (1, ones (2), ones (3), 1)
636s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
636s  hygeinv (1, 1, ones (2), ones (3))
636s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
636s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
636s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
636s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
636s 26 tests, 26 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/hninv.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hninv.m
636s ***** demo
636s  ## Plot various iCDFs from the half-normal distribution
636s  p = 0.001:0.001:0.999;
636s  x1 = hninv (p, 0, 1);
636s  x2 = hninv (p, 0, 2);
636s  x3 = hninv (p, 0, 3);
636s  x4 = hninv (p, 0, 5);
636s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
636s  grid on
636s  ylim ([0, 10])
636s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
636s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
636s  title ("Half-normal iCDF")
636s  xlabel ("probability")
636s  ylabel ("x")
636s ***** shared p, x
636s  p = [0, 0.3829, 0.6827, 1];
636s  x = [0, 1/2, 1, Inf];
636s ***** assert (hninv (p, 0, 1), x, 1e-4);
636s ***** assert (hninv (p, 5, 1), x + 5, 1e-4);
636s ***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
636s ***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
636s ***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
636s ***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
636s ***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
636s ***** error<hninv: function called with too few input arguments.> hninv (1)
636s ***** error<hninv: function called with too few input arguments.> hninv (1, 2)
636s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
636s  hninv (1, ones (2), ones (3))
636s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
636s  hninv (ones (2), 1, ones (3))
636s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
636s  hninv (ones (2), ones (3), 1)
636s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
636s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
636s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
636s 15 tests, 15 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/normcdf.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/normcdf.m
636s ***** demo
636s  ## Plot various CDFs from the normal distribution
636s  x = -5:0.01:5;
636s  p1 = normcdf (x, 0, 0.5);
636s  p2 = normcdf (x, 0, 1);
636s  p3 = normcdf (x, 0, 2);
636s  p4 = normcdf (x, -2, 0.8);
636s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
636s  grid on
636s  xlim ([-5, 5])
636s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
636s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
636s  title ("Normal CDF")
636s  xlabel ("values in x")
636s  ylabel ("probability")
636s ***** shared x, y
636s  x = [-Inf 1 2 Inf];
636s  y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
636s ***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
636s ***** assert (normcdf (x, 1, ones (1,4)), y)
636s ***** assert (normcdf (x, ones (1,4), 1), y)
636s ***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
636s ***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
636s ***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
636s ***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
636s ***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
636s ***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
636s ***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
636s ***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
636s ***** error<normcdf: invalid number of input arguments.> normcdf ()
636s ***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
636s ***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
636s ***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
636s  normcdf (ones (3), ones (2), ones (2))
636s ***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
636s ***** error<normcdf: covariance matrix is required for confidence bounds.> ...
636s  [p, plo, pup] = normcdf (1, 2, 3)
636s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
636s  normcdf (1, 2, 3, [1, 0; 0, 1], 0)
636s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
636s  normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
636s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
636s  normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
636s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
636s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
636s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
636s ***** error<normcdf: bad covariance matrix.> ...
636s  [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
636s 24 tests, 24 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/vmrnd.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/vmrnd.m
636s ***** assert (size (vmrnd (1, 1)), [1 1])
636s ***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
636s ***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
636s ***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
636s ***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
636s ***** assert (size (vmrnd (1, 1, 3)), [3, 3])
636s ***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
636s ***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
636s ***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
636s ***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
636s ***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
636s ***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
636s ***** assert (class (vmrnd (1, 1)), "double")
636s ***** assert (class (vmrnd (1, single (1))), "single")
636s ***** assert (class (vmrnd (1, single ([1, 1]))), "single")
636s ***** assert (class (vmrnd (single (1), 1)), "single")
636s ***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
636s ***** error<vmrnd: function called with too few input arguments.> vmrnd ()
636s ***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
636s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
636s  vmrnd (ones (3), ones (2))
636s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
636s  vmrnd (ones (2), ones (3))
636s ***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
636s ***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
636s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  vmrnd (1, 2, -1)
636s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  vmrnd (1, 2, 1.2)
636s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  vmrnd (1, 2, ones (2))
636s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  vmrnd (1, 2, [2 -1 2])
636s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
636s  vmrnd (1, 2, [2 0 2.5])
636s ***** error<vmrnd: dimensions must be non-negative integers.> ...
636s  vmrnd (1, 2, 2, -1, 5)
636s ***** error<vmrnd: dimensions must be non-negative integers.> ...
636s  vmrnd (1, 2, 2, 1.5, 5)
636s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
636s  vmrnd (2, ones (2), 3)
636s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
636s  vmrnd (2, ones (2), [3, 2])
636s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
636s  vmrnd (2, ones (2), 3, 2)
636s 33 tests, 33 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/gevinv.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gevinv.m
636s ***** demo
636s  ## Plot various iCDFs from the generalized extreme value distribution
636s  p = 0.001:0.001:0.999;
636s  x1 = gevinv (p, 1, 1, 1);
636s  x2 = gevinv (p, 0.5, 1, 1);
636s  x3 = gevinv (p, 1, 1, 5);
636s  x4 = gevinv (p, 1, 2, 5);
636s  x5 = gevinv (p, 1, 5, 5);
636s  x6 = gevinv (p, 1, 0.5, 5);
636s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
636s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
636s  grid on
636s  ylim ([-1, 10])
636s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
636s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
636s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
636s          "location", "northwest")
636s  title ("Generalized extreme value iCDF")
636s  xlabel ("probability")
636s  ylabel ("values in x")
636s ***** test
636s  p = 0.1:0.1:0.9;
636s  k = 0;
636s  sigma = 1;
636s  mu = 0;
636s  x = gevinv (p, k, sigma, mu);
636s  c = gevcdf(x, k, sigma, mu);
636s  assert (c, p, 0.001);
636s ***** test
636s  p = 0.1:0.1:0.9;
636s  k = 1;
636s  sigma = 1;
636s  mu = 0;
636s  x = gevinv (p, k, sigma, mu);
636s  c = gevcdf(x, k, sigma, mu);
636s  assert (c, p, 0.001);
636s ***** test
636s  p = 0.1:0.1:0.9;
636s  k = 0.3;
636s  sigma = 1;
636s  mu = 0;
636s  x = gevinv (p, k, sigma, mu);
636s  c = gevcdf(x, k, sigma, mu);
636s  assert (c, p, 0.001);
636s ***** error<gevinv: function called with too few input arguments.> gevinv ()
636s ***** error<gevinv: function called with too few input arguments.> gevinv (1)
636s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
636s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
636s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
636s  gevinv (ones (3), ones (2), ones(2), ones(2))
636s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
636s  gevinv (ones (2), ones (3), ones(2), ones(2))
636s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
636s  gevinv (ones (2), ones (2), ones(3), ones(2))
636s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
636s  gevinv (ones (2), ones (2), ones(2), ones(3))
636s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
636s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
636s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
636s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
636s 15 tests, 15 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/expinv.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/expinv.m
636s ***** demo
636s  ## Plot various iCDFs from the exponential distribution
636s  p = 0.001:0.001:0.999;
636s  x1 = expinv (p, 2/3);
636s  x2 = expinv (p, 1.0);
636s  x3 = expinv (p, 2.0);
636s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
636s  grid on
636s  ylim ([0, 5])
636s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
636s  title ("Exponential iCDF")
636s  xlabel ("probability")
636s  ylabel ("values in x")
636s ***** shared p
636s  p = [-1 0 0.3934693402873666 1 2];
636s ***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
636s ***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
636s ***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
636s ***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
636s ***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
636s ***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
636s ***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
636s ***** error<expinv: invalid number of input arguments.> expinv ()
636s ***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
636s ***** error<expinv: P and MU must be of common size or scalars.> ...
636s  expinv (ones (3), ones (2))
636s ***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
636s  expinv (2, 3, [1, 2])
636s ***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
636s  [x, xlo, xup] = expinv (1, 2)
636s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
636s  expinv (1, 2, 3, 0)
636s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
636s  expinv (1, 2, 3, 1.22)
636s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
636s  expinv (1, 2, 3, [0.05, 0.1])
636s ***** error<expinv: P and MU must not be complex.> expinv (i, 2)
636s ***** error<expinv: P and MU must not be complex.> expinv (2, i)
636s ***** error<expinv: variance, PCOV, cannot be negative.> ...
636s  [x, xlo, xup] = expinv (1, 2, -1, 0.04)
636s 18 tests, 18 passed, 0 known failure, 0 skipped
636s [inst/dist_fun/invgcdf.m]
636s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/invgcdf.m
636s ***** demo
636s  ## Plot various CDFs from the inverse Gaussian distribution
636s  x = 0:0.001:3;
636s  p1 = invgcdf (x, 1, 0.2);
636s  p2 = invgcdf (x, 1, 1);
636s  p3 = invgcdf (x, 1, 3);
636s  p4 = invgcdf (x, 3, 0.2);
636s  p5 = invgcdf (x, 3, 1);
636s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
636s  grid on
636s  xlim ([0, 3])
636s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
636s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
636s  title ("Inverse Gaussian CDF")
636s  xlabel ("values in x")
636s  ylabel ("probability")
636s ***** shared x, p1, p1u, y2, y2u, y3, y3u
636s  x = [-Inf, -1, 0, 1/2, 1, Inf];
636s  p1 = [0, 0, 0, 0.3650, 0.6681, 1];
636s  p1u = [1, 1, 1, 0.6350, 0.3319, 0];
636s ***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
637s ***** assert (invgcdf (x, 1, 1), p1, 1e-4)
637s ***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
637s ***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
637s ***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
637s ***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
637s ***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
637s ***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
637s ***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
637s ***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
637s ***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
637s ***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
637s ***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
637s ***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
637s ***** error<invgcdf: function called with too few input arguments.> invgcdf ()
637s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
637s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
637s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
637s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
637s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
637s  invgcdf (ones (3), ones (2), ones(2))
637s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
637s  invgcdf (ones (2), ones (3), ones(2))
637s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
637s  invgcdf (ones (2), ones (2), ones(3))
637s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
637s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
637s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
637s 25 tests, 25 passed, 0 known failure, 0 skipped
637s [inst/dist_fun/plrnd.m]
637s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/plrnd.m
637s ***** shared x, Fx
637s  x = [0, 1, 3, 4, 7, 10];
637s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
637s ***** assert (size (plrnd (x, Fx)), [1, 1])
637s ***** assert (size (plrnd (x, Fx, 3)), [3, 3])
637s ***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
637s ***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
637s ***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
637s ***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
637s ***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
637s ***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
637s ***** assert (class (plrnd (x, Fx)), "double")
637s ***** assert (class (plrnd (x, single (Fx))), "single")
637s ***** assert (class (plrnd (single (x), Fx)), "single")
637s ***** error<plrnd: function called with too few input arguments.> plrnd ()
637s ***** error<plrnd: function called with too few input arguments.> plrnd (1)
637s ***** error<plrnd: X and FX must be vectors of equal size.> ...
637s  plrnd ([0, 1, 2], [0, 1])
637s ***** error<plrnd: X and FX must be at least two-elements long.> ...
637s  plrnd ([0], [1])
637s ***** error<plrnd: FX must be bounded in the range> ...
637s  plrnd ([0, 1, 2], [0, 1, 1.5])
637s ***** error<plrnd: FX must be bounded in the range> ...
637s  plrnd ([0, 1, 2], [0, i, 1])
637s ***** error<plrnd: X and FX must not be complex.> ...
637s  plrnd ([0, i, 2], [0, 0.5, 1])
637s ***** error<plrnd: X and FX must not be complex.> ...
637s  plrnd ([0, i, 2], [0, 0.5i, 1])
637s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
637s  plrnd (x, Fx, -1)
637s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
637s  plrnd (x, Fx, 1.2)
637s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
637s  plrnd (x, Fx, ones (2))
637s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
637s  plrnd (x, Fx, [2 -1 2])
637s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
637s  plrnd (x, Fx, [2 0 2.5])
637s ***** error<plrnd: dimensions must be non-negative integers.> ...
637s  plrnd (x, Fx, 2, -1, 5)
637s ***** error<plrnd: dimensions must be non-negative integers.> ...
637s  plrnd (x, Fx, 2, 1.5, 5)
637s 26 tests, 26 passed, 0 known failure, 0 skipped
637s [inst/dist_fun/nakapdf.m]
637s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nakapdf.m
637s ***** demo
637s  ## Plot various PDFs from the Nakagami distribution
637s  x = 0:0.01:3;
637s  y1 = nakapdf (x, 0.5, 1);
637s  y2 = nakapdf (x, 1, 1);
637s  y3 = nakapdf (x, 1, 2);
637s  y4 = nakapdf (x, 1, 3);
637s  y5 = nakapdf (x, 2, 1);
637s  y6 = nakapdf (x, 2, 2);
637s  y7 = nakapdf (x, 5, 1);
637s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
637s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
637s  grid on
637s  xlim ([0, 3])
637s  ylim ([0, 2])
637s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
637s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
637s           "μ = 5, ω = 1"}, "location", "northeast")
637s  title ("Nakagami PDF")
637s  xlabel ("values in x")
637s  ylabel ("density")
637s ***** shared x, y
637s  x = [-1, 0, 1, 2, Inf];
637s  y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
637s ***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
637s ***** assert (nakapdf (x, 1, 1), y, eps)
637s ***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
637s ***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
637s ***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
637s ***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
637s ***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
637s ***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
637s ***** error<nakapdf: function called with too few input arguments.> nakapdf ()
637s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
637s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
637s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
637s  nakapdf (ones (3), ones (2), ones(2))
637s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
637s  nakapdf (ones (2), ones (3), ones(2))
637s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
637s  nakapdf (ones (2), ones (2), ones(3))
637s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
637s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
637s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
637s 17 tests, 17 passed, 0 known failure, 0 skipped
637s [inst/dist_fun/vmcdf.m]
637s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/vmcdf.m
637s ***** demo
637s  ## Plot various CDFs from the von Mises distribution
637s  x1 = [-pi:0.1:pi];
637s  p1 = vmcdf (x1, 0, 0.5);
637s  p2 = vmcdf (x1, 0, 1);
637s  p3 = vmcdf (x1, 0, 2);
637s  p4 = vmcdf (x1, 0, 4);
637s  plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
637s  grid on
637s  xlim ([-pi, pi])
637s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
637s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
637s  title ("Von Mises CDF")
637s  xlabel ("values in x")
637s  ylabel ("probability")
637s ***** shared x, p0, p1
637s  x = [-pi:pi/2:pi];
637s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
637s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
637s ***** assert (vmcdf (x, 0, 1), p0, 1e-5)
637s ***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
637s ***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
637s ***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
637s ***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
637s ***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
637s ***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
637s ***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
637s ***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
637s ***** error<vmcdf: function called with too few input arguments.> vmcdf ()
637s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
637s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
637s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
637s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
637s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
637s  vmcdf (ones (3), ones (2), ones (2))
637s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
637s  vmcdf (ones (2), ones (3), ones (2))
637s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
637s  vmcdf (ones (2), ones (2), ones (3))
637s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
637s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
637s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
637s 20 tests, 20 passed, 0 known failure, 0 skipped
637s [inst/dist_fun/unifpdf.m]
637s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unifpdf.m
637s ***** demo
637s  ## Plot various PDFs from the continuous uniform distribution
637s  x = 0:0.001:10;
637s  y1 = unifpdf (x, 2, 5);
637s  y2 = unifpdf (x, 3, 9);
637s  plot (x, y1, "-b", x, y2, "-g")
637s  grid on
637s  xlim ([0, 10])
637s  ylim ([0, 0.4])
637s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
637s  title ("Continuous uniform PDF")
637s  xlabel ("values in x")
637s  ylabel ("density")
637s ***** shared x, y
637s  x = [-1 0 0.5 1 2] + 1;
637s  y = [0 1 1 1 0];
637s ***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
637s ***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
637s ***** assert (unifpdf (x, ones (1,5), 2), y)
637s ***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
637s ***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
637s ***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
637s ***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
637s ***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
637s ***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
637s ***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
637s ***** error<unifpdf: function called with too few input arguments.> unifpdf ()
637s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
637s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
637s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
637s  unifpdf (ones (3), ones (2), ones (2))
637s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
637s  unifpdf (ones (2), ones (3), ones (2))
637s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
637s  unifpdf (ones (2), ones (2), ones (3))
637s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
637s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
637s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
637s 19 tests, 19 passed, 0 known failure, 0 skipped
637s [inst/dist_fun/gampdf.m]
637s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gampdf.m
637s ***** demo
637s  ## Plot various PDFs from the Gamma distribution
637s  x = 0:0.01:20;
637s  y1 = gampdf (x, 1, 2);
637s  y2 = gampdf (x, 2, 2);
637s  y3 = gampdf (x, 3, 2);
637s  y4 = gampdf (x, 5, 1);
637s  y5 = gampdf (x, 9, 0.5);
637s  y6 = gampdf (x, 7.5, 1);
637s  y7 = gampdf (x, 0.5, 1);
637s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
637s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
637s  grid on
637s  ylim ([0,0.5])
637s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
637s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
637s           "α = 0.5, β = 1"}, "location", "northeast")
637s  title ("Gamma PDF")
637s  xlabel ("values in x")
637s  ylabel ("density")
637s ***** shared x, y
637s  x = [-1 0 0.5 1 Inf];
637s  y = [0 exp(-x(2:end))];
637s ***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
637s ***** assert (gampdf (x, 1, ones (1,5)), y)
638s ***** assert (gampdf (x, ones (1,5), 1), y)
638s ***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN 0 y(5)])
638s ***** assert (gampdf (x, [0 Inf NaN Inf 1], 1), [NaN 0 NaN 0 y(5)])
638s ***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
638s ***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
638s ***** assert (gampdf (2, Inf, 4), 0)
638s ***** assert (gampdf (2, 4, Inf), 0)
638s ***** assert (gampdf (2, Inf, Inf), 0)
638s ***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
638s ***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
638s ***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
638s ***** error<gampdf: function called with too few input arguments.> gampdf ()
638s ***** error<gampdf: function called with too few input arguments.> gampdf (1)
638s ***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
638s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
638s  gampdf (ones (3), ones (2), ones (2))
638s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
638s  gampdf (ones (2), ones (3), ones (2))
638s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
638s  gampdf (ones (2), ones (2), ones (3))
638s ***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
638s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
638s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
638s 22 tests, 22 passed, 0 known failure, 0 skipped
638s [inst/dist_fun/gppdf.m]
638s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gppdf.m
638s ***** demo
638s  ## Plot various PDFs from the generalized Pareto distribution
638s  x = 0:0.001:5;
638s  y1 = gppdf (x, 1, 1, 0);
638s  y2 = gppdf (x, 5, 1, 0);
638s  y3 = gppdf (x, 20, 1, 0);
638s  y4 = gppdf (x, 1, 2, 0);
638s  y5 = gppdf (x, 5, 2, 0);
638s  y6 = gppdf (x, 20, 2, 0);
638s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
638s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
638s  grid on
638s  xlim ([0, 5])
638s  ylim ([0, 1])
638s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
638s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
638s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
638s          "location", "northeast")
638s  title ("Generalized Pareto PDF")
638s  xlabel ("values in x")
638s  ylabel ("density")
638s ***** shared x, y1, y2, y3
638s  x = [-Inf, -1, 0, 1/2, 1, Inf];
638s  y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
638s  y2 = [0, 0, 1, 4/9, 1/4, 0];
638s  y3 = [0, 0, 1, 1, 1, 0];
638s ***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
638s ***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
638s ***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
638s ***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
638s ***** assert (gppdf (x, 0, 1, 0), y1, eps)
638s ***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
638s ***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
638s ***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
638s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
638s ***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
638s ***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
638s ***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
638s ***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
638s ***** assert (gppdf (x, 1, 1, 0), y2, eps)
638s ***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
638s ***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
638s ***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
638s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
638s ***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
638s ***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
638s ***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
638s ***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
638s ***** assert (gppdf (x, -1, 1, 0), y3, eps)
638s ***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
638s ***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
638s ***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
638s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
638s ***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
638s ***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
638s ***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
638s ***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
638s ***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
638s ***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
638s ***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
638s ***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
638s ***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
638s ***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
638s ***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
638s ***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
638s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
638s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
638s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
638s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
638s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
638s  gpcdf (ones (3), ones (2), ones(2), ones(2))
638s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
638s  gpcdf (ones (2), ones (3), ones(2), ones(2))
638s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
638s  gpcdf (ones (2), ones (2), ones(3), ones(2))
638s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
638s  gpcdf (ones (2), ones (2), ones(2), ones(3))
638s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
638s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
638s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
638s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
638s 51 tests, 51 passed, 0 known failure, 0 skipped
638s [inst/dist_fun/loglpdf.m]
638s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/loglpdf.m
638s ***** demo
638s  ## Plot various PDFs from the log-logistic distribution
638s  x = 0.001:0.001:2;
638s  y1 = loglpdf (x, log (1), 1/0.5);
638s  y2 = loglpdf (x, log (1), 1);
638s  y3 = loglpdf (x, log (1), 1/2);
638s  y4 = loglpdf (x, log (1), 1/4);
638s  y5 = loglpdf (x, log (1), 1/8);
638s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
638s  grid on
638s  ylim ([0,3])
638s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
638s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
638s  title ("Log-logistic PDF")
638s  xlabel ("values in x")
638s  ylabel ("density")
638s  text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
638s ***** shared out1, out2
638s  out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
638s  out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
638s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
638s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
638s ***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
638s ***** assert (class (loglpdf (single (1), 2, 3)), "single")
638s ***** assert (class (loglpdf (1, single (2), 3)), "single")
638s ***** assert (class (loglpdf (1, 2, single (3))), "single")
638s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
638s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
638s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
638s  loglpdf (1, ones (2), ones (3))
638s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
638s  loglpdf (ones (2), 1, ones (3))
638s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
638s  loglpdf (ones (2), ones (3), 1)
638s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
638s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
638s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
638s 14 tests, 14 passed, 0 known failure, 0 skipped
638s [inst/dist_fun/vminv.m]
638s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/vminv.m
638s ***** demo
638s  ## Plot various iCDFs from the von Mises distribution
638s  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];
638s  x1 = vminv (p1, 0, 0.5);
638s  x2 = vminv (p1, 0, 1);
638s  x3 = vminv (p1, 0, 2);
638s  x4 = vminv (p1, 0, 4);
638s  plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
638s  grid on
638s  ylim ([-pi, pi])
638s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
638s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
638s  title ("Von Mises iCDF")
638s  xlabel ("probability")
638s  ylabel ("values in x")
638s ***** shared x, p0, p1
638s  x = [-pi:pi/2:pi];
638s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
638s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
638s ***** assert (vminv (p0, 0, 1), x, 5e-5)
638s ***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
639s ***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
639s ***** error<vminv: function called with too few input arguments.> vminv ()
639s ***** error<vminv: function called with too few input arguments.> vminv (1)
639s ***** error<vminv: function called with too few input arguments.> vminv (1, 2)
639s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
639s  vminv (ones (3), ones (2), ones (2))
639s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
639s  vminv (ones (2), ones (3), ones (2))
639s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
639s  vminv (ones (2), ones (2), ones (3))
639s ***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
639s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
639s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
639s 12 tests, 12 passed, 0 known failure, 0 skipped
639s [inst/dist_fun/binoinv.m]
639s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/binoinv.m
639s ***** demo
639s  ## Plot various iCDFs from the binomial distribution
639s  p = 0.001:0.001:0.999;
639s  x1 = binoinv (p, 20, 0.5);
639s  x2 = binoinv (p, 20, 0.7);
639s  x3 = binoinv (p, 40, 0.5);
639s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
639s  grid on
639s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
639s           "n = 40, ps = 0.5"}, "location", "southeast")
639s  title ("Binomial iCDF")
639s  xlabel ("probability")
639s  ylabel ("values in x (number of successes)")
639s ***** shared p
639s  p = [-1 0 0.5 1 2];
639s ***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
639s ***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
639s ***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
639s ***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
639s ***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
639s ***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
639s ***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
639s ***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
639s ***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
639s ***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
639s ***** shared x, tol
639s  x = magic (3) + 1;
639s  tol = 1;
639s ***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
639s ***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
639s ***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
639s ***** error<binoinv: function called with too few input arguments.> binoinv ()
639s ***** error<binoinv: function called with too few input arguments.> binoinv (1)
639s ***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
639s ***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
639s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
639s  binoinv (ones (3), ones (2), ones (2))
639s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
639s  binoinv (ones (2), ones (3), ones (2))
639s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
639s  binoinv (ones (2), ones (2), ones (3))
639s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
639s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
639s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
639s 23 tests, 23 passed, 0 known failure, 0 skipped
639s [inst/dist_fun/binopdf.m]
639s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/binopdf.m
639s ***** demo
639s  ## Plot various PDFs from the binomial distribution
639s  x = 0:40;
639s  y1 = binopdf (x, 20, 0.5);
639s  y2 = binopdf (x, 20, 0.7);
639s  y3 = binopdf (x, 40, 0.5);
639s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
639s  grid on
639s  ylim ([0, 0.25])
639s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
639s           "n = 40, ps = 0.5"}, "location", "northeast")
639s  title ("Binomial PDF")
639s  xlabel ("values in x (number of successes)")
639s  ylabel ("density")
639s ***** shared x, y
639s  x = [-1 0 1 2 3];
639s  y = [0 1/4 1/2 1/4 0];
639s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
639s ***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
639s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
639s ***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
639s ***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
639s ***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
639s ***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
639s ***** assert (binopdf (1, 1, 1), 1)
639s ***** assert (binopdf (0, 3, 0), 1)
639s ***** assert (binopdf (2, 2, 1), 1)
640s ***** assert (binopdf (1, 2, 1), 0)
640s ***** assert (binopdf (0, 1.1, 0), NaN)
640s ***** assert (binopdf (1, 2, -1), NaN)
640s ***** assert (binopdf (1, 2, 1.5), NaN)
640s ***** assert (binopdf ([], 1, 1), [])
640s ***** assert (binopdf (1, [], 1), [])
640s ***** assert (binopdf (1, 1, []), [])
640s ***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
640s ***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
640s ***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
640s ***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
640s ***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
640s ***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
640s ***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
640s ***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
640s ***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
640s ***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
640s ***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
640s ***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
640s ***** error<binopdf: function called with too few input arguments.> binopdf ()
640s ***** error<binopdf: function called with too few input arguments.> binopdf (1)
640s ***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
640s ***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
640s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
640s  binopdf (ones (3), ones (2), ones (2))
640s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
640s  binopdf (ones (2), ones (3), ones (2))
640s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
640s  binopdf (ones (2), ones (2), ones (3))
640s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
640s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
640s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
640s 39 tests, 39 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/poissrnd.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/poissrnd.m
640s ***** assert (size (poissrnd (2)), [1, 1])
640s ***** assert (size (poissrnd (ones (2,1))), [2, 1])
640s ***** assert (size (poissrnd (ones (2,2))), [2, 2])
640s ***** assert (size (poissrnd (1, 3)), [3, 3])
640s ***** assert (size (poissrnd (1, [4 1])), [4, 1])
640s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
640s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
640s ***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
640s ***** assert (size (poissrnd (1, 0, 1)), [0, 1])
640s ***** assert (size (poissrnd (1, 1, 0)), [1, 0])
640s ***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
640s ***** assert (poissrnd (0, 1, 1), 0)
640s ***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
640s ***** assert (class (poissrnd (2)), "double")
640s ***** assert (class (poissrnd (single (2))), "single")
640s ***** assert (class (poissrnd (single ([2 2]))), "single")
640s ***** error<poissrnd: function called with too few input arguments.> poissrnd ()
640s ***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (1, -1)
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (1, 1.2)
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (1, ones (2))
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (1, [2 -1 2])
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (1, [2 0 2.5])
640s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  poissrnd (ones (2), ones (2))
640s ***** error<poissrnd: dimensions must be non-negative integers.> ...
640s  poissrnd (1, 2, -1, 5)
640s ***** error<poissrnd: dimensions must be non-negative integers.> ...
640s  poissrnd (1, 2, 1.5, 5)
640s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
640s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
640s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
640s 29 tests, 29 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/binornd.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/binornd.m
640s ***** assert (size (binornd (2, 1/2)), [1 1])
640s ***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
640s ***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
640s ***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
640s ***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
640s ***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
640s ***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
640s ***** assert (size (binornd (2, 1/2, 3)), [3, 3])
640s ***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
640s ***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
640s ***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
640s ***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
640s ***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
640s ***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
640s ***** assert (class (binornd (1, 1)), "double")
640s ***** assert (class (binornd (1, single (0))), "single")
640s ***** assert (class (binornd (1, single ([0, 0]))), "single")
640s ***** assert (class (binornd (1, single (1), 2)), "single")
640s ***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
640s ***** assert (class (binornd (single (1), 1, 2)), "single")
640s ***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
640s ***** error<binornd: function called with too few input arguments.> binornd ()
640s ***** error<binornd: function called with too few input arguments.> binornd (1)
640s ***** error<binornd: N and PS must be of common size or scalars.> ...
640s  binornd (ones (3), ones (2))
640s ***** error<binornd: N and PS must be of common size or scalars.> ...
640s  binornd (ones (2), ones (3))
640s ***** error<binornd: N and PS must not be complex.> binornd (i, 2)
640s ***** error<binornd: N and PS must not be complex.> binornd (1, i)
640s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  binornd (1, 1/2, -1)
640s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  binornd (1, 1/2, 1.2)
640s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  binornd (1, 1/2, ones (2))
640s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  binornd (1, 1/2, [2 -1 2])
640s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  binornd (1, 1/2, [2 0 2.5])
640s ***** error<binornd: dimensions must be non-negative integers.> ...
640s  binornd (1, 1/2, 2, -1, 5)
640s ***** error<binornd: dimensions must be non-negative integers.> ...
640s  binornd (1, 1/2, 2, 1.5, 5)
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2, 1/2 * ones (2), 3)
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2, 1/2 * ones (2), [3, 2])
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2, 1/2 * ones (2), 3, 2)
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2 * ones (2), 1/2, 3)
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2 * ones (2), 1/2, [3, 2])
640s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
640s  binornd (2 * ones (2), 1/2, 3, 2)
640s 40 tests, 40 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/burrpdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/burrpdf.m
640s ***** demo
640s  ## Plot various PDFs from the Burr type XII distribution
640s  x = 0.001:0.001:3;
640s  y1 = burrpdf (x, 1, 1, 1);
640s  y2 = burrpdf (x, 1, 1, 2);
640s  y3 = burrpdf (x, 1, 1, 3);
640s  y4 = burrpdf (x, 1, 2, 1);
640s  y5 = burrpdf (x, 1, 3, 1);
640s  y6 = burrpdf (x, 1, 0.5, 2);
640s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
640s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
640s  grid on
640s  ylim ([0, 2])
640s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
640s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
640s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
640s          "location", "northeast")
640s  title ("Burr type XII PDF")
640s  xlabel ("values in x")
640s  ylabel ("density")
640s ***** shared x, y
640s  x = [-1, 0, 1, 2, Inf];
640s  y = [0, 1, 1/4, 1/9, 0];
640s ***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
640s ***** assert (burrpdf (x, 1, 1, 1), y)
640s ***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
640s ***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
640s ***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
640s ***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
640s ***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
640s ***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
640s ***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
640s ***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
640s ***** error<burrpdf: function called with too few input arguments.> burrpdf ()
640s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
640s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
640s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
640s ***** error<burrpdf: function called with too many inputs> ...
640s  burrpdf (1, 2, 3, 4, 5)
640s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
640s  burrpdf (ones (3), ones (2), ones(2), ones(2))
640s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
640s  burrpdf (ones (2), ones (3), ones(2), ones(2))
640s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
640s  burrpdf (ones (2), ones (2), ones(3), ones(2))
640s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
640s  burrpdf (ones (2), ones (2), ones(2), ones(3))
640s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
640s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
640s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
640s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
640s 23 tests, 23 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/gamcdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gamcdf.m
640s ***** demo
640s  ## Plot various CDFs from the Gamma distribution
640s  x = 0:0.01:20;
640s  p1 = gamcdf (x, 1, 2);
640s  p2 = gamcdf (x, 2, 2);
640s  p3 = gamcdf (x, 3, 2);
640s  p4 = gamcdf (x, 5, 1);
640s  p5 = gamcdf (x, 9, 0.5);
640s  p6 = gamcdf (x, 7.5, 1);
640s  p7 = gamcdf (x, 0.5, 1);
640s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
640s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
640s  grid on
640s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
640s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
640s           "α = 0.5, β = 1"}, "location", "southeast")
640s  title ("Gamma CDF")
640s  xlabel ("values in x")
640s  ylabel ("probability")
640s ***** shared x, y, u
640s  x = [-1, 0, 0.5, 1, 2, Inf];
640s  y = [0, gammainc(x(2:end), 1)];
640s  u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
640s ***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
640s ***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
640s ***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
640s ***** assert (gamcdf (x, ones (1,6), 1), y, eps)
640s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
640s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
640s ***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
640s ***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
640s ***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
640s ***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
640s ***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
640s ***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
640s ***** error<gamcdf: invalid number of input arguments.> gamcdf ()
640s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
640s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
640s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
640s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
640s ***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
640s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
640s  [p, plo, pup] = gamcdf (1, 2, 3)
640s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
640s  [p, plo, pup] = gamcdf (1, 2, 3, "upper")
640s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
640s  gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
640s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
640s  gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
640s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
640s  gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
640s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
640s  gamcdf (ones (3), ones (2), ones (2))
640s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
640s  gamcdf (ones (2), ones (3), ones (2))
640s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
640s  gamcdf (ones (2), ones (2), ones (3))
640s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
640s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
640s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
640s ***** error<gamcdf: bad covariance matrix.> ...
640s  [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
640s 30 tests, 30 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/copulapdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/copulapdf.m
640s ***** test
640s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
640s  theta = [1; 2];
640s  y = copulapdf ("Clayton", x, theta);
640s  expected_p = [0.9872; 0.7295];
640s  assert (y, expected_p, 0.001);
640s ***** test
640s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
640s  y = copulapdf ("Gumbel", x, 2);
640s  expected_p = [0.9468; 0.9468];
640s  assert (y, expected_p, 0.001);
640s ***** test
640s  x = [0.2, 0.6; 0.2, 0.6];
640s  theta = [1; 2];
640s  y = copulapdf ("Frank", x, theta);
640s  expected_p = [0.9378; 0.8678];
640s  assert (y, expected_p, 0.001);
640s ***** test
640s  x = [0.2, 0.6; 0.2, 0.6];
640s  theta = [0.3; 0.7];
640s  y = copulapdf ("AMH", x, theta);
640s  expected_p = [0.9540; 0.8577];
640s  assert (y, expected_p, 0.001);
640s 4 tests, 4 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/nakacdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nakacdf.m
640s ***** demo
640s  ## Plot various CDFs from the Nakagami distribution
640s  x = 0:0.01:3;
640s  p1 = nakacdf (x, 0.5, 1);
640s  p2 = nakacdf (x, 1, 1);
640s  p3 = nakacdf (x, 1, 2);
640s  p4 = nakacdf (x, 1, 3);
640s  p5 = nakacdf (x, 2, 1);
640s  p6 = nakacdf (x, 2, 2);
640s  p7 = nakacdf (x, 5, 1);
640s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
640s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
640s  grid on
640s  xlim ([0, 3])
640s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
640s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
640s           "μ = 5, ω = 1"}, "location", "southeast")
640s  title ("Nakagami CDF")
640s  xlabel ("values in x")
640s  ylabel ("probability")
640s ***** shared x, y
640s  x = [-1, 0, 1, 2, Inf];
640s  y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
640s ***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
640s ***** assert (nakacdf (x, 1, 1), y, eps)
640s ***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
640s ***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
640s ***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
640s ***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
640s ***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
640s ***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
640s ***** error<nakacdf: function called with too few input arguments.> nakacdf ()
640s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
640s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
640s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
640s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
640s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
640s  nakacdf (ones (3), ones (2), ones (2))
640s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
640s  nakacdf (ones (2), ones (3), ones (2))
640s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
640s  nakacdf (ones (2), ones (2), ones (3))
640s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
640s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
640s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
640s 19 tests, 19 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/gamrnd.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gamrnd.m
640s ***** assert (size (gamrnd (1, 1)), [1 1])
640s ***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
640s ***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
640s ***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
640s ***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
640s ***** assert (size (gamrnd (1, 1, 3)), [3, 3])
640s ***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
640s ***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
640s ***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
640s ***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
640s ***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
640s ***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
640s ***** assert (class (gamrnd (1, 1)), "double")
640s ***** assert (class (gamrnd (1, single (1))), "single")
640s ***** assert (class (gamrnd (1, single ([1, 1]))), "single")
640s ***** assert (class (gamrnd (single (1), 1)), "single")
640s ***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
640s ***** error<gamrnd: function called with too few input arguments.> gamrnd ()
640s ***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
640s ***** error<gamrnd: A and B must be of common size or scalars.> ...
640s  gamrnd (ones (3), ones (2))
640s ***** error<gamrnd: A and B must be of common size or scalars.> ...
640s  gamrnd (ones (2), ones (3))
640s ***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
640s ***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
640s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  gamrnd (1, 2, -1)
640s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  gamrnd (1, 2, 1.2)
640s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  gamrnd (1, 2, ones (2))
640s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  gamrnd (1, 2, [2 -1 2])
640s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
640s  gamrnd (1, 2, [2 0 2.5])
640s ***** error<gamrnd: dimensions must be non-negative integers.> ...
640s  gamrnd (1, 2, 2, -1, 5)
640s ***** error<gamrnd: dimensions must be non-negative integers.> ...
640s  gamrnd (1, 2, 2, 1.5, 5)
640s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
640s  gamrnd (2, ones (2), 3)
640s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
640s  gamrnd (2, ones (2), [3, 2])
640s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
640s  gamrnd (2, ones (2), 3, 2)
640s 33 tests, 33 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/mvnpdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvnpdf.m
640s ***** demo
640s  mu = [1, -1];
640s  sigma = [0.9, 0.4; 0.4, 0.3];
640s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
640s  x = [X1(:), X2(:)];
640s  p = mvnpdf (x, mu, sigma);
640s  surf (X1, X2, reshape (p, 25, 25));
640s ***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
640s ***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
640s ***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
640s ***** error<mvnpdf: columns in X and MU mismatch.> ...
640s  y = mvnpdf (ones (10, 2), [4, 2, 3]);
640s ***** error<mvnpdf: rows in X and MU mismatch.> ...
640s  y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
640s ***** error<mvnpdf: wrong size of MU.> ...
640s  y = mvnpdf (ones (10, 2), ones (3, 3, 3));
640s ***** shared x, mu, sigma
640s  x = [1, 2, 5, 4, 6];
640s  mu = [2, 0, -1, 1, 4];
640s  sigma = [2, 2, 2, 2, 2];
640s ***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
640s ***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
640s ***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
640s 9 tests, 9 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/betacdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/betacdf.m
640s ***** demo
640s  ## Plot various CDFs from the Beta distribution
640s  x = 0:0.005:1;
640s  p1 = betacdf (x, 0.5, 0.5);
640s  p2 = betacdf (x, 5, 1);
640s  p3 = betacdf (x, 1, 3);
640s  p4 = betacdf (x, 2, 2);
640s  p5 = betacdf (x, 2, 5);
640s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
640s  grid on
640s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
640s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
640s  title ("Beta CDF")
640s  xlabel ("values in x")
640s  ylabel ("probability")
640s ***** shared x, y, x1, x2
640s  x = [-1 0 0.5 1 2];
640s  y = [0 0 0.75 1 1];
640s ***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
640s ***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
640s ***** assert (betacdf (x, ones (1, 5), 2), y)
640s ***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
640s ***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
640s ***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
640s  x1 = [0.1:0.2:0.9];
640s ***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
640s ***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
640s         1e-14);
640s  x2 = [1, 2, 3];
640s ***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
640s ***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
640s ***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
640s ***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
640s ***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
640s ***** error<betacdf: function called with too few input arguments.> betacdf ()
640s ***** error<betacdf: function called with too few input arguments.> betacdf (1)
640s ***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
640s ***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
640s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
640s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
640s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
640s  betacdf (ones (3), ones (2), ones (2))
640s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
640s  betacdf (ones (2), ones (3), ones (2))
640s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
640s  betacdf (ones (2), ones (2), ones (3))
640s ***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
640s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
640s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
640s 25 tests, 25 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/tricdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tricdf.m
640s ***** demo
640s  ## Plot various CDFs from the triangular distribution
640s  x = 0.001:0.001:10;
640s  p1 = tricdf (x, 3, 4, 6);
640s  p2 = tricdf (x, 1, 2, 5);
640s  p3 = tricdf (x, 2, 3, 9);
640s  p4 = tricdf (x, 2, 5, 9);
640s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
640s  grid on
640s  xlim ([0, 10])
640s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
640s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
640s          "location", "southeast")
640s  title ("Triangular CDF")
640s  xlabel ("values in x")
640s  ylabel ("probability")
640s ***** shared x, y
640s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
640s  y = [0, 0, 0.02, 0.5, 0.98, 1 1];
640s ***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
640s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
640s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
640s ***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
640s ***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
640s ***** assert (tricdf (x, 1, 1.5, 2), y, eps)
640s ***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
640s  [y(1:2), NaN, y(4:7)], eps)
640s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
640s  [y(1:2), NaN, y(4:7)], eps)
640s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
640s  [y(1:2), NaN, y(4:7)], eps)
640s ***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
640s ***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
640s  single ([y, NaN]), eps("single"))
640s ***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
640s  single ([y, NaN]), eps("single"))
640s ***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
640s  single ([y, NaN]), eps("single"))
640s ***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
640s  single ([y, NaN]), eps("single"))
640s ***** error<tricdf: function called with too few input arguments.> tricdf ()
640s ***** error<tricdf: function called with too few input arguments.> tricdf (1)
640s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
640s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
640s ***** error<tricdf: function called with too many inputs> ...
640s  tricdf (1, 2, 3, 4, 5, 6)
640s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
640s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
640s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
640s  tricdf (ones (3), ones (2), ones(2), ones(2))
640s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
640s  tricdf (ones (2), ones (3), ones(2), ones(2))
640s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
640s  tricdf (ones (2), ones (2), ones(3), ones(2))
640s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
640s  tricdf (ones (2), ones (2), ones(2), ones(3))
640s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
640s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
640s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
640s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
640s 29 tests, 29 passed, 0 known failure, 0 skipped
640s [inst/dist_fun/plcdf.m]
640s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/plcdf.m
640s ***** demo
640s  ## Plot various CDFs from the Piecewise linear distribution
640s  data = 0:0.01:10;
640s  x1 = [0, 1, 3, 4, 7, 10];
640s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
640s  x2 = [0, 2, 5, 6, 7, 8];
640s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
640s  p1 = plcdf (data, x1, Fx1);
640s  p2 = plcdf (data, x2, Fx2);
640s  plot (data, p1, "-b", data, p2, "g")
640s  grid on
640s  ylim ([0, 1])
640s  xlim ([0, 10])
640s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
640s  title ("Piecewise linear CDF")
640s  xlabel ("values in data")
640s  ylabel ("probability")
640s ***** test
640s  data = 0:0.2:1;
640s  p = plcdf (data, [0, 1], [0, 1]);
640s  assert (p, data);
640s ***** test
640s  data = 0:0.2:1;
640s  p = plcdf (data, [0, 2], [0, 1]);
640s  assert (p, 0.5 * data);
640s ***** test
640s  data = 0:0.2:1;
640s  p = plcdf (data, [0, 1], [0, 0.5]);
640s  assert (p, 0.5 * data);
640s ***** test
640s  data = 0:0.2:1;
640s  p = plcdf (data, [0, 0.5], [0, 1]);
640s  assert (p, [0, 0.4, 0.8, 1, 1, 1]);
640s ***** test
640s  data = 0:0.2:1;
640s  p = plcdf (data, [0, 1], [0, 1], "upper");
640s  assert (p, 1 - data);
641s ***** error<plcdf: function called with too few input arguments.> plcdf ()
641s ***** error<plcdf: function called with too few input arguments.> plcdf (1)
641s ***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
641s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
641s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
641s ***** error<plcdf: X and FX must be vectors of equal size.> ...
641s  plcdf (1, [0, 1, 2], [0, 1])
641s ***** error<plcdf: X and FX must be at least two-elements long.> ...
641s  plcdf (1, [0], [1])
641s ***** error<plcdf: FX must be bounded in the range> ...
641s  plcdf (1, [0, 1, 2], [0, 1, 1.5])
641s ***** error<plcdf: FX must be bounded in the range> ...
641s  plcdf (1, [0, 1, 2], [0, i, 1])
641s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
641s  plcdf (i, [0, 1, 2], [0, 0.5, 1])
641s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
641s  plcdf (1, [0, i, 2], [0, 0.5, 1])
641s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
641s  plcdf (1, [0, 1, 2], [0, 0.5i, 1])
641s 17 tests, 17 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/betarnd.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/betarnd.m
641s ***** assert (size (betarnd (2, 1/2)), [1 1])
641s ***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
641s ***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
641s ***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
641s ***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
641s ***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
641s ***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
641s ***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
641s ***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
641s ***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
641s ***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
641s ***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
641s ***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
641s ***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
641s ***** assert (class (betarnd (1, 1)), "double")
641s ***** assert (class (betarnd (1, single (0))), "single")
641s ***** assert (class (betarnd (1, single ([0, 0]))), "single")
641s ***** assert (class (betarnd (1, single (1), 2)), "single")
641s ***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
641s ***** assert (class (betarnd (single (1), 1, 2)), "single")
641s ***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
641s ***** error<betarnd: function called with too few input arguments.> betarnd ()
641s ***** error<betarnd: function called with too few input arguments.> betarnd (1)
641s ***** error<betarnd: A and B must be of common size or scalars.> ...
641s  betarnd (ones (3), ones (2))
641s ***** error<betarnd: A and B must be of common size or scalars.> ...
641s  betarnd (ones (2), ones (3))
641s ***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
641s ***** error<betarnd: A and B must not be complex.> betarnd (1, i)
641s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  betarnd (1, 1/2, -1)
641s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  betarnd (1, 1/2, 1.2)
641s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  betarnd (1, 1/2, ones (2))
641s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  betarnd (1, 1/2, [2 -1 2])
641s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  betarnd (1, 1/2, [2 0 2.5])
641s ***** error<betarnd: dimensions must be non-negative integers.> ...
641s  betarnd (1, 1/2, 2, -1, 5)
641s ***** error<betarnd: dimensions must be non-negative integers.> ...
641s  betarnd (1, 1/2, 2, 1.5, 5)
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2, 1/2 * ones (2), 3)
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2, 1/2 * ones (2), [3, 2])
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2, 1/2 * ones (2), 3, 2)
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2 * ones (2), 1/2, 3)
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2 * ones (2), 1/2, [3, 2])
641s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
641s  betarnd (2 * ones (2), 1/2, 3, 2)
641s 40 tests, 40 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/lognrnd.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/lognrnd.m
641s ***** assert (size (lognrnd (1, 1)), [1 1])
641s ***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
641s ***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
641s ***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
641s ***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
641s ***** assert (size (lognrnd (1, 1, 3)), [3, 3])
641s ***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
641s ***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
641s ***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
641s ***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
641s ***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
641s ***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
641s ***** assert (class (lognrnd (1, 1)), "double")
641s ***** assert (class (lognrnd (1, single (1))), "single")
641s ***** assert (class (lognrnd (1, single ([1, 1]))), "single")
641s ***** assert (class (lognrnd (single (1), 1)), "single")
641s ***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
641s ***** error<lognrnd: function called with too few input arguments.> lognrnd ()
641s ***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
641s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
641s  lognrnd (ones (3), ones (2))
641s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
641s  lognrnd (ones (2), ones (3))
641s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
641s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
641s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  lognrnd (1, 2, -1)
641s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  lognrnd (1, 2, 1.2)
641s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  lognrnd (1, 2, ones (2))
641s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  lognrnd (1, 2, [2 -1 2])
641s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  lognrnd (1, 2, [2 0 2.5])
641s ***** error<lognrnd: dimensions must be non-negative integers.> ...
641s  lognrnd (1, 2, 2, -1, 5)
641s ***** error<lognrnd: dimensions must be non-negative integers.> ...
641s  lognrnd (1, 2, 2, 1.5, 5)
641s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
641s  lognrnd (2, ones (2), 3)
641s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
641s  lognrnd (2, ones (2), [3, 2])
641s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
641s  lognrnd (2, ones (2), 3, 2)
641s 33 tests, 33 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/ncx2pdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncx2pdf.m
641s ***** demo
641s  ## Plot various PDFs from the noncentral chi-squared distribution
641s  x = 0:0.1:10;
641s  y1 = ncx2pdf (x, 2, 1);
641s  y2 = ncx2pdf (x, 2, 2);
641s  y3 = ncx2pdf (x, 2, 3);
641s  y4 = ncx2pdf (x, 4, 1);
641s  y5 = ncx2pdf (x, 4, 2);
641s  y6 = ncx2pdf (x, 4, 3);
641s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
641s        x, y4, "-m", x, y5, "-c", x, y6, "-y")
641s  grid on
641s  xlim ([0, 10])
641s  ylim ([0, 0.32])
641s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
641s           "df = 2, λ = 3", "df = 4, λ = 1", ...
641s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
641s  title ("Noncentral chi-squared PDF")
641s  xlabel ("values in x")
641s  ylabel ("density")
641s ***** demo
641s  ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
641s  ## chi-squared PDF with the same number of degrees of freedom (4).
641s 
641s  x = 0:0.1:10;
641s  y1 = ncx2pdf (x, 4, 2);
641s  y2 = chi2pdf (x, 4);
641s  plot (x, y1, "-", x, y2, "-");
641s  grid on
641s  xlim ([0, 10])
641s  ylim ([0, 0.32])
641s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
641s  title ("Noncentral chi-squared vs chi-squared PDFs")
641s  xlabel ("values in x")
641s  ylabel ("density")
641s ***** shared x1, df, d1
641s  x1 = [-Inf, 2, NaN, 4, Inf];
641s  df = [2, 0, -1, 1, 4];
641s  d1 = [1, NaN, 3, -1, 2];
641s ***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
641s ***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
641s                               0.06160064323277038, 0], 1e-14);
641s ***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
641s                               0.09631299762429098, 0], 1e-14);
641s ***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
641s                               0.08430464047296625, 0], 1e-14);
641s ***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
641s ***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
641s                               NaN, 0.1076346446244688], 1e-14);
641s ***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
641s                               NaN, 0.1192317192431485], 1e-14);
641s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
641s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
641s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
641s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
641s  ncx2pdf (ones (3), ones (2), ones (2))
641s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
641s  ncx2pdf (ones (2), ones (3), ones (2))
641s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
641s  ncx2pdf (ones (2), ones (2), ones (3))
641s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
641s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
641s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
641s 16 tests, 16 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/exprnd.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/exprnd.m
641s ***** assert (size (exprnd (2)), [1, 1])
641s ***** assert (size (exprnd (ones (2,1))), [2, 1])
641s ***** assert (size (exprnd (ones (2,2))), [2, 2])
641s ***** assert (size (exprnd (1, 3)), [3, 3])
641s ***** assert (size (exprnd (1, [4 1])), [4, 1])
641s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
641s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
641s ***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
641s ***** assert (size (exprnd (1, 0, 1)), [0, 1])
641s ***** assert (size (exprnd (1, 1, 0)), [1, 0])
641s ***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
641s ***** assert (class (exprnd (2)), "double")
641s ***** assert (class (exprnd (single (2))), "single")
641s ***** assert (class (exprnd (single ([2 2]))), "single")
641s ***** error<exprnd: function called with too few input arguments.> exprnd ()
641s ***** error<exprnd: MU must not be complex.> exprnd (i)
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (1, -1)
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (1, 1.2)
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (1, ones (2))
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (1, [2 -1 2])
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (1, [2 0 2.5])
641s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
641s  exprnd (ones (2), ones (2))
641s ***** error<exprnd: dimensions must be non-negative integers.> ...
641s  exprnd (1, 2, -1, 5)
641s ***** error<exprnd: dimensions must be non-negative integers.> ...
641s  exprnd (1, 2, 1.5, 5)
641s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
641s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
641s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
641s 27 tests, 27 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/unifcdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unifcdf.m
641s ***** demo
641s  ## Plot various CDFs from the continuous uniform distribution
641s  x = 0:0.1:10;
641s  p1 = unifcdf (x, 2, 5);
641s  p2 = unifcdf (x, 3, 9);
641s  plot (x, p1, "-b", x, p2, "-g")
641s  grid on
641s  xlim ([0, 10])
641s  ylim ([0, 1])
641s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
641s  title ("Continuous uniform CDF")
641s  xlabel ("values in x")
641s  ylabel ("probability")
641s ***** shared x, y
641s  x = [-1 0 0.5 1 2] + 1;
641s  y = [0 0 0.5 1 1];
641s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
641s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
641s ***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
641s ***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
641s ***** assert (unifcdf (x, ones (1,5), 2), y)
641s ***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
641s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
641s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
641s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
641s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
641s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
641s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
641s ***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
641s ***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
641s ***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
641s ***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
641s ***** error<unifcdf: function called with too few input arguments.> unifcdf ()
641s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
641s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
641s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
641s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
641s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
641s  unifcdf (ones (3), ones (2), ones (2))
641s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
641s  unifcdf (ones (2), ones (3), ones (2))
641s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
641s  unifcdf (ones (2), ones (2), ones (3))
641s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
641s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
641s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
641s 27 tests, 27 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/geocdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/geocdf.m
641s ***** demo
641s  ## Plot various CDFs from the geometric distribution
641s  x = 0:10;
641s  p1 = geocdf (x, 0.2);
641s  p2 = geocdf (x, 0.5);
641s  p3 = geocdf (x, 0.7);
641s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
641s  grid on
641s  xlim ([0, 10])
641s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
641s  title ("Geometric CDF")
641s  xlabel ("values in x (number of failures)")
641s  ylabel ("probability")
641s ***** test
641s  p = geocdf ([1, 2, 3, 4], 0.25);
641s  assert (p(1), 0.4375000000, 1e-14);
641s  assert (p(2), 0.5781250000, 1e-14);
641s  assert (p(3), 0.6835937500, 1e-14);
641s  assert (p(4), 0.7626953125, 1e-14);
641s ***** test
641s  p = geocdf ([1, 2, 3, 4], 0.25, "upper");
641s  assert (p(1), 0.5625000000, 1e-14);
641s  assert (p(2), 0.4218750000, 1e-14);
641s  assert (p(3), 0.3164062500, 1e-14);
641s  assert (p(4), 0.2373046875, 1e-14);
641s ***** shared x, p
641s  x = [-1 0 1 Inf];
641s  p = [0 0.5 0.75 1];
641s ***** assert (geocdf (x, 0.5*ones (1,4)), p)
641s ***** assert (geocdf (x, 0.5), p)
641s ***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
641s ***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
641s ***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
641s ***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
641s ***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
641s ***** error<geocdf: function called with too few input arguments.> geocdf ()
641s ***** error<geocdf: function called with too few input arguments.> geocdf (1)
641s ***** error<geocdf: X and PS must be of common size or scalars.> ...
641s  geocdf (ones (3), ones (2))
641s ***** error<geocdf: X and PS must be of common size or scalars.> ...
641s  geocdf (ones (2), ones (3))
641s ***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
641s ***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
641s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
641s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
641s 17 tests, 17 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/expcdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/expcdf.m
641s ***** demo
641s  ## Plot various CDFs from the exponential distribution
641s  x = 0:0.01:5;
641s  p1 = expcdf (x, 2/3);
641s  p2 = expcdf (x, 1.0);
641s  p3 = expcdf (x, 2.0);
641s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
641s  grid on
641s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
641s  title ("Exponential CDF")
641s  xlabel ("values in x")
641s  ylabel ("probability")
641s ***** shared x, p
641s  x = [-1 0 0.5 1 Inf];
641s  p = [0, 1 - exp(-x(2:end)/2)];
641s ***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
641s ***** assert (expcdf (x, 2), p, 1e-16)
641s ***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
641s ***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
641s ***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
641s ***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
641s ***** test
641s  [p, plo, pup] = expcdf (1, 2, 3);
641s  assert (p, 0.39346934028737, 1e-14);
641s  assert (plo, 0.08751307220484, 1e-14);
641s  assert (pup, 0.93476821257933, 1e-14);
641s ***** test
641s  [p, plo, pup] = expcdf (1, 2, 2, 0.1);
641s  assert (p, 0.39346934028737, 1e-14);
641s  assert (plo, 0.14466318041675, 1e-14);
641s  assert (pup, 0.79808291849140, 1e-14);
641s ***** test
641s  [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
641s  assert (p, 0.60653065971263, 1e-14);
641s  assert (plo, 0.20191708150860, 1e-14);
641s  assert (pup, 0.85533681958325, 1e-14);
641s ***** error<expcdf: invalid number of input arguments.> expcdf ()
641s ***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
641s ***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
641s ***** error<expcdf: X and MU must be of common size or scalars.> ...
641s  expcdf (ones (3), ones (2))
641s ***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
641s  expcdf (2, 3, [1, 2])
641s ***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
641s  [p, plo, pup] = expcdf (1, 2)
641s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
641s  expcdf (1, 2, 3, 0)
641s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
641s  expcdf (1, 2, 3, 1.22)
641s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
641s  expcdf (1, 2, 3, "alpha", "upper")
641s ***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
641s ***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
641s ***** error<expcdf: variance, PCOV, cannot be negative.> ...
641s  [p, plo, pup] = expcdf (1, 2, -1, 0.04)
641s 21 tests, 21 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/triinv.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/triinv.m
641s ***** demo
641s  ## Plot various iCDFs from the triangular distribution
641s  p = 0.001:0.001:0.999;
641s  x1 = triinv (p, 3, 6, 4);
641s  x2 = triinv (p, 1, 5, 2);
641s  x3 = triinv (p, 2, 9, 3);
641s  x4 = triinv (p, 2, 9, 5);
641s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
641s  grid on
641s  ylim ([0, 10])
641s  legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
641s           "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
641s          "location", "northwest")
641s  title ("Triangular CDF")
641s  xlabel ("probability")
641s  ylabel ("values in x")
641s ***** shared p, y
641s  p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
641s  y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
641s ***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
641s ***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
641s ***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
641s ***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
641s ***** assert (triinv (p, 1, 1.5, 2), y, eps)
641s ***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
641s ***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
641s ***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
641s ***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
641s ***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
641s ***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
641s ***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
641s ***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
641s ***** error<triinv: function called with too few input arguments.> triinv ()
641s ***** error<triinv: function called with too few input arguments.> triinv (1)
641s ***** error<triinv: function called with too few input arguments.> triinv (1, 2)
641s ***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
641s ***** error<triinv: function called with too many inputs> ...
641s  triinv (1, 2, 3, 4, 5)
641s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
641s  triinv (ones (3), ones (2), ones(2), ones(2))
641s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
641s  triinv (ones (2), ones (3), ones(2), ones(2))
641s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
641s  triinv (ones (2), ones (2), ones(3), ones(2))
641s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
641s  triinv (ones (2), ones (2), ones(2), ones(3))
641s ***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
641s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
641s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
641s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
641s 26 tests, 26 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/logicdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logicdf.m
641s ***** demo
641s  ## Plot various CDFs from the logistic distribution
641s  x = -5:0.01:20;
641s  p1 = logicdf (x, 5, 2);
641s  p2 = logicdf (x, 9, 3);
641s  p3 = logicdf (x, 9, 4);
641s  p4 = logicdf (x, 6, 2);
641s  p5 = logicdf (x, 2, 1);
641s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
641s  grid on
641s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
641s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
641s  title ("Logistic CDF")
641s  xlabel ("values in x")
641s  ylabel ("probability")
641s ***** shared x, y
641s  x = [-Inf -log(3) 0 log(3) Inf];
641s  y = [0, 1/4, 1/2, 3/4, 1];
641s ***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
641s ***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1], eps)
641s ***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
641s ***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
641s ***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
641s ***** error<logicdf: function called with too few input arguments.> logicdf ()
641s ***** error<logicdf: function called with too few input arguments.> logicdf (1)
641s ***** error<logicdf: function called with too few input arguments.> ...
641s  logicdf (1, 2)
641s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
641s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
641s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
641s  logicdf (1, ones (2), ones (3))
641s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
641s  logicdf (ones (2), 1, ones (3))
641s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
641s  logicdf (ones (2), ones (3), 1)
641s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
641s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
641s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
641s 16 tests, 16 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/mvnrnd.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvnrnd.m
641s ***** error<mvnrnd: too few input arguments.> mvnrnd ()
641s ***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
641s ***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
641s ***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
641s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
641s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
641s 6 tests, 6 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/unidcdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unidcdf.m
641s ***** demo
641s  ## Plot various CDFs from the discrete uniform distribution
641s  x = 0:10;
641s  p1 = unidcdf (x, 5);
641s  p2 = unidcdf (x, 9);
641s  plot (x, p1, "*b", x, p2, "*g")
641s  grid on
641s  xlim ([0, 10])
641s  ylim ([0, 1])
641s  legend ({"N = 5", "N = 9"}, "location", "southeast")
641s  title ("Discrete uniform CDF")
641s  xlabel ("values in x")
641s  ylabel ("probability")
641s ***** shared x, y
641s  x = [0 1 2.5 10 11];
641s  y = [0, 0.1 0.2 1.0 1.0];
641s ***** assert (unidcdf (x, 10*ones (1,5)), y)
641s ***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
641s ***** assert (unidcdf (x, 10), y)
641s ***** assert (unidcdf (x, 10, "upper"), 1 - y)
641s ***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
641s ***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
641s ***** assert (unidcdf ([x, NaN], 10), [y, NaN])
641s ***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
641s ***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
641s ***** error<unidcdf: function called with too few input arguments.> unidcdf ()
641s ***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
641s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
641s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
641s ***** error<unidcdf: X and N must be of common size or scalars.> ...
641s  unidcdf (ones (3), ones (2))
641s ***** error<unidcdf: X and N must be of common size or scalars.> ...
641s  unidcdf (ones (2), ones (3))
641s ***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
641s ***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
641s 17 tests, 17 passed, 0 known failure, 0 skipped
641s [inst/dist_fun/ncfpdf.m]
641s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncfpdf.m
641s ***** demo
641s  ## Plot various PDFs from the noncentral F distribution
641s  x = 0:0.01:5;
641s  y1 = ncfpdf (x, 2, 5, 1);
641s  y2 = ncfpdf (x, 2, 5, 2);
641s  y3 = ncfpdf (x, 5, 10, 1);
641s  y4 = ncfpdf (x, 10, 20, 10);
641s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
641s  grid on
641s  xlim ([0, 5])
641s  ylim ([0, 0.8])
641s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
641s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
641s          "location", "northeast")
641s  title ("Noncentral F PDF")
641s  xlabel ("values in x")
641s  ylabel ("density")
641s ***** demo
641s  ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
641s  ## same number of numerator and denominator degrees of freedom (5, 20)
641s 
641s  x = 0.01:0.1:10.01;
641s  y1 = ncfpdf (x, 5, 20, 10);
641s  y2 = fpdf (x, 5, 20);
641s  plot (x, y1, "-", x, y2, "-");
641s  grid on
641s  xlim ([0, 10])
641s  ylim ([0, 0.8])
641s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
641s  title ("Noncentral F vs F PDFs")
641s  xlabel ("values in x")
641s  ylabel ("density")
641s ***** shared x1, df1, df2, lambda
641s  x1 = [-Inf, 2, NaN, 4, Inf];
641s  df1 = [2, 0, -1, 1, 4];
641s  df2 = [2, 4, 5, 6, 8];
641s  lambda = [1, NaN, 3, -1, 2];
641s ***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
641s ***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
641s                                    0.05607937264237208, NaN], 1e-14);
641s ***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
641s                                    0.080125760971946518, NaN], 1e-14);
641s ***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
641s                                    0.0715902008258656, NaN], 1e-14);
641s ***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
641s ***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
641s                                       NaN, 0.2152571783045893], 1e-14);
641s ***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
641s                                       NaN, 0.05560846335398539], 1e-14);
641s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
641s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
641s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
641s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
641s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
641s  ncfpdf (ones (3), ones (2), ones (2), ones (2))
641s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
641s  ncfpdf (ones (2), ones (3), ones (2), ones (2))
641s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
641s  ncfpdf (ones (2), ones (2), ones (3), ones (2))
641s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
641s  ncfpdf (ones (2), ones (2), ones (2), ones (3))
641s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
642s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
642s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
642s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
642s 19 tests, 19 passed, 0 known failure, 0 skipped
642s [inst/dist_fun/ricernd.m]
642s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ricernd.m
642s ***** assert (size (ricernd (2, 1/2)), [1, 1])
642s ***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
642s ***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
642s ***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
642s ***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
642s ***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
642s ***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
642s ***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
642s ***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
642s ***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
642s ***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
642s ***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
642s ***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
642s ***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
642s ***** assert (class (ricernd (1, 1)), "double")
642s ***** assert (class (ricernd (1, single (0))), "single")
642s ***** assert (class (ricernd (1, single ([0, 0]))), "single")
642s ***** assert (class (ricernd (1, single (1), 2)), "single")
642s ***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
642s ***** assert (class (ricernd (single (1), 1, 2)), "single")
642s ***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
642s ***** error<ricernd: function called with too few input arguments.> ricernd ()
642s ***** error<ricernd: function called with too few input arguments.> ricernd (1)
642s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
642s  ricernd (ones (3), ones (2))
642s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
642s  ricernd (ones (2), ones (3))
642s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
642s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
642s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  ricernd (1, 1/2, -1)
642s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  ricernd (1, 1/2, 1.2)
642s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  ricernd (1, 1/2, ones (2))
642s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  ricernd (1, 1/2, [2 -1 2])
642s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  ricernd (1, 1/2, [2 0 2.5])
642s ***** error<ricernd: dimensions must be non-negative integers.> ...
642s  ricernd (1, 1/2, 2, -1, 5)
642s ***** error<ricernd: dimensions must be non-negative integers.> ...
642s  ricernd (1, 1/2, 2, 1.5, 5)
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2, 1/2 * ones (2), 3)
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2, 1/2 * ones (2), [3, 2])
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2, 1/2 * ones (2), 3, 2)
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2 * ones (2), 1/2, 3)
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2 * ones (2), 1/2, [3, 2])
642s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
642s  ricernd (2 * ones (2), 1/2, 3, 2)
642s 40 tests, 40 passed, 0 known failure, 0 skipped
642s [inst/dist_fun/chi2cdf.m]
642s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/chi2cdf.m
642s ***** demo
642s  ## Plot various CDFs from the chi-squared distribution
642s  x = 0:0.01:8;
642s  p1 = chi2cdf (x, 1);
642s  p2 = chi2cdf (x, 2);
642s  p3 = chi2cdf (x, 3);
642s  p4 = chi2cdf (x, 4);
642s  p5 = chi2cdf (x, 6);
642s  p6 = chi2cdf (x, 9);
642s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
642s        x, p4, "-c", x, p5, "-m", x, p6, "-y")
642s  grid on
642s  xlim ([0, 8])
642s  legend ({"df = 1", "df = 2", "df = 3", ...
642s           "df = 4", "df = 6", "df = 9"}, "location", "southeast")
642s  title ("Chi-squared CDF")
642s  xlabel ("values in x")
642s  ylabel ("probability")
642s ***** shared x, p, u
642s  x = [-1, 0, 0.5, 1, 2];
642s  p = [0, (1 - exp (-x(2:end) / 2))];
642s  u = [1, 0, NaN, 0.606530659712633, 0.367879441171442];
642s ***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
642s ***** assert (chi2cdf (x, 2), p, eps)
642s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [0, 1, NaN, p(4:5)], eps)
642s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), u, 3 * eps)
642s ***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
642s ***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
642s ***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
642s ***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
642s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
642s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
642s ***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
642s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
642s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
642s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
642s  chi2cdf (ones (3), ones (2))
642s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
642s  chi2cdf (ones (2), ones (3))
642s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
642s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
642s 17 tests, 17 passed, 0 known failure, 0 skipped
642s [inst/dist_fun/invgrnd.m]
642s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/invgrnd.m
642s ***** assert (size (invgrnd (1, 1, 1)), [1, 1])
642s ***** assert (size (invgrnd (1, 1, 2)), [2, 2])
642s ***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
642s ***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
642s ***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
642s ***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
642s ***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
642s ***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
642s ***** assert (size (invgrnd (1, 1, 3)), [3, 3])
642s ***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
642s ***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
642s ***** test
642s  r =  invgrnd (1, [1, 0, -1]);
642s  assert (r([2:3]), [NaN, NaN])
642s ***** assert (class (invgrnd (1, 0)), "double")
642s ***** assert (class (invgrnd (1, single (0))), "single")
642s ***** assert (class (invgrnd (1, single ([0 0]))), "single")
642s ***** assert (class (invgrnd (1, single (1))), "single")
642s ***** assert (class (invgrnd (1, single ([1 1]))), "single")
642s ***** assert (class (invgrnd (single (1), 1)), "single")
642s ***** assert (class (invgrnd (single ([1 1]), 1)), "single")
642s ***** error<invgrnd: function called with too few input arguments.> invgrnd ()
642s ***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
642s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
642s  invgrnd (ones (3), ones (2))
642s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
642s  invgrnd (ones (2), ones (3))
642s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
642s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
642s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  invgrnd (1, 2, -1)
642s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  invgrnd (1, 2, 1.2)
642s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  invgrnd (1, 2, ones (2))
642s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  invgrnd (1, 2, [2 -1 2])
642s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
642s  invgrnd (1, 2, [2 0 2.5])
642s ***** error<invgrnd: dimensions must be non-negative integers.> ...
642s  invgrnd (1, 2, 2, -1, 5)
642s ***** error<invgrnd: dimensions must be non-negative integers.> ...
642s  invgrnd (1, 2, 2, 1.5, 5)
642s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
642s  invgrnd (2, ones (2), 3)
642s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
642s  invgrnd (2, ones (2), [3, 2])
642s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
642s  invgrnd (2, ones (2), 3, 2)
642s 35 tests, 35 passed, 0 known failure, 0 skipped
642s [inst/dist_fun/bisacdf.m]
642s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bisacdf.m
642s ***** demo
642s  ## Plot various CDFs from the Birnbaum-Saunders distribution
642s  x = 0.01:0.01:4;
642s  p1 = bisacdf (x, 1, 0.5);
642s  p2 = bisacdf (x, 1, 1);
642s  p3 = bisacdf (x, 1, 2);
642s  p4 = bisacdf (x, 1, 5);
642s  p5 = bisacdf (x, 1, 10);
642s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
642s  grid on
642s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
642s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
642s  title ("Birnbaum-Saunders CDF")
642s  xlabel ("values in x")
642s  ylabel ("probability")
642s ***** demo
642s  ## Plot various CDFs from the Birnbaum-Saunders distribution
642s  x = 0.01:0.01:6;
642s  p1 = bisacdf (x, 1, 0.3);
642s  p2 = bisacdf (x, 2, 0.3);
642s  p3 = bisacdf (x, 1, 0.5);
642s  p4 = bisacdf (x, 3, 0.5);
642s  p5 = bisacdf (x, 5, 0.5);
642s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
642s  grid on
642s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
642s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
642s  title ("Birnbaum-Saunders CDF")
642s  xlabel ("values in x")
642s  ylabel ("probability")
642s ***** shared x, y
642s  x = [-1, 0, 1, 2, Inf];
642s  y = [0, 0, 1/2, 0.76024993890652337, 1];
642s ***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
642s ***** assert (bisacdf (x, 1, 1), y, eps)
642s ***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
642s ***** assert (bisacdf (x, ones (1,5), 1), y, eps)
642s ***** assert (bisacdf (x, 1, 1), y, eps)
642s ***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
642s ***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
642s ***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
644s ***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
644s ***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
644s ***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
644s ***** error<bisacdf: function called with too few input arguments.> bisacdf ()
644s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
644s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
644s ***** error<bisacdf: function called with too many inputs> ...
644s  bisacdf (1, 2, 3, 4, 5)
644s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
644s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
644s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
644s  bisacdf (ones (3), ones (2), ones(2))
644s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
644s  bisacdf (ones (2), ones (3), ones(2))
644s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
644s  bisacdf (ones (2), ones (2), ones(3))
644s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
644s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
644s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
644s 23 tests, 23 passed, 0 known failure, 0 skipped
644s [inst/dist_fun/hncdf.m]
644s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hncdf.m
644s ***** demo
644s  ## Plot various CDFs from the half-normal distribution
644s  x = 0:0.001:10;
644s  p1 = hncdf (x, 0, 1);
644s  p2 = hncdf (x, 0, 2);
644s  p3 = hncdf (x, 0, 3);
644s  p4 = hncdf (x, 0, 5);
644s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
644s  grid on
644s  xlim ([0, 10])
644s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
644s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
644s  title ("Half-normal CDF")
644s  xlabel ("values in x")
644s  ylabel ("probability")
644s ***** demo
644s  ## Plot half-normal against normal cumulative distribution function
644s  x = -5:0.001:5;
644s  p1 = hncdf (x, 0, 1);
644s  p2 = normcdf (x);
644s  plot (x, p1, "-b", x, p2, "-g")
644s  grid on
644s  xlim ([-5, 5])
644s  legend ({"half-normal with μ = 0, σ = 1", ...
644s           "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
644s  title ("Half-normal against standard normal CDF")
644s  xlabel ("values in x")
644s  ylabel ("probability")
644s ***** shared x, p1, p1u, y2, y2u, y3, y3u
644s  x = [-Inf, -1, 0, 1/2, 1, Inf];
644s  p1 = [0, 0, 0, 0.3829, 0.6827, 1];
644s  p1u = [1, 1, 1, 0.6171, 0.3173, 0];
644s ***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
644s ***** assert (hncdf (x, 0, 1), p1, 1e-4)
644s ***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
644s ***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
644s ***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
644s ***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
644s ***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
644s ***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
644s ***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
644s ***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
644s ***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
644s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
644s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
644s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
644s ***** error<hncdf: function called with too few input arguments.> hncdf ()
644s ***** error<hncdf: function called with too few input arguments.> hncdf (1)
644s ***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
644s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
644s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
644s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
644s  hncdf (ones (3), ones (2), ones(2))
644s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
644s  hncdf (ones (2), ones (3), ones(2))
644s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
644s  hncdf (ones (2), ones (2), ones(3))
645s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
645s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
645s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
645s 25 tests, 25 passed, 0 known failure, 0 skipped
645s [inst/dist_fun/nbininv.m]
645s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nbininv.m
645s ***** demo
645s  ## Plot various iCDFs from the negative binomial distribution
645s  p = 0.001:0.001:0.999;
645s  x1 = nbininv (p, 2, 0.15);
645s  x2 = nbininv (p, 5, 0.2);
645s  x3 = nbininv (p, 4, 0.4);
645s  x4 = nbininv (p, 10, 0.3);
645s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
645s  grid on
645s  ylim ([0, 40])
645s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
645s           "r = 10, ps = 0.3"}, "location", "northwest")
645s  title ("Negative binomial iCDF")
645s  xlabel ("probability")
645s  ylabel ("values in x (number of failures)")
645s ***** shared p
645s  p = [-1 0 3/4 1 2];
645s ***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
645s ***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
645s ***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
645s ***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
645s ***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
645s ***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
645s ***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
645s ***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
645s ***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
645s ***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
645s ***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
645s ***** shared y, tol
645s  y = magic (3) + 1;
645s  tol = 1;
645s ***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
645s ***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
645s ***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
645s ***** error<nbininv: function called with too few input arguments.> nbininv ()
645s ***** error<nbininv: function called with too few input arguments.> nbininv (1)
645s ***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
645s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
645s  nbininv (ones (3), ones (2), ones (2))
645s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
645s  nbininv (ones (2), ones (3), ones (2))
645s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
645s  nbininv (ones (2), ones (2), ones (3))
645s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
645s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
645s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
645s 23 tests, 23 passed, 0 known failure, 0 skipped
645s [inst/dist_fun/gevrnd.m]
645s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gevrnd.m
645s ***** assert(size (gevrnd (1,2,1)), [1, 1]);
645s ***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
645s ***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
645s ***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
645s ***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
645s ***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
645s ***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
645s ***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
645s ***** assert (class (gevrnd (1,1,1)), "double")
645s ***** assert (class (gevrnd (single (1),1,1)), "single")
645s ***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
645s ***** assert (class (gevrnd (1,single (1),1)), "single")
645s ***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
645s ***** assert (class (gevrnd (1,1,single (1))), "single")
645s ***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
645s ***** error<gevrnd: function called with too few input arguments.> gevrnd ()
645s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
645s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
645s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
645s  gevrnd (ones (3), ones (2), ones (2))
645s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
645s  gevrnd (ones (2), ones (3), ones (2))
645s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
645s  gevrnd (ones (2), ones (2), ones (3))
645s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
645s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
645s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
645s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
645s  gevrnd (1, 2, 3, -1)
645s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
645s  gevrnd (1, 2, 3, 1.2)
645s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
645s  gevrnd (1, 2, 3, ones (2))
645s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
645s  gevrnd (1, 2, 3, [2 -1 2])
645s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
645s  gevrnd (1, 2, 3, [2 0 2.5])
645s ***** error<gevrnd: dimensions must be non-negative integers.> ...
645s  gevrnd (1, 2, 3, 2, -1, 5)
645s ***** error<gevrnd: dimensions must be non-negative integers.> ...
645s  gevrnd (1, 2, 3, 2, 1.5, 5)
645s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
645s  gevrnd (2, ones (2), 2, 3)
645s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
645s  gevrnd (2, ones (2), 2, [3, 2])
645s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
645s  gevrnd (2, ones (2), 2, 3, 2)
645s 34 tests, 34 passed, 0 known failure, 0 skipped
645s [inst/dist_fun/mvncdf.m]
645s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvncdf.m
645s ***** demo
645s  mu = [1, -1];
645s  Sigma = [0.9, 0.4; 0.4, 0.3];
645s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
645s  X = [X1(:), X2(:)];
645s  p = mvncdf (X, mu, Sigma);
645s  Z = reshape (p, 25, 25);
645s  surf (X1, X2, Z);
645s  title ("Bivariate Normal Distribution");
645s  ylabel "X1"
645s  xlabel "X2"
645s ***** demo
645s  mu = [0, 0];
645s  Sigma = [0.25, 0.3; 0.3, 1];
645s  p = mvncdf ([0 0], [1 1], mu, Sigma);
645s  x1 = -3:.2:3;
645s  x2 = -3:.2:3;
645s  [X1, X2] = meshgrid (x1, x2);
645s  X = [X1(:), X2(:)];
645s  p = mvnpdf (X, mu, Sigma);
645s  p = reshape (p, length (x2), length (x1));
645s  contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
645s  xlabel ("x");
645s  ylabel ("p");
645s  title ("Probability over Rectangular Region");
645s  line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
645s ***** test
645s  fD = (-2:2)';
645s  X = repmat (fD, 1, 4);
645s  p = mvncdf (X);
645s  assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
645s ***** test
645s  mu = [1, -1];
645s  Sigma = [0.9, 0.4; 0.4, 0.3];
645s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
645s  X = [X1(:), X2(:)];
645s  p = mvncdf (X, mu, Sigma);
645s  p_out = [0.00011878988774500, 0.00034404112322371, ...
645s           0.00087682502191813, 0.00195221905058185, ...
645s           0.00378235566873474, 0.00638175749734415, ...
645s           0.00943764224329656, 0.01239164888125426, ...
645s           0.01472750274376648, 0.01623228313374828]';
645s  assert (p([1:10]), p_out, 1e-16);
645s ***** test
645s  mu = [1, -1];
645s  Sigma = [0.9, 0.4; 0.4, 0.3];
645s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
645s  X = [X1(:), X2(:)];
645s  p = mvncdf (X, mu, Sigma);
645s  p_out = [0.8180695783608276, 0.8854485749482751, ...
645s           0.9308108777385832, 0.9579855743025508, ...
645s           0.9722897881414742, 0.9788150170059926, ...
645s           0.9813597788804785, 0.9821977956568989, ...
645s           0.9824283794464095, 0.9824809345614861]';
645s  assert (p([616:625]), p_out, 3e-16);
645s ***** test
645s  mu = [0, 0];
645s  Sigma = [0.25, 0.3; 0.3, 1];
645s  [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
645s  assert (p, 0.2097424404755626, 1e-16);
645s  assert (err, 1e-08);
645s ***** test
645s  x = [1 2];
645s  mu = [0.5 1.5];
645s  sigma = [1.0, 0.5; 0.5, 1.0];
645s  p = mvncdf (x, mu, sigma);
645s  assert (p, 0.546244443857090, 1e-15);
645s ***** test
645s  x = [1 2];
645s  mu = [0.5 1.5];
645s  sigma = [1.0, 0.5; 0.5, 1.0];
645s  a = [-inf 0];
645s  p = mvncdf (a, x, mu, sigma);
645s  assert (p, 0.482672935215631, 1e-15);
645s ***** error p = mvncdf (randn (25,26), [], eye (26));
645s ***** error p = mvncdf (randn (25,8), [], eye (9));
645s ***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
645s ***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
645s ***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
645s ***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
645s 12 tests, 12 passed, 0 known failure, 0 skipped
645s [inst/dist_fun/jsucdf.m]
645s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/jsucdf.m
645s ***** error jsucdf ()
645s ***** error jsucdf (1, 2, 3, 4)
645s ***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
645s  jsucdf (1, ones (2), ones (3))
645s 3 tests, 3 passed, 0 known failure, 0 skipped
645s [inst/dist_fun/plpdf.m]
645s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/plpdf.m
645s ***** demo
645s  ## Plot various PDFs from the Piecewise linear distribution
645s  data = 0:0.01:10;
645s  x1 = [0, 1, 3, 4, 7, 10];
645s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
645s  x2 = [0, 2, 5, 6, 7, 8];
645s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
645s  y1 = plpdf (data, x1, Fx1);
645s  y2 = plpdf (data, x2, Fx2);
645s  plot (data, y1, "-b", data, y2, "g")
645s  grid on
645s  ylim ([0, 0.6])
645s  xlim ([0, 10])
645s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
645s  title ("Piecewise linear CDF")
645s  xlabel ("values in data")
645s  ylabel ("density")
645s ***** shared x, Fx
645s  x = [0, 1, 3, 4, 7, 10];
645s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
645s ***** assert (plpdf (0.5, x, Fx), 0.2, eps);
645s ***** assert (plpdf (1.5, x, Fx), 0.15, eps);
645s ***** assert (plpdf (3.5, x, Fx), 0.1, eps);
645s ***** assert (plpdf (5, x, Fx), 0.1/3, eps);
645s ***** assert (plpdf (8, x, Fx), 0.1, eps);
645s ***** error<plpdf: function called with too few input arguments.> plpdf ()
645s ***** error<plpdf: function called with too few input arguments.> plpdf (1)
645s ***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
645s ***** error<plpdf: X and FX must be vectors of equal size.> ...
645s  plpdf (1, [0, 1, 2], [0, 1])
645s ***** error<plpdf: X and FX must be at least two-elements long.> ...
645s  plpdf (1, [0], [1])
646s ***** error<plpdf: FX must be bounded in the range> ...
646s  plpdf (1, [0, 1, 2], [0, 1, 1.5])
646s ***** error<plpdf: FX must be bounded in the range> ...
646s  plpdf (1, [0, 1, 2], [0, i, 1])
646s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
646s  plpdf (i, [0, 1, 2], [0, 0.5, 1])
646s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
646s  plpdf (1, [0, i, 2], [0, 0.5, 1])
646s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
646s  plpdf (1, [0, 1, 2], [0, 0.5i, 1])
646s 15 tests, 15 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/iwishrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/iwishrnd.m
646s ***** assert(size (iwishrnd (1,2,1)), [1, 1]);
646s ***** assert(size (iwishrnd ([],2,1)), [1, 1]);
646s ***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
646s ***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
646s ***** error iwishrnd ()
646s ***** error iwishrnd (1)
646s ***** error iwishrnd ([-3 1; 1 3],1)
646s ***** error iwishrnd ([1; 1],1)
646s 8 tests, 8 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/ncx2inv.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncx2inv.m
646s ***** demo
646s  ## Plot various iCDFs from the noncentral chi-squared distribution
646s  p = 0.001:0.001:0.999;
646s  x1 = ncx2inv (p, 2, 1);
646s  x2 = ncx2inv (p, 2, 2);
646s  x3 = ncx2inv (p, 2, 3);
646s  x4 = ncx2inv (p, 4, 1);
646s  x5 = ncx2inv (p, 4, 2);
646s  x6 = ncx2inv (p, 4, 3);
646s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
646s        p, x4, "-m", p, x5, "-c", p, x6, "-y")
646s  grid on
646s  ylim ([0, 10])
646s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
646s           "df = 2, λ = 3", "df = 4, λ = 1", ...
646s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
646s  title ("Noncentral chi-squared iCDF")
646s  xlabel ("probability")
646s  ylabel ("values in x")
646s ***** demo
646s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
646s  ## chi-squared CDF with the same number of degrees of freedom (4).
646s 
646s  p = 0.001:0.001:0.999;
646s  x1 = ncx2inv (p, 4, 2);
646s  x2 = chi2inv (p, 4);
646s  plot (p, x1, "-", p, x2, "-");
646s  grid on
646s  ylim ([0, 10])
646s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
646s  title ("Noncentral chi-squared vs chi-squared quantile functions")
646s  xlabel ("probability")
646s  ylabel ("values in x")
646s ***** test
646s  x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
646s  assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
646s ***** test
646s  x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
646s  assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
646s ***** test
646s  x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
646s  assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
646s ***** test
646s  x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
646s  assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
646s ***** test
646s  x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
646s  assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
646s ***** test
646s  x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
646s  assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
646s ***** test
646s  assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
646s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
646s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
646s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
646s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
646s  ncx2inv (ones (3), ones (2), ones (2))
646s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
646s  ncx2inv (ones (2), ones (3), ones (2))
646s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
646s  ncx2inv (ones (2), ones (2), ones (3))
646s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
646s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
646s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
646s 16 tests, 16 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/ncfrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncfrnd.m
646s ***** assert (size (ncfrnd (1, 1, 1)), [1 1])
646s ***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
646s ***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
646s ***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
646s ***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
646s ***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
646s ***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
646s ***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
646s ***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
646s ***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
646s ***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
646s ***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
646s ***** assert (class (ncfrnd (1, 1, 1)), "double")
646s ***** assert (class (ncfrnd (1, single (1), 1)), "single")
646s ***** assert (class (ncfrnd (1, 1, single (1))), "single")
646s ***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
646s ***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
646s ***** assert (class (ncfrnd (single (1), 1, 1)), "single")
646s ***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
646s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
646s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
646s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
646s  ncfrnd (ones (3), ones (2), ones (2))
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
646s  ncfrnd (ones (2), ones (3), ones (2))
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
646s  ncfrnd (ones (2), ones (2), ones (3))
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
646s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  ncfrnd (1, 2, 3, -1)
646s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  ncfrnd (1, 2, 3, 1.2)
646s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  ncfrnd (1, 2, 3, ones (2))
646s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  ncfrnd (1, 2, 3, [2 -1 2])
646s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  ncfrnd (1, 2, 3, [2 0 2.5])
646s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
646s  ncfrnd (1, 2, 3, 2, -1, 5)
646s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
646s  ncfrnd (1, 2, 3, 2, 1.5, 5)
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
646s  ncfrnd (2, ones (2), 2, 3)
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
646s  ncfrnd (2, ones (2), 2, [3, 2])
646s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
646s  ncfrnd (2, ones (2), 2, 3, 2)
646s 38 tests, 38 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/mvtpdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvtpdf.m
646s ***** demo
646s  ## Compute the pdf of a multivariate t distribution with correlation
646s  ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.
646s 
646s  rho = [1, 0.4; 0.4, 1];
646s  df = 2;
646s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
646s  X = [X1(:), X2(:)];
646s  y = mvtpdf (X, rho, df);
646s  surf (X1, X2, reshape (y, 25, 25));
646s  title ("Bivariate Student's t probability density function");
646s ***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
646s ***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
646s ***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
646s  [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
646s  [0.04713313 0.03722421 0.02069011]', 1E-7)
646s 3 tests, 3 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/hygernd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hygernd.m
646s ***** assert (size (hygernd (4,2,2)), [1, 1])
646s ***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
646s ***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
646s ***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
646s ***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
646s ***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
646s ***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
646s ***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
646s ***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
646s ***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
646s ***** assert (class (hygernd (4,2,2)), "double")
646s ***** assert (class (hygernd (single (4),2,2)), "single")
646s ***** assert (class (hygernd (single ([4 4]),2,2)), "single")
646s ***** assert (class (hygernd (4,single (2),2)), "single")
646s ***** assert (class (hygernd (4,single ([2 2]),2)), "single")
646s ***** assert (class (hygernd (4,2,single (2))), "single")
646s ***** assert (class (hygernd (4,2,single ([2 2]))), "single")
646s ***** error<hygernd: function called with too few input arguments.> hygernd ()
646s ***** error<hygernd: function called with too few input arguments.> hygernd (1)
646s ***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
646s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
646s  hygernd (ones (3), ones (2), ones (2))
646s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
646s  hygernd (ones (2), ones (3), ones (2))
646s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
646s  hygernd (ones (2), ones (2), ones (3))
646s ***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
646s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
646s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
646s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hygernd (1, 2, 3, -1)
646s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hygernd (1, 2, 3, 1.2)
646s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hygernd (1, 2, 3, ones (2))
646s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hygernd (1, 2, 3, [2 -1 2])
646s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hygernd (1, 2, 3, [2 0 2.5])
646s ***** error<hygernd: dimensions must be non-negative integers.> ...
646s  hygernd (1, 2, 3, 2, -1, 5)
646s ***** error<hygernd: dimensions must be non-negative integers.> ...
646s  hygernd (1, 2, 3, 2, 1.5, 5)
646s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
646s  hygernd (2, ones (2), 2, 3)
646s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
646s  hygernd (2, ones (2), 2, [3, 2])
646s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
646s  hygernd (2, ones (2), 2, 3, 2)
646s 36 tests, 36 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/hnrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hnrnd.m
646s ***** assert (size (hnrnd (1, 1, 1)), [1, 1])
646s ***** assert (size (hnrnd (1, 1, 2)), [2, 2])
646s ***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
646s ***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
646s ***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
646s ***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
646s ***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
646s ***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
646s ***** assert (size (hnrnd (1, 1, 3)), [3, 3])
646s ***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
646s ***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
646s ***** test
646s  r =  hnrnd (1, [1, 0, -1]);
646s  assert (r([2:3]), [NaN, NaN])
646s ***** assert (class (hnrnd (1, 0)), "double")
646s ***** assert (class (hnrnd (1, single (0))), "single")
646s ***** assert (class (hnrnd (1, single ([0 0]))), "single")
646s ***** assert (class (hnrnd (1, single (1))), "single")
646s ***** assert (class (hnrnd (1, single ([1 1]))), "single")
646s ***** assert (class (hnrnd (single (1), 1)), "single")
646s ***** assert (class (hnrnd (single ([1 1]), 1)), "single")
646s ***** error<hnrnd: function called with too few input arguments.> hnrnd ()
646s ***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
646s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
646s  hnrnd (ones (3), ones (2))
646s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
646s  hnrnd (ones (2), ones (3))
646s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
646s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
646s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hnrnd (1, 2, -1)
646s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hnrnd (1, 2, 1.2)
646s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hnrnd (1, 2, ones (2))
646s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hnrnd (1, 2, [2 -1 2])
646s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  hnrnd (1, 2, [2 0 2.5])
646s ***** error<hnrnd: dimensions must be non-negative integers.> ...
646s  hnrnd (1, 2, 2, -1, 5)
646s ***** error<hnrnd: dimensions must be non-negative integers.> ...
646s  hnrnd (1, 2, 2, 1.5, 5)
646s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  hnrnd (2, ones (2), 3)
646s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  hnrnd (2, ones (2), [3, 2])
646s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  hnrnd (2, ones (2), 3, 2)
646s 35 tests, 35 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/gumbelinv.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gumbelinv.m
646s ***** demo
646s  ## Plot various iCDFs from the Gumbel distribution
646s  p = 0.001:0.001:0.999;
646s  x1 = gumbelinv (p, 0.5, 2);
646s  x2 = gumbelinv (p, 1.0, 2);
646s  x3 = gumbelinv (p, 1.5, 3);
646s  x4 = gumbelinv (p, 3.0, 4);
646s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
646s  grid on
646s  ylim ([-5, 20])
646s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
646s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
646s  title ("Gumbel iCDF")
646s  xlabel ("probability")
646s  ylabel ("values in x")
646s ***** shared p, x
646s  p = [0, 0.05, 0.5 0.95];
646s  x = [-Inf, -1.0972, 0.3665, 2.9702];
646s ***** assert (gumbelinv (p), x, 1e-4)
646s ***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
646s ***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
646s ***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
646s ***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
646s ***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
646s ***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
646s ***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
646s ***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
646s ***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
646s ***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
646s  p = [0.05, 0.5, 0.95];
646s  x = gumbelinv(p);
646s ***** assert (gumbelcdf(x), p, 1e-4)
646s ***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
646s ***** error gumbelinv (1,2,3,4,5,6)
646s ***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
646s  gumbelinv (ones (3), ones (2), ones (2))
646s ***** error<gumbelinv: invalid size of covariance matrix.> ...
646s  [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
646s ***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
646s  [p, plo, pup] = gumbelinv (1, 2, 3)
646s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
646s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
646s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
646s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
646s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
646s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
646s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
646s ***** error<gumbelinv: bad covariance matrix.> ...
646s  [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
646s 23 tests, 23 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/betapdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/betapdf.m
646s ***** demo
646s  ## Plot various PDFs from the Beta distribution
646s  x = 0.001:0.001:0.999;
646s  y1 = betapdf (x, 0.5, 0.5);
646s  y2 = betapdf (x, 5, 1);
646s  y3 = betapdf (x, 1, 3);
646s  y4 = betapdf (x, 2, 2);
646s  y5 = betapdf (x, 2, 5);
646s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
646s  grid on
646s  ylim ([0, 2.5])
646s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
646s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
646s  title ("Beta PDF")
646s  xlabel ("values in x")
646s  ylabel ("density")
646s ***** shared x, y
646s  x = [-1 0 0.5 1 2];
646s  y = [0 2 1 0 0];
646s ***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
646s ***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
646s ***** assert (betapdf (x, ones (1, 5), 2), y)
646s ***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
646s ***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
646s ***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
646s ***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
646s ***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
646s ***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
646s ***** test
646s  x = rand (10,1);
646s  y = 1 ./ (pi * sqrt (x .* (1 - x)));
646s  assert (betapdf (x, 1/2, 1/2), y, 1e-12);
646s ***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
646s ***** error<betapdf: function called with too few input arguments.> betapdf ()
646s ***** error<betapdf: function called with too few input arguments.> betapdf (1)
646s ***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
646s ***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
646s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
646s  betapdf (ones (3), ones (2), ones (2))
646s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
646s  betapdf (ones (2), ones (3), ones (2))
646s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
646s  betapdf (ones (2), ones (2), ones (3))
646s ***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
646s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
646s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
646s 21 tests, 21 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/gprnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gprnd.m
646s ***** assert (size (gprnd (0, 1, 0)), [1, 1])
646s ***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
646s ***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
646s ***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
646s ***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
646s ***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
646s ***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
646s ***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
646s ***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
646s ***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
646s ***** assert (size (gprnd (1,1,0)), [1, 1])
646s ***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
646s ***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
646s ***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
646s ***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
646s ***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
646s ***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
646s ***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
646s ***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
646s ***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
646s ***** assert (size (gprnd (-1, 1, 0)), [1, 1])
646s ***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
646s ***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
646s ***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
646s ***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
646s ***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
646s ***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
646s ***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
646s ***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
646s ***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
646s ***** assert (class (gprnd (0, 1, 0)), "double")
646s ***** assert (class (gprnd (0, 1, single (0))), "single")
646s ***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
646s ***** assert (class (gprnd (0, single (1),0)), "single")
646s ***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
646s ***** assert (class (gprnd (single (0), 1, 0)), "single")
646s ***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
646s ***** error<gprnd: function called with too few input arguments.> gprnd ()
646s ***** error<gprnd: function called with too few input arguments.> gprnd (1)
646s ***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
646s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gprnd (ones (3), ones (2), ones (2))
646s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gprnd (ones (2), ones (3), ones (2))
646s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gprnd (ones (2), ones (2), ones (3))
646s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
646s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
646s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
646s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  gprnd (1, 2, 3, -1)
646s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  gprnd (1, 2, 3, 1.2)
646s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  gprnd (1, 2, 3, ones (2))
646s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  gprnd (1, 2, 3, [2 -1 2])
646s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  gprnd (1, 2, 3, [2 0 2.5])
646s ***** error<gprnd: dimensions must be non-negative integers.> ...
646s  gprnd (1, 2, 3, 2, -1, 5)
646s ***** error<gprnd: dimensions must be non-negative integers.> ...
646s  gprnd (1, 2, 3, 2, 1.5, 5)
646s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
646s  gprnd (2, ones (2), 2, 3)
646s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
646s  gprnd (2, ones (2), 2, [3, 2])
646s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
646s  gprnd (2, ones (2), 2, 3, 2)
646s 56 tests, 56 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/loglcdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/loglcdf.m
646s ***** demo
646s  ## Plot various CDFs from the log-logistic distribution
646s  x = 0:0.001:2;
646s  p1 = loglcdf (x, log (1), 1/0.5);
646s  p2 = loglcdf (x, log (1), 1);
646s  p3 = loglcdf (x, log (1), 1/2);
646s  p4 = loglcdf (x, log (1), 1/4);
646s  p5 = loglcdf (x, log (1), 1/8);
646s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
646s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
646s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
646s  grid on
646s  title ("Log-logistic CDF")
646s  xlabel ("values in x")
646s  ylabel ("probability")
646s  text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
646s ***** shared out1, out2
646s  out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
646s  out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
646s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
646s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
646s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
646s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
646s ***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
646s ***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
646s ***** assert (class (loglcdf (single (1), 2, 3)), "single")
646s ***** assert (class (loglcdf (1, single (2), 3)), "single")
646s ***** assert (class (loglcdf (1, 2, single (3))), "single")
646s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
646s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
646s ***** error<loglcdf: invalid argument for upper tail.> ...
646s  loglcdf (1, 2, 3, 4)
646s ***** error<loglcdf: invalid argument for upper tail.> ...
646s  loglcdf (1, 2, 3, "uper")
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (1, ones (2), ones (3))
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (1, ones (2), ones (3), "upper")
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (ones (2), 1, ones (3))
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (ones (2), 1, ones (3), "upper")
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (ones (2), ones (3), 1)
646s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  loglcdf (ones (2), ones (3), 1, "upper")
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
646s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
646s 25 tests, 25 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/gpcdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gpcdf.m
646s ***** demo
646s  ## Plot various CDFs from the generalized Pareto distribution
646s  x = 0:0.001:5;
646s  p1 = gpcdf (x, 1, 1, 0);
646s  p2 = gpcdf (x, 5, 1, 0);
646s  p3 = gpcdf (x, 20, 1, 0);
646s  p4 = gpcdf (x, 1, 2, 0);
646s  p5 = gpcdf (x, 5, 2, 0);
646s  p6 = gpcdf (x, 20, 2, 0);
646s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
646s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
646s  grid on
646s  xlim ([0, 5])
646s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
646s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
646s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
646s          "location", "northwest")
646s  title ("Generalized Pareto CDF")
646s  xlabel ("values in x")
646s  ylabel ("probability")
646s ***** shared x, y1, y1u, y2, y2u, y3, y3u
646s  x = [-Inf, -1, 0, 1/2, 1, Inf];
646s  y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
646s  y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
646s  y2 = [0, 0, 0, 1/3, 1/2, 1];
646s  y2u = [1, 1, 1, 2/3, 1/2, 0];
646s  y3 = [0, 0, 0, 1/2, 1, 1];
646s  y3u = [1, 1, 1, 1/2, 0, 0];
646s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
646s ***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
646s ***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
646s ***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
646s ***** assert (gpcdf (x, 0, 1, 0), y1, eps)
646s ***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
646s ***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
646s ***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
646s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
646s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
646s ***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
646s ***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
646s ***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
646s ***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
646s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
646s ***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
646s ***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
646s ***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
646s ***** assert (gpcdf (x, 1, 1, 0), y2, eps)
646s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
646s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
646s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
646s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
646s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
646s ***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
646s ***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
646s ***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
646s ***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
646s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
646s                         [y2u(1:3), NaN, y2u(5:6)], eps)
646s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
646s                         [y2u(1:3), NaN, y2u(5:6)], eps)
646s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
646s                         [y2u(1:3), NaN, y2u(5:6)], eps)
646s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
646s                         [y2u(1:3), NaN, y2u(5:6)], eps)
646s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
646s ***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
646s ***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
646s ***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
646s ***** assert (gpcdf (x, -1, 1, 0), y3, eps)
646s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
646s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
646s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
646s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
646s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
646s ***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
646s ***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
646s ***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
646s ***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
646s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
646s                          [y3u(1:3), NaN, y3u(5:6)], eps)
646s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
646s                           [y3u(1:3), NaN, y3u(5:6)], eps)
646s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
646s                           [y3u(1:3), NaN, y3u(5:6)], eps)
646s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
646s                           [y3u(1:3), NaN, y3u(5:6)], eps)
646s ***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
646s ***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
646s ***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
646s ***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
646s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
646s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
646s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
646s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
646s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
646s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
646s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gpcdf (ones (3), ones (2), ones(2), ones(2))
646s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gpcdf (ones (2), ones (3), ones(2), ones(2))
646s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gpcdf (ones (2), ones (2), ones(3), ones(2))
646s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
646s  gpcdf (ones (2), ones (2), ones(2), ones(3))
646s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
646s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
646s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
646s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
646s 76 tests, 76 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/tripdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tripdf.m
646s ***** demo
646s  ## Plot various CDFs from the triangular distribution
646s  x = 0.001:0.001:10;
646s  y1 = tripdf (x, 3, 4, 6);
646s  y2 = tripdf (x, 1, 2, 5);
646s  y3 = tripdf (x, 2, 3, 9);
646s  y4 = tripdf (x, 2, 5, 9);
646s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
646s  grid on
646s  xlim ([0, 10])
646s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
646s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
646s          "location", "northeast")
646s  title ("Triangular CDF")
646s  xlabel ("values in x")
646s  ylabel ("probability")
646s ***** shared x, y, deps
646s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
646s  y = [0, 0, 0.4, 2, 0.4, 0, 0];
646s  deps = 2*eps;
646s ***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
646s ***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
646s ***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
646s ***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
646s ***** assert (tripdf (x, 1, 1.5, 2), y, deps)
646s ***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
646s ***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
646s ***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
646s ***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
646s ***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
646s ***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
646s ***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
646s ***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
646s ***** error<tripdf: function called with too few input arguments.> tripdf ()
646s ***** error<tripdf: function called with too few input arguments.> tripdf (1)
646s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
646s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
646s ***** error<tripdf: function called with too many inputs> ...
646s  tripdf (1, 2, 3, 4, 5)
646s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
646s  tripdf (ones (3), ones (2), ones(2), ones(2))
646s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
646s  tripdf (ones (2), ones (3), ones(2), ones(2))
646s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
646s  tripdf (ones (2), ones (2), ones(3), ones(2))
646s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
646s  tripdf (ones (2), ones (2), ones(2), ones(3))
646s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
646s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
646s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
646s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
646s 26 tests, 26 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/hygepdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hygepdf.m
646s ***** demo
646s  ## Plot various PDFs from the hypergeometric distribution
646s  x = 0:60;
646s  y1 = hygepdf (x, 500, 50, 100);
646s  y2 = hygepdf (x, 500, 60, 200);
646s  y3 = hygepdf (x, 500, 70, 300);
646s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
646s  grid on
646s  xlim ([0, 60])
646s  ylim ([0, 0.18])
646s  legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
646s           "m = 500, k = 70, μ = 300"}, "location", "northeast")
646s  title ("Hypergeometric PDF")
646s  xlabel ("values in x (number of successes)")
646s  ylabel ("density")
646s ***** shared x, y
646s  x = [-1 0 1 2 3];
646s  y = [0 1/6 4/6 1/6 0];
646s ***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
646s ***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
646s ***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
646s ***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
646s ***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
646s ***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
646s ***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
646s ***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
646s ***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
646s ***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
646s ***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
646s ***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
646s ***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
646s ***** test
646s  z = zeros(3,5);
646s  z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
646s  assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
646s  assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
646s  assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
646s  assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
646s  assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
646s  assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
646s ***** error<hygepdf: function called with too few input arguments.> hygepdf ()
646s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
646s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
646s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
646s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
646s  hygepdf (1, ones (3), ones (2), ones (2))
646s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
646s  hygepdf (1, ones (2), ones (3), ones (2))
646s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
646s  hygepdf (1, ones (2), ones (2), ones (3))
646s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
646s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
646s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
646s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
646s 25 tests, 25 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/burrrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/burrrnd.m
646s ***** assert (size (burrrnd (1, 1, 1)), [1 1])
646s ***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
646s ***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
646s ***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
646s ***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
646s ***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
646s ***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
646s ***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
646s ***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
646s ***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
646s ***** assert (class (burrrnd (1,1,1)), "double")
646s ***** assert (class (burrrnd (single (1),1,1)), "single")
646s ***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
646s ***** assert (class (burrrnd (1,single (1),1)), "single")
646s ***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
646s ***** assert (class (burrrnd (1,1,single (1))), "single")
646s ***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
646s ***** error<burrrnd: function called with too few input arguments.> burrrnd ()
646s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
646s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
646s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
646s  burrrnd (ones (3), ones (2), ones (2))
646s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
646s  burrrnd (ones (2), ones (3), ones (2))
646s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
646s  burrrnd (ones (2), ones (2), ones (3))
646s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
646s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
646s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
646s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  burrrnd (1, 2, 3, -1)
646s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  burrrnd (1, 2, 3, 1.2)
646s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  burrrnd (1, 2, 3, ones (2))
646s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  burrrnd (1, 2, 3, [2 -1 2])
646s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  burrrnd (1, 2, 3, [2 0 2.5])
646s ***** error<burrrnd: dimensions must be non-negative integers.> ...
646s  burrrnd (1, 2, 3, 2, -1, 5)
646s ***** error<burrrnd: dimensions must be non-negative integers.> ...
646s  burrrnd (1, 2, 3, 2, 1.5, 5)
646s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
646s  burrrnd (2, ones (2), 2, 3)
646s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
646s  burrrnd (2, ones (2), 2, [3, 2])
646s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
646s  burrrnd (2, ones (2), 2, 3, 2)
646s 36 tests, 36 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/loglinv.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/loglinv.m
646s ***** demo
646s  ## Plot various iCDFs from the log-logistic distribution
646s  p = 0.001:0.001:0.999;
646s  x1 = loglinv (p, log (1), 1/0.5);
646s  x2 = loglinv (p, log (1), 1);
646s  x3 = loglinv (p, log (1), 1/2);
646s  x4 = loglinv (p, log (1), 1/4);
646s  x5 = loglinv (p, log (1), 1/8);
646s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
646s  ylim ([0, 20])
646s  grid on
646s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
646s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
646s  title ("Log-logistic iCDF")
646s  xlabel ("probability")
646s  ylabel ("x")
646s  text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
646s ***** shared p, out1, out2
646s  p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
646s  out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
646s  out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
646s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
646s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
646s ***** assert (loglinv (p, 1, 3), out2, 1e-6)
646s ***** assert (class (loglinv (single (1), 2, 3)), "single")
646s ***** assert (class (loglinv (1, single (2), 3)), "single")
646s ***** assert (class (loglinv (1, 2, single (3))), "single")
646s ***** error<loglinv: function called with too few input arguments.> loglinv (1)
646s ***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
646s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
646s  loglinv (1, ones (2), ones (3))
646s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
646s  loglinv (ones (2), 1, ones (3))
646s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
646s  loglinv (ones (2), ones (3), 1)
646s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
646s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
646s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
646s 14 tests, 14 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/hnpdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/hnpdf.m
646s ***** demo
646s  ## Plot various PDFs from the half-normal distribution
646s  x = 0:0.001:10;
646s  y1 = hnpdf (x, 0, 1);
646s  y2 = hnpdf (x, 0, 2);
646s  y3 = hnpdf (x, 0, 3);
646s  y4 = hnpdf (x, 0, 5);
646s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
646s  grid on
646s  xlim ([0, 10])
646s  ylim ([0, 0.9])
646s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
646s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
646s  title ("Half-normal PDF")
646s  xlabel ("values in x")
646s  ylabel ("density")
646s ***** demo
646s  ## Plot half-normal against normal probability density function
646s  x = -5:0.001:5;
646s  y1 = hnpdf (x, 0, 1);
646s  y2 = normpdf (x);
646s  plot (x, y1, "-b", x, y2, "-g")
646s  grid on
646s  xlim ([-5, 5])
646s  ylim ([0, 0.9])
646s  legend ({"half-normal with μ = 0, σ = 1", ...
646s           "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
646s  title ("Half-normal against standard normal PDF")
646s  xlabel ("values in x")
646s  ylabel ("density")
646s ***** shared x, y
646s  x = [-Inf, -1, 0, 1/2, 1, Inf];
646s  y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
646s ***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
646s ***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
646s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
646s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
646s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
646s ***** error<hnpdf: function called with too few input arguments.> hnpdf ()
646s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
646s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
646s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  hnpdf (1, ones (2), ones (3))
646s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  hnpdf (ones (2), 1, ones (3))
646s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
646s  hnpdf (ones (2), ones (3), 1)
646s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
646s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
646s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
646s 14 tests, 14 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/poisspdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/poisspdf.m
646s ***** demo
646s  ## Plot various PDFs from the Poisson distribution
646s  x = 0:20;
646s  y1 = poisspdf (x, 1);
646s  y2 = poisspdf (x, 4);
646s  y3 = poisspdf (x, 10);
646s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
646s  grid on
646s  ylim ([0, 0.4])
646s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
646s  title ("Poisson PDF")
646s  xlabel ("values in x (number of occurences)")
646s  ylabel ("density")
646s ***** shared x, y
646s  x = [-1 0 1 2 Inf];
646s  y = [0, exp(-1)*[1 1 0.5], 0];
646s ***** assert (poisspdf (x, ones (1,5)), y, eps)
646s ***** assert (poisspdf (x, 1), y, eps)
646s ***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
646s ***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
646s ***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
646s ***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
646s ***** error<poisspdf: function called with too few input arguments.> poisspdf ()
646s ***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
646s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
646s  poisspdf (ones (3), ones (2))
646s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
646s  poisspdf (ones (2), ones (3))
646s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
646s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
646s 12 tests, 12 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/mnrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mnrnd.m
646s ***** test
646s  n = 10;
646s  pk = [0.2, 0.5, 0.3];
646s  r = mnrnd (n, pk);
646s  assert (size (r), size (pk));
646s  assert (all (r >= 0));
646s  assert (all (round (r) == r));
646s  assert (sum (r) == n);
646s ***** test
646s  n = 10 * ones (3, 1);
646s  pk = [0.2, 0.5, 0.3];
646s  r = mnrnd (n, pk);
646s  assert (size (r), [length(n), length(pk)]);
646s  assert (all (r >= 0));
646s  assert (all (round (r) == r));
646s  assert (all (sum (r, 2) == n));
646s ***** test
646s  n = (1:2)';
646s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
646s  r = mnrnd (n, pk);
646s  assert (size (r), size (pk));
646s  assert (all (r >= 0));
646s  assert (all (round (r) == r));
646s  assert (all (sum (r, 2) == n));
646s 3 tests, 3 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/gevpdf.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gevpdf.m
646s ***** demo
646s  ## Plot various PDFs from the generalized extreme value distribution
646s  x = -1:0.001:10;
646s  y1 = gevpdf (x, 1, 1, 1);
646s  y2 = gevpdf (x, 0.5, 1, 1);
646s  y3 = gevpdf (x, 1, 1, 5);
646s  y4 = gevpdf (x, 1, 2, 5);
646s  y5 = gevpdf (x, 1, 5, 5);
646s  y6 = gevpdf (x, 1, 0.5, 5);
646s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
646s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
646s  grid on
646s  xlim ([-1, 10])
646s  ylim ([0, 1.1])
646s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
646s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
646s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
646s          "location", "northeast")
646s  title ("Generalized extreme value PDF")
646s  xlabel ("values in x")
646s  ylabel ("density")
646s ***** test
646s  x = 0:0.5:2.5;
646s  sigma = 1:6;
646s  k = 1;
646s  mu = 0;
646s  y = gevpdf (x, k, sigma, mu);
646s  expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
646s  assert (y, expected_y, 0.001);
646s ***** test
646s  x = -0.5:0.5:2.5;
646s  sigma = 0.5;
646s  k = 1;
646s  mu = 0;
646s  y = gevpdf (x, k, sigma, mu);
646s  expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
646s  assert (y, expected_y, 0.001);
646s ***** test # check for continuity for k near 0
646s  x = 1;
646s  sigma = 0.5;
646s  k = -0.03:0.01:0.03;
646s  mu = 0;
646s  y = gevpdf (x, k, sigma, mu);
646s  expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
646s  assert (y, expected_y, 0.001);
646s ***** error<gevpdf: function called with too few input arguments.> gevpdf ()
646s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
646s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
646s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
646s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
646s  gevpdf (ones (3), ones (2), ones(2), ones(2))
646s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
646s  gevpdf (ones (2), ones (3), ones(2), ones(2))
646s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
646s  gevpdf (ones (2), ones (2), ones(3), ones(2))
646s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
646s  gevpdf (ones (2), ones (2), ones(2), ones(3))
646s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
646s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
646s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
646s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
646s 15 tests, 15 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/normrnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/normrnd.m
646s ***** assert (size (normrnd (1, 1)), [1 1])
646s ***** assert (size (normrnd (1, ones (2,1))), [2, 1])
646s ***** assert (size (normrnd (1, ones (2,2))), [2, 2])
646s ***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
646s ***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
646s ***** assert (size (normrnd (1, 1, 3)), [3, 3])
646s ***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
646s ***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
646s ***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
646s ***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
646s ***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
646s ***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
646s ***** assert (class (normrnd (1, 1)), "double")
646s ***** assert (class (normrnd (1, single (1))), "single")
646s ***** assert (class (normrnd (1, single ([1, 1]))), "single")
646s ***** assert (class (normrnd (single (1), 1)), "single")
646s ***** assert (class (normrnd (single ([1, 1]), 1)), "single")
646s ***** error<normrnd: function called with too few input arguments.> normrnd ()
646s ***** error<normrnd: function called with too few input arguments.> normrnd (1)
646s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
646s  normrnd (ones (3), ones (2))
646s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
646s  normrnd (ones (2), ones (3))
646s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
646s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
646s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  normrnd (1, 2, -1)
646s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  normrnd (1, 2, 1.2)
646s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  normrnd (1, 2, ones (2))
646s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  normrnd (1, 2, [2 -1 2])
646s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
646s  normrnd (1, 2, [2 0 2.5])
646s ***** error<normrnd: dimensions must be non-negative integers.> ...
646s  normrnd (1, 2, 2, -1, 5)
646s ***** error<normrnd: dimensions must be non-negative integers.> ...
646s  normrnd (1, 2, 2, 1.5, 5)
646s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  normrnd (2, ones (2), 3)
646s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  normrnd (2, ones (2), [3, 2])
646s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
646s  normrnd (2, ones (2), 3, 2)
646s 33 tests, 33 passed, 0 known failure, 0 skipped
646s [inst/dist_fun/ncx2rnd.m]
646s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncx2rnd.m
646s ***** assert (size (ncx2rnd (1, 1)), [1 1])
646s ***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
646s ***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
646s ***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
646s ***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
646s ***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
646s ***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
646s ***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
646s ***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
646s ***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
647s ***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
647s ***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
647s ***** assert (class (ncx2rnd (1, 1)), "double")
647s ***** assert (class (ncx2rnd (1, single (1))), "single")
647s ***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
647s ***** assert (class (ncx2rnd (single (1), 1)), "single")
647s ***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
647s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
647s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
647s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
647s  ncx2rnd (ones (3), ones (2))
647s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
647s  ncx2rnd (ones (2), ones (3))
647s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
647s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
647s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  ncx2rnd (1, 2, -1)
647s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  ncx2rnd (1, 2, 1.2)
647s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  ncx2rnd (1, 2, ones (2))
647s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  ncx2rnd (1, 2, [2 -1 2])
647s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  ncx2rnd (1, 2, [2 0 2.5])
647s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
647s  ncx2rnd (1, 2, 2, -1, 5)
647s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
647s  ncx2rnd (1, 2, 2, 1.5, 5)
647s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
647s  ncx2rnd (2, ones (2), 3)
647s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
647s  ncx2rnd (2, ones (2), [3, 2])
647s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
647s  ncx2rnd (2, ones (2), 3, 2)
647s 33 tests, 33 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/logncdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logncdf.m
647s ***** demo
647s  ## Plot various CDFs from the log-normal distribution
647s  x = 0:0.01:3;
647s  p1 = logncdf (x, 0, 1);
647s  p2 = logncdf (x, 0, 0.5);
647s  p3 = logncdf (x, 0, 0.25);
647s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
647s  grid on
647s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
647s          "location", "southeast")
647s  title ("Log-normal CDF")
647s  xlabel ("values in x")
647s  ylabel ("probability")
647s ***** shared x, y
647s  x = [-1, 0, 1, e, Inf];
647s  y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
647s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
647s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
647s ***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
647s ***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
647s ***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
647s ***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
647s ***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
647s ***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
647s ***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
647s ***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
647s ***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
647s ***** error<logncdf: invalid number of input arguments.> logncdf ()
647s ***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
647s ***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
647s ***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
647s  logncdf (ones (3), ones (2), ones (2))
647s ***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
647s ***** error<logncdf: covariance matrix is required for confidence bounds.> ...
647s  [p, plo, pup] = logncdf (1, 2, 3)
647s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
647s  logncdf (1, 2, 3, [1, 0; 0, 1], 0)
647s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
647s  logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
647s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
647s  logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
647s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
647s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
647s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
647s ***** error<logncdf: bad covariance matrix.> ...
647s  [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
647s 24 tests, 24 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/binocdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/binocdf.m
647s ***** demo
647s  ## Plot various CDFs from the binomial distribution
647s  x = 0:40;
647s  p1 = binocdf (x, 20, 0.5);
647s  p2 = binocdf (x, 20, 0.7);
647s  p3 = binocdf (x, 40, 0.5);
647s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
647s  grid on
647s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
647s           "n = 40, ps = 0.5"}, "location", "southeast")
647s  title ("Binomial CDF")
647s  xlabel ("values in x (number of successes)")
647s  ylabel ("probability")
647s ***** shared x, p, p1
647s  x = [-1 0 1 2 3];
647s  p = [0 1/4 3/4 1 1];
647s  p1 = 1 - p;
647s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
647s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
647s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
647s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
647s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
647s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
647s ***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
647s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
647s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
647s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
647s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
647s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
647s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
647s ***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
647s ***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
647s ***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
647s ***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
647s ***** error<binocdf: function called with too few input arguments.> binocdf ()
647s ***** error<binocdf: function called with too few input arguments.> binocdf (1)
647s ***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
647s ***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
647s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
647s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
647s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
647s  binocdf (ones (3), ones (2), ones (2))
647s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
647s  binocdf (ones (2), ones (3), ones (2))
647s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
647s  binocdf (ones (2), ones (2), ones (3))
647s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
647s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
647s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
647s 29 tests, 29 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/cauchyinv.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/cauchyinv.m
647s ***** demo
647s  ## Plot various iCDFs from the Cauchy distribution
647s  p = 0.001:0.001:0.999;
647s  x1 = cauchyinv (p, 0, 0.5);
647s  x2 = cauchyinv (p, 0, 1);
647s  x3 = cauchyinv (p, 0, 2);
647s  x4 = cauchyinv (p, -2, 1);
647s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
647s  grid on
647s  ylim ([-5, 5])
647s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
647s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
647s  title ("Cauchy iCDF")
647s  xlabel ("probability")
647s  ylabel ("values in x")
647s ***** shared p
647s  p = [-1 0 0.5 1 2];
647s ***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
647s ***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
647s ***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
647s ***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
647s ***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
647s ***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
647s ***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
647s ***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
647s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
647s ***** assert (cauchyinv ([p, NaN], single (1), 2), ...
647s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
647s ***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
647s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
647s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
647s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
647s ***** error<cauchyinv: function called with too few input arguments.> ...
647s  cauchyinv (1, 2)
647s ***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
647s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
647s  cauchyinv (ones (3), ones (2), ones(2))
647s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
647s  cauchyinv (ones (2), ones (3), ones(2))
647s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
647s  cauchyinv (ones (2), ones (2), ones(3))
647s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
647s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
647s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
647s 20 tests, 20 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/tlscdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tlscdf.m
647s ***** demo
647s  ## Plot various CDFs from the location-scale Student's T distribution
647s  x = -8:0.01:8;
647s  p1 = tlscdf (x, 0, 1, 1);
647s  p2 = tlscdf (x, 0, 2, 2);
647s  p3 = tlscdf (x, 3, 2, 5);
647s  p4 = tlscdf (x, -1, 3, Inf);
647s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
647s  grid on
647s  xlim ([-8, 8])
647s  ylim ([0, 1])
647s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
647s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
647s          "location", "northwest")
647s  title ("Location-scale Student's T CDF")
647s  xlabel ("values in x")
647s  ylabel ("probability")
647s ***** shared x,y
647s  x = [-Inf 0 1 Inf];
647s  y = [0 1/2 3/4 1];
647s ***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
647s ***** assert (tlscdf (x, 0, 1, 1), y, eps)
647s ***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
647s ***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
647s ***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
647s ***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
647s ***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
647s ***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
647s ***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
647s ***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
647s ***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
647s ***** error<tlscdf: function called with too few input arguments.> tlscdf ()
647s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
647s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
647s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
647s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
647s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), ones (2), 1, 1)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), 1, ones (2), 1)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), 1, 1, ones (2))
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), ones (2), 1, 1, "upper")
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), 1, ones (2), 1, "upper")
647s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
647s  tlscdf (ones (3), 1, 1, ones (2), "upper")
647s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
647s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
647s 27 tests, 27 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/evcdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/evcdf.m
647s ***** demo
647s  ## Plot various CDFs from the extreme value distribution
647s  x = -10:0.01:10;
647s  p1 = evcdf (x, 0.5, 2);
647s  p2 = evcdf (x, 1.0, 2);
647s  p3 = evcdf (x, 1.5, 3);
647s  p4 = evcdf (x, 3.0, 4);
647s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
647s  grid on
647s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
647s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
647s  title ("Extreme value CDF")
647s  xlabel ("values in x")
647s  ylabel ("probability")
647s ***** shared x, y
647s  x = [-Inf, 1, 2, Inf];
647s  y = [0, 0.6321, 0.9340, 1];
647s ***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
647s ***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
647s ***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
647s ***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
647s ***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
647s ***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
647s ***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
647s ***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
647s ***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
647s ***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
647s ***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
647s ***** error<evcdf: invalid number of input arguments.> evcdf ()
647s ***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
647s ***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
647s ***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
647s  evcdf (ones (3), ones (2), ones (2))
647s ***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
647s ***** error<evcdf: covariance matrix is required for confidence bounds.> ...
647s  [p, plo, pup] = evcdf (1, 2, 3)
647s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  evcdf (1, 2, 3, [1, 0; 0, 1], 0)
647s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
647s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
647s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
647s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
647s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
647s ***** error<evcdf: bad covariance matrix.> ...
647s  [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
647s 24 tests, 24 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/wblrnd.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wblrnd.m
647s ***** assert (size (wblrnd (1, 1)), [1 1])
647s ***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
647s ***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
647s ***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
647s ***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
647s ***** assert (size (wblrnd (1, 1, 3)), [3, 3])
647s ***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
647s ***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
647s ***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
647s ***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
647s ***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
647s ***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
647s ***** assert (class (wblrnd (1, 1)), "double")
647s ***** assert (class (wblrnd (1, single (1))), "single")
647s ***** assert (class (wblrnd (1, single ([1, 1]))), "single")
647s ***** assert (class (wblrnd (single (1), 1)), "single")
647s ***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
647s ***** error<wblrnd: function called with too few input arguments.> wblrnd ()
647s ***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
647s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
647s  wblrnd (ones (3), ones (2))
647s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
647s  wblrnd (ones (2), ones (3))
647s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
647s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
647s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  wblrnd (1, 2, -1)
647s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  wblrnd (1, 2, 1.2)
647s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  wblrnd (1, 2, ones (2))
647s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  wblrnd (1, 2, [2 -1 2])
647s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  wblrnd (1, 2, [2 0 2.5])
647s ***** error<wblrnd: dimensions must be non-negative integers.> ...
647s  wblrnd (1, 2, 2, -1, 5)
647s ***** error<wblrnd: dimensions must be non-negative integers.> ...
647s  wblrnd (1, 2, 2, 1.5, 5)
647s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
647s  wblrnd (2, ones (2), 3)
647s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
647s  wblrnd (2, ones (2), [3, 2])
647s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
647s  wblrnd (2, ones (2), 3, 2)
647s 33 tests, 33 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/logninv.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logninv.m
647s ***** demo
647s  ## Plot various iCDFs from the log-normal distribution
647s  p = 0.001:0.001:0.999;
647s  x1 = logninv (p, 0, 1);
647s  x2 = logninv (p, 0, 0.5);
647s  x3 = logninv (p, 0, 0.25);
647s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
647s  grid on
647s  ylim ([0, 3])
647s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
647s          "location", "northwest")
647s  title ("Log-normal iCDF")
647s  xlabel ("probability")
647s  ylabel ("values in x")
647s ***** shared p
647s  p = [-1 0 0.5 1 2];
647s ***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
647s ***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
647s ***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN], 2*eps)
647s ***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
647s ***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
647s ***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
647s ***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN], 2*eps)
647s ***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
647s ***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
647s ***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
647s ***** error logninv ()
647s ***** error logninv (1,2,3,4)
647s ***** error logninv (ones (3), ones (2), ones (2))
647s ***** error logninv (ones (2), ones (3), ones (2))
647s ***** error logninv (ones (2), ones (2), ones (3))
647s ***** error logninv (i, 2, 2)
647s ***** error logninv (2, i, 2)
647s ***** error logninv (2, 2, i)
647s 18 tests, 18 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/unifrnd.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unifrnd.m
647s ***** assert (size (unifrnd (1, 1)), [1 1])
647s ***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
647s ***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
647s ***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
647s ***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
647s ***** assert (size (unifrnd (1, 1, 3)), [3, 3])
647s ***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
647s ***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
647s ***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
647s ***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
647s ***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
647s ***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
647s ***** assert (class (unifrnd (1, 1)), "double")
647s ***** assert (class (unifrnd (1, single (1))), "single")
647s ***** assert (class (unifrnd (1, single ([1, 1]))), "single")
647s ***** assert (class (unifrnd (single (1), 1)), "single")
647s ***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
647s ***** error<unifrnd: function called with too few input arguments.> unifrnd ()
647s ***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
647s ***** error<unifrnd: A and B must be of common size or scalars.> ...
647s  unifrnd (ones (3), ones (2))
647s ***** error<unifrnd: A and B must be of common size or scalars.> ...
647s  unifrnd (ones (2), ones (3))
647s ***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
647s ***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
647s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  unifrnd (1, 2, -1)
647s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  unifrnd (1, 2, 1.2)
647s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  unifrnd (1, 2, ones (2))
647s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  unifrnd (1, 2, [2 -1 2])
647s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
647s  unifrnd (1, 2, [2 0 2.5])
647s ***** error<unifrnd: dimensions must be non-negative integers.> ...
647s  unifrnd (1, 2, 2, -1, 5)
647s ***** error<unifrnd: dimensions must be non-negative integers.> ...
647s  unifrnd (1, 2, 2, 1.5, 5)
647s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
647s  unifrnd (2, ones (2), 3)
647s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
647s  unifrnd (2, ones (2), [3, 2])
647s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
647s  unifrnd (2, ones (2), 3, 2)
647s 33 tests, 33 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/copularnd.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/copularnd.m
647s ***** test
647s  theta = 0.5;
647s  r = copularnd ("Gaussian", theta);
647s  assert (size (r), [1, 2]);
647s  assert (all ((r >= 0) & (r <= 1)));
647s ***** test
647s  theta = 0.5;
647s  df = 2;
647s  r = copularnd ("t", theta, df);
647s  assert (size (r), [1, 2]);
647s  assert (all ((r >= 0) & (r <= 1)));
647s ***** test
647s  theta = 0.5;
647s  r = copularnd ("Clayton", theta);
647s  assert (size (r), [1, 2]);
647s  assert (all ((r >= 0) & (r <= 1)));
647s ***** test
647s  theta = 0.5;
647s  n = 2;
647s  r = copularnd ("Clayton", theta, n);
647s  assert (size (r), [n, 2]);
647s  assert (all ((r >= 0) & (r <= 1)));
647s ***** test
647s  theta = [1; 2];
647s  n = 2;
647s  d = 3;
647s  r = copularnd ("Clayton", theta, n, d);
647s  assert (size (r), [n, d]);
647s  assert (all ((r >= 0) & (r <= 1)));
647s 5 tests, 5 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/chi2pdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/chi2pdf.m
647s ***** demo
647s  ## Plot various PDFs from the chi-squared distribution
647s  x = 0:0.01:8;
647s  y1 = chi2pdf (x, 1);
647s  y2 = chi2pdf (x, 2);
647s  y3 = chi2pdf (x, 3);
647s  y4 = chi2pdf (x, 4);
647s  y5 = chi2pdf (x, 6);
647s  y6 = chi2pdf (x, 9);
647s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
647s        x, y4, "-c", x, y5, "-m", x, y6, "-y")
647s  grid on
647s  xlim ([0, 8])
647s  ylim ([0, 0.5])
647s  legend ({"df = 1", "df = 2", "df = 3", ...
647s           "df = 4", "df = 6", "df = 9"}, "location", "northeast")
647s  title ("Chi-squared PDF")
647s  xlabel ("values in x")
647s  ylabel ("density")
647s ***** shared x, y
647s  x = [-1 0 0.5 1 Inf];
647s  y = [0, 1/2 * exp(-x(2:5)/2)];
647s ***** assert (chi2pdf (x, 2*ones (1,5)), y)
647s ***** assert (chi2pdf (x, 2), y)
647s ***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
647s ***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
647s ***** assert (chi2pdf (2, Inf), 0)
647s ***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
647s ***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
647s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
647s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
647s ***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
647s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
647s  chi2pdf (ones (3), ones (2))
647s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
647s  chi2pdf (ones (2), ones (3))
647s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
647s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
647s 14 tests, 14 passed, 0 known failure, 0 skipped
647s [inst/dist_fun/wblcdf.m]
647s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wblcdf.m
647s ***** demo
647s  ## Plot various CDFs from the Weibull distribution
647s  x = 0:0.001:2.5;
647s  p1 = wblcdf (x, 1, 0.5);
647s  p2 = wblcdf (x, 1, 1);
647s  p3 = wblcdf (x, 1, 1.5);
647s  p4 = wblcdf (x, 1, 5);
647s  plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
647s  grid on
647s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
647s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
647s  title ("Weibull CDF")
647s  xlabel ("values in x")
647s  ylabel ("probability")
647s ***** shared x, y
647s  x = [-1 0 0.5 1 Inf];
647s  y = [0, 1-exp(-x(2:4)), 1];
647s ***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
647s ***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
647s ***** assert (wblcdf (x, "upper"), 1 - y)
647s ***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
647s ***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
647s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
647s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
647s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
647s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
647s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
647s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
647s ***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
647s ***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
647s ***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
647s ***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
647s ***** error<wblcdf: invalid number of input arguments.> wblcdf ()
647s ***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
647s ***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
647s ***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
647s  wblcdf (ones (3), ones (2), ones (2))
647s ***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
647s ***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
647s  [p, plo, pup] = wblcdf (1, 2, 3)
647s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
647s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
647s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
647s  wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
648s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
648s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
648s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
648s ***** error<wblcdf: bad covariance matrix.> ...
648s  [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
648s 28 tests, 28 passed, 0 known failure, 0 skipped
648s [inst/dist_fun/bisapdf.m]
648s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bisapdf.m
648s ***** demo
648s  ## Plot various PDFs from the Birnbaum-Saunders distribution
648s  x = 0.01:0.01:4;
648s  y1 = bisapdf (x, 1, 0.5);
648s  y2 = bisapdf (x, 1, 1);
648s  y3 = bisapdf (x, 1, 2);
648s  y4 = bisapdf (x, 1, 5);
648s  y5 = bisapdf (x, 1, 10);
648s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
648s  grid on
648s  ylim ([0, 1.5])
648s  legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
648s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
648s  title ("Birnbaum-Saunders PDF")
648s  xlabel ("values in x")
648s  ylabel ("density")
648s ***** demo
648s  ## Plot various PDFs from the Birnbaum-Saunders distribution
648s  x = 0.01:0.01:6;
648s  y1 = bisapdf (x, 1, 0.3);
648s  y2 = bisapdf (x, 2, 0.3);
648s  y3 = bisapdf (x, 1, 0.5);
648s  y4 = bisapdf (x, 3, 0.5);
648s  y5 = bisapdf (x, 5, 0.5);
648s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
648s  grid on
648s  ylim ([0, 1.5])
648s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
648s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
648s  title ("Birnbaum-Saunders CDF")
648s  xlabel ("values in x")
648s  ylabel ("density")
648s ***** shared x, y
648s  x = [-1, 0, 1, 2, Inf];
648s  y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
648s ***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
648s ***** assert (bisapdf (x, 1, 1), y, eps)
648s ***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
648s ***** assert (bisapdf (x, ones (1,5), 1), y, eps)
648s ***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
648s ***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
648s ***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
648s ***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
648s ***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
648s ***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
648s ***** error<bisapdf: function called with too few input arguments.> bisapdf ()
648s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
648s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
648s ***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
648s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
648s  bisapdf (ones (3), ones (2), ones(2))
648s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
648s  bisapdf (ones (2), ones (3), ones(2))
648s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
648s  bisapdf (ones (2), ones (2), ones(3))
648s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
648s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
648s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
648s 20 tests, 20 passed, 0 known failure, 0 skipped
648s [inst/dist_fun/cauchypdf.m]
648s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/cauchypdf.m
648s ***** demo
648s  ## Plot various PDFs from the Cauchy distribution
648s  x = -5:0.01:5;
648s  y1 = cauchypdf (x, 0, 0.5);
648s  y2 = cauchypdf (x, 0, 1);
648s  y3 = cauchypdf (x, 0, 2);
648s  y4 = cauchypdf (x, -2, 1);
648s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
648s  grid on
648s  xlim ([-5, 5])
648s  ylim ([0, 0.7])
648s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
648s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
648s  title ("Cauchy PDF")
648s  xlabel ("values in x")
648s  ylabel ("density")
648s ***** shared x, y
648s  x = [-1 0 0.5 1 2];
648s  y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
648s ***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
648s ***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
648s ***** assert (cauchypdf (x, ones (1,5), 2), y)
648s ***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
648s ***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
648s ***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
648s ***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
648s ***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
648s ***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
648s ***** test
648s  x = rand (10, 1);
648s  assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
648s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
648s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
648s ***** error<cauchypdf: function called with too few input arguments.> ...
648s  cauchypdf (1, 2)
648s ***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
648s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
648s  cauchypdf (ones (3), ones (2), ones(2))
648s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
648s  cauchypdf (ones (2), ones (3), ones(2))
648s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
648s  cauchypdf (ones (2), ones (2), ones(3))
648s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
648s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
648s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
648s 20 tests, 20 passed, 0 known failure, 0 skipped
648s [inst/dist_fun/unifinv.m]
648s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unifinv.m
648s ***** demo
648s  ## Plot various iCDFs from the continuous uniform distribution
648s  p = 0.001:0.001:0.999;
648s  x1 = unifinv (p, 2, 5);
648s  x2 = unifinv (p, 3, 9);
648s  plot (p, x1, "-b", p, x2, "-g")
648s  grid on
648s  xlim ([0, 1])
648s  ylim ([0, 10])
648s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
648s  title ("Continuous uniform iCDF")
648s  xlabel ("probability")
648s  ylabel ("values in x")
648s ***** shared p
648s  p = [-1 0 0.5 1 2];
648s ***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
648s ***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
648s ***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
648s ***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
648s ***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
648s ***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
648s ***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
648s ***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
648s ***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
648s ***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
648s ***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
648s ***** error<unifinv: function called with too few input arguments.> unifinv ()
648s ***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
648s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
648s  unifinv (ones (3), ones (2), ones (2))
648s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
648s  unifinv (ones (2), ones (3), ones (2))
648s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
648s  unifinv (ones (2), ones (2), ones (3))
648s ***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
648s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
648s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
648s 19 tests, 19 passed, 0 known failure, 0 skipped
648s [inst/dist_fun/nctinv.m]
648s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nctinv.m
648s ***** demo
648s  ## Plot various iCDFs from the noncentral T distribution
648s  p = 0.001:0.001:0.999;
648s  x1 = nctinv (p, 1, 0);
648s  x2 = nctinv (p, 4, 0);
648s  x3 = nctinv (p, 1, 2);
648s  x4 = nctinv (p, 4, 2);
648s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
648s  grid on
648s  ylim ([-5, 5])
648s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
648s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
648s  title ("Noncentral T iCDF")
648s  xlabel ("probability")
648s  ylabel ("values in x")
648s ***** demo
648s  ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
648s  ## with the same number of degrees of freedom (10).
648s 
648s  p = 0.001:0.001:0.999;
648s  x1 = nctinv (p, 10, 1);
648s  x2 = tinv (p, 10);
648s  plot (p, x1, "-", p, x2, "-");
648s  grid on
648s  ylim ([-5, 5])
648s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
648s  title ("Noncentral T vs T quantile functions")
648s  xlabel ("probability")
648s  ylabel ("values in x")
648s ***** test
648s  x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
648s  assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
649s ***** test
649s  x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
649s  assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
650s ***** test
650s  x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
650s  assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
651s ***** test
651s  x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
651s  assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
652s ***** test
652s  x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
652s  assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
654s ***** test
654s  x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
654s  assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
656s ***** test
656s  assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
659s ***** error<nctinv: function called with too few input arguments.> nctinv ()
659s ***** error<nctinv: function called with too few input arguments.> nctinv (1)
659s ***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
659s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
659s  nctinv (ones (3), ones (2), ones (2))
659s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
659s  nctinv (ones (2), ones (3), ones (2))
659s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
659s  nctinv (ones (2), ones (2), ones (3))
659s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
659s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
659s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
659s 16 tests, 16 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/jsupdf.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/jsupdf.m
659s ***** error jsupdf ()
659s ***** error jsupdf (1, 2, 3, 4)
659s ***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
659s  jsupdf (1, ones (2), ones (3))
659s 3 tests, 3 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/plinv.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/plinv.m
659s ***** demo
659s  ## Plot various iCDFs from the Piecewise linear distribution
659s  p = 0.001:0.001:0.999;
659s  x1 = [0, 1, 3, 4, 7, 10];
659s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
659s  x2 = [0, 2, 5, 6, 7, 8];
659s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
659s  data1 = plinv (p, x1, Fx1);
659s  data2 = plinv (p, x2, Fx2);
659s  plot (p, data1, "-b", p, data2, "-g")
659s  grid on
659s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
659s  title ("Piecewise linear iCDF")
659s  xlabel ("probability")
659s  ylabel ("values in data")
659s ***** test
659s  p = 0:0.2:1;
659s  data = plinv (p, [0, 1], [0, 1]);
659s  assert (data, p);
659s ***** test
659s  p = 0:0.2:1;
659s  data = plinv (p, [0, 2], [0, 1]);
659s  assert (data, 2 * p);
659s ***** test
659s  p = 0:0.2:1;
659s  data_out = 1:6;
659s  data = plinv (p, [0, 1], [0, 0.5]);
659s  assert (data, [0, 0.4, 0.8, NA, NA, NA]);
659s ***** test
659s  p = 0:0.2:1;
659s  data_out = 1:6;
659s  data = plinv (p, [0, 0.5], [0, 1]);
659s  assert (data, [0:0.1:0.5]);
659s ***** error<plinv: function called with too few input arguments.> plinv ()
659s ***** error<plinv: function called with too few input arguments.> plinv (1)
659s ***** error<plinv: function called with too few input arguments.> plinv (1, 2)
659s ***** error<plinv: X and FX must be vectors of equal size.> ...
659s  plinv (1, [0, 1, 2], [0, 1])
659s ***** error<plinv: X and FX must be at least two-elements long.> ...
659s  plinv (1, [0], [1])
659s ***** error<plinv: FX must be bounded in the range> ...
659s  plinv (1, [0, 1, 2], [0, 1, 1.5])
659s ***** error<plinv: FX must be bounded in the range> ...
659s  plinv (1, [0, 1, 2], [0, i, 1])
659s ***** error<plinv: P, X, and FX must not be complex.> ...
659s  plinv (i, [0, 1, 2], [0, 0.5, 1])
659s ***** error<plinv: P, X, and FX must not be complex.> ...
659s  plinv (1, [0, i, 2], [0, 0.5, 1])
659s ***** error<plinv: P, X, and FX must not be complex.> ...
659s  plinv (1, [0, 1, 2], [0, 0.5i, 1])
659s 14 tests, 14 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/gumbelrnd.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gumbelrnd.m
659s ***** assert (size (gumbelrnd (1, 1)), [1 1])
659s ***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
659s ***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
659s ***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
659s ***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
659s ***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
659s ***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
659s ***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
659s ***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
659s ***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
659s ***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
659s ***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
659s ***** assert (class (gumbelrnd (1, 1)), "double")
659s ***** assert (class (gumbelrnd (1, single (1))), "single")
659s ***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
659s ***** assert (class (gumbelrnd (single (1), 1)), "single")
659s ***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
659s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
659s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
659s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
659s  gumbelrnd (ones (3), ones (2))
659s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
659s  gumbelrnd (ones (2), ones (3))
659s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
659s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
659s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  gumbelrnd (1, 2, -1)
659s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  gumbelrnd (1, 2, 1.2)
659s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  gumbelrnd (1, 2, ones (2))
659s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  gumbelrnd (1, 2, [2 -1 2])
659s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  gumbelrnd (1, 2, [2 0 2.5])
659s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
659s  gumbelrnd (1, 2, 2, -1, 5)
659s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
659s  gumbelrnd (1, 2, 2, 1.5, 5)
659s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
659s  gumbelrnd (2, ones (2), 3)
659s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
659s  gumbelrnd (2, ones (2), [3, 2])
659s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
659s  gumbelrnd (2, ones (2), 3, 2)
659s 33 tests, 33 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/raylcdf.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/raylcdf.m
659s ***** demo
659s  ## Plot various CDFs from the Rayleigh distribution
659s  x = 0:0.01:10;
659s  p1 = raylcdf (x, 0.5);
659s  p2 = raylcdf (x, 1);
659s  p3 = raylcdf (x, 2);
659s  p4 = raylcdf (x, 3);
659s  p5 = raylcdf (x, 4);
659s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
659s  grid on
659s  ylim ([0, 1])
659s  legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
659s           "σ = 3", "σ = 4"}, "location", "southeast")
659s  title ("Rayleigh CDF")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** test
659s  x = 0:0.5:2.5;
659s  sigma = 1:6;
659s  p = raylcdf (x, sigma);
659s  expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
659s  assert (p, expected_p, 0.001);
659s ***** test
659s  x = 0:0.5:2.5;
659s  p = raylcdf (x, 0.5);
659s  expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
659s  assert (p, expected_p, 0.001);
659s ***** shared x, p
659s  x = [-1, 0, 1, 2, Inf];
659s  p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
659s ***** assert (raylcdf (x, 1), p, 1e-14)
659s ***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
659s ***** error<raylcdf: function called with too few input arguments.> raylcdf ()
659s ***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
659s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
659s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
659s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
659s  raylcdf (ones (3), ones (2))
659s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
659s  raylcdf (ones (2), ones (3))
659s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
659s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
659s 12 tests, 12 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/ncx2cdf.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncx2cdf.m
659s ***** demo
659s  ## Plot various CDFs from the noncentral chi-squared distribution
659s  x = 0:0.1:10;
659s  p1 = ncx2cdf (x, 2, 1);
659s  p2 = ncx2cdf (x, 2, 2);
659s  p3 = ncx2cdf (x, 2, 3);
659s  p4 = ncx2cdf (x, 4, 1);
659s  p5 = ncx2cdf (x, 4, 2);
659s  p6 = ncx2cdf (x, 4, 3);
659s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
659s        x, p4, "-m", x, p5, "-c", x, p6, "-y")
659s  grid on
659s  xlim ([0, 10])
659s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
659s           "df = 2, λ = 3", "df = 4, λ = 1", ...
659s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
659s  title ("Noncentral chi-squared CDF")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** demo
659s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
659s  ## chi-squared CDF with the same number of degrees of freedom (4).
659s 
659s  x = 0:0.1:10;
659s  p1 = ncx2cdf (x, 4, 2);
659s  p2 = chi2cdf (x, 4);
659s  plot (x, p1, "-", x, p2, "-")
659s  grid on
659s  xlim ([0, 10])
659s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
659s  title ("Noncentral chi-squared vs chi-squared CDFs")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** test
659s  x = -2:0.1:2;
659s  p = ncx2cdf (x, 10, 1);
659s  assert (p([1:21]), zeros (1, 21), 3e-84);
659s  assert (p(22), 1.521400636466575e-09, 1e-14);
659s  assert (p(30), 6.665480510026046e-05, 1e-14);
659s  assert (p(41), 0.002406447308399836, 1e-14);
659s ***** test
659s  p = ncx2cdf (12, 10, 3);
659s  assert (p, 0.4845555602398649, 1e-14);
659s ***** test
659s  p = ncx2cdf (2, 3, 2);
659s  assert (p, 0.2207330870741212, 1e-14);
659s ***** test
659s  p = ncx2cdf (2, 3, 2, "upper");
659s  assert (p, 0.7792669129258789, 1e-14);
659s ***** test
659s  p = ncx2cdf ([3, 6], 3, 2, "upper");
659s  assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
659s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
659s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
659s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
659s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
659s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
659s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
659s  ncx2cdf (ones (3), ones (2), ones (2))
659s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
659s  ncx2cdf (ones (2), ones (3), ones (2))
659s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
659s  ncx2cdf (ones (2), ones (2), ones (3))
659s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
659s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
659s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
659s 16 tests, 16 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/raylinv.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/raylinv.m
659s ***** demo
659s  ## Plot various iCDFs from the Rayleigh distribution
659s  p = 0.001:0.001:0.999;
659s  x1 = raylinv (p, 0.5);
659s  x2 = raylinv (p, 1);
659s  x3 = raylinv (p, 2);
659s  x4 = raylinv (p, 3);
659s  x5 = raylinv (p, 4);
659s  plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
659s  grid on
659s  ylim ([0, 10])
659s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
659s           "σ = 3", "σ = 4"}, "location", "northwest")
659s  title ("Rayleigh iCDF")
659s  xlabel ("probability")
659s  ylabel ("values in x")
659s ***** test
659s  p = 0:0.1:0.5;
659s  sigma = 1:6;
659s  x = raylinv (p, sigma);
659s  expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
659s  assert (x, expected_x, 0.001);
659s ***** test
659s  p = 0:0.1:0.5;
659s  x = raylinv (p, 0.5);
659s  expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
659s  assert (x, expected_x, 0.001);
659s ***** error<raylinv: function called with too few input arguments.> raylinv ()
659s ***** error<raylinv: function called with too few input arguments.> raylinv (1)
659s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
659s  raylinv (ones (3), ones (2))
659s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
659s  raylinv (ones (2), ones (3))
659s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
659s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
659s 8 tests, 8 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/tcdf.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tcdf.m
659s ***** demo
659s  ## Plot various CDFs from the Student's T distribution
659s  x = -5:0.01:5;
659s  p1 = tcdf (x, 1);
659s  p2 = tcdf (x, 2);
659s  p3 = tcdf (x, 5);
659s  p4 = tcdf (x, Inf);
659s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
659s  grid on
659s  xlim ([-5, 5])
659s  ylim ([0, 1])
659s  legend ({"df = 1", "df = 2", ...
659s           "df = 5", 'df = \infty'}, "location", "southeast")
659s  title ("Student's T CDF")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** shared x,y
659s  x = [-Inf 0 1 Inf];
659s  y = [0 1/2 3/4 1];
659s ***** assert (tcdf (x, ones (1,4)), y, eps)
659s ***** assert (tcdf (x, 1), y, eps)
659s ***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
659s ***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
659s ***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
659s ***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
659s ***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
659s ***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
659s ***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
659s ***** error<tcdf: function called with too few input arguments.> tcdf ()
659s ***** error<tcdf: function called with too few input arguments.> tcdf (1)
659s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
659s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
659s ***** error<tcdf: X and DF must be of common size or scalars.> ...
659s  tcdf (ones (3), ones (2))
659s ***** error<tcdf: X and DF must be of common size or scalars.> ...
659s  tcdf (ones (3), ones (2))
659s ***** error<tcdf: X and DF must be of common size or scalars.> ...
659s  tcdf (ones (3), ones (2), "upper")
659s ***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
659s ***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
659s ***** shared tol_rel
659s  tol_rel = 10 * eps;
659s ***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
659s ***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
659s ***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
659s ***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
659s ***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
659s ***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
659s ***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
659s ***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
659s ***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
659s ***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
659s ***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
659s ***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
659s 30 tests, 30 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/laplaceinv.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/laplaceinv.m
659s ***** demo
659s  ## Plot various iCDFs from the Laplace distribution
659s  p = 0.001:0.001:0.999;
659s  x1 = cauchyinv (p, 0, 1);
659s  x2 = cauchyinv (p, 0, 2);
659s  x3 = cauchyinv (p, 0, 4);
659s  x4 = cauchyinv (p, -5, 4);
659s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
659s  grid on
659s  ylim ([-10, 10])
659s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
659s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
659s  title ("Laplace iCDF")
659s  xlabel ("probability")
659s  ylabel ("values in x")
659s ***** shared p, x
659s  p = [-1 0 0.5 1 2];
659s  x = [NaN, -Inf, 0, Inf, NaN];
659s ***** assert (laplaceinv (p, 0, 1), x)
659s ***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
659s ***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
659s ***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
659s ***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
659s ***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
659s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
659s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
659s ***** error<laplaceinv: function called with too few input arguments.> ...
659s  laplaceinv (1, 2)
659s ***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
659s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
659s  laplaceinv (1, ones (2), ones (3))
659s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
659s  laplaceinv (ones (2), 1, ones (3))
659s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
659s  laplaceinv (ones (2), ones (3), 1)
659s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
659s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
659s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
659s 16 tests, 16 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/poisscdf.m]
659s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/poisscdf.m
659s ***** demo
659s  ## Plot various CDFs from the Poisson distribution
659s  x = 0:20;
659s  p1 = poisscdf (x, 1);
659s  p2 = poisscdf (x, 4);
659s  p3 = poisscdf (x, 10);
659s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
659s  grid on
659s  ylim ([0, 1])
659s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
659s  title ("Poisson CDF")
659s  xlabel ("values in x (number of occurences)")
659s  ylabel ("probability")
659s ***** shared x, y
659s  x = [-1 0 1 2 Inf];
659s  y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
659s ***** assert (poisscdf (x, ones (1,5)), y)
659s ***** assert (poisscdf (x, 1), y)
659s ***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
660s ***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
660s ***** assert (poisscdf ([x, NaN], 1), [y, NaN])
660s ***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
660s ***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
660s ***** error<poisscdf: function called with too few input arguments.> poisscdf ()
660s ***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
660s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
660s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
660s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
660s  poisscdf (ones (3), ones (2))
660s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
660s  poisscdf (ones (2), ones (3))
660s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
660s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
660s 15 tests, 15 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/ricecdf.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ricecdf.m
660s ***** demo
660s  ## Plot various CDFs from the Rician distribution
660s  x = 0:0.01:10;
660s  p1 = ricecdf (x, 0, 1);
660s  p2 = ricecdf (x, 0.5, 1);
660s  p3 = ricecdf (x, 1, 1);
660s  p4 = ricecdf (x, 2, 1);
660s  p5 = ricecdf (x, 4, 1);
660s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
660s  grid on
660s  ylim ([0, 1])
660s  xlim ([0, 8])
660s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
660s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
660s  title ("Rician CDF")
660s  xlabel ("values in x")
660s  ylabel ("probability")
660s ***** demo
660s  ## Plot various CDFs from the Rician distribution
660s  x = 0:0.01:10;
660s  p1 = ricecdf (x, 0, 0.5);
660s  p2 = ricecdf (x, 0, 2);
660s  p3 = ricecdf (x, 0, 3);
660s  p4 = ricecdf (x, 2, 2);
660s  p5 = ricecdf (x, 4, 2);
660s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
660s  grid on
660s  ylim ([0, 1])
660s  xlim ([0, 8])
660s  legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
660s           "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
660s  title ("Rician CDF")
660s  xlabel ("values in x")
660s  ylabel ("probability")
660s ***** test
660s  x = 0:0.5:2.5;
660s  s = 1:6;
660s  p = ricecdf (x, s, 1);
660s  expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
660s  assert (p, expected_p, 0.001);
660s ***** test
660s  x = 0:0.5:2.5;
660s  sigma = 1:6;
660s  p = ricecdf (x, 1, sigma);
660s  expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
660s  assert (p, expected_p, 0.001);
660s ***** test
660s  x = 0:0.5:2.5;
660s  p = ricecdf (x, 0, 1);
660s  expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
660s  assert (p, expected_p, 0.001);
660s ***** test
660s  x = 0:0.5:2.5;
660s  p = ricecdf (x, 1, 1);
660s  expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
660s  assert (p, expected_p, 0.001);
660s ***** shared x, p
660s  x = [-1, 0, 1, 2, Inf];
660s  p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
660s ***** assert (ricecdf (x, 1, 1), p, 1e-14)
660s ***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
660s ***** error<ricecdf: function called with too few input arguments.> ricecdf ()
660s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
660s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
660s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
660s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
660s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricecdf (ones (3), ones (2), ones (2))
660s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricecdf (ones (2), ones (3), ones (2))
660s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricecdf (ones (2), ones (2), ones (3))
660s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
660s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
660s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
660s 17 tests, 17 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/tlspdf.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tlspdf.m
660s ***** demo
660s  ## Plot various PDFs from the Student's T distribution
660s  x = -8:0.01:8;
660s  y1 = tlspdf (x, 0, 1, 1);
660s  y2 = tlspdf (x, 0, 2, 2);
660s  y3 = tlspdf (x, 3, 2, 5);
660s  y4 = tlspdf (x, -1, 3, Inf);
660s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
660s  grid on
660s  xlim ([-8, 8])
660s  ylim ([0, 0.41])
660s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
660s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
660s          "location", "northwest")
660s  title ("Location-scale Student's T PDF")
660s  xlabel ("values in x")
660s  ylabel ("density")
660s ***** test
660s  x = rand (10,1);
660s  y = 1./(pi * (1 + x.^2));
660s  assert (tlspdf (x, 0, 1, 1), y, 5*eps);
660s  assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
660s  assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
660s ***** shared x, y
660s  x = [-Inf 0 0.5 1 Inf];
660s  y = 1./(pi * (1 + x.^2));
660s ***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
660s ***** assert (tlspdf (x, 0, 1, 1), y, eps)
660s ***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
660s ***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
660s ***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
660s ***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
660s ***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
660s ***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
660s ***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
660s ***** error<tlspdf: function called with too few input arguments.> tlspdf ()
660s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
660s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
660s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlspdf (ones (3), ones (2), 1, 1)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlspdf (ones (2), 1, ones (3), 1)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlspdf (ones (2), 1, 1, ones (3))
660s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
660s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
660s 21 tests, 21 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/norminv.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/norminv.m
660s ***** demo
660s  ## Plot various iCDFs from the normal distribution
660s  p = 0.001:0.001:0.999;
660s  x1 = norminv (p, 0, 0.5);
660s  x2 = norminv (p, 0, 1);
660s  x3 = norminv (p, 0, 2);
660s  x4 = norminv (p, -2, 0.8);
660s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
660s  grid on
660s  ylim ([-5, 5])
660s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
660s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
660s  title ("Normal iCDF")
660s  xlabel ("probability")
660s  ylabel ("values in x")
660s ***** shared p
660s  p = [-1 0 0.5 1 2];
660s ***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
660s ***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
660s ***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
660s ***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
660s ***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
660s ***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
660s ***** assert (norminv (p), probit (p))
660s ***** assert (norminv (0.31254), probit (0.31254))
660s ***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
660s ***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
660s ***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
660s ***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
660s ***** error<norminv: function called with too few input arguments.> norminv ()
660s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
660s  norminv (ones (3), ones (2), ones (2))
660s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
660s  norminv (ones (2), ones (3), ones (2))
660s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
660s  norminv (ones (2), ones (2), ones (3))
660s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
660s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
660s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
660s 19 tests, 19 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/ricepdf.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ricepdf.m
660s ***** demo
660s  ## Plot various PDFs from the Rician distribution
660s  x = 0:0.01:8;
660s  y1 = ricepdf (x, 0, 1);
660s  y2 = ricepdf (x, 0.5, 1);
660s  y3 = ricepdf (x, 1, 1);
660s  y4 = ricepdf (x, 2, 1);
660s  y5 = ricepdf (x, 4, 1);
660s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
660s  grid on
660s  ylim ([0, 0.65])
660s  xlim ([0, 8])
660s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
660s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
660s  title ("Rician PDF")
660s  xlabel ("values in x")
660s  ylabel ("density")
660s ***** shared x, y
660s  x = [-1 0 0.5 1 2];
660s  y = [0 0 0.1073 0.1978 0.2846];
660s ***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
660s ***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
660s ***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
660s ***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
660s ***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
660s ***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
660s ***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
660s ***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
660s ***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
660s ***** error<ricepdf: function called with too few input arguments.> ricepdf ()
660s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
660s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
660s ***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
660s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricepdf (ones (3), ones (2), ones (2))
660s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricepdf (ones (2), ones (3), ones (2))
660s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
660s  ricepdf (ones (2), ones (2), ones (3))
660s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
660s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
660s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
660s 19 tests, 19 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/mvtrnd.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvtrnd.m
660s ***** test
660s  rho = [1, 0.5; 0.5, 1];
660s  df = 3;
660s  n = 10;
660s  r = mvtrnd (rho, df, n);
660s  assert (size (r), [10, 2]);
660s ***** test
660s  rho = [1, 0.5; 0.5, 1];
660s  df = [2; 3];
660s  n = 2;
660s  r = mvtrnd (rho, df, 2);
660s  assert (size (r), [2, 2]);
660s 2 tests, 2 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/tlsrnd.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/tlsrnd.m
660s ***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
660s ***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
660s ***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
660s ***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
660s ***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
660s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
660s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
660s ***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
660s ***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
660s ***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
660s ***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
660s ***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
660s ***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
660s ***** assert (class (tlsrnd (1, 2, 3)), "double")
660s ***** assert (class (tlsrnd (single (1), 2, 3)), "single")
660s ***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
660s ***** assert (class (tlsrnd (1, single (2), 3)), "single")
660s ***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
660s ***** assert (class (tlsrnd (1, 2, single (3))), "single")
660s ***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
660s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
660s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
660s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlsrnd (ones (3), ones (2), 1)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlsrnd (ones (2), 1, ones (3))
660s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
660s  tlsrnd (1, ones (2), ones (3))
660s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
660s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
660s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (1, 2, 3, -1)
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (1, 2, 3, 1.2)
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (1, 2, 3, ones (2))
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (1, 2, 3, [2 -1 2])
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (1, 2, 3, [2 0 2.5])
660s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
660s  tlsrnd (ones (2), 2, 3, ones (2))
660s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
660s  tlsrnd (1, 2, 3, 2, -1, 5)
660s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
660s  tlsrnd (1, 2, 3, 2, 1.5, 5)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
660s  tlsrnd (ones (2,2), 2, 3, 3)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
660s  tlsrnd (1, ones (2,2), 3, 3)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
660s  tlsrnd (1, 2, ones (2,2), 3)
660s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
660s  tlsrnd (1, 2, ones (2,2), [3, 3])
660s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
660s  tlsrnd (1, 2, ones (2,2), 2, 3)
660s 42 tests, 42 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/gpinv.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gpinv.m
660s ***** demo
660s  ## Plot various iCDFs from the generalized Pareto distribution
660s  p = 0.001:0.001:0.999;
660s  x1 = gpinv (p, 1, 1, 0);
660s  x2 = gpinv (p, 5, 1, 0);
660s  x3 = gpinv (p, 20, 1, 0);
660s  x4 = gpinv (p, 1, 2, 0);
660s  x5 = gpinv (p, 5, 2, 0);
660s  x6 = gpinv (p, 20, 2, 0);
660s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
660s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
660s  grid on
660s  ylim ([0, 5])
660s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
660s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
660s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
660s          "location", "southeast")
660s  title ("Generalized Pareto iCDF")
660s  xlabel ("probability")
660s  ylabel ("values in x")
660s ***** shared p, y1, y2, y3
660s  p = [-1, 0, 1/2, 1, 2];
660s  y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
660s  y2 = [NaN, 0, 1, Inf, NaN];
660s  y3 = [NaN, 0, 1/2, 1, NaN];
660s ***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
660s ***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
660s ***** assert (gpinv (p, 0, ones (1,5), 0), y1)
660s ***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
660s ***** assert (gpinv (p, 0, 1, 0), y1)
660s ***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
660s ***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
660s ***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
660s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
660s ***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
660s ***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
660s ***** assert (gpinv (p, 1, ones (1,5), 0), y2)
660s ***** assert (gpinv (p, ones (1,5), 1, 0), y2)
660s ***** assert (gpinv (p, 1, 1, 0), y2)
660s ***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
660s ***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
660s ***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
660s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
660s ***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
660s ***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
660s ***** assert (gpinv (p, -1, ones (1,5), 0), y3)
660s ***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
660s ***** assert (gpinv (p, -1, 1, 0), y3)
660s ***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
660s ***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
660s ***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
660s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
660s ***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
660s ***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
660s ***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
660s ***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
660s ***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
660s ***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
660s ***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
660s ***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
660s ***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
660s ***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
660s ***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
660s ***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
660s ***** error<gpinv: function called with too few input arguments.> gpinv ()
660s ***** error<gpinv: function called with too few input arguments.> gpinv (1)
660s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
660s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
660s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
660s  gpinv (ones (3), ones (2), ones(2), ones(2))
660s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
660s  gpinv (ones (2), ones (3), ones(2), ones(2))
660s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
660s  gpinv (ones (2), ones (2), ones(3), ones(2))
660s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
660s  gpinv (ones (2), ones (2), ones(2), ones(3))
660s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
660s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
660s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
660s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
660s 51 tests, 51 passed, 0 known failure, 0 skipped
660s [inst/dist_fun/mvtcdf.m]
660s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvtcdf.m
660s ***** demo
660s  ## Compute the cdf of a multivariate Student's t distribution with
660s  ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.
660s 
660s  rho = [1, 0.4; 0.4, 1];
660s  df = 2;
660s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
660s  X = [X1(:), X2(:)];
660s  p = mvtcdf (X, rho, df);
660s  surf (X1, X2, reshape (p, 25, 25));
660s  title ("Bivariate Student's t cumulative distribution function");
660s ***** test
660s  x = [1, 2];
660s  rho = [1, 0.5; 0.5, 1];
660s  df = 4;
660s  a = [-1, 0];
660s  assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
660s ***** test
660s  x = [1, 2;2, 4;1, 5];
660s  rho = [1, 0.5; 0.5, 1];
660s  df = 4;
660s  p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
660s  assert (mvtcdf(x, rho, df), p, 1e-14);
660s ***** test
660s  x = [1, 2, 2, 4, 1, 5];
660s  rho = eye (6);
660s  rho(rho == 0) = 0.5;
660s  df = 4;
660s  assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
685s ***** error mvtcdf (1)
685s ***** error mvtcdf (1, 2)
685s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
685s  mvtcdf (1, [2, 3; 3, 2], 1)
685s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
685s  mvtcdf ([2, 3, 4], ones (2), 1)
685s ***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
685s  mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
685s ***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
685s  mvtcdf ([2, 3], ones (2), [1, 2, 3])
685s ***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
685s  mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
685s 10 tests, 10 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/mnpdf.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mnpdf.m
685s ***** test
685s  x = [1, 4, 2];
685s  pk = [0.2, 0.5, 0.3];
685s  y = mnpdf (x, pk);
685s  assert (y, 0.11812, 0.001);
685s ***** test
685s  x = [1, 4, 2; 1, 0, 9];
685s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
685s  y = mnpdf (x, pk);
685s  assert (y, [0.11812; 0.13422], 0.001);
685s 2 tests, 2 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/bisainv.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bisainv.m
685s ***** demo
685s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
685s  p = 0.001:0.001:0.999;
685s  x1 = bisainv (p, 1, 0.5);
685s  x2 = bisainv (p, 1, 1);
685s  x3 = bisainv (p, 1, 2);
685s  x4 = bisainv (p, 1, 5);
685s  x5 = bisainv (p, 1, 10);
685s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
685s  grid on
685s  ylim ([0, 10])
685s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
685s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
685s  title ("Birnbaum-Saunders iCDF")
685s  xlabel ("probability")
685s  ylabel ("values in x")
685s ***** demo
685s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
685s  p = 0.001:0.001:0.999;
685s  x1 = bisainv (p, 1, 0.3);
685s  x2 = bisainv (p, 2, 0.3);
685s  x3 = bisainv (p, 1, 0.5);
685s  x4 = bisainv (p, 3, 0.5);
685s  x5 = bisainv (p, 5, 0.5);
685s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
685s  grid on
685s  ylim ([0, 10])
685s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
685s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
685s  title ("Birnbaum-Saunders iCDF")
685s  xlabel ("probability")
685s  ylabel ("values in x")
685s ***** shared p, y, f
685s  f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
685s  p = [-1, 0, 1/4, 1/2, 1, 2];
685s  y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
685s ***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
685s ***** assert (bisainv (p, 1, ones (1,6)), y)
685s ***** assert (bisainv (p, ones (1,6), 1), y)
685s ***** assert (bisainv (p, 1, 1), y)
685s ***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
685s ***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
685s ***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
685s ***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
685s ***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
685s ***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
685s ***** error<bisainv: function called with too few input arguments.> bisainv ()
685s ***** error<bisainv: function called with too few input arguments.> bisainv (1)
685s ***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
685s ***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
685s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
685s  bisainv (ones (3), ones (2), ones(2))
685s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
685s  bisainv (ones (2), ones (3), ones(2))
685s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
685s  bisainv (ones (2), ones (2), ones(3))
685s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
685s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
685s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
685s 20 tests, 20 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/bvncdf.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/bvncdf.m
685s ***** demo
685s  mu = [1, -1];
685s  sigma = [0.9, 0.4; 0.4, 0.3];
685s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
685s  x = [X1(:), X2(:)];
685s  p = bvncdf (x, mu, sigma);
685s  Z = reshape (p, 25, 25);
685s  surf (X1, X2, Z);
685s  title ("Bivariate Normal Distribution");
685s  ylabel "X1"
685s  xlabel "X2"
685s ***** test
685s  mu = [1, -1];
685s  sigma = [0.9, 0.4; 0.4, 0.3];
685s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
685s  x = [X1(:), X2(:)];
685s  p = bvncdf (x, mu, sigma);
685s  p_out = [0.00011878988774500, 0.00034404112322371, ...
685s           0.00087682502191813, 0.00195221905058185, ...
685s           0.00378235566873474, 0.00638175749734415, ...
685s           0.00943764224329656, 0.01239164888125426, ...
685s           0.01472750274376648, 0.01623228313374828]';
685s  assert (p([1:10]), p_out, 1e-16);
685s ***** test
685s  mu = [1, -1];
685s  sigma = [0.9, 0.4; 0.4, 0.3];
685s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
685s  x = [X1(:), X2(:)];
685s  p = bvncdf (x, mu, sigma);
685s  p_out = [0.8180695783608276, 0.8854485749482751, ...
685s           0.9308108777385832, 0.9579855743025508, ...
685s           0.9722897881414742, 0.9788150170059926, ...
685s           0.9813597788804785, 0.9821977956568989, ...
685s           0.9824283794464095, 0.9824809345614861]';
685s  assert (p([616:625]), p_out, 3e-16);
685s ***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
685s ***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
685s ***** error bvncdf (randn (25,2), [], ones (3, 2));
685s 5 tests, 5 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/unidrnd.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unidrnd.m
685s ***** assert (size (unidrnd (2)), [1, 1])
685s ***** assert (size (unidrnd (ones (2,1))), [2, 1])
685s ***** assert (size (unidrnd (ones (2,2))), [2, 2])
685s ***** assert (size (unidrnd (1, 3)), [3, 3])
685s ***** assert (size (unidrnd (1, [4 1])), [4, 1])
685s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
685s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
685s ***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
685s ***** assert (size (unidrnd (1, 0, 1)), [0, 1])
685s ***** assert (size (unidrnd (1, 1, 0)), [1, 0])
685s ***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
685s ***** assert (unidrnd (0, 1, 1), NaN)
685s ***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
685s ***** assert (class (unidrnd (2)), "double")
685s ***** assert (class (unidrnd (single (2))), "single")
685s ***** assert (class (unidrnd (single ([2 2]))), "single")
685s ***** error<unidrnd: function called with too few input arguments.> unidrnd ()
685s ***** error<unidrnd: N must not be complex.> unidrnd (i)
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (1, -1)
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (1, 1.2)
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (1, ones (2))
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (1, [2 -1 2])
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (1, [2 0 2.5])
685s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  unidrnd (ones (2), ones (2))
685s ***** error<unidrnd: dimensions must be non-negative integers.> ...
685s  unidrnd (1, 2, -1, 5)
685s ***** error<unidrnd: dimensions must be non-negative integers.> ...
685s  unidrnd (1, 2, 1.5, 5)
685s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
685s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
685s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
685s 29 tests, 29 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/mvtcdfqmc.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/mvtcdfqmc.m
685s ***** error mvtcdfqmc (1, 2, 3);
685s ***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
685s 2 tests, 2 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/finv.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/finv.m
685s ***** demo
685s  ## Plot various iCDFs from the F distribution
685s  p = 0.001:0.001:0.999;
685s  x1 = finv (p, 1, 1);
685s  x2 = finv (p, 2, 1);
685s  x3 = finv (p, 5, 2);
685s  x4 = finv (p, 10, 1);
685s  x5 = finv (p, 100, 100);
685s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
685s  grid on
685s  ylim ([0, 4])
685s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
685s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
685s           "df1 = 100, df2 = 100"}, "location", "northwest")
685s  title ("F iCDF")
685s  xlabel ("probability")
685s  ylabel ("values in x")
685s ***** shared p
685s  p = [-1 0 0.5 1 2];
685s ***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
685s ***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
685s ***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
685s ***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN Inf NaN])
685s ***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN Inf NaN])
685s ***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
685s ***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
685s ***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
685s ***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
685s ***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
685s ***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
685s ***** test
685s  x = finv (0.35, Inf, 4);
685s  assert (x, 0.9014, 1e-4)
685s ***** test
685s  x = finv (0, Inf, 4);
685s  assert (x, 0)
685s ***** test
685s  x = finv (1, Inf, 4);
685s  assert (x, Inf)
685s ***** test
685s  x = finv (0.35, 4, Inf);
685s  assert (x, 0.6175, 1e-4)
685s ***** test
685s  x = finv (0, 4, Inf);
685s  assert (x, 0)
685s ***** test
685s  x = finv (1, 4, Inf);
685s  assert (x, Inf)
685s ***** test
685s  x = finv ([0, 0.000001, 0.35, 1, 1.2], Inf, Inf);
685s  assert (x, [0, 1, 1, 1, NaN]);
685s ***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
685s ***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
685s ***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
685s ***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
685s ***** error<finv: function called with too few input arguments.> finv ()
685s ***** error<finv: function called with too few input arguments.> finv (1)
685s ***** error<finv: function called with too few input arguments.> finv (1,2)
685s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
685s  finv (ones (3), ones (2), ones (2))
685s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
685s  finv (ones (2), ones (3), ones (2))
685s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
685s  finv (ones (2), ones (2), ones (3))
685s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
685s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
685s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
685s 31 tests, 31 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/gumbelpdf.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gumbelpdf.m
685s ***** demo
685s  ## Plot various PDFs from the Extreme value distribution
685s  x = -5:0.001:20;
685s  y1 = gumbelpdf (x, 0.5, 2);
685s  y2 = gumbelpdf (x, 1.0, 2);
685s  y3 = gumbelpdf (x, 1.5, 3);
685s  y4 = gumbelpdf (x, 3.0, 4);
685s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
685s  grid on
685s  ylim ([0, 0.2])
685s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
685s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
685s  title ("Extreme value PDF")
685s  xlabel ("values in x")
685s  ylabel ("density")
685s ***** shared x, y0, y1
685s  x = [-5, 0, 1, 2, 3];
685s  y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
685s  y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
685s ***** assert (gumbelpdf (x), y0, 1e-4)
685s ***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
685s ***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
685s ***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
685s ***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
685s  gumbelpdf (ones (3), ones (2), ones (2))
685s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
685s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
685s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
685s 8 tests, 8 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/nctrnd.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nctrnd.m
685s ***** assert (size (nctrnd (1, 1)), [1 1])
685s ***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
685s ***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
685s ***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
685s ***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
685s ***** assert (size (nctrnd (1, 1, 3)), [3, 3])
685s ***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
685s ***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
685s ***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
685s ***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
685s ***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
685s ***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
685s ***** assert (class (nctrnd (1, 1)), "double")
685s ***** assert (class (nctrnd (1, single (1))), "single")
685s ***** assert (class (nctrnd (1, single ([1, 1]))), "single")
685s ***** assert (class (nctrnd (single (1), 1)), "single")
685s ***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
685s ***** error<nctrnd: function called with too few input arguments.> nctrnd ()
685s ***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
685s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
685s  nctrnd (ones (3), ones (2))
685s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
685s  nctrnd (ones (2), ones (3))
685s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
685s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
685s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nctrnd (1, 2, -1)
685s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nctrnd (1, 2, 1.2)
685s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nctrnd (1, 2, ones (2))
685s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nctrnd (1, 2, [2 -1 2])
685s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nctrnd (1, 2, [2 0 2.5])
685s ***** error<nctrnd: dimensions must be non-negative integers.> ...
685s  nctrnd (1, 2, 2, -1, 5)
685s ***** error<nctrnd: dimensions must be non-negative integers.> ...
685s  nctrnd (1, 2, 2, 1.5, 5)
685s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
685s  nctrnd (2, ones (2), 3)
685s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
685s  nctrnd (2, ones (2), [3, 2])
685s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
685s  nctrnd (2, ones (2), 3, 2)
685s 33 tests, 33 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/frnd.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/frnd.m
685s ***** assert (size (frnd (1, 1)), [1 1])
685s ***** assert (size (frnd (1, ones (2,1))), [2, 1])
685s ***** assert (size (frnd (1, ones (2,2))), [2, 2])
685s ***** assert (size (frnd (ones (2,1), 1)), [2, 1])
685s ***** assert (size (frnd (ones (2,2), 1)), [2, 2])
685s ***** assert (size (frnd (1, 1, 3)), [3, 3])
685s ***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
685s ***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
685s ***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
685s ***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
685s ***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
685s ***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
685s ***** assert (class (frnd (1, 1)), "double")
685s ***** assert (class (frnd (1, single (1))), "single")
685s ***** assert (class (frnd (1, single ([1, 1]))), "single")
685s ***** assert (class (frnd (single (1), 1)), "single")
685s ***** assert (class (frnd (single ([1, 1]), 1)), "single")
685s ***** error<frnd: function called with too few input arguments.> frnd ()
685s ***** error<frnd: function called with too few input arguments.> frnd (1)
685s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
685s  frnd (ones (3), ones (2))
685s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
685s  frnd (ones (2), ones (3))
685s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
685s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
685s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  frnd (1, 2, -1)
685s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  frnd (1, 2, 1.2)
685s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  frnd (1, 2, ones (2))
685s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  frnd (1, 2, [2 -1 2])
685s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  frnd (1, 2, [2 0 2.5])
685s ***** error<frnd: dimensions must be non-negative integers.> ...
685s  frnd (1, 2, 2, -1, 5)
685s ***** error<frnd: dimensions must be non-negative integers.> ...
685s  frnd (1, 2, 2, 1.5, 5)
685s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
685s  frnd (2, ones (2), 3)
685s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
685s  frnd (2, ones (2), [3, 2])
685s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
685s  frnd (2, ones (2), 3, 2)
685s 33 tests, 33 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/wienrnd.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/wienrnd.m
685s ***** error wienrnd (0)
685s ***** error wienrnd (1, 3, -50)
685s ***** error wienrnd (5, 0)
685s ***** error wienrnd (0.4, 3, 5)
685s ***** error wienrnd ([1 4], 3, 5)
685s 5 tests, 5 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/invginv.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/invginv.m
685s ***** demo
685s  ## Plot various iCDFs from the inverse Gaussian distribution
685s  p = 0.001:0.001:0.999;
685s  x1 = invginv (p, 1, 0.2);
685s  x2 = invginv (p, 1, 1);
685s  x3 = invginv (p, 1, 3);
685s  x4 = invginv (p, 3, 0.2);
685s  x5 = invginv (p, 3, 1);
685s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
685s  grid on
685s  ylim ([0, 3])
685s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
685s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
685s  title ("Inverse Gaussian iCDF")
685s  xlabel ("probability")
685s  ylabel ("x")
685s ***** shared p, x
685s  p = [0, 0.3829, 0.6827, 1];
685s  x = [0, 0.5207, 1.0376, Inf];
685s ***** assert (invginv (p, 1, 1), x, 1e-4);
685s ***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
685s ***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
685s ***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
685s ***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
685s ***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
685s ***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
685s ***** error<invginv: function called with too few input arguments.> invginv (1)
685s ***** error<invginv: function called with too few input arguments.> invginv (1, 2)
685s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
685s  invginv (1, ones (2), ones (3))
685s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
685s  invginv (ones (2), 1, ones (3))
685s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
685s  invginv (ones (2), ones (3), 1)
685s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
685s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
685s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
685s 15 tests, 15 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/laplacepdf.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/laplacepdf.m
685s ***** demo
685s  ## Plot various PDFs from the Laplace distribution
685s  x = -10:0.01:10;
685s  y1 = laplacepdf (x, 0, 1);
685s  y2 = laplacepdf (x, 0, 2);
685s  y3 = laplacepdf (x, 0, 4);
685s  y4 = laplacepdf (x, -5, 4);
685s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
685s  grid on
685s  xlim ([-10, 10])
685s  ylim ([0, 0.6])
685s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
685s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
685s  title ("Laplace PDF")
685s  xlabel ("values in x")
685s  ylabel ("density")
685s ***** shared x, y
685s  x = [-Inf -log(2) 0 log(2) Inf];
685s  y = [0, 1/4, 1/2, 1/4, 0];
685s ***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
685s ***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
685s ***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
685s ***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
685s ***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
685s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
685s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
685s ***** error<laplacepdf: function called with too few input arguments.> ...
685s  laplacepdf (1, 2)
685s ***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
685s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
685s  laplacepdf (1, ones (2), ones (3))
685s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
685s  laplacepdf (ones (2), 1, ones (3))
685s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
685s  laplacepdf (ones (2), ones (3), 1)
685s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
685s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
685s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
685s 15 tests, 15 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/nbinrnd.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nbinrnd.m
685s ***** assert (size (nbinrnd (1, 0.5)), [1 1])
685s ***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
685s ***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
685s ***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
685s ***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
685s ***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
685s ***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
685s ***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
685s ***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
685s ***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
685s ***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
685s ***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
685s ***** assert (class (nbinrnd (1, 0.5)), "double")
685s ***** assert (class (nbinrnd (1, single (0.5))), "single")
685s ***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
685s ***** assert (class (nbinrnd (single (1), 0.5)), "single")
685s ***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
685s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
685s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
685s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
685s  nbinrnd (ones (3), ones (2))
685s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
685s  nbinrnd (ones (2), ones (3))
685s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
685s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
685s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nbinrnd (1, 2, -1)
685s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nbinrnd (1, 2, 1.2)
685s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nbinrnd (1, 2, ones (2))
685s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nbinrnd (1, 2, [2 -1 2])
685s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
685s  nbinrnd (1, 2, [2 0 2.5])
685s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
685s  nbinrnd (1, 2, 2, -1, 5)
685s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
685s  nbinrnd (1, 2, 2, 1.5, 5)
685s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
685s  nbinrnd (2, ones (2), 3)
685s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
685s  nbinrnd (2, ones (2), [3, 2])
685s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
685s  nbinrnd (2, ones (2), 3, 2)
685s 33 tests, 33 passed, 0 known failure, 0 skipped
685s [inst/dist_fun/normpdf.m]
685s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/normpdf.m
685s ***** demo
685s  ## Plot various PDFs from the normal distribution
685s  x = -5:0.01:5;
685s  y1 = normpdf (x, 0, 0.5);
685s  y2 = normpdf (x, 0, 1);
685s  y3 = normpdf (x, 0, 2);
685s  y4 = normpdf (x, -2, 0.8);
685s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
685s  grid on
685s  xlim ([-5, 5])
685s  ylim ([0, 0.9])
685s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
685s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
685s  title ("Normal PDF")
685s  xlabel ("values in x")
685s  ylabel ("density")
685s ***** shared x, y
685s  x = [-Inf, 1, 2, Inf];
685s  y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
685s ***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
685s ***** assert (normpdf (x, 1, ones (1,4)), y, eps)
685s ***** assert (normpdf (x, ones (1,4), 1), y, eps)
685s ***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
685s ***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
686s ***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
686s ***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
686s ***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
686s ***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
686s ***** error<normpdf: function called with too few input arguments.> normpdf ()
686s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  normpdf (ones (3), ones (2), ones (2))
686s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  normpdf (ones (2), ones (3), ones (2))
686s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  normpdf (ones (2), ones (2), ones (3))
686s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
686s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
686s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
686s 16 tests, 16 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/logipdf.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logipdf.m
686s ***** demo
686s  ## Plot various PDFs from the logistic distribution
686s  x = -5:0.01:20;
686s  y1 = logipdf (x, 5, 2);
686s  y2 = logipdf (x, 9, 3);
686s  y3 = logipdf (x, 9, 4);
686s  y4 = logipdf (x, 6, 2);
686s  y5 = logipdf (x, 2, 1);
686s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
686s  grid on
686s  ylim ([0, 0.3])
686s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
686s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
686s  title ("Logistic PDF")
686s  xlabel ("values in x")
686s  ylabel ("density")
686s ***** shared x, y
686s  x = [-Inf -log(4) 0 log(4) Inf];
686s  y = [0, 0.16, 1/4, 0.16, 0];
686s ***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
686s ***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
686s ***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
686s ***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
686s ***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
686s ***** error<logipdf: function called with too few input arguments.> logipdf ()
686s ***** error<logipdf: function called with too few input arguments.> logipdf (1)
686s ***** error<logipdf: function called with too few input arguments.> ...
686s  logipdf (1, 2)
686s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  logipdf (1, ones (2), ones (3))
686s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  logipdf (ones (2), 1, ones (3))
686s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
686s  logipdf (ones (2), ones (3), 1)
686s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
686s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
686s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
686s 14 tests, 14 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/gevcdf.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gevcdf.m
686s ***** demo
686s  ## Plot various CDFs from the generalized extreme value distribution
686s  x = -1:0.001:10;
686s  p1 = gevcdf (x, 1, 1, 1);
686s  p2 = gevcdf (x, 0.5, 1, 1);
686s  p3 = gevcdf (x, 1, 1, 5);
686s  p4 = gevcdf (x, 1, 2, 5);
686s  p5 = gevcdf (x, 1, 5, 5);
686s  p6 = gevcdf (x, 1, 0.5, 5);
686s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
686s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
686s  grid on
686s  xlim ([-1, 10])
686s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
686s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
686s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
686s          "location", "southeast")
686s  title ("Generalized extreme value CDF")
686s  xlabel ("values in x")
686s  ylabel ("probability")
686s ***** test
686s  x = 0:0.5:2.5;
686s  sigma = 1:6;
686s  k = 1;
686s  mu = 0;
686s  p = gevcdf (x, k, sigma, mu);
686s  expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
686s  assert (p, expected_p, 0.001);
686s ***** test
686s  x = -0.5:0.5:2.5;
686s  sigma = 0.5;
686s  k = 1;
686s  mu = 0;
686s  p = gevcdf (x, k, sigma, mu);
686s  expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
686s  assert (p, expected_p, 0.001);
686s ***** test # check for continuity for k near 0
686s  x = 1;
686s  sigma = 0.5;
686s  k = -0.03:0.01:0.03;
686s  mu = 0;
686s  p = gevcdf (x, k, sigma, mu);
686s  expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
686s  assert (p, expected_p, 0.001);
686s ***** error<gevcdf: function called with too few input arguments.> gevcdf ()
686s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
686s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
686s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
686s ***** error<gevcdf: function called with too many inputs> ...
686s  gevcdf (1, 2, 3, 4, 5, 6)
686s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
686s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
686s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
686s  gevcdf (ones (3), ones (2), ones(2), ones(2))
686s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
686s  gevcdf (ones (2), ones (3), ones(2), ones(2))
686s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
686s  gevcdf (ones (2), ones (2), ones(3), ones(2))
686s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
686s  gevcdf (ones (2), ones (2), ones(2), ones(3))
686s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
686s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
686s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
686s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
686s 18 tests, 18 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/unidinv.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/unidinv.m
686s ***** demo
686s  ## Plot various iCDFs from the discrete uniform distribution
686s  p = 0.001:0.001:0.999;
686s  x1 = unidinv (p, 5);
686s  x2 = unidinv (p, 9);
686s  plot (p, x1, "-b", p, x2, "-g")
686s  grid on
686s  xlim ([0, 1])
686s  ylim ([0, 10])
686s  legend ({"N = 5", "N = 9"}, "location", "northwest")
686s  title ("Discrete uniform iCDF")
686s  xlabel ("probability")
686s  ylabel ("values in x")
686s ***** shared p
686s  p = [-1 0 0.5 1 2];
686s ***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
686s ***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
686s ***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
686s ***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
686s ***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
686s ***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
686s ***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
686s ***** error<unidinv: function called with too few input arguments.> unidinv ()
686s ***** error<unidinv: function called with too few input arguments.> unidinv (1)
686s ***** error<unidinv: P and N must be of common size or scalars.> ...
686s  unidinv (ones (3), ones (2))
686s ***** error<unidinv: P and N must be of common size or scalars.> ...
686s  unidinv (ones (2), ones (3))
686s ***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
686s ***** error<unidinv: P and N must not be complex.> unidinv (2, i)
686s 13 tests, 13 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/logiinv.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/logiinv.m
686s ***** demo
686s  ## Plot various iCDFs from the logistic distribution
686s  p = 0.001:0.001:0.999;
686s  x1 = logiinv (p, 5, 2);
686s  x2 = logiinv (p, 9, 3);
686s  x3 = logiinv (p, 9, 4);
686s  x4 = logiinv (p, 6, 2);
686s  x5 = logiinv (p, 2, 1);
686s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
686s  grid on
686s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
686s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
686s  title ("Logistic iCDF")
686s  xlabel ("probability")
686s  ylabel ("x")
686s ***** test
686s  p = [0.01:0.01:0.99];
686s  assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
686s ***** shared p
686s  p = [-1 0 0.5 1 2];
686s ***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
686s ***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
686s ***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
686s ***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
686s ***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
686s ***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
686s ***** error<logiinv: function called with too few input arguments.> logiinv ()
686s ***** error<logiinv: function called with too few input arguments.> logiinv (1)
686s ***** error<logiinv: function called with too few input arguments.> ...
686s  logiinv (1, 2)
686s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
686s  logiinv (1, ones (2), ones (3))
686s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
686s  logiinv (ones (2), 1, ones (3))
686s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
686s  logiinv (ones (2), ones (3), 1)
686s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
686s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
686s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
686s 16 tests, 16 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/trirnd.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/trirnd.m
686s ***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
686s ***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
686s ***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
686s ***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
686s ***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
686s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
686s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
686s ***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
686s ***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
686s ***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
686s ***** assert (class (trirnd (1, 1.5, 2)), "double")
686s ***** assert (class (trirnd (single (1), 1.5, 2)), "single")
686s ***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
686s ***** assert (class (trirnd (1, single (1.5), 2)), "single")
686s ***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
686s ***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
686s ***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
686s ***** error<trirnd: function called with too few input arguments.> trirnd ()
686s ***** error<trirnd: function called with too few input arguments.> trirnd (1)
686s ***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
686s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
686s  trirnd (ones (3), 5 * ones (2), ones (2))
686s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
686s  trirnd (ones (2), 5 * ones (3), ones (2))
686s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
686s  trirnd (ones (2), 5 * ones (2), ones (3))
686s ***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
686s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
686s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
686s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
686s  trirnd (1, 5, 3, -1)
686s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
686s  trirnd (1, 5, 3, 1.2)
686s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
686s  trirnd (1, 5, 3, ones (2))
686s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
686s  trirnd (1, 5, 3, [2 -1 2])
686s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
686s  trirnd (1, 5, 3, [2 0 2.5])
686s ***** error<trirnd: dimensions must be non-negative integers.> ...
686s  trirnd (1, 5, 3, 2, -1, 5)
686s ***** error<trirnd: dimensions must be non-negative integers.> ...
686s  trirnd (1, 5, 3, 2, 1.5, 5)
686s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
686s  trirnd (2, 5 * ones (2), 2, 3)
686s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
686s  trirnd (2, 5 * ones (2), 2, [3, 2])
686s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
686s  trirnd (2, 5 * ones (2), 2, 3, 2)
686s 36 tests, 36 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/gumbelcdf.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/gumbelcdf.m
686s ***** demo
686s  ## Plot various CDFs from the Gumbel distribution
686s  x = -5:0.01:20;
686s  p1 = gumbelcdf (x, 0.5, 2);
686s  p2 = gumbelcdf (x, 1.0, 2);
686s  p3 = gumbelcdf (x, 1.5, 3);
686s  p4 = gumbelcdf (x, 3.0, 4);
686s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
686s  grid on
686s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
686s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
686s  title ("Gumbel CDF")
686s  xlabel ("values in x")
686s  ylabel ("probability")
686s ***** shared x, y
686s  x = [-Inf, 1, 2, Inf];
686s  y = [0, 0.3679, 0.6922, 1];
686s ***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
686s ***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
686s ***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
686s ***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
686s ***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
686s ***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
686s ***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
686s ***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
686s ***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
686s ***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
686s ***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
686s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
686s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
686s ***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
686s ***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
686s  gumbelcdf (ones (3), ones (2), ones (2))
686s ***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
686s ***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
686s  [p, plo, pup] = gumbelcdf (1, 2, 3)
686s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
686s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
686s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
686s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
686s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
686s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
686s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
686s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
686s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
686s ***** error<gumbelcdf: bad covariance matrix.> ...
686s  [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
686s 24 tests, 24 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/raylpdf.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/raylpdf.m
686s ***** demo
686s  ## Plot various PDFs from the Rayleigh distribution
686s  x = 0:0.01:10;
686s  y1 = raylpdf (x, 0.5);
686s  y2 = raylpdf (x, 1);
686s  y3 = raylpdf (x, 2);
686s  y4 = raylpdf (x, 3);
686s  y5 = raylpdf (x, 4);
686s  plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
686s  grid on
686s  ylim ([0, 1.25])
686s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
686s           "σ = 3", "σ = 4"}, "location", "northeast")
686s  title ("Rayleigh PDF")
686s  xlabel ("values in x")
686s  ylabel ("density")
686s ***** test
686s  x = 0:0.5:2.5;
686s  sigma = 1:6;
686s  y = raylpdf (x, sigma);
686s  expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
686s  assert (y, expected_y, 0.001);
686s ***** test
686s  x = 0:0.5:2.5;
686s  y = raylpdf (x, 0.5);
686s  expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
686s  assert (y, expected_y, 0.001);
686s ***** error<raylpdf: function called with too few input arguments.> raylpdf ()
686s ***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
686s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
686s  raylpdf (ones (3), ones (2))
686s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
686s  raylpdf (ones (2), ones (3))
686s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
686s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
686s 8 tests, 8 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/iwishpdf.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/iwishpdf.m
686s ***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
686s ***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
686s ***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
686s  [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
686s ***** error iwishpdf ()
686s ***** error iwishpdf (1, 2)
686s ***** error iwishpdf (1, 2, 0)
686s 6 tests, 6 passed, 0 known failure, 0 skipped
686s [inst/dist_fun/ncfinv.m]
686s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/ncfinv.m
686s ***** demo
686s  ## Plot various iCDFs from the noncentral F distribution
686s  p = 0.001:0.001:0.999;
686s  x1 = ncfinv (p, 2, 5, 1);
686s  x2 = ncfinv (p, 2, 5, 2);
686s  x3 = ncfinv (p, 5, 10, 1);
686s  x4 = ncfinv (p, 10, 20, 10);
686s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
686s  grid on
686s  ylim ([0, 5])
686s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
686s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
686s          "location", "northwest")
686s  title ("Noncentral F iCDF")
686s  xlabel ("probability")
686s  ylabel ("values in x")
686s ***** demo
686s  ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
686s  ## same number of numerator and denominator degrees of freedom (5, 20)
686s 
686s  p = 0.001:0.001:0.999;
686s  x1 = ncfinv (p, 5, 20, 10);
686s  x2 = finv (p, 5, 20);
686s  plot (p, x1, "-", p, x2, "-");
686s  grid on
686s  ylim ([0, 10])
686s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
686s  title ("Noncentral F vs F quantile functions")
686s  xlabel ("probability")
686s  ylabel ("values in x")
686s ***** test
686s  x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
686s  assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
686s ***** test
686s  x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
686s  assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
686s ***** test
686s  x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
686s  assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
686s ***** test
686s  x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
686s  assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
686s ***** test
686s  x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
686s  assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
687s ***** test
687s  x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
687s  assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
687s ***** test
687s  assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
687s ***** error<ncfinv: function called with too few input arguments.> ncfinv ()
687s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
687s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
687s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
687s  ncfinv (ones (3), ones (2), ones (2), ones (2))
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
687s  ncfinv (ones (2), ones (3), ones (2), ones (2))
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
687s  ncfinv (ones (2), ones (2), ones (3), ones (2))
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
687s  ncfinv (ones (2), ones (2), ones (2), ones (3))
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
687s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
687s 19 tests, 19 passed, 0 known failure, 0 skipped
687s [inst/dist_fun/nbinpdf.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_fun/nbinpdf.m
687s ***** demo
687s  ## Plot various PDFs from the negative binomial distribution
687s  x = 0:40;
687s  y1 = nbinpdf (x, 2, 0.15);
687s  y2 = nbinpdf (x, 5, 0.2);
687s  y3 = nbinpdf (x, 4, 0.4);
687s  y4 = nbinpdf (x, 10, 0.3);
687s  plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
687s  grid on
687s  xlim ([0, 40])
687s  ylim ([0, 0.12])
687s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
687s           "r = 10, ps = 0.3"}, "location", "northeast")
687s  title ("Negative binomial PDF")
687s  xlabel ("values in x (number of failures)")
687s  ylabel ("density")
687s ***** shared x, y
687s  x = [-1 0 1 2 Inf];
687s  y = [0 1/2 1/4 1/8 NaN];
687s ***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
687s ***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
687s ***** assert (nbinpdf (x, ones (1,5), 0.5), y)
687s ***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
687s ***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
687s ***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
687s ***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
687s ***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
687s ***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
687s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
687s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
687s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
687s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
687s  nbinpdf (ones (3), ones (2), ones (2))
687s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
687s  nbinpdf (ones (2), ones (3), ones (2))
687s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
687s  nbinpdf (ones (2), ones (2), ones (3))
687s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
687s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
687s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
687s 18 tests, 18 passed, 0 known failure, 0 skipped
687s [inst/glmfit.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/glmfit.m
687s ***** demo
687s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
687s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
687s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
687s  b = glmfit (x, [y n], "binomial", "Link", "probit");
687s  yfit = glmval (b, x, "probit", "Size", n);
687s  plot (x, y./n, 'o', x, yfit ./ n, '-')
687s ***** demo
687s  load fisheriris
687s  X = meas (51:end, :);
687s  y = strcmp ("versicolor", species(51:end));
687s  b = glmfit (X, y, "binomial", "link", "logit")
687s ***** test
687s  load fisheriris;
687s  X = meas(51:end,:);
687s  y = strcmp ("versicolor", species(51:end));
687s  b = glmfit (X, y, "binomial", "link", "logit");
687s  assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
687s ***** test
687s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
687s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
687s  [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
687s  b_matlab = [-0.7631; 0.1113];
687s  dev_matlab = 0.0111;
687s  assert (Bnew, b_matlab, 0.001);
687s  assert (dev, dev_matlab, 0.001);
687s ***** test
687s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
687s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
687s  p_input = 1;
687s  [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
687s  b_matlab = [0.3813; 0.0950];
687s  dev_matlab = 0.0051;
687s  assert (Bnew, b_matlab, 0.001);
687s  assert (dev, dev_matlab, 0.001);
687s ***** error <glmfit: too few input arguments.> glmfit ()
687s ***** error <glmfit: too few input arguments.> glmfit (1)
687s ***** error <glmfit: too few input arguments.> glmfit (1, 2)
687s ***** error <glmfit: Name-Value arguments must be in pairs.> ...
687s  glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
687s ***** error <glmfit: X must be a numeric matrix.> ...
687s  glmfit ('abc', rand (6, 1), 'poisson')
687s ***** error <glmfit: X must be a numeric matrix.> ...
687s  glmfit ([], rand (6, 1), 'poisson')
687s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
687s  glmfit (rand (5, 2), 'abc', 'poisson')
687s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
687s  glmfit (rand (5, 2), [], 'poisson')
687s ***** error <glmfit: X and Y must have the same number of observations.> ...
687s  glmfit (rand (5, 2), rand (6, 1), 'poisson')
687s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
687s  glmfit (rand (6, 2), rand (6, 1), 3)
687s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
687s  glmfit (rand (6, 2), rand (6, 1), {'poisson'})
687s ***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
687s  glmfit (rand (5, 2), rand (5, 3), 'binomial')
687s ***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
687s  glmfit (rand (2, 2), [true, true; false, false], 'binomial')
687s ***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
687s  glmfit (rand (5, 2), rand (5, 2), 'normal')
687s ***** error <glmfit: unsupported distribution.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'chebychev')
687s ***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
687s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
687s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
687s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
687s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
687s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
687s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
687s ***** error <glmfit: structure with custom link functions must be a scalar.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
687s ***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
687s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
687s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
687s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
687s ***** error <glmfit: invalid custom 'Link' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
687s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
687s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
687s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
687s ***** error <glmfit: invalid custom 'Derivative' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
687s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
687s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
687s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
687s ***** error <glmfit: invalid custom 'Inverse' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
687s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
687s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
687s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
687s ***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
687s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
687s ***** error <glmfit: invalid custom 'Link' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
687s ***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
687s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
687s ***** error <glmfit: invalid custom 'Derivative' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
687s ***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
687s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
687s ***** error <glmfit: invalid custom 'Inverse' function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
687s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
687s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
687s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
687s ***** error <glmfit: canonical link function name must be a character vector.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
687s ***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
687s ***** error <glmfit: invalid value for custom link function.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
687s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
687s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
687s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
687s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
687s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
687s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
687s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
687s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
687s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
687s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
687s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
687s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
687s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
687s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
687s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
687s 70 tests, 70 passed, 0 known failure, 0 skipped
687s [inst/monotone_smooth.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/monotone_smooth.m
687s ***** error<Invalid call to monotone_smooth.  Correct usage is> ...
687s  monotone_smooth (1)
687s ***** error<monotone_smooth: X must be a numeric vector.> ...
687s  monotone_smooth ("char", 1)
687s ***** error<monotone_smooth: X must be a numeric vector.> ...
687s  monotone_smooth ({1,2,3}, 1)
687s ***** error<monotone_smooth: X must be a numeric vector.> ...
687s  monotone_smooth (ones(20,3), 1)
687s ***** error<monotone_smooth: Y must be a numeric vector.> ...
687s  monotone_smooth (1, "char")
687s ***** error<monotone_smooth: Y must be a numeric vector.> ...
687s  monotone_smooth (1, {1,2,3})
687s ***** error<monotone_smooth: Y must be a numeric vector.> ...
687s  monotone_smooth (1, ones(20,3))
687s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
687s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
687s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
687s 10 tests, 10 passed, 0 known failure, 0 skipped
687s [inst/pcacov.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/pcacov.m
687s ***** demo
687s  x = [ 7    26     6    60;
687s        1    29    15    52;
687s       11    56     8    20;
687s       11    31     8    47;
687s        7    52     6    33;
687s       11    55     9    22;
687s        3    71    17     6;
687s        1    31    22    44;
687s        2    54    18    22;
687s       21    47     4    26;
687s        1    40    23    34;
687s       11    66     9    12;
687s       10    68     8    12
687s      ];
687s  Kxx = cov (x);
687s  [coeff, latent, explained] = pcacov (Kxx)
687s ***** test
687s  load hald
687s  Kxx = cov (ingredients);
687s  [coeff,latent,explained] = pcacov(Kxx);
687s  c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
687s           -0.6785, -0.0200, -0.5440, 0.4933; ...
687s            0.0290,  0.7553,  0.4036, 0.5156; ...
687s            0.7309, -0.1085, -0.4684, 0.4844];
687s  l_out = [517.7969; 67.4964; 12.4054; 0.2372];
687s  e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
687s  assert (coeff, c_out, 1e-4);
687s  assert (latent, l_out, 1e-4);
687s  assert (explained, e_out, 1e-4);
687s ***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
687s ***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
687s 3 tests, 3 passed, 0 known failure, 0 skipped
687s [inst/ztest2.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ztest2.m
687s ***** error ztest2 ();
687s ***** error ztest2 (1);
687s ***** error ztest2 (1, 2);
687s ***** error ztest2 (1, 2, 3);
687s ***** error ztest2 (1, 2, 3, 2);
687s ***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
687s  ztest2 (1, 2, 3, 4, "alpha")
687s ***** error<ztest2: invalid VALUE for alpha.> ...
687s  ztest2 (1, 2, 3, 4, "alpha", 0);
687s ***** error<ztest2: invalid VALUE for alpha.> ...
687s  ztest2 (1, 2, 3, 4, "alpha", 1.2);
687s ***** error<ztest2: invalid VALUE for alpha.> ...
687s  ztest2 (1, 2, 3, 4, "alpha", "val");
687s ***** error<ztest2: invalid VALUE for tail.>  ...
687s  ztest2 (1, 2, 3, 4, "tail", "val");
687s ***** error<ztest2: invalid VALUE for tail.>  ...
687s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
687s ***** error<ztest2: invalid NAME for optional arguments.> ...
687s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
687s 12 tests, 12 passed, 0 known failure, 0 skipped
687s [inst/normplot.m]
687s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/normplot.m
687s ***** demo
687s  h = normplot([1:20]);
687s ***** demo
687s  h = normplot([1:20;5:2:44]');
687s ***** demo
687s  ax = newplot();
687s  h = normplot(ax, [1:20]);
687s  ax = gca;
687s  h = normplot(ax, [-10:10]);
687s  set (ax, "xlim", [-11, 21]);
687s ***** error normplot ();
687s ***** error normplot (23);
687s ***** error normplot (23, [1:20]);
687s ***** error normplot (ones(3,4,5));
687s ***** test
687s  hf = figure ("visible", "off");
687s  unwind_protect
687s    ax = newplot (hf);
687s    h = normplot (ax, [1:20]);
687s    ax = gca;
687s    h = normplot(ax, [-10:10]);
687s    set (ax, "xlim", [-11, 21]);
687s  unwind_protect_cleanup
687s    close (hf);
687s  end_unwind_protect
687s ***** test
687s  hf = figure ("visible", "off");
687s  unwind_protect
687s    h = normplot([1:20;5:2:44]');
687s  unwind_protect_cleanup
687s    close (hf);
687s  end_unwind_protect
688s 6 tests, 6 passed, 0 known failure, 0 skipped
688s [inst/tabulate.m]
688s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/tabulate.m
688s ***** demo
688s  ## Generate a frequency table for a vector of data in a cell array
688s  load patients
688s 
688s  ## Display the first seven entries of the Gender variable
688s  gender = Gender(1:7)
688s 
688s  ## Compute the frequency table that shows the number and
688s  ## percentage of Male and Female patients
688s  tabulate (Gender)
688s ***** demo
688s  ## Create a frequency table for a vector of positive integers
688s  load patients
688s 
688s  ## Display the first seven entries of the Gender variable
688s  height = Height(1:7)
688s 
688s  ## Create a frequency table that shows, in its second and third columns,
688s  ## the number and percentage of patients with a particular height.
688s  table = tabulate (Height);
688s 
688s  ## Display the first and last seven entries of the frequency table
688s  first = table(1:7,:)
688s 
688s  last = table(end-6:end,:)
688s ***** demo
688s  ## Create a frequency table from a character array
688s  load carsmall
688s 
688s  ## Tabulate the data in the Origin variable, which shows the
688s  ## country of origin of each car in the data set
688s  tabulate (Origin)
688s ***** demo
688s  ## Create a frequency table from a numeric vector with NaN values
688s  load carsmall
688s 
688s  ## The carsmall dataset contains measurements of 100 cars
688s  total_cars = length (MPG)
688s  ## For six cars, the MPG value is missing
688s  missingMPG = length (MPG(isnan (MPG)))
688s 
688s  ## Create a frequency table using MPG
688s  tabulate (MPG)
688s  table = tabulate (MPG);
688s 
688s  ## Only 94 cars were used
688s  valid_cars = sum (table(:,2))
688s ***** test
688s  load patients
688s  table = tabulate (Gender);
688s  assert (table{1,1}, "Male");
688s  assert (table{2,1}, "Female");
688s  assert (table{1,2}, 47);
688s  assert (table{2,2}, 53);
688s ***** test
688s  load patients
688s  table = tabulate (Height);
688s  assert (table(end-4,:), [68, 15, 15]);
688s  assert (table(end-3,:), [69, 8, 8]);
688s  assert (table(end-2,:), [70, 11, 11]);
688s  assert (table(end-1,:), [71, 10, 10]);
688s  assert (table(end,:), [72, 4, 4]);
688s ***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
688s ***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
688s ***** error<tabulate: X must be either a numeric vector> ...
688s  tabulate ({"a", "b"; "a", "c"})
688s 5 tests, 5 passed, 0 known failure, 0 skipped
688s [inst/standardizeMissing.m]
688s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/standardizeMissing.m
688s ***** assert (standardizeMissing (1, 1), NaN)
688s ***** assert (standardizeMissing (1, 0), 1)
688s ***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
688s ***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
688s ***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
688s ***** assert (standardizeMissing ('foo', 'a'), 'foo')
688s ***** assert (standardizeMissing ('foo', 'f'), ' oo')
688s ***** assert (standardizeMissing ('foo', 'o'), 'f  ')
688s ***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
688s ***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
688s ***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
688s ***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
688s ***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
688s ***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
688s ***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
688s ***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
688s ***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
688s ***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
688s ***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
688s ***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
688s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
688s ***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
688s ***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
688s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
688s ***** assert (standardizeMissing (double (1), single (1)), double (NaN))
688s ***** assert (standardizeMissing (single (1), single (1)), single (NaN))
688s ***** assert (standardizeMissing (single (1), double (1)), single (NaN))
688s ***** assert (standardizeMissing (single (1), true), single (NaN))
688s ***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
688s ***** assert (standardizeMissing (true, true), true)
688s ***** assert (standardizeMissing (true, 1), true)
688s ***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
688s ***** assert (standardizeMissing (int32 (1), 1), int32 (1))
688s ***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
688s ***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
688s ***** error standardizeMissing ();
688s ***** error standardizeMissing (1);
688s ***** error standardizeMissing (1,2,3);
688s ***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
688s ***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
688s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
688s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
688s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
688s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
688s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
688s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
688s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
688s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
688s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
688s 49 tests, 49 passed, 0 known failure, 0 skipped
688s [inst/fitcnet.m]
688s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fitcnet.m
688s ***** demo
688s  ## Train a Neural Network on the Fisher's Iris data set and display
688s  ## a confusion chart with the classification results.
688s 
688s  load fisheriris
688s  Mdl = fitcnet (meas, species);
688s  pred_species = resubPredict (Mdl);
688s  confusionchart (species, pred_species, "Title", ...
688s  "Fully Connected Neural Network classification on Fisher's Iris dataset");
688s ***** test
688s  load fisheriris
688s  x = meas;
688s  y = grp2idx (species);
688s  Mdl = fitcnet (x, y, "IterationLimit", 50);
688s  assert (class (Mdl), "ClassificationNeuralNetwork");
688s  assert (numel (Mdl.ModelParameters.LayerWeights), 2);
688s  assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
688s  assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
688s ***** error<fitcnet: too few arguments.> fitcnet ()
688s ***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
688s ***** error<fitcnet: Name-Value arguments must be in pairs.>
688s  fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
688s ***** error<fitcnet: number of rows in X and Y must be equal.>
688s  fitcnet (ones (4,2), ones (3, 1))
688s ***** error<fitcnet: number of rows in X and Y must be equal.>
688s  fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
688s 6 tests, 6 passed, 0 known failure, 0 skipped
688s [inst/multiway.m]
688s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/multiway.m
688s ***** test
688s  numbers = [4, 5, 6, 7, 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
688s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
688s ***** test
688s  numbers = [1, 2, 3, 4, 5, 6];
688s  num_parts = 3;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
688s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
688s ***** test
688s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
688s  num_parts = 3;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
688s  assert (sort (cellfun (@sum, partition)), sort ([38, 38, 37]));
688s ***** test
688s  numbers = [10, 10, 10];
688s  num_parts = 3;
688s  [~, partition] = multiway (numbers, num_parts, "completeKK");
688s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
688s ***** test
688s  numbers = 1:10;
688s  num_parts = 2;
688s  [~, partition] = multiway (numbers, num_parts, "completeKK");
688s  assert (sort (cellfun (@sum, partition)), [27, 28]);
688s ***** test
688s  numbers = [4, 5, 6, 7, 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
688s ***** test
688s  numbers = [1, 2, 3, 4, 5, 6];
688s  num_parts = 3;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
688s ***** test
688s  numbers = [10, 7, 5, 5, 6, 4, 10, 11, 12, 9, 10, 4, 3, 4, 5];
688s  num_parts = 4;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), sort ([27, 27, 27, 24]));
688s ***** test
688s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
688s  num_parts = 3;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), sort ([35, 37, 41]));
688s ***** test
688s  numbers = [10, 10, 10];
688s  num_parts = 3;
688s  [~, partition] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
688s ***** test
688s  numbers = 1:10;
688s  num_parts = 2;
688s  [~, partition] = multiway (numbers, num_parts, "greedy");
688s  assert (sort (cellfun (@sum, partition)), [27, 28]);
688s ***** test
688s  grpidx_ckk = multiway ([3 2 4 3 9 3 64], 3);
688s  grpidx_greedy = multiway ([3 2 4 3 9 3 64], 3, 'greedy');
688s  assert (isequal (grpidx_ckk, grpidx_greedy), false);
688s ***** test
688s  numbers = [4; 5; 6; 7; 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
688s  assert (iscolumn (groupindex), true)
688s  assert (iscolumn (groupsizes), true);
688s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
688s  numbers = [4; 5; 6; 7; 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (iscolumn (groupindex), true)
688s  assert (iscolumn (groupsizes), true);
688s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
688s ***** test
688s  numbers = [4, 5, 6, 7, 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
688s  assert (isrow (groupindex), true)
688s  assert (isrow (groupsizes), true);
688s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
688s ***** test
688s  numbers = [4, 5, 6, 7, 8];
688s  num_parts = 2;
688s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
688s  assert (isrow (groupindex), true)
688s  assert (isrow (groupsizes), true);
688s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
688s ***** error<multiway: too few input arguments.> multiway ()
688s ***** error<multiway: too few input arguments.> multiway ([1, 2])
688s ***** error<multiway: METHOD value must be a character vector.> ...
688s  multiway ([1, 2, 3], 2, 1)
688s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway ([], 2)
688s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway (ones (2, 2), 2)
688s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
688s  multiway ({1, 2, 3}, 2)
688s ***** error<multiway: NUMBERS must be non-negative.> multiway ([1, -2, 3], 2)
688s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
688s  multiway ([1, 2, NaN], 2)
688s ***** error<multiway: NUM_PARTS must be a scalar.> multiway ([1,2,3], [1,2])
688s ***** error<multiway: NUM_PARTS must be a real numeric value.> ...
688s  multiway ([1, 2, 3], "2")
688s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 0)
688s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 1.5)
688s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], -1)
688s ***** error<multiway: NUM_PARTS cannot be greater than number of elements in NUMBERS.> ...
688s  multiway ([1, 2], 3)
688s ***** error <multiway: unsupported method 'greedyalgo'.> ...
688s  multiway ([1,2,3], 2, "greedyalgo")
688s 30 tests, 30 passed, 0 known failure, 0 skipped
688s [inst/anovan.m]
689s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/anovan.m
689s ***** demo
689s 
689s  # Two-sample unpaired test on independent samples (equivalent to Student's
689s  # t-test). Note that the absolute value of t-statistic can be obtained by
689s  # taking the square root of the reported F statistic. In this example,
689s  # t = sqrt (1.44) = 1.20.
689s 
689s  score = [54 23 45 54 45 43 34 65 77 46 65]';
689s  gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
689s            "female" "female" "female"}';
689s 
689s  [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
689s ***** demo
689s 
689s  # Two-sample paired test on dependent or matched samples equivalent to a
689s  # paired t-test. As for the first example, the t-statistic can be obtained by
689s  # taking the square root of the reported F statistic. Note that the interaction
689s  # between treatment x subject was dropped from the full model by assigning
689s  # subject as a random factor (').
689s 
689s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
689s  treatment = {"before" "after"; "before" "after"; "before" "after";
689s               "before" "after"; "before" "after"}';
689s  subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
689s 
689s  [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
689s                             "model", "full", "random", 2, "sstype", 2, ...
689s                             "varnames", {"treatment", "subject"}, ...
689s                             "display", "on");
689s ***** demo
689s 
689s  # One-way ANOVA on the data from a study on the strength of structural beams,
689s  # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
689s 
689s  strength = [82 86 79 83 84 85 86 87 74 82 ...
689s             78 75 76 77 79 79 77 78 82 79]';
689s  alloy = {"st","st","st","st","st","st","st","st", ...
689s           "al1","al1","al1","al1","al1","al1", ...
689s           "al2","al2","al2","al2","al2","al2"}';
689s 
689s  [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
689s                             "varnames", "alloy");
689s ***** demo
689s 
689s  # One-way repeated measures ANOVA on the data from a study on the number of
689s  # words recalled by 10 subjects for three time conditions, in Loftus & Masson
689s  # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
689s  # between seconds x subject was dropped from the full model by assigning
689s  # subject as a random factor (').
689s 
689s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
689s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
689s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
689s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
689s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
689s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
689s 
689s  [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
689s                             "model", "full", "random", 2, "sstype", 2, ...
689s                             "display", "on", "varnames", {"seconds", "subject"});
689s ***** demo
689s 
689s  # Balanced two-way ANOVA with interaction on the data from a study of popcorn
689s  # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
689s  # New York: MacMillan
689s 
689s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
689s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
689s  brands = {"Gourmet", "National", "Generic"; ...
689s            "Gourmet", "National", "Generic"; ...
689s            "Gourmet", "National", "Generic"; ...
689s            "Gourmet", "National", "Generic"; ...
689s            "Gourmet", "National", "Generic"; ...
689s            "Gourmet", "National", "Generic"};
689s  popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
689s            "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
689s 
689s  [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
689s                             "display", "on", "model", "full", ...
689s                             "varnames", {"brands", "popper"});
689s ***** demo
689s 
689s  # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
689s  # gender and having a college degree on salaries of company employees,
689s  # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
689s 
689s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
689s            25 29 27 19 18 21 20 21 22 19]';
689s  gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
689s            "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
689s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
689s 
689s  [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
689s                             "sstype", 3, "display", "on", "varnames", ...
689s                             {"gender", "degree"});
689s ***** demo
689s 
689s  # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
689s  # adding sugar and/or milk on the tendency of coffee to make people babble,
689s  # in from Navarro (2019): 16.10
689s 
689s  sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
689s           "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
689s  milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
689s          "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
689s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
689s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
689s 
689s  [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
689s                             "sstype", 3, "display", "on", ...
689s                             "varnames", {"sugar", "milk"});
689s ***** demo
689s 
689s  # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
689s  # of three different drugs, biofeedback and diet on patient blood pressure,
689s  # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
689s  # * Missing values introduced to make the sample sizes unequal to test the
689s  #   calculation of different types of sums-of-squares
689s 
689s  drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
689s          "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
689s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
689s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
689s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
689s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
689s  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;
689s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
689s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
689s  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;
689s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
689s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
689s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
689s        173 194 197 190 176 198 164 190 169 164 176 175;
689s        186 194 201 215 219 209 164 166 159 182 187 174 ...
689s        189 194 217 206 199 195 171 173 196 199 180 NaN;
689s        180 187 199 170 204 194 162 184 183 156 180 173 ...
689s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
689s 
689s  [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
689s                                     "model", "full", "sstype", 3, ...
689s                                     "display", "on", ...
689s                                     "varnames", {"drug", "feedback", "diet"});
689s ***** demo
689s 
689s  # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
689s  # factor. The data is from a randomized block design study on the effects
689s  # of antioxidant treatment on glutathione-S-transferase (GST) levels in
689s  # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
689s  # Note that all interactions involving block were dropped from the full model
689s  # by assigning block as a random factor (').
689s 
689s  measurement = [444 614 423 625 408  856 447 719 ...
689s                 764 831 586 782 609 1002 606 766]';
689s  strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
689s           "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
689s  treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
689s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
689s 
689s  [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
689s                             "sstype", 2, "model", "full", "random", 3, ...
689s                             "display", "on", ...
689s                             "varnames", {"strain", "treatment", "block"});
689s ***** demo
689s 
689s  # One-way ANCOVA on data from a study of the additive effects of species
689s  # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
689s  # Text eveR
689s 
689s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
689s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
689s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
689s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
689s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
689s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
689s  species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
689s             "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
689s             "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
689s 
689s  [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
689s                            "continuous", 2, "sstype", "h", "display", "on", ...
689s                            "varnames", {"species", "temp"});
689s ***** demo
689s 
689s  # Factorial ANCOVA on data from a study of the effects of treatment and
689s  # exercise on stress reduction score after adjusting for age. Data from R
689s  # datarium package).
689s 
689s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
689s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
689s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
689s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
689s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
689s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
689s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
689s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
689s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
689s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
689s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
689s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
689s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
689s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
689s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
689s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
689s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
689s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
689s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
689s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
689s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
689s 
689s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
689s                             "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
689s                             "continuous", 3, "sstype", "h", "display", "on", ...
689s                             "varnames", {"treatment", "exercise", "age"});
689s ***** demo
689s 
689s  # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
689s  # relating to the contrasts are shown in the table of model parameters, and
689s  # can be retrieved from the STATS.coeffs output.
689s 
689s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
689s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
689s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
689s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
689s         25.694 ]';
689s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
689s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
689s  C = [ 0.4001601  0.3333333  0.5  0.0
689s        0.4001601  0.3333333 -0.5  0.0
689s        0.4001601 -0.6666667  0.0  0.0
689s       -0.6002401  0.0000000  0.0  0.5
689s       -0.6002401  0.0000000  0.0 -0.5];
689s 
689s  [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
689s                           "alpha", 0.05, "display", "on");
689s ***** demo
689s 
689s  # One-way ANOVA with the linear model fit by weighted least squares to
689s  # account for heteroskedasticity. In this example, the variance appears
689s  # proportional to the outcome, so weights have been estimated by initially
689s  # fitting the model without weights and regressing the absolute residuals on
689s  # the fitted values. Although this data could have been analysed by Welch's
689s  # ANOVA test, the approach here can generalize to ANOVA models with more than
689s  # one factor.
689s 
689s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
689s       2, 2, 2, 2, 2, 2, 2, 2, ...
689s       3, 3, 3, 3, 3, 3, 3, 3]';
689s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
689s       10, 25, 66, 43, 47, 56,  6, 39, ...
689s       11, 39, 26, 35, 25, 14, 24, 17]';
689s 
689s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
689s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
689s  b = polyfit (fitted, abs (STATS.resid), 1);
689s  v = polyval (b, fitted);  # Variance as a function of the fitted values
689s  figure("Name", "Regression of the absolute residuals on the fitted values");
689s  plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
689s  xlabel("Fitted values"); ylabel("Absolute residuals");
689s 
689s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
689s ***** test
689s  score = [54 23 45 54 45 43 34 65 77 46 65]';
689s  gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
689s            'female' 'female' 'female'}';
689s 
689s  [P, T, STATS] = anovan (score,gender,'display','off');
689s  assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
689s  assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
689s  assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
689s  assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
689s ***** test
689s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
689s  treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
689s               'before' 'after'; 'before' 'after'}';
689s  subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
689s 
689s  [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
689s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
689s  assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
689s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
689s  assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
689s ***** test
689s  strength = [82 86 79 83 84 85 86 87 74 82 ...
689s             78 75 76 77 79 79 77 78 82 79]';
689s  alloy = {'st','st','st','st','st','st','st','st', ...
689s           'al1','al1','al1','al1','al1','al1', ...
689s           'al2','al2','al2','al2','al2','al2'}';
689s 
689s  [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
689s  assert (P(1), 0.000152643638830491, 1e-09);
689s  assert (ATAB{2,6}, 15.4, 1e-09);
689s ***** test
689s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
689s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
689s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
689s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
689s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
689s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
689s 
689s  [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
689s  assert (P(1), 1.51865926758752e-07, 1e-09);
689s  assert (ATAB{2,2}, 52.2666666666667, 1e-09);
689s  assert (ATAB{3,2}, 942.533333333333, 1e-09);
689s  assert (ATAB{4,2}, 11.0666666666667, 1e-09);
689s ***** test
689s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
689s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
689s  brands = {'Gourmet', 'National', 'Generic'; ...
689s            'Gourmet', 'National', 'Generic'; ...
689s            'Gourmet', 'National', 'Generic'; ...
689s            'Gourmet', 'National', 'Generic'; ...
689s            'Gourmet', 'National', 'Generic'; ...
689s            'Gourmet', 'National', 'Generic'};
689s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
689s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
689s 
689s  [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
689s  assert (P(1), 7.67895738278171e-07, 1e-09);
689s  assert (P(2), 0.000100373896304998, 1e-09);
689s  assert (P(3), 0.746215396636649, 1e-09);
689s  assert (ATAB{2,6}, 56.7, 1e-09);
689s  assert (ATAB{3,6}, 32.4, 1e-09);
689s  assert (ATAB{4,6}, 0.29999999999997, 1e-09);
689s ***** test
689s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
689s            25 29 27 19 18 21 20 21 22 19]';
689s  gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
689s            'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
689s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
689s 
689s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
689s  assert (P(1), 0.747462549227232, 1e-09);
689s  assert (P(2), 1.03809316857694e-08, 1e-09);
689s  assert (P(3), 0.523689833702691, 1e-09);
689s  assert (ATAB{2,2}, 0.296969696969699, 1e-09);
689s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
689s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
689s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
689s  [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
689s  assert (P(1), 2.53445097305047e-08, 1e-09);
689s  assert (P(2), 0.00388133678528749, 1e-09);
689s  assert (P(3), 0.523689833702671, 1e-09);
689s  assert (ATAB{2,2}, 242.227272727273, 1e-09);
689s  assert (ATAB{3,2}, 30.4615384615384, 1e-09);
689s  assert (ATAB{4,2}, 1.17482517482523, 1e-09);
689s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
689s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
689s  assert (P(1), 0.00388133678528743, 1e-09);
689s  assert (P(2), 1.03809316857694e-08, 1e-09);
689s  assert (P(3), 0.523689833702691, 1e-09);
689s  assert (ATAB{2,2}, 30.4615384615385, 1e-09);
689s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
689s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
689s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
689s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
689s  assert (P(1), 0.00442898146583742, 1e-09);
689s  assert (P(2), 1.30634252053587e-08, 1e-09);
689s  assert (P(3), 0.523689833702691, 1e-09);
689s  assert (ATAB{2,2}, 29.3706293706294, 1e-09);
689s  assert (ATAB{3,2}, 264.335664335664, 1e-09);
689s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
689s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
689s ***** test
689s  sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
689s           'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
689s  milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
689s          'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
689s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
689s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
689s 
689s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
689s  assert (P(1), 0.0108632139833963, 1e-09);
689s  assert (P(2), 0.0810606976703546, 1e-09);
689s  assert (P(3), 0.00175433329935627, 1e-09);
689s  assert (ATAB{2,2}, 3.55752380952381, 1e-09);
689s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
689s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
689s  assert (ATAB{5,2}, 3.1625, 1e-09);
689s  [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
689s  assert (P(1), 0.0373333189297505, 1e-09);
689s  assert (P(2), 0.017075098787169, 1e-09);
689s  assert (P(3), 0.00175433329935627, 1e-09);
689s  assert (ATAB{2,2}, 1.444, 1e-09);
689s  assert (ATAB{3,2}, 3.06963228699552, 1e-09);
689s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
689s  assert (ATAB{5,2}, 3.1625, 1e-09);
689s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
689s  assert (P(1), 0.017075098787169, 1e-09);
689s  assert (P(2), 0.0810606976703546, 1e-09);
689s  assert (P(3), 0.00175433329935627, 1e-09);
689s  assert (ATAB{2,2}, 3.06963228699552, 1e-09);
689s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
689s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
689s  assert (ATAB{5,2}, 3.1625,  1e-09);
689s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
689s  assert (P(1), 0.0454263063473954, 1e-09);
689s  assert (P(2), 0.0746719907091438, 1e-09);
689s  assert (P(3), 0.00175433329935627, 1e-09);
689s  assert (ATAB{2,2}, 2.13184977578476, 1e-09);
689s  assert (ATAB{3,2}, 1.00413461538462, 1e-09);
689s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
689s  assert (ATAB{5,2}, 3.1625, 1e-09);
689s ***** test
689s  drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
689s          'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
689s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
689s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
689s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
689s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
689s  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;
689s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
689s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
689s  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;
689s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
689s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
689s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
689s        173 194 197 190 176 198 164 190 169 164 176 175;
689s        186 194 201 215 219 209 164 166 159 182 187 174 ...
689s        189 194 217 206 199 195 171 173 196 199 180 NaN;
689s        180 187 199 170 204 194 162 184 183 156 180 173 ...
689s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
689s 
689s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
689s  assert (P(1), 7.02561843825325e-05, 1e-09);
689s  assert (P(2), 0.000425806013389362, 1e-09);
689s  assert (P(3), 6.16780773446401e-07, 1e-09);
689s  assert (P(4), 0.261347622678438, 1e-09);
689s  assert (P(5), 0.0542278432357043, 1e-09);
689s  assert (P(6), 0.590353225626655, 1e-09);
689s  assert (P(7), 0.0861628249564267, 1e-09);
689s  assert (ATAB{2,2}, 3614.70355731226, 1e-09);
689s  assert (ATAB{3,2}, 2227.46639771024, 1e-09);
689s  assert (ATAB{4,2}, 5008.25614451819, 1e-09);
689s  assert (ATAB{5,2}, 437.066007908781, 1e-09);
689s  assert (ATAB{6,2}, 976.180770397332, 1e-09);
689s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
689s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
689s  assert (ATAB{9,2}, 9065.8,  1e-09);
689s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
689s  assert (P(1), 9.4879638470754e-05, 1e-09);
689s  assert (P(2), 0.00124177666315809, 1e-09);
689s  assert (P(3), 6.86162012732911e-07, 1e-09);
689s  assert (P(4), 0.260856132341256, 1e-09);
689s  assert (P(5), 0.0523758623892078, 1e-09);
689s  assert (P(6), 0.590353225626655, 1e-09);
689s  assert (P(7), 0.0861628249564267, 1e-09);
689s  assert (ATAB{2,2}, 3481.72176560122, 1e-09);
689s  assert (ATAB{3,2}, 1837.08812970469, 1e-09);
689s  assert (ATAB{4,2}, 4957.20277938622, 1e-09);
689s  assert (ATAB{5,2}, 437.693674777847, 1e-09);
689s  assert (ATAB{6,2}, 988.431929811402, 1e-09);
689s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
689s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
689s  assert (ATAB{9,2}, 9065.8,  1e-09);
689s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
689s  assert (P(1), 0.000106518678028207, 1e-09);
689s  assert (P(2), 0.00125371366571508, 1e-09);
689s  assert (P(3), 5.30813260778464e-07, 1e-09);
689s  assert (P(4), 0.308353667232981, 1e-09);
689s  assert (P(5), 0.0562901327343161, 1e-09);
689s  assert (P(6), 0.599091042141092, 1e-09);
689s  assert (P(7), 0.0861628249564267, 1e-09);
689s  assert (ATAB{2,2}, 3430.88156424581, 1e-09);
689s  assert (ATAB{3,2}, 1833.68031496063, 1e-09);
689s  assert (ATAB{4,2}, 5080.48346456693, 1e-09);
689s  assert (ATAB{5,2}, 382.07709497207, 1e-09);
689s  assert (ATAB{6,2}, 963.037988826813, 1e-09);
689s  assert (ATAB{7,2}, 44.4519685039322, 1e-09);
689s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
689s  assert (ATAB{9,2}, 9065.8, 1e-09);
689s ***** test
689s  measurement = [444 614 423 625 408  856 447 719 ...
689s                 764 831 586 782 609 1002 606 766]';
689s  strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
689s           'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
689s  treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
689s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
689s 
689s  [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
689s  assert (P(1), 0.0914352969909372, 1e-09);
689s  assert (P(2), 5.04077373924908e-05, 1e-09);
689s  assert (P(4), 0.0283196918836667, 1e-09);
689s  assert (ATAB{2,2}, 286.132500000002, 1e-09);
689s  assert (ATAB{3,2}, 2275.29, 1e-09);
689s  assert (ATAB{4,2}, 1242.5625, 1e-09);
689s  assert (ATAB{5,2}, 495.905000000001, 1e-09);
689s  assert (ATAB{6,2}, 207.007499999999, 1e-09);
689s ***** test
689s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
689s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
689s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
689s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
689s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
689s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
689s  species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
689s             'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
689s             'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
689s 
689s  [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
689s  assert (P(1), 6.27153318786007e-14, 1e-09);
689s  assert (P(2), 2.48773241196644e-25, 1e-09);
689s  assert (ATAB{2,2}, 598.003953318404, 1e-09);
689s  assert (ATAB{3,2}, 4376.08256843712, 1e-09);
689s  assert (ATAB{4,2}, 89.3498685376726, 1e-09);
689s  assert (ATAB{2,6}, 187.399388123951, 1e-09);
689s  assert (ATAB{3,6}, 1371.35413763454, 1e-09);
689s ***** test
689s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
689s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
689s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
689s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
689s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
689s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
689s  treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
689s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
689s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
689s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
689s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
689s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
689s  exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
689s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
689s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
689s              'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
689s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
689s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
689s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
689s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
689s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
689s 
689s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
689s  assert (P(5), 0.9245630968248468, 1e-09);
689s  assert (P(6), 0.791115159521822, 1e-09);
689s  assert (P(7), 0.9296668751457956, 1e-09);
689s  [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');
689s  assert (P(1), 0.00158132928938933, 1e-09);
689s  assert (P(2), 2.12537505039986e-07, 1e-09);
689s  assert (P(3), 0.00390292555160047, 1e-09);
689s  assert (P(4), 0.0164086580775543, 1e-09);
689s  assert (ATAB{2,6}, 11.0956027650549, 1e-09);
689s  assert (ATAB{3,6}, 20.8195665467178, 1e-09);
689s  assert (ATAB{4,6}, 9.10966630720186, 1e-09);
689s  assert (ATAB{5,6}, 4.4457923698584, 1e-09);
689s ***** test
689s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
689s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
689s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
689s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
689s         25.694 ]';
689s  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]';
689s  C = [ 0.4001601  0.3333333  0.5  0.0
689s        0.4001601  0.3333333 -0.5  0.0
689s        0.4001601 -0.6666667  0.0  0.0
689s       -0.6002401  0.0000000  0.0  0.5
689s       -0.6002401  0.0000000  0.0 -0.5];
689s 
689s  [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
689s  assert (STATS.coeffs(1,1), 19.4001, 1e-04);
689s  assert (STATS.coeffs(2,1), -9.3297, 1e-04);
689s  assert (STATS.coeffs(3,1), -5.0000, 1e-04);
689s  assert (STATS.coeffs(4,1), -8.0000, 1e-04);
689s  assert (STATS.coeffs(5,1), -8.0000, 1e-04);
689s  assert (STATS.coeffs(1,2), 0.4831, 1e-04);
689s  assert (STATS.coeffs(2,2), 0.9694, 1e-04);
689s  assert (STATS.coeffs(3,2), 1.3073, 1e-04);
689s  assert (STATS.coeffs(4,2), 1.6411, 1e-04);
689s  assert (STATS.coeffs(5,2), 1.4507, 1e-04);
689s  assert (STATS.coeffs(1,5), 40.161, 1e-03);
689s  assert (STATS.coeffs(2,5), -9.624, 1e-03);
689s  assert (STATS.coeffs(3,5), -3.825, 1e-03);
689s  assert (STATS.coeffs(4,5), -4.875, 1e-03);
689s  assert (STATS.coeffs(5,5), -5.515, 1e-03);
689s  assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
689s  assert (STATS.coeffs(3,6), 0.000572, 1e-06);
689s  assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
689s  assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
690s 12 tests, 12 passed, 0 known failure, 0 skipped
690s [inst/violin.m]
690s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/violin.m
690s ***** demo
690s  clf
690s  x = zeros (9e2, 10);
690s  for i=1:10
690s    x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
690s  endfor
690s  h = violin (x, "color", "c");
690s  axis tight
690s  set (h.violin, "linewidth", 2);
690s  set (gca, "xgrid", "on");
690s  xlabel ("Variables")
690s  ylabel ("Values")
690s ***** demo
690s  clf
690s  data = {randn(100,1)*5+140, randn(130,1)*8+135};
690s  subplot (1,2,1)
690s  title ("Grade 3 heights - vertical");
690s  set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
690s  violin (data, "Nbins", 10);
690s  axis tight
690s 
690s  subplot(1,2,2)
690s  title ("Grade 3 heights - horizontal");
690s  set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
690s  violin (data, "horizontal", "Nbins", 10);
690s  axis tight
690s ***** demo
690s  clf
690s  data = exprnd (0.1, 500,4);
690s  violin (data, "nbins", {5,10,50,100});
690s  axis ([0 5 0 max(data(:))])
690s ***** demo
690s  clf
690s  data = exprnd (0.1, 500,4);
690s  violin (data, "color", jet(4));
690s  axis ([0 5 0 max(data(:))])
690s ***** demo
690s  clf
690s  data = repmat(exprnd (0.1, 500,1), 1, 4);
690s  violin (data, "width", linspace (0.1,0.5,4));
690s  axis ([0 5 0 max(data(:))])
690s ***** demo
690s  clf
690s  data = repmat(exprnd (0.1, 500,1), 1, 4);
690s  violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
690s  axis ([0 5 0 max(data(:))])
690s ***** test
690s  hf = figure ("visible", "off");
690s  unwind_protect
690s    data = exprnd (0.1, 500,4);
690s    violin (data, "color", jet(4));
690s    axis ([0 5 0 max(data(:))])
690s  unwind_protect_cleanup
690s    close (hf);
690s  end_unwind_protect
690s ***** test
690s  hf = figure ("visible", "off");
690s  unwind_protect
690s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
690s    subplot (1,2,1)
690s    title ("Grade 3 heights - vertical");
690s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
690s    violin (data, "Nbins", 10);
690s    axis tight
690s  unwind_protect_cleanup
690s    close (hf);
690s  end_unwind_protect
690s ***** test
690s  hf = figure ("visible", "off");
690s  unwind_protect
690s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
690s    subplot (1,2,1)
690s    title ("Grade 3 heights - vertical");
690s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
690s    violin (data, "Nbins", 10);
690s    axis tight
690s    subplot(1,2,2)
690s    title ("Grade 3 heights - horizontal");
690s    set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
690s    violin (data, "horizontal", "Nbins", 10);
690s    axis tight
690s  unwind_protect_cleanup
690s    close (hf);
690s  end_unwind_protect
691s ***** test
691s  hf = figure ("visible", "off");
691s  unwind_protect
691s    data = repmat(exprnd (0.1, 500,1), 1, 4);
691s    violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
691s    axis ([0 5 0 max(data(:))])
691s  unwind_protect_cleanup
691s    close (hf);
691s  end_unwind_protect
692s ***** test
692s  hf = figure ("visible", "off");
692s  unwind_protect
692s    data = repmat(exprnd (0.1, 500,1), 1, 4);
692s    violin (data, "width", linspace (0.1,0.5,4));
692s    axis ([0 5 0 max(data(:))])
692s  unwind_protect_cleanup
692s    close (hf);
692s  end_unwind_protect
692s 5 tests, 5 passed, 0 known failure, 0 skipped
692s [inst/dcov.m]
692s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dcov.m
692s ***** demo
692s  base=@(x) (x- min(x))./(max(x)-min(x));
692s  N = 5e2;
692s  x = randn (N,1); x = base (x);
692s  z = randn (N,1); z = base (z);
692s  # Linear relations
692s  cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
692s  ly = x .* cy;
692s  ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
692s  # Correlated Gaussian
692s  cz = 1 - abs (cy);
692s  gy = base ( ly + cz.*z);
692s  # Shapes
692s  sx      = repmat (x,1,7);
692s  sy      = zeros (size (ly));
692s  v       = 2 * rand (size(x,1),2) - 1;
692s  sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
692s  R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
692s  tmp     = R(35) * v.';
692s  sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
692s  tmp     = R(45) * v.';
692s  sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
692s  sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
692s  sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
692s  sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
692s  sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
692s  sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
692s  sy      = base (sy);
692s  sx      = base (sx);
692s  # scaled shape
692s  sc  = 1/3;
692s  ssy = (sy-0.5) * sc + 0.5;
692s  n = size (ly,2);
692s  ym = 1.2;
692s  xm = 0.5;
692s  fmt={'horizontalalignment','center'};
692s  ff = "% .2f";
692s  figure (1)
692s  for i=1:n
692s    subplot(4,n,i);
692s    plot (x, gy(:,i), '.b');
692s    axis tight
692s    axis off
692s    text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})
692s 
692s    subplot(4,n,i+n);
692s    plot (x, ly(:,i), '.b');
692s    axis tight
692s    axis off
692s    text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})
692s 
692s    subplot(4,n,i+2*n);
692s    plot (sx(:,i), sy(:,i), '.b');
692s    axis tight
692s    axis off
692s    text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
692s    v = axis ();
692s 
692s    subplot(4,n,i+3*n);
692s    plot (sx(:,i), ssy(:,i), '.b');
692s    axis (v)
692s    axis off
692s    text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
692s  endfor
692s ***** error dcov (randn (30, 5), randn (25,5))
692s 1 test, 1 passed, 0 known failure, 0 skipped
692s [inst/chi2gof.m]
692s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/chi2gof.m
692s ***** demo
692s  x = normrnd (50, 5, 100, 1);
692s  [h, p, stats] = chi2gof (x)
692s  [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
692s  [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
692s ***** demo
692s  x = rand (100,1 );
692s  n = length (x);
692s  binedges = linspace (0, 1, 11);
692s  expectedCounts = n * diff (binedges);
692s  [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
692s ***** demo
692s  bins = 0:5;
692s  obsCounts = [6 16 10 12 4 2];
692s  n = sum(obsCounts);
692s  lambdaHat = sum(bins.*obsCounts) / n;
692s  expCounts = n * poisspdf(bins,lambdaHat);
692s  [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
692s                           "expected", expCounts, "nparams",1)
692s ***** error chi2gof ()
692s ***** error chi2gof ([2,3;3,4])
692s ***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
692s ***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
692s ***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
692s ***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
692s ***** test
692s  x = [1 2 1 3 2 4 3 2 4 3 2 2];
692s  [h, p, stats] = chi2gof (x);
692s  assert (h, 0);
692s  assert (p, NaN);
692s  assert (stats.chi2stat, 0.1205375022748029, 1e-14);
692s  assert (stats.df, 0);
692s  assert (stats.edges, [1, 2.5, 4], 1e-14);
692s  assert (stats.O, [7, 5], 1e-14);
692s  assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
692s 11 tests, 11 passed, 0 known failure, 0 skipped
692s [inst/jackknife.m]
692s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/jackknife.m
692s ***** demo
692s  for k = 1:1000
692s    rand ("seed", k);  # for reproducibility
692s    x = rand (10, 1);
692s    s(k) = std (x);
692s    jackstat = jackknife (@std, x);
692s    j(k) = 10 * std (x) - 9 * mean (jackstat);
692s  endfor
692s  figure();
692s  hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
692s ***** demo
692s  for k = 1:1000
692s    randn ("seed", k); # for reproducibility
692s    x = randn (1, 50);
692s    rand ("seed", k);  # for reproducibility
692s    y = rand (1, 50);
692s    jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
692s    j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
692s    v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
692s  endfor
692s  t = (j - sqrt (1 / 12)) ./ sqrt (v);
692s  figure();
692s  plot (sort (tcdf (t, 49)), ...
692s        "-;Almost linear mapping indicates good fit with t-distribution.;")
692s ***** test
692s  ##Example from Quenouille, Table 1
692s  d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
692s  jackstat = jackknife ( @(x) 1/mean(x), d );
692s  assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
692s 1 test, 1 passed, 0 known failure, 0 skipped
692s [inst/manovacluster.m]
692s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/manovacluster.m
692s ***** demo
692s  load carbig
692s  X = [MPG Acceleration Weight Displacement];
692s  [d, p, stats] = manova1 (X, Origin);
692s  manovacluster (stats)
692s ***** test
692s  hf = figure ("visible", "off");
692s  unwind_protect
692s    load carbig
692s    X = [MPG Acceleration Weight Displacement];
692s    [d, p, stats] = manova1 (X, Origin);
692s    manovacluster (stats);
692s  unwind_protect_cleanup
692s    close (hf);
692s  end_unwind_protect
692s ***** error manovacluster (stats, "some");
693s 2 tests, 2 passed, 0 known failure, 0 skipped
693s [inst/kruskalwallis.m]
693s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/kruskalwallis.m
693s ***** demo
693s  x = meshgrid (1:6);
693s  x = x + normrnd (0, 1, 6, 6);
693s  kruskalwallis (x, [], 'off');
693s ***** demo
693s  x = meshgrid (1:6);
693s  x = x + normrnd (0, 1, 6, 6);
693s  [p, atab] = kruskalwallis(x);
693s ***** demo
693s  x = ones (30, 4) .* [-2, 0, 1, 5];
693s  x = x + normrnd (0, 2, 30, 4);
693s  group = {"A", "B", "C", "D"};
693s  kruskalwallis (x, group);
693s ***** test
693s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
693s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
693s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
693s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
693s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
693s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
693s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
693s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
693s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
693s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
693s  group = [1:10] .* ones (10,10);
693s  group = group(:);
693s  [p, tbl] = kruskalwallis (data, group, "off");
693s  assert (p, 0.048229, 1e-6);
693s  assert (tbl{2,5}, 17.03124, 1e-5);
693s  assert (tbl{2,3}, 9, 0);
693s  assert (tbl{4,2}, 82655.5, 1e-16);
693s  data = reshape (data, 10, 10);
693s  [p, tbl, stats] = kruskalwallis (data, [], "off");
693s  assert (p, 0.048229, 1e-6);
693s  assert (tbl{2,5}, 17.03124, 1e-5);
693s  assert (tbl{2,3}, 9, 0);
693s  assert (tbl{4,2}, 82655.5, 1e-16);
693s  means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
693s  N = 10 * ones (1, 10);
693s  assert (stats.meanranks, means, 1e-6);
693s  assert (length (stats.gnames), 10, 0);
693s  assert (stats.n, N, 0);
693s 1 test, 1 passed, 0 known failure, 0 skipped
693s [inst/kstest2.m]
693s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/kstest2.m
693s ***** error kstest2 ([1,2,3,4,5,5])
693s ***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
693s ***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
693s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
693s ***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
693s ***** test
693s  load examgrades
693s  [h, p] = kstest2 (grades(:,1), grades(:,2));
693s  assert (h, false);
693s  assert (p, 0.1222791870137312, 1e-14);
693s ***** test
693s  load examgrades
693s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
693s  assert (h, false);
693s  assert (p, 0.1844421391011258, 1e-14);
693s ***** test
693s  load examgrades
693s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
693s  assert (h, false);
693s  assert (p, 0.06115357930171663, 1e-14);
693s ***** test
693s  load examgrades
693s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
693s  assert (h, true);
693s  assert (p, 0.06115357930171663, 1e-14);
693s 14 tests, 14 passed, 0 known failure, 0 skipped
693s [inst/evalclusters.m]
693s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/evalclusters.m
693s ***** demo
693s  load fisheriris;
693s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
693s  plot (eva)
693s ***** error evalclusters ()
693s ***** error evalclusters ([1 1;0 1])
693s ***** error evalclusters ([1 1;0 1], "kmeans")
693s ***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
693s ***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
693s ***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
693s ***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
693s ***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
693s ***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
693s ***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
693s ***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
693s ***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
693s ***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
693s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
693s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
693s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
693s ***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
693s                                         "distance", "a")
693s ***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
693s                                        "distance", "a")
693s ***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
693s                                      "clusterpriors", "equal")
693s ***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
693s                                          "silhouette", "clusterpriors", "xxx")
693s ***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
693s ***** test
693s  load fisheriris;
693s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
693s  assert (isa (eva, "CalinskiHarabaszEvaluation"));
693s  assert (eva.NumObservations, 150);
693s  assert (eva.OptimalK, 3);
693s  assert (eva.InspectedK, [1 2 3 4 5 6]);
693s 22 tests, 22 passed, 0 known failure, 0 skipped
693s [inst/regress_gp.m]
693s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/regress_gp.m
693s ***** demo
693s  ## Linear fitting of 1D Data
693s  rand ("seed", 125);
693s  X = 2 * rand (5, 1) - 1;
693s  randn ("seed", 25);
693s  Y = 2 * X - 1 + 0.3 * randn (5, 1);
693s 
693s  ## Points for interpolation/extrapolation
693s  Xfit = linspace (-2, 2, 10)';
693s 
693s  ## Fit regression model
693s  [Yfit, Yint, m] = regress_gp (X, Y, Xfit);
693s 
693s  ## Plot fitted data
693s  plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
693s  title ("Gaussian process regression with linear kernel");
693s ***** demo
693s  ## Linear fitting of 2D Data
693s  rand ("seed", 135);
693s  X = 2 * rand (4, 2) - 1;
693s  randn ("seed", 35);
693s  Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);
693s 
693s  ## Mesh for interpolation/extrapolation
693s  [x1, x2] = meshgrid (linspace (-1, 1, 10));
693s  Xfit = [x1(:), x2(:)];
693s 
693s  ## Fit regression model
693s  [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
693s  Ypred = reshape (Ypred, 10, 10);
693s  YintU = reshape (Yint(:,1), 10, 10);
693s  YintL = reshape (Yint(:,2), 10, 10);
693s 
693s  ## Plot fitted data
693s  plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
693s  hold on;
693s  h = mesh (x1, x2, Ypred, zeros (10, 10));
693s  set (h, "facecolor", "none", "edgecolor", "yellow");
693s  h = mesh (x1, x2, YintU, ones (10, 10));
693s  set (h, "facecolor", "none", "edgecolor", "cyan");
693s  h = mesh (x1, x2, YintL, ones (10, 10));
693s  set (h, "facecolor", "none", "edgecolor", "cyan");
693s  hold off
693s  axis tight
693s  view (75, 25)
693s  title ("Gaussian process regression with linear kernel");
693s ***** demo
693s  ## Projection over basis function with linear kernel
693s  pp = [2, 2, 0.3, 1];
693s  n = 10;
693s  rand ("seed", 145);
693s  X = 2 * rand (n, 1) - 1;
693s  randn ("seed", 45);
693s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
693s 
693s  ## Powers
693s  px = [sqrt(abs(X)), X, X.^2, X.^3];
693s 
693s  ## Points for interpolation/extrapolation
693s  Xfit = linspace (-1, 1, 100)';
693s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
693s 
693s  ## Define a prior covariance assuming that the sqrt component is not present
693s  Sp = 100 * eye (size (px, 2) + 1);
693s  Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present
693s 
693s  ## Fit regression model
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);
693s 
693s  ## Plot fitted data
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("Linear kernel over basis function with prior covariance");
693s ***** demo
693s  ## Projection over basis function with linear kernel
693s  pp = [2, 2, 0.3, 1];
693s  n = 10;
693s  rand ("seed", 145);
693s  X = 2 * rand (n, 1) - 1;
693s  randn ("seed", 45);
693s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
693s 
693s  ## Powers
693s  px = [sqrt(abs(X)), X, X.^2, X.^3];
693s 
693s  ## Points for interpolation/extrapolation
693s  Xfit = linspace (-1, 1, 100)';
693s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
693s 
693s  ## Fit regression model without any assumption on prior covariance
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);
693s 
693s  ## Plot fitted data
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("Linear kernel over basis function without prior covariance");
693s ***** demo
693s  ## Projection over basis function with rbf kernel
693s  pp = [2, 2, 0.3, 1];
693s  n = 10;
693s  rand ("seed", 145);
693s  X = 2 * rand (n, 1) - 1;
693s  randn ("seed", 45);
693s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
693s 
693s  ## Powers
693s  px = [sqrt(abs(X)), X, X.^2, X.^3];
693s 
693s  ## Points for interpolation/extrapolation
693s  Xfit = linspace (-1, 1, 100)';
693s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
693s 
693s  ## Fit regression model with RBF kernel (standard parameters)
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");
693s 
693s  ## Plot fitted data
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("RBF kernel over basis function with standard parameters");
693s  text (-0.5, 4, "theta = 5\n g = 0.01");
693s ***** demo
693s  ## Projection over basis function with rbf kernel
693s  pp = [2, 2, 0.3, 1];
693s  n = 10;
693s  rand ("seed", 145);
693s  X = 2 * rand (n, 1) - 1;
693s  randn ("seed", 45);
693s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
693s 
693s  ## Powers
693s  px = [sqrt(abs(X)), X, X.^2, X.^3];
693s 
693s  ## Points for interpolation/extrapolation
693s  Xfit = linspace (-1, 1, 100)';
693s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
693s 
693s  ## Fit regression model with RBF kernel with different parameters
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);
693s 
693s  ## Plot fitted data
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("GP regression with RBF kernel and non default parameters");
693s  text (-0.5, 4, "theta = 10\n g = 0.01");
693s 
693s  ## Fit regression model with RBF kernel with different parameters
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);
693s 
693s  ## Plot fitted data
693s  figure
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("GP regression with RBF kernel and non default parameters");
693s  text (-0.5, 4, "theta = 50\n g = 0.01");
693s 
693s  ## Fit regression model with RBF kernel with different parameters
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);
693s 
693s  ## Plot fitted data
693s  figure
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("GP regression with RBF kernel and non default parameters");
693s  text (-0.5, 4, "theta = 50\n g = 0.001");
693s 
693s  ## Fit regression model with RBF kernel with different parameters
693s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);
693s 
693s  ## Plot fitted data
693s  figure
693s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
693s                          Xfit, polyval (pp, Xfit), "g-;True;");
693s  axis tight
693s  axis manual
693s  hold on
693s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
693s  hold off
693s  title ("GP regression with RBF kernel and non default parameters");
693s  text (-0.5, 4, "theta = 50\n g = 0.05");
693s ***** demo
693s  ## RBF fitting on noiseless 1D Data
693s  x = [0:2*pi/7:2*pi]';
693s  y = 5 * sin (x);
693s 
693s  ## Predictive grid of 500 equally spaced locations
693s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
693s 
693s  ## Fit regression model with RBF kernel
693s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
693s 
693s  ## Plot fitted data
693s  r = mvnrnd (Yfit, diag (Ysd)', 50);
693s  plot (xi, r', "c-");
693s  hold on
693s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
693s  plot (x, y, ".k;Predictor points;", "markersize", 20)
693s  plot (xi, 5 * sin (xi), "-y;True Function;");
693s  xlim ([-0.5,2*pi+0.5]);
693s  ylim ([-10,10]);
693s  hold off
693s  title ("GP regression with RBF kernel on noiseless 1D data");
693s  text (0, -7, "theta = 5\n g = 0.01");
693s ***** demo
693s  ## RBF fitting on noisy 1D Data
693s  x = [0:2*pi/7:2*pi]';
693s  x = [x; x];
693s  y = 5 * sin (x) + randn (size (x));
693s 
693s  ## Predictive grid of 500 equally spaced locations
693s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
693s 
693s  ## Fit regression model with RBF kernel
693s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
693s 
693s  ## Plot fitted data
693s  r = mvnrnd (Yfit, diag (Ysd)', 50);
693s  plot (xi, r', "c-");
693s  hold on
693s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
693s  plot (x, y, ".k;Predictor points;", "markersize", 20)
693s  plot (xi, 5 * sin (xi), "-y;True Function;");
693s  xlim ([-0.5,2*pi+0.5]);
693s  ylim ([-10,10]);
693s  hold off
693s  title ("GP regression with RBF kernel on noisy 1D data");
693s  text (0, -7, "theta = 5\n g = 0.01");
693s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
693s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
693s ***** error<regress_gp: X must be a 2-D matrix.> ...
693s  regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
693s ***** error<regress_gp: Y must be a column vector.> ...
693s  regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
693s ***** error<regress_gp: rows in X must equal the length of Y.> ...
693s  regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
693s ***** error<regress_gp: X and XI must have the same number of columns.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
693s ***** error<regress_gp: invalid 4th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
693s ***** error<regress_gp: invalid 4th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
693s ***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
693s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
693s ***** error<regress_gp: invalid 5th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
693s ***** error<regress_gp: invalid 5th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
693s ***** error<regress_gp: invalid 5th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
693s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
693s ***** error<regress_gp: invalid 6th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
693s ***** error<regress_gp: invalid 6th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
693s ***** error<regress_gp: invalid 6th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
693s ***** error<regress_gp: invalid 6th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
693s ***** error<regress_gp: invalid 6th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
693s ***** error<regress_gp: invalid 7th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
693s ***** error<regress_gp: invalid 7th argument.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
693s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
693s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
693s 22 tests, 22 passed, 0 known failure, 0 skipped
693s [inst/anova2.m]
693s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/anova2.m
693s ***** demo
693s 
693s  # Factorial (Crossed) Two-way ANOVA with Interaction
693s 
693s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
693s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
693s 
693s  [p, atab, stats] = anova2(popcorn, 3, "on");
693s ***** demo
693s 
693s  # One-way Repeated Measures ANOVA (Rows are a crossed random factor)
693s 
693s  data = [54, 43, 78, 111;
693s          23, 34, 37, 41;
693s          45, 65, 99, 78;
693s          31, 33, 36, 35;
693s          15, 25, 30, 26];
693s 
693s  [p, atab, stats] = anova2 (data, 1, "on", "linear");
693s ***** demo
693s 
693s  # Balanced Nested One-way ANOVA (Rows are a nested random factor)
693s 
693s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
693s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
693s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
693s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
693s 
693s  [p, atab, stats] = anova2 (data, 4, "on", "nested");
693s ***** test
693s  ## Test for anova2 ("interaction")
693s  ## comparison with results from Matlab for column effect
693s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
693s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
693s  [p, atab, stats] = anova2 (popcorn, 3, "off");
693s  assert (p(1), 7.678957383294716e-07, 1e-14);
693s  assert (p(2), 0.0001003738963050171, 1e-14);
693s  assert (p(3), 0.7462153966366274, 1e-14);
693s  assert (atab{2,5}, 56.700, 1e-14);
693s  assert (atab{2,3}, 2, 0);
693s  assert (atab{4,2}, 0.08333333333333348, 1e-14);
693s  assert (atab{5,4}, 0.1388888888888889, 1e-14);
693s  assert (atab{5,2}, 1.666666666666667, 1e-14);
693s  assert (atab{6,2}, 22);
693s  assert (stats.source, "anova2");
693s  assert (stats.colmeans, [6.25, 4.75, 4]);
693s  assert (stats.inter, 1, 0);
693s  assert (stats.pval, 0.7462153966366274, 1e-14);
693s  assert (stats.df, 12);
693s ***** test
693s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
693s  data = [54, 43, 78, 111;
693s          23, 34, 37, 41;
693s          45, 65, 99, 78;
693s          31, 33, 36, 35;
693s          15, 25, 30, 26];
693s  [p, atab, stats] = anova2 (data, 1, "off", "linear");
693s  assert (atab{2,2}, 2174.95, 1e-10);
693s  assert (atab{3,2}, 8371.7, 1e-10);
693s  assert (atab{4,2}, 2404.3, 1e-10);
693s  assert (atab{5,2}, 12950.95, 1e-10);
693s  assert (atab{2,4}, 724.983333333333, 1e-10);
693s  assert (atab{3,4}, 2092.925, 1e-10);
693s  assert (atab{4,4}, 200.358333333333, 1e-10);
693s  assert (atab{2,5}, 3.61843363972882, 1e-10);
693s  assert (atab{3,5}, 10.445909412303, 1e-10);
693s  assert (atab{2,6}, 0.087266112738617, 1e-10);
693s  assert (atab{3,6}, 0.000698397753556, 1e-10);
693s ***** test
693s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
693s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
693s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
693s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
693s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
693s  [p, atab, stats] = anova2 (data, 4, "off", "nested");
693s  assert (atab{2,2}, 745.360306290833, 1e-10);
693s  assert (atab{3,2}, 278.01854140125, 1e-10);
693s  assert (atab{4,2}, 180.180377467501, 1e-10);
693s  assert (atab{5,2}, 1203.55922515958, 1e-10);
693s  assert (atab{2,4}, 372.680153145417, 1e-10);
693s  assert (atab{3,4}, 92.67284713375, 1e-10);
693s  assert (atab{4,4}, 10.0100209704167, 1e-10);
693s  assert (atab{2,5}, 4.02146005730833, 1e-10);
693s  assert (atab{3,5}, 9.25800729165627, 1e-10);
693s  assert (atab{2,6}, 0.141597630656771, 1e-10);
693s  assert (atab{3,6}, 0.000636643812875719, 1e-10);
694s 3 tests, 3 passed, 0 known failure, 0 skipped
694s [inst/histfit.m]
694s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/histfit.m
694s ***** demo
694s  histfit (randn (100, 1))
694s ***** demo
694s  histfit (poissrnd (2, 1000, 1), 10, "Poisson")
694s ***** demo
694s  histfit (betarnd (3, 10, 1000, 1), 10, "beta")
694s ***** test
694s  hf = figure ("visible", "off");
694s  unwind_protect
694s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
694s    histfit (x);
694s  unwind_protect_cleanup
694s    close (hf);
694s  end_unwind_protect
694s ***** test
694s  hf = figure ("visible", "off");
694s  unwind_protect
694s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
694s    histfit (x);
694s  unwind_protect_cleanup
694s    close (hf);
694s  end_unwind_protect
694s ***** test
694s  hf = figure ("visible", "off");
694s  unwind_protect
694s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
694s    histfit (x, 3);
694s  unwind_protect_cleanup
694s    close (hf);
694s  end_unwind_protect
694s ***** test
694s  hf = figure ("visible", "off");
694s  unwind_protect
694s    histfit (randn (100, 1));
694s  unwind_protect_cleanup
694s    close (hf);
694s  end_unwind_protect
694s ***** test
694s  hf = figure ("visible", "off");
694s  unwind_protect
694s    histfit (poissrnd (2, 1000, 1), 10, "Poisson");
694s  unwind_protect_cleanup
694s    close (hf);
694s  end_unwind_protect
695s ***** test
695s  hf = figure ("visible", "off");
695s  unwind_protect
695s    histfit (betarnd (3, 10, 1000, 1), 10, "beta");
695s  unwind_protect_cleanup
695s    close (hf);
695s  end_unwind_protect
695s ***** test
695s  hf = figure ("visible", "off");
695s  unwind_protect
695s    ax = gca ();
695s    histfit (ax, randn (100, 1));
695s  unwind_protect_cleanup
695s    close (hf);
695s  end_unwind_protect
695s ***** test
695s  hf = figure ("visible", "off");
695s  unwind_protect
695s    ax = gca ();
695s    histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
695s  unwind_protect_cleanup
695s    close (hf);
695s  end_unwind_protect
695s ***** test
695s  hf = figure ("visible", "off");
695s  unwind_protect
695s    ax = gca ();
695s    histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
695s  unwind_protect_cleanup
695s    close (hf);
695s  end_unwind_protect
695s ***** test
695s  hf = figure ("visible", "off");
695s  unwind_protect
695s    ax = axes ("parent", hf);
695s    fail ("histfit (ax)", "histfit: too few input arguments.");
695s  unwind_protect_cleanup
695s    close (hf);
695s  end_unwind_protect
695s ***** error<histfit: X must be a numeric vector of real numbers.> ...
695s  histfit ('wer')
695s ***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
695s ***** error<histfit: NBINS must be a real scalar integer value.> ...
695s  histfit (randn (100, 1), 5.6)
695s ***** error<histfit: DISTNAME must be a character vector.> ...
695s  histfit (randn (100, 1), 8, 5)
695s ***** error<histfit: DISTNAME must be a character vector.> ...
695s  histfit (randn (100, 1), 8, {'normal'})
695s ***** error<histfit: 'Kernel' distribution is not supported yet.> ...
695s  histfit (randn (100, 1), 8, 'Kernel')
695s ***** error<histfit: unrecognized distribution name.> ...
695s  histfit (randn (100, 1), 8, 'ASDASDASD')
695s 17 tests, 17 passed, 0 known failure, 0 skipped
695s [inst/runstest.m]
695s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/runstest.m
695s ***** test
695s  ## NIST beam deflection data
695s  ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
695s  data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
695s           194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
695s          -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
695s          -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
695s           131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
695s          -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
695s           162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
695s          -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
695s           -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
695s           134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
695s          -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
695s          -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
695s          -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
695s           103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
695s          -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
695s          -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
695s            15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
695s  [h, p, stats] = runstest (data, median (data));
695s  expected_h = 1;
695s  expected_p = 0.008562;
695s  expected_z = 2.6229;
695s  assert (h, expected_h);
695s  assert (p, expected_p, 1E-6);
695s  assert (stats.z, expected_z, 1E-4);
695s ***** shared x
695s  x = [45, -60, 1.225, 55.4, -9 27];
695s ***** test
695s  [h, p, stats] = runstest (x);
695s  assert (h, 0);
695s  assert (p, 0.6, 1e-14);
695s  assert (stats.nruns, 5);
695s  assert (stats.n1, 3);
695s  assert (stats.n0, 3);
695s  assert (stats.z, 0.456435464587638, 1e-14);
695s ***** test
695s  [h, p, stats] = runstest (x, [], "method", "approximate");
695s  assert (h, 0);
695s  assert (p, 0.6481, 1e-4);
695s  assert (stats.z, 0.456435464587638, 1e-14);
695s ***** test
695s  [h, p, stats] = runstest (x, [], "tail", "left");
695s  assert (h, 0);
695s  assert (p, 0.9, 1e-14);
695s  assert (stats.z, 1.369306393762915, 1e-14);
695s ***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
695s ***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
695s ***** error<runstest: V must be either a scalar number or> ...
695s  runstest ([2 3 4 3 2 3 4], "updown")
695s ***** error<runstest: invalid value for alpha.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
695s ***** error<runstest: invalid value for alpha.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
695s ***** error<runstest: invalid value for alpha.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
695s ***** error<runstest: invalid value for alpha.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
695s ***** error<runstest: invalid value for method.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "method", "some")
695s ***** error<runstest: invalid value for tail.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "tail", "some")
695s ***** error<runstest: invalid optional argument.> ...
695s  runstest ([2 3 4 3 2 3 4], [], "option", "some")
695s 14 tests, 14 passed, 0 known failure, 0 skipped
695s [inst/manova1.m]
695s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/manova1.m
695s ***** demo
695s  load carbig
695s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
695s ***** test
695s  load carbig
695s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
695s  assert (d, 3);
695s  assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
695s              0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
695s ***** test
695s  load carbig
695s  [d,p] = manova1([MPG, Acceleration, Weight], Origin);
695s  assert (d, 2);
695s  assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
695s             [1e-12, 1e-12, 1e-12]');
695s 2 tests, 2 passed, 0 known failure, 0 skipped
695s [inst/hotelling_t2test2.m]
695s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hotelling_t2test2.m
695s ***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
695s ***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
695s  hotelling_t2test2 ([2, 3, 4, 5, 6]);
695s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
695s  hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
695s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
695s  hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
695s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
695s  hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
695s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
695s  hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
695s ***** error<hotelling_t2test2: invalid value for alpha.> ...
695s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
695s ***** error<hotelling_t2test2: invalid value for alpha.> ...
695s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
695s ***** error<hotelling_t2test2: invalid value for alpha.> ...
695s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
695s ***** error<hotelling_t2test2: invalid value for alpha.> ...
695s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
695s ***** error<hotelling_t2test2: invalid Name argument.> ...
695s  hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
695s ***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
695s  hotelling_t2test2 (ones (20,1), ones (20,2));
695s ***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
695s  hotelling_t2test2 (ones (20,2), ones (25,3));
696s ***** test
696s  randn ("seed", 1);
696s  x1 = randn (60000, 5);
696s  randn ("seed", 5);
696s  x2 = randn (30000, 5);
696s  [h, pval, stats] = hotelling_t2test2 (x1, x2);
696s  assert (h, 0);
696s  assert (stats.df1, 5);
696s  assert (stats.df2, 89994);
696s 14 tests, 14 passed, 0 known failure, 0 skipped
696s [inst/cdfcalc.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cdfcalc.m
696s ***** test
696s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
696s  [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
696s  assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
696s  assert (xCDF, [2, 3, 4, 5, 6]');
696s  assert (n, 10);
696s ***** shared x
696s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
696s ***** error yCDF = cdfcalc (x);
696s ***** error [yCDF, xCDF] = cdfcalc ();
696s ***** error [yCDF, xCDF] = cdfcalc (x, x);
696s ***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
696s 5 tests, 5 passed, 0 known failure, 0 skipped
696s [inst/kmeans.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/kmeans.m
696s ***** demo
696s  ## Generate a two-cluster problem
696s  randn ("seed", 31)  # for reproducibility
696s  C1 = randn (100, 2) + 1;
696s  randn ("seed", 32)  # for reproducibility
696s  C2 = randn (100, 2) - 1;
696s  data = [C1; C2];
696s 
696s  ## Perform clustering
696s  rand ("seed", 1)  # for reproducibility
696s  [idx, centers] = kmeans (data, 2);
696s 
696s  ## Plot the result
696s  figure;
696s  plot (data (idx==1, 1), data (idx==1, 2), "ro");
696s  hold on;
696s  plot (data (idx==2, 1), data (idx==2, 2), "bs");
696s  plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
696s  title ("A simple two-clusters example");
696s  hold off;
696s ***** demo
696s  ## Cluster data using k-means clustering, then plot the cluster regions
696s  ## Load Fisher's iris data set and use the petal lengths and widths as
696s  ## predictors
696s 
696s  load fisheriris
696s  X = meas(:,3:4);
696s 
696s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
696s  title ("Fisher's Iris Data");
696s  xlabel ("Petal Lengths (cm)");
696s  ylabel ("Petal Widths (cm)");
696s 
696s  ## Cluster the data. Specify k = 3 clusters
696s  rand ("seed", 1)  # for reproducibility
696s  [idx, C] = kmeans (X, 3);
696s  x1 = min (X(:,1)):0.01:max (X(:,1));
696s  x2 = min (X(:,2)):0.01:max (X(:,2));
696s  [x1G, x2G] = meshgrid (x1, x2);
696s  XGrid = [x1G(:), x2G(:)];
696s 
696s  idx2Region = kmeans (XGrid, 3, "MaxIter", 10, "Start", C);
696s  figure;
696s  gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
696s            [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
696s  hold on;
696s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
696s  title ("Fisher's Iris Data");
696s  xlabel ("Petal Lengths (cm)");
696s  ylabel ("Petal Widths (cm)");
696s  legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
696s  hold off
696s ***** demo
696s  ## Partition Data into Two Clusters
696s 
696s  randn ("seed", 1)  # for reproducibility
696s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
696s  randn ("seed", 2)  # for reproducibility
696s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
696s  X = [r1; r2];
696s 
696s  plot (X(:,1), X(:,2), ".");
696s  title ("Randomly Generated Data");
696s  rand ("seed", 1)  # for reproducibility
696s  [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
696s                           "Replicates", 5, "Display", "final");
696s  figure;
696s  plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
696s  hold on
696s  plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
696s  plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
696s  legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
696s  title ("Cluster Assignments and Centroids");
696s  hold off
696s ***** demo
696s  ## Assign New Data to Existing Clusters
696s 
696s  ## Generate a training data set using three distributions
696s  randn ("seed", 5)  # for reproducibility
696s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
696s  randn ("seed", 7)  # for reproducibility
696s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
696s  randn ("seed", 9)  # for reproducibility
696s  r3 = randn (100, 2) * 0.75;
696s  X = [r1; r2; r3];
696s 
696s  ## Partition the training data into three clusters by using kmeans
696s 
696s  rand ("seed", 1)  # for reproducibility
696s  [idx, C] = kmeans (X, 3);
696s 
696s  ## Plot the clusters and the cluster centroids
696s 
696s  gscatter (X(:,1), X(:,2), idx, "bgm", "***");
696s  hold on
696s  plot (C(:,1), C(:,2), "kx");
696s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")
696s 
696s  ## Generate a test data set
696s  randn ("seed", 25)  # for reproducibility
696s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
696s  randn ("seed", 27)  # for reproducibility
696s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
696s  randn ("seed", 29)  # for reproducibility
696s  r3 = randn (100, 2) * 0.75;
696s  Xtest = [r1; r2; r3];
696s 
696s  ## Classify the test data set using the existing clusters
696s  ## Find the nearest centroid from each test data point by using pdist2
696s 
696s  D = pdist2 (C, Xtest, "euclidean");
696s  [group, ~] = find (D == min (D));
696s 
696s  ## Plot the test data and label the test data using idx_test with gscatter
696s 
696s  gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
696s  box on;
696s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
696s          "Data classified to Cluster 1", "Data classified to Cluster 2", ...
696s          "Data classified to Cluster 3", "Location", "NorthWest");
696s  title ("Assign New Data to Existing Clusters");
696s ***** test
696s  samples = 4;
696s  dims = 3;
696s  k = 2;
696s  [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
696s                           "emptyAction", "singleton");
696s  assert (size (cls), [samples, 1]);
696s  assert (size (c), [k, dims]);
696s  assert (size (d), [k, 1]);
696s  assert (size (z), [samples, k]);
696s ***** test
696s  samples = 4;
696s  dims = 3;
696s  k = 2;
696s  [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
696s                           "emptyAction", "singleton");
696s  assert (size (cls), [samples, 1]);
696s  assert (size (c), [k, dims]);
696s  assert (size (d), [k, 1]);
696s  assert (size (z), [samples, k]);
696s ***** test
696s  [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
696s  assert (cls, [1; 1]);
696s  assert (c, [1.5, 0; NA, NA]);
696s ***** test
696s  kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
696s          "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
696s ***** test
696s  kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
696s ***** test
696s  kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
696s ***** error kmeans (rand (3,2), 4);
696s ***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
696s ***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
696s ***** error kmeans (rand (4,3), 2, "start", rand (2,2));
696s ***** error kmeans (rand (4,3), 2, "distance", "manhattan");
696s ***** error kmeans (rand (3,4), 2, "start", "normal");
696s ***** error kmeans (rand (4,3), 2, "replicates", i);
696s ***** error kmeans (rand (4,3), 2, "replicates", -1);
696s ***** error kmeans (rand (4,3), 2, "replicates", []);
696s ***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
696s ***** error kmeans (rand (4,3), 2, "replicates", "one");
696s ***** error kmeans (rand (4,3), 2, "MAXITER", i);
696s ***** error kmeans (rand (4,3), 2, "MaxIter", -1);
696s ***** error kmeans (rand (4,3), 2, "maxiter", []);
696s ***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
696s ***** error kmeans (rand (4,3), 2, "maxiter", "one");
696s ***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
696s 31 tests, 31 passed, 0 known failure, 0 skipped
696s [inst/sigma_pts.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/sigma_pts.m
696s ***** demo
696s  K      = [1 0.5; 0.5 1]; # covariance matrix
696s  # calculate and build associated ellipse
696s  [R,S,~] = svd (K);
696s  theta   = atan2 (R(2,1), R(1,1));
696s  v       = sqrt (diag (S));
696s  v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
696s  t       = linspace (0, 2*pi, 100).';
696s  xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
696s  ye      = v(1,2) * cos (t) + v(2,2) * sin (t);
696s 
696s  figure(1); clf; hold on
696s  # Plot ellipse and axes
696s  line ([0 0; v(:,1).'],[0 0; v(:,2).'])
696s  plot (xe,ye,'-r');
696s 
696s  col = 'rgb';
696s  l     = [-1.8 -1 1.5];
696s  for li = 1:3
696s   p     = sigma_pts (2, [], K, l(li));
696s   tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
696s                p(1,1), p(1,2), ['o' col(li)]);
696s   h(li) = tmp(1);
696s  endfor
696s  hold off
696s  axis image
696s  legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
696s ***** test
696s  p = sigma_pts (5);
696s  assert (mean (p), zeros(1,5), sqrt(eps));
696s  assert (cov (p), eye(5), sqrt(eps));
696s ***** test
696s  m = randn(1, 5);
696s  p = sigma_pts (5, m);
696s  assert (mean (p), m, sqrt(eps));
696s  assert (cov (p), eye(5), sqrt(eps));
696s ***** test
696s  x = linspace (0,1,5);
696s  K = exp (- (x.' - x).^2/ 0.5);
696s  p = sigma_pts (5, [], K);
696s  assert (mean (p), zeros(1,5), sqrt(eps));
696s  assert (cov (p), K, sqrt(eps));
696s ***** error sigma_pts(2,1);
696s ***** error sigma_pts(2,[],1);
696s ***** error sigma_pts(2,1,1);
696s ***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
696s 7 tests, 7 passed, 0 known failure, 0 skipped
696s [inst/nanmax.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/nanmax.m
696s ***** demo
696s  ## Find the column maximum values and their indices
696s  ## for matrix data with missing values.
696s 
696s  x = magic (3);
696s  x([1, 6:9]) = NaN
696s  [y, ind] = nanmax (x)
696s ***** demo
696s  ## Find the maximum of all the values in an array, ignoring missing values.
696s  ## Create a 2-by-5-by-3 array x with some missing values.
696s 
696s  x = reshape (1:30, [2, 5, 3]);
696s  x([10:12, 25]) = NaN
696s 
696s  ## Find the maximum of the elements of x.
696s 
696s  y = nanmax (x, [], 'all')
696s ***** assert (nanmax ([2, 4, NaN, 7]), 7)
696s ***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
696s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
696s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
696s ***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
696s ***** shared x, y
696s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
696s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
696s  y = x;
696s  y(2,3,1) = 0.51;
696s ***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
696s ***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
696s ***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
696s ***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
696s ***** assert (nanmax (x, [], 'all'), 6.77)
696s ***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
696s ***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
696s ***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
696s ***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
696s ***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
696s ***** test
696s  xx = repmat ([1:20;6:25], [5 2 6 3]);
696s  assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
696s  assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
696s  assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
696s  assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
696s  assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
696s ***** assert (nanmax (ones (2), [], 3), ones (2, 2))
696s ***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
696s ***** assert (nanmax (magic (3), [], 3), magic (3))
696s ***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
696s ***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
696s ***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
696s ***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
696s  nanmax (y, [], [1, 1, 2])
696s ***** error <nanmax: a second output is not supported with this syntax.> ...
696s  [v, idx] = nanmax(x, y, [1 2])
696s 24 tests, 24 passed, 0 known failure, 0 skipped
696s [inst/correlation_test.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/correlation_test.m
696s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
696s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
696s ***** error<correlation_test: X must contain finite real numbers.> ...
696s  correlation_test ([1 2 NaN]', [2 3 4]');
696s ***** error<correlation_test: X must contain finite real numbers.> ...
696s  correlation_test ([1 2 Inf]', [2 3 4]');
696s ***** error<correlation_test: X must contain finite real numbers.> ...
696s  correlation_test ([1 2 3+i]', [2 3 4]');
696s ***** error<correlation_test: Y must contain finite real numbers.> ...
696s  correlation_test ([1 2 3]', [2 3 NaN]');
696s ***** error<correlation_test: Y must contain finite real numbers.> ...
696s  correlation_test ([1 2 3]', [2 3 Inf]');
696s ***** error<correlation_test: Y must contain finite real numbers.> ...
696s  correlation_test ([1 2 3]', [3 4 3+i]');
696s ***** error<correlation_test: X and Y must be vectors of equal length.> ...
696s  correlation_test ([1 2 3]', [3 4 4 5]');
696s ***** error<correlation_test: invalid value for alpha.> ...
696s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
696s ***** error<correlation_test: invalid value for alpha.> ...
696s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
696s ***** error<correlation_test: invalid value for alpha.> ...
696s  correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
696s ***** error<correlation_test: invalid value for alpha.> ...
696s  correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
696s ***** error<correlation_test: invalid Name argument.> ...
696s  correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
696s ***** error<correlation_test: invalid value for tail.>  ...
696s  correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
696s ***** error<correlation_test: invalid value for tail.>  ...
696s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
696s ***** error<correlation_test: invalid value for method.>  ...
696s  correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
696s ***** error<correlation_test: invalid value for method.>  ...
696s  correlation_test ([1 2 3]', [2 3 4]', "method", "some");
696s ***** test
696s  x = [6 7 7 9 10 12 13 14 15 17];
696s  y = [19 22 27 25 30 28 30 29 25 32];
696s  [h, pval, stats] = correlation_test (x, y);
696s  assert (stats.corrcoef, corr (x', y'), 1e-14);
696s  assert (pval, 0.0223, 1e-4);
696s ***** test
696s  x = [6 7 7 9 10 12 13 14 15 17]';
696s  y = [19 22 27 25 30 28 30 29 25 32]';
696s  [h, pval, stats] = correlation_test (x, y);
696s  assert (stats.corrcoef, corr (x, y), 1e-14);
696s  assert (pval, 0.0223, 1e-4);
696s 20 tests, 20 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/ricestat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/ricestat.m
696s ***** error<ricestat: function called with too few input arguments.> ricestat ()
696s ***** error<ricestat: function called with too few input arguments.> ricestat (1)
696s ***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
696s ***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
696s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
696s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
696s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
696s  ricestat (ones (3), ones (2))
696s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
696s  ricestat (ones (2), ones (3))
696s ***** shared s, sigma
696s  s = [2, 0, -1, 1, 4];
696s  sigma = [1, NaN, 3, -1, 2];
696s ***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
696s ***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
696s ***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
696s ***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
696s ***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
696s ***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
696s ***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
696s ***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
696s 16 tests, 16 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/lognstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/lognstat.m
696s ***** error<lognstat: function called with too few input arguments.> lognstat ()
696s ***** error<lognstat: function called with too few input arguments.> lognstat (1)
696s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
696s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
696s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
696s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
696s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
696s  lognstat (ones (3), ones (2))
696s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
696s  lognstat (ones (2), ones (3))
696s ***** test
696s  mu = 0:0.2:1;
696s  sigma = 0.2:0.2:1.2;
696s  [m, v] = lognstat (mu, sigma);
696s  expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
696s  expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
696s  assert (m, expected_m, 0.001);
696s  assert (v, expected_v, 0.001);
696s ***** test
696s  sigma = 0.2:0.2:1.2;
696s  [m, v] = lognstat (0, sigma);
696s  expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
696s  expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
696s  assert (m, expected_m, 0.001);
696s  assert (v, expected_v, 0.001);
696s 10 tests, 10 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/hnstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/hnstat.m
696s ***** error<hnstat: function called with too few input arguments.> hnstat ()
696s ***** error<hnstat: function called with too few input arguments.> hnstat (1)
696s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
696s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
696s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
696s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
696s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
696s  hnstat (ones (3), ones (2))
696s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
696s  hnstat (ones (2), ones (3))
696s ***** test
696s  [m, v] = hnstat (0, 1);
696s  assert (m, 0.7979, 1e-4);
696s  assert (v, 0.3634, 1e-4);
696s ***** test
696s  [m, v] = hnstat (2, 1);
696s  assert (m, 2.7979, 1e-4);
696s  assert (v, 0.3634, 1e-4);
696s ***** test
696s  [m, v] = hnstat (2, 2);
696s  assert (m, 3.5958, 1e-4);
696s  assert (v, 1.4535, 1e-4);
696s ***** test
696s  [m, v] = hnstat (2, 2.5);
696s  assert (m, 3.9947, 1e-4);
696s  assert (v, 2.2711, 1e-4);
696s ***** test
696s  [m, v] = hnstat (1.5, 0.5);
696s  assert (m, 1.8989, 1e-4);
696s  assert (v, 0.0908, 1e-4);
696s ***** test
696s  [m, v] = hnstat (-1.5, 0.5);
696s  assert (m, -1.1011, 1e-4);
696s  assert (v, 0.0908, 1e-4);
696s 14 tests, 14 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/gevstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/gevstat.m
696s ***** error<gevstat: function called with too few input arguments.> gevstat ()
696s ***** error<gevstat: function called with too few input arguments.> gevstat (1)
696s ***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
696s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
696s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
696s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
696s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
696s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
696s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
696s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
696s  gevstat (ones (3), ones (2), 3)
696s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
696s  gevstat (ones (2), 2, ones (3))
696s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
696s  gevstat (1, ones (2), ones (3))
696s ***** test
696s  k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
696s  sigma = 2;
696s  mu = 1;
696s  [m, v] = gevstat (k, sigma, mu);
696s  expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
696s  expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
696s  assert (m, expected_m, -0.001);
696s  assert (v, expected_v, -0.001);
696s 13 tests, 13 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/normstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/normstat.m
696s ***** error<normstat: function called with too few input arguments.> normstat ()
696s ***** error<normstat: function called with too few input arguments.> normstat (1)
696s ***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
696s ***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
696s ***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
696s ***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
696s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
696s  normstat (ones (3), ones (2))
696s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
696s  normstat (ones (2), ones (3))
696s ***** test
696s  mu = 1:6;
696s  sigma = 0.2:0.2:1.2;
696s  [m, v] = normstat (mu, sigma);
696s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
696s  assert (m, mu);
696s  assert (v, expected_v, 0.001);
696s ***** test
696s  sigma = 0.2:0.2:1.2;
696s  [m, v] = normstat (0, sigma);
696s  expected_mn = [0, 0, 0, 0, 0, 0];
696s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
696s  assert (m, expected_mn, 0.001);
696s  assert (v, expected_v, 0.001);
696s 10 tests, 10 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/tristat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/tristat.m
696s ***** error<tristat: function called with too few input arguments.> tristat ()
696s ***** error<tristat: function called with too few input arguments.> tristat (1)
696s ***** error<tristat: function called with too few input arguments.> tristat (1, 2)
696s ***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
696s ***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
696s ***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
696s ***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
696s ***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
696s ***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
696s ***** test
696s  a = 1:5;
696s  b = 3:7;
696s  c = 5:9;
696s  [m, v] = tristat (a, b, c);
696s  expected_m = [3, 4, 5, 6, 7];
696s  assert (m, expected_m);
696s  assert (v, ones (1, 5) * (2/3));
696s 10 tests, 10 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/nakastat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/nakastat.m
696s ***** error<nakastat: function called with too few input arguments.> nakastat ()
696s ***** error<nakastat: function called with too few input arguments.> nakastat (1)
696s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
696s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
696s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
696s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
696s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
696s  nakastat (ones (3), ones (2))
696s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
696s  nakastat (ones (2), ones (3))
696s ***** test
696s  [m, v] = nakastat (1, 1);
696s  assert (m, 0.8862269254, 1e-10);
696s  assert (v, 0.2146018366, 1e-10);
696s ***** test
696s  [m, v] = nakastat (1, 2);
696s  assert (m, 1.25331413731, 1e-10);
696s  assert (v, 0.42920367321, 1e-10);
696s ***** test
696s  [m, v] = nakastat (2, 1);
696s  assert (m, 0.93998560299, 1e-10);
696s  assert (v, 0.11642706618, 1e-10);
696s 11 tests, 11 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/betastat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/betastat.m
696s ***** error<betastat: function called with too few input arguments.> betastat ()
696s ***** error<betastat: function called with too few input arguments.> betastat (1)
696s ***** error<betastat: A and B must be numeric.> betastat ({}, 2)
696s ***** error<betastat: A and B must be numeric.> betastat (1, "")
696s ***** error<betastat: A and B must not be complex.> betastat (i, 2)
696s ***** error<betastat: A and B must not be complex.> betastat (1, i)
696s ***** error<betastat: A and B must be of common size or scalars.> ...
696s  betastat (ones (3), ones (2))
696s ***** error<betastat: A and B must be of common size or scalars.> ...
696s  betastat (ones (2), ones (3))
696s ***** test
696s  a = -2:6;
696s  b = 0.4:0.2:2;
696s  [m, v] = betastat (a, b);
696s  expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
696s  expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
696s  assert (m, expected_m, eps*100);
696s  assert (v, expected_v, 0.001);
696s ***** test
696s  a = -2:1:6;
696s  [m, v] = betastat (a, 1.5);
696s  expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
696s  expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
696s  assert (m, expected_m);
696s  assert (v, expected_v, 0.001);
696s ***** test
696s  a = [14  Inf   10  NaN  10];
696s  b = [12    9  NaN  Inf  12];
696s  [m, v] = betastat (a, b);
696s  expected_m = [14/26 NaN NaN NaN 10/22];
696s  expected_v = [168/18252 NaN NaN NaN 120/11132];
696s  assert (m, expected_m);
696s  assert (v, expected_v);
696s ***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
696s ***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
696s ***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
696s ***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
696s 15 tests, 15 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/ncfstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/ncfstat.m
696s ***** error<ncfstat: function called with too few input arguments.> ncfstat ()
696s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
696s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
696s  ncfstat (ones (3), ones (2), 3)
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
696s  ncfstat (ones (2), 2, ones (3))
696s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
696s  ncfstat (1, ones (2), ones (3))
696s ***** shared df1, df2, lambda
696s  df1 = [2, 0, -1, 1, 4, 5];
696s  df2 = [2, 4, -1, 5, 6, 7];
696s  lambda = [1, NaN, 3, 0, 2, -1];
696s ***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
696s ***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
696s ***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
696s ***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
696s ***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
696s ***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
696s ***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
696s ***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
696s 20 tests, 20 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/hygestat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/hygestat.m
696s ***** error<hygestat: function called with too few input arguments.> hygestat ()
696s ***** error<hygestat: function called with too few input arguments.> hygestat (1)
696s ***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
696s ***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
696s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
696s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
696s ***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
696s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
696s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
696s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
696s  hygestat (ones (3), ones (2), 3)
696s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
696s  hygestat (ones (2), 2, ones (3))
696s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
696s  hygestat (1, ones (2), ones (3))
696s ***** test
696s  m = 4:9;
696s  k = 0:5;
696s  n = 1:6;
696s  [mn, v] = hygestat (m, k, n);
696s  expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
696s  expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
696s  assert (mn, expected_mn, 0.001);
696s  assert (v, expected_v, 0.001);
696s ***** test
696s  m = 4:9;
696s  k = 0:5;
696s  [mn, v] = hygestat (m, k, 2);
696s  expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
696s  expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
696s  assert (mn, expected_mn, 0.001);
696s  assert (v, expected_v, 0.001);
696s 14 tests, 14 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/geostat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/geostat.m
696s ***** error<geostat: function called with too few input arguments.> geostat ()
696s ***** error<geostat: PS must be numeric.> geostat ({})
696s ***** error<geostat: PS must be numeric.> geostat ("")
696s ***** error<geostat: PS must not be complex.> geostat (i)
696s ***** test
696s  ps = 1 ./ (1:6);
696s  [m, v] = geostat (ps);
696s  assert (m, [0, 1, 2, 3, 4, 5], 0.001);
696s  assert (v, [0, 2, 6, 12, 20, 30], 0.001);
696s 5 tests, 5 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/chi2stat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/chi2stat.m
696s ***** error<chi2stat: function called with too few input arguments.> chi2stat ()
696s ***** error<chi2stat: DF must be numeric.> chi2stat ({})
696s ***** error<chi2stat: DF must be numeric.> chi2stat ("")
696s ***** error<chi2stat: DF must not be complex.> chi2stat (i)
696s ***** test
696s  df = 1:6;
696s  [m, v] = chi2stat (df);
696s  assert (m, df);
696s  assert (v, [2, 4, 6, 8, 10, 12], 0.001);
696s 5 tests, 5 passed, 0 known failure, 0 skipped
696s [inst/dist_stat/expstat.m]
696s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/expstat.m
696s ***** error<expstat: function called with too few input arguments.> expstat ()
696s ***** error<expstat: MU must be numeric.> expstat ({})
696s ***** error<expstat: MU must be numeric.> expstat ("")
696s ***** error<expstat: MU must not be complex.> expstat (i)
696s ***** test
696s  mu = 1:6;
696s  [m, v] = expstat (mu);
696s  assert (m, [1, 2, 3, 4, 5, 6], 0.001);
696s  assert (v, [1, 4, 9, 16, 25, 36], 0.001);
697s 5 tests, 5 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/burrstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/burrstat.m
697s ***** error<burrstat: function called with too few input arguments.> burrstat ()
697s ***** error<burrstat: function called with too few input arguments.> burrstat (1)
697s ***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
697s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
697s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
697s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
697s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
697s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
697s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
697s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
697s  burrstat (ones (3), ones (2), 3)
697s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
697s  burrstat (ones (2), 2, ones (3))
697s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
697s  burrstat (1, ones (2), ones (3))
697s ***** test
697s  [m, v] = burrstat (1, 2, 5);
697s  assert (m, 0.4295, 1e-4);
697s  assert (v, 0.0655, 1e-4);
697s ***** test
697s  [m, v] = burrstat (1, 1, 1);
697s  assert (m, Inf);
697s  assert (v, Inf);
697s ***** test
697s  [m, v] = burrstat (2, 4, 1);
697s  assert (m, 2.2214, 1e-4);
697s  assert (v, 1.3484, 1e-4);
697s 15 tests, 15 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/evstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/evstat.m
697s ***** error<evstat: function called with too few input arguments.> evstat ()
697s ***** error<evstat: function called with too few input arguments.> evstat (1)
697s ***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
697s ***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
697s ***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
697s ***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
697s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
697s  evstat (ones (3), ones (2))
697s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
697s  evstat (ones (2), ones (3))
697s ***** shared x, y0, y1
697s  x = [-5, 0, 1, 2, 3];
697s  y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
697s  y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
697s ***** assert (evstat (x, x), y0, 1e-4)
697s ***** assert (evstat (x, x+6), y1, 1e-4)
697s ***** assert (evstat (x, x-6), NaN (1,5))
697s 11 tests, 11 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/plstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/plstat.m
697s ***** shared x, Fx
697s  x = [0, 1, 3, 4, 7, 10];
697s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
697s ***** assert (plstat (x, Fx), 4.15)
697s ***** test
697s  [m, v] = plstat (x, Fx);
697s  assert (v, 10.3775, 1e-14)
697s ***** error<plstat: function called with too few input arguments.> plstat ()
697s ***** error<plstat: function called with too few input arguments.> plstat (1)
697s ***** error<plstat: X and FX must be vectors of equal size.> ...
697s  plstat ([0, 1, 2], [0, 1])
697s ***** error<plstat: X and FX must be at least two-elements long.> ...
697s  plstat ([0], [1])
697s ***** error<plstat: FX must be bounded in the range> ...
697s  plstat ([0, 1, 2], [0, 1, 1.5])
697s ***** error<plstat: FX must be bounded in the range> ...
697s  plstat ([0, 1, 2], [0, i, 1])
697s ***** error<plstat: X and FX must not be complex.> ...
697s  plstat ([0, i, 2], [0, 0.5, 1])
697s ***** error<plstat: X and FX must not be complex.> ...
697s  plstat ([0, i, 2], [0, 0.5i, 1])
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/raylstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/raylstat.m
697s ***** error<raylstat: function called with too few input arguments.> raylstat ()
697s ***** error<raylstat: SIGMA must be numeric.> raylstat ({})
697s ***** error<raylstat: SIGMA must be numeric.> raylstat ("")
697s ***** error<raylstat: SIGMA must not be complex.> raylstat (i)
697s ***** test
697s  sigma = 1:6;
697s  [m, v] = raylstat (sigma);
697s  expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
697s  expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 5 tests, 5 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/wblstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/wblstat.m
697s ***** error<wblstat: function called with too few input arguments.> wblstat ()
697s ***** error<wblstat: function called with too few input arguments.> wblstat (1)
697s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
697s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
697s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
697s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
697s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
697s  wblstat (ones (3), ones (2))
697s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
697s  wblstat (ones (2), ones (3))
697s ***** test
697s  lambda = 3:8;
697s  k = 1:6;
697s  [m, v] = wblstat (lambda, k);
697s  expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
697s  expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  k = 1:6;
697s  [m, v] = wblstat (6, k);
697s  expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
697s  expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/tlsstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/tlsstat.m
697s ***** error<tlsstat: function called with too few input arguments.> tlsstat ()
697s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
697s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
697s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
697s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
697s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
697s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
697s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
697s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
697s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
697s  tlsstat (ones (3), ones (2), 1)
697s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
697s  tlsstat (ones (2), 1, ones (3))
697s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
697s  tlsstat (1, ones (2), ones (3))
697s ***** test
697s  [m, v] = tlsstat (0, 1, 0);
697s  assert (m, NaN);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (0, 1, 1);
697s  assert (m, NaN);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (2, 1, 1);
697s  assert (m, NaN);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (-2, 1, 1);
697s  assert (m, NaN);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (0, 1, 2);
697s  assert (m, 0);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (2, 1, 2);
697s  assert (m, 2);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (-2, 1, 2);
697s  assert (m, -2);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (0, 2, 2);
697s  assert (m, 0);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (2, 2, 2);
697s  assert (m, 2);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (-2, 2, 2);
697s  assert (m, -2);
697s  assert (v, NaN);
697s ***** test
697s  [m, v] = tlsstat (0, 1, 3);
697s  assert (m, 0);
697s  assert (v, 3);
697s ***** test
697s  [m, v] = tlsstat (0, 2, 3);
697s  assert (m, 0);
697s  assert (v, 6);
697s ***** test
697s  [m, v] = tlsstat (2, 1, 3);
697s  assert (m, 2);
697s  assert (v, 3);
697s ***** test
697s  [m, v] = tlsstat (2, 2, 3);
697s  assert (m, 2);
697s  assert (v, 6);
697s ***** test
697s  [m, v] = tlsstat (-2, 1, 3);
697s  assert (m, -2);
697s  assert (v, 3);
697s ***** test
697s  [m, v] = tlsstat (-2, 2, 3);
697s  assert (m, -2);
697s  assert (v, 6);
697s 28 tests, 28 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/ncx2stat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/ncx2stat.m
697s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
697s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
697s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
697s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
697s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
697s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
697s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
697s  ncx2stat (ones (3), ones (2))
697s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
697s  ncx2stat (ones (2), ones (3))
697s ***** shared df, d1
697s  df = [2, 0, -1, 1, 4];
697s  d1 = [1, NaN, 3, -1, 2];
697s ***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
697s ***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
697s ***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
697s ***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
697s ***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
697s ***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
697s ***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
697s ***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
697s 16 tests, 16 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/nctstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/nctstat.m
697s ***** error<nctstat: function called with too few input arguments.> nctstat ()
697s ***** error<nctstat: function called with too few input arguments.> nctstat (1)
697s ***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
697s ***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
697s ***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
697s ***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
697s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
697s  nctstat (ones (3), ones (2))
697s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
697s  nctstat (ones (2), ones (3))
697s ***** shared df, mu
697s  df = [2, 0, -1, 1, 4];
697s  mu = [1, NaN, 3, -1, 2];
697s ***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
697s ***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
697s ***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
697s ***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
697s ***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
697s ***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
697s ***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
697s ***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
697s 16 tests, 16 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/binostat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/binostat.m
697s ***** error<binostat: function called with too few input arguments.> binostat ()
697s ***** error<binostat: function called with too few input arguments.> binostat (1)
697s ***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
697s ***** error<binostat: N and PS must be numeric.> binostat (1, "")
697s ***** error<binostat: N and PS must not be complex.> binostat (i, 2)
697s ***** error<binostat: N and PS must not be complex.> binostat (1, i)
697s ***** error<binostat: N and PS must be of common size or scalars.> ...
697s  binostat (ones (3), ones (2))
697s ***** error<binostat: N and PS must be of common size or scalars.> ...
697s  binostat (ones (2), ones (3))
697s ***** test
697s  n = 1:6;
697s  ps = 0:0.2:1;
697s  [m, v] = binostat (n, ps);
697s  expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
697s  expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  n = 1:6;
697s  [m, v] = binostat (n, 0.5);
697s  expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
697s  expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  n = [-Inf -3 5 0.5 3 NaN 100, Inf];
697s  [m, v] = binostat (n, 0.5);
697s  assert (isnan (m), [true true false true false true false false])
697s  assert (isnan (v), [true true false true false true false false])
697s  assert (m(end), Inf);
697s  assert (v(end), Inf);
697s ***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
697s ***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
697s ***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
697s ***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
697s 15 tests, 15 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/nbinstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/nbinstat.m
697s ***** error<nbinstat: function called with too few input arguments.> nbinstat ()
697s ***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
697s ***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
697s ***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
697s ***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
697s ***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
697s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
697s  nbinstat (ones (3), ones (2))
697s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
697s  nbinstat (ones (2), ones (3))
697s ***** test
697s  r = 1:4;
697s  ps = 0.2:0.2:0.8;
697s  [m, v] = nbinstat (r, ps);
697s  expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
697s  expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  r = 1:4;
697s  [m, v] = nbinstat (r, 0.5);
697s  expected_m = [1, 2, 3, 4];
697s  expected_v = [2, 4, 6, 8];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/bisastat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/bisastat.m
697s ***** error<bisastat: function called with too few input arguments.> bisastat ()
697s ***** error<bisastat: function called with too few input arguments.> bisastat (1)
697s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
697s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
697s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
697s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
697s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
697s  bisastat (ones (3), ones (2))
697s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
697s  bisastat (ones (2), ones (3))
697s ***** test
697s  beta = 1:6;
697s  gamma = 1:0.2:2;
697s  [m, v] = bisastat (beta, gamma);
697s  expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
697s  expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
697s  assert (m, expected_m, 1e-2);
697s  assert (v, expected_v, 1e-3);
697s ***** test
697s  beta = 1:6;
697s  [m, v] = bisastat (beta, 1.5);
697s  expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
697s  expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
697s  assert (m, expected_m, 1e-3);
697s  assert (v, expected_v, 1e-4);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/loglstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/loglstat.m
697s ***** error<loglstat: function called with too few input arguments.> loglstat ()
697s ***** error<loglstat: function called with too few input arguments.> loglstat (1)
697s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
697s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
697s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
697s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
697s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
697s  loglstat (ones (3), ones (2))
697s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
697s  loglstat (ones (2), ones (3))
697s ***** test
697s  [m, v] = loglstat (0, 1);
697s  assert (m, Inf, 0.001);
697s  assert (v, Inf, 0.001);
697s ***** test
697s  [m, v] = loglstat (0, 0.8);
697s  assert (m, 4.2758, 0.001);
697s  assert (v, Inf, 0.001);
697s ***** test
697s  [m, v] = loglstat (0, 0.6);
697s  assert (m, 1.9820, 0.001);
697s  assert (v, Inf, 0.001);
697s ***** test
697s  [m, v] = loglstat (0, 0.4);
697s  assert (m, 1.3213, 0.001);
697s  assert (v, 2.5300, 0.001);
697s ***** test
697s  [m, v] = loglstat (0, 0.2);
697s  assert (m, 1.0690, 0.001);
697s  assert (v, 0.1786, 0.001);
697s 13 tests, 13 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/unifstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/unifstat.m
697s ***** error<unifstat: function called with too few input arguments.> unifstat ()
697s ***** error<unifstat: function called with too few input arguments.> unifstat (1)
697s ***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
697s ***** error<unifstat: A and B must be numeric.> unifstat (1, "")
697s ***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
697s ***** error<unifstat: A and B must not be complex.> unifstat (1, i)
697s ***** error<unifstat: A and B must be of common size or scalars.> ...
697s  unifstat (ones (3), ones (2))
697s ***** error<unifstat: A and B must be of common size or scalars.> ...
697s  unifstat (ones (2), ones (3))
697s ***** test
697s  a = 1:6;
697s  b = 2:2:12;
697s  [m, v] = unifstat (a, b);
697s  expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
697s  expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  a = 1:6;
697s  [m, v] = unifstat (a, 10);
697s  expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
697s  expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/tstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/tstat.m
697s ***** error<tstat: function called with too few input arguments.> tstat ()
697s ***** error<tstat: DF must be numeric.> tstat ({})
697s ***** error<tstat: DF must be numeric.> tstat ("")
697s ***** error<tstat: DF must not be complex.> tstat (i)
697s ***** test
697s  df = 3:8;
697s  [m, v] = tstat (df);
697s  expected_m = [0, 0, 0, 0, 0, 0];
697s  expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
697s  assert (m, expected_m);
697s  assert (v, expected_v, 0.001);
697s 5 tests, 5 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/logistat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/logistat.m
697s ***** error<logistat: function called with too few input arguments.> logistat ()
697s ***** error<logistat: function called with too few input arguments.> logistat (1)
697s ***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
697s ***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
697s ***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
697s ***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
697s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
697s  logistat (ones (3), ones (2))
697s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
697s  logistat (ones (2), ones (3))
697s ***** test
697s  [m, v] = logistat (0, 1);
697s  assert (m, 0);
697s  assert (v, 3.2899, 0.001);
697s ***** test
697s  [m, v] = logistat (0, 0.8);
697s  assert (m, 0);
697s  assert (v, 2.1055, 0.001);
697s ***** test
697s  [m, v] = logistat (1, 0.6);
697s  assert (m, 1);
697s  assert (v, 1.1844, 0.001);
697s ***** test
697s  [m, v] = logistat (0, 0.4);
697s  assert (m, 0);
697s  assert (v, 0.5264, 0.001);
697s ***** test
697s  [m, v] = logistat (-1, 0.2);
697s  assert (m, -1);
697s  assert (v, 0.1316, 0.001);
697s 13 tests, 13 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/invgstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/invgstat.m
697s ***** error<invgstat: function called with too few input arguments.> invgstat ()
697s ***** error<invgstat: function called with too few input arguments.> invgstat (1)
697s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
697s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
697s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
697s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
697s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
697s  invgstat (ones (3), ones (2))
697s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
697s  invgstat (ones (2), ones (3))
697s ***** test
697s  [m, v] = invgstat (1, 1);
697s  assert (m, 1);
697s  assert (v, 1);
697s ***** test
697s  [m, v] = invgstat (2, 1);
697s  assert (m, 2);
697s  assert (v, 8);
697s ***** test
697s  [m, v] = invgstat (2, 2);
697s  assert (m, 2);
697s  assert (v, 4);
697s ***** test
697s  [m, v] = invgstat (2, 2.5);
697s  assert (m, 2);
697s  assert (v, 3.2);
697s ***** test
697s  [m, v] = invgstat (1.5, 0.5);
697s  assert (m, 1.5);
697s  assert (v, 6.75);
697s 13 tests, 13 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/gamstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/gamstat.m
697s ***** error<gamstat: function called with too few input arguments.> gamstat ()
697s ***** error<gamstat: function called with too few input arguments.> gamstat (1)
697s ***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
697s ***** error<gamstat: A and B must be numeric.> gamstat (1, "")
697s ***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
697s ***** error<gamstat: A and B must not be complex.> gamstat (1, i)
697s ***** error<gamstat: A and B must be of common size or scalars.> ...
697s  gamstat (ones (3), ones (2))
697s ***** error<gamstat: A and B must be of common size or scalars.> ...
697s  gamstat (ones (2), ones (3))
697s ***** test
697s  a = 1:6;
697s  b = 1:0.2:2;
697s  [m, v] = gamstat (a, b);
697s  expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
697s  expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  a = 1:6;
697s  [m, v] = gamstat (a, 1.5);
697s  expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
697s  expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/fstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/fstat.m
697s ***** error<fstat: function called with too few input arguments.> fstat ()
697s ***** error<fstat: function called with too few input arguments.> fstat (1)
697s ***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
697s ***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
697s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
697s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
697s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
697s  fstat (ones (3), ones (2))
697s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
697s  fstat (ones (2), ones (3))
697s ***** test
697s  df1 = 1:6;
697s  df2 = 5:10;
697s  [m, v] = fstat (df1, df2);
697s  expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
697s  expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
697s  assert (m, expected_mn, 0.001);
697s  assert (v, expected_v, 0.001);
697s ***** test
697s  df1 = 1:6;
697s  [m, v] = fstat (df1, 5);
697s  expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
697s  expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
697s  assert (m, expected_mn, 0.001);
697s  assert (v, expected_v, 0.001);
697s 10 tests, 10 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/poisstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/poisstat.m
697s ***** error<poisstat: function called with too few input arguments.> poisstat ()
697s ***** error<poisstat: SIGMA must be numeric.> poisstat ({})
697s ***** error<poisstat: SIGMA must be numeric.> poisstat ("")
697s ***** error<poisstat: SIGMA must not be complex.> poisstat (i)
697s ***** test
697s  lambda = 1 ./ (1:6);
697s  [m, v] = poisstat (lambda);
697s  assert (m, lambda);
697s  assert (v, lambda);
697s 5 tests, 5 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/unidstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/unidstat.m
697s ***** error<unidstat: function called with too few input arguments.> unidstat ()
697s ***** error<unidstat: N must be numeric.> unidstat ({})
697s ***** error<unidstat: N must be numeric.> unidstat ("")
697s ***** error<unidstat: N must not be complex.> unidstat (i)
697s ***** test
697s  N = 1:6;
697s  [m, v] = unidstat (N);
697s  expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
697s  expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
697s  assert (m, expected_m, 0.001);
697s  assert (v, expected_v, 0.001);
697s 5 tests, 5 passed, 0 known failure, 0 skipped
697s [inst/dist_stat/gpstat.m]
697s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dist_stat/gpstat.m
697s ***** error<gpstat: function called with too few input arguments.> gpstat ()
697s ***** error<gpstat: function called with too few input arguments.> gpstat (1)
697s ***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
697s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
697s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
697s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
697s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
697s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
697s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
697s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
697s  gpstat (ones (3), ones (2), 3)
697s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
697s  gpstat (ones (2), 2, ones (3))
697s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
697s  gpstat (1, ones (2), ones (3))
698s ***** shared x, y
698s  x = [-Inf, -1, 0, 1/2, 1, Inf];
698s  y = [0, 0.5, 1, 2, Inf, Inf];
698s ***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
698s ***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
698s ***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
698s ***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
698s ***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
698s ***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
698s ***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
698s 19 tests, 19 passed, 0 known failure, 0 skipped
698s [inst/x2fx.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/x2fx.m
698s ***** test
698s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
698s  D = x2fx(X,'quadratic');
698s  assert (D(1,:), [1, 1, 10, 10, 1, 100]);
698s  assert (D(2,:), [1, 2, 20, 40, 4, 400]);
698s ***** test
698s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
698s  model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
698s  D = x2fx(X,model);
698s  assert (D(1,:), [1, 1, 10, 10, 1]);
698s  assert (D(2,:), [1, 2, 20, 40, 4]);
698s  assert (D(4,:), [1, 4, 20, 80, 16]);
698s ***** test
698s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
698s  D = x2fx (x, 'linear');
698s  assert (D, [1, 1, 2, 3; 1, 2, 3, 4;, 1, 3, 4, 5]);
698s  D = x2fx (x, 'interaction');
698s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6]);
698s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12]);
698s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20]);
698s  D = x2fx (x, 'quadratic');
698s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6, 1, 4, 9]);
698s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12, 4, 9, 16]);
698s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20, 9, 16, 25]);
698s  D = x2fx (x, 'purequadratic');
698s  assert (D(1,:), [1, 1, 2, 3, 1, 4, 9]);
698s  assert (D(2,:), [1, 2, 3, 4, 4, 9, 16]);
698s  assert (D(3,:), [1, 3, 4, 5, 9, 16, 25]);
698s ***** test
698s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
698s  D = x2fx (x, [0, 0, 1; 1, 0, 2]);
698s  assert (D, [3, 9; 4, 32; 5, 75]);
698s ***** test
698s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
698s  D = x2fx (x, 'linear', [1, 3]);
698s  assert (D, [1, 1, 0, 2, 1, 0; 1, 0, 1, 3, 0, 1; 1, 0, 0, 4, 0, 0]);
698s ***** test
698s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
698s  D = x2fx (x, 'quadratic', [1, 3]);
698s  assert (D(1,:), [1, 1, 0, 2, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 4]);
698s  assert (D(2,:), [1, 0, 1, 3, 0, 1, 0, 3, 0, 0, 0, 1, 0, 3, 9]);
698s  assert (D(3,:), [1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16]);
698s ***** test
698s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
698s  D = x2fx (x, 'cos');
698s  assert (D(1,:), [0.5403, -0.4161, -0.9900], 1e-4);
698s  assert (D(2,:), [-0.4161, -0.9900, -0.6536], 1e-4);
698s  assert (D(3,:), [-0.9900, -0.6536, 0.2837], 1e-4);
698s ***** error <x2fx: category index exceeds number of columns in X.> ...
698s  x2fx ([1, 2, 3; 2, 3, 4], 'quadratic', [1, 4])
698s ***** error <x2fx: unrecognized function 'cosine'.> ...
698s  D = x2fx ([1, 2, 3; 2, 3, 4; 3, 4, 5], 'cosine')
698s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
698s  x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
698s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
698s  x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
698s 11 tests, 11 passed, 0 known failure, 0 skipped
698s [inst/createns.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/createns.m
698s ***** test
698s  ## Default ExhaustiveSearcher
698s  X = [1, 2; 3, 4; 5, 6];
698s  obj = createns (X);
698s  assert (isa (obj, "ExhaustiveSearcher"));
698s  assert (obj.X, X);
698s  assert (obj.Distance, "euclidean");
698s ***** test
698s  ## KDTreeSearcher with default parameters
698s  X = [1, 2; 3, 4; 5, 6];
698s  obj = createns (X, "NSMethod", "kdtree");
698s  assert (isa (obj, "KDTreeSearcher"));
698s  assert (obj.X, X);
698s  assert (obj.Distance, "euclidean");
698s ***** test
698s  ## hnswSearcher with custom parameters
698s  X = [1, 2; 3, 4; 5, 6];
698s  obj = createns (X, "NSMethod", "hnsw", "MaxNumLinksPerNode", 2, "TrainSetSize", 3);
698s  assert (isa (obj, "hnswSearcher"));
698s  assert (obj.X, X);
698s  assert (obj.MaxNumLinksPerNode, 2);
698s  assert (obj.TrainSetSize, 3);
698s ***** test
698s  ## ExhaustiveSearcher with custom distance
698s  X = [1, 2; 3, 4];
698s  obj = createns (X, "NSMethod", "exhaustive", "Distance", "cityblock");
698s  assert (isa (obj, "ExhaustiveSearcher"));
698s  assert (obj.Distance, "cityblock");
698s ***** error<createns: too few input arguments.>
698s  createns ()
698s ***** error<createns: Name-Value arguments must be in pairs.>
698s  X = [1, 2; 3, 4]; createns (X, "NSMethod")
698s ***** error<createns: X must be a finite numeric matrix.>
698s  createns ([1; Inf; 3])
698s ***** error<createns: 'NSMethod' must be a string.>
698s  X = [1, 2; 3, 4]; createns (X, "NSMethod", 1)
698s ***** error<createns: invalid 'NSMethod' value: 'invalid'.>
698s  X = [1, 2; 3, 4]; createns (X, "NSMethod", "invalid")
698s 9 tests, 9 passed, 0 known failure, 0 skipped
698s [inst/canoncorr.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/canoncorr.m
698s ***** shared X,Y,A,B,r,U,V,k
698s  k = 10;
698s  X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
698s  [A,B,r,U,V,stats] = canoncorr (X,Y);
698s ***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
698s ***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
698s ***** assert (r, [0.99590   0.26754], 1E-5);
698s ***** assert (U, center(X) * A, 10*eps);
698s ***** assert (V, center(Y) * B, 10*eps);
698s ***** assert (cov(U), eye(size(U, 2)), 10*eps);
698s ***** assert (cov(V), eye(size(V, 2)), 10*eps);
698s  rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
698s ***** assert (r, ones(1, 5), 10*eps);
698s 8 tests, 8 passed, 0 known failure, 0 skipped
698s [inst/harmmean.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/harmmean.m
698s ***** test
698s  x = [0:10];
698s  y = [x;x+5;x+10];
698s  assert (harmmean (x), 0);
698s  m = [0 8.907635160795225 14.30854471766802];
698s  assert (harmmean (y, 2), m', 4e-14);
698s  assert (harmmean (y, "all"), 0);
698s  y(2,4) = NaN;
698s  m(2) = 9.009855936313949;
698s  assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
698s  assert (harmmean (y', "omitnan"), m, 4e-14);
698s  z = y + 20;
698s  assert (harmmean (z, "all"), NaN);
698s  assert (harmmean (z, "all", "includenan"), NaN);
698s  assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
698s  m = [24.59488458841874 NaN 34.71244385944397];
698s  assert (harmmean (z'), m, 4e-14);
698s  assert (harmmean (z', "includenan"), m, 4e-14);
698s  m(2) = 29.84104075528277;
698s  assert (harmmean (z', "omitnan"), m, 4e-14);
698s  assert (harmmean (z, 2, "omitnan"), m', 4e-14);
698s ***** test
698s  x = repmat ([1:20;6:25], [5 2 6 3]);
698s  assert (size (harmmean (x, [3 2])), [10 1 1 3]);
698s  assert (size (harmmean (x, [1 2])), [1 1 6 3]);
698s  assert (size (harmmean (x, [1 2 4])), [1 1 6]);
698s  assert (size (harmmean (x, [1 4 3])), [1 40]);
698s  assert (size (harmmean (x, [1 2 3 4])), [1 1]);
698s ***** test
698s  x = repmat ([1:20;6:25], [5 2 6 3]);
698s  m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
698s  assert (harmmean (x, [3 2]), m, 4e-14);
698s  x(2,5,6,3) = NaN;
698s  m(2,3) = NaN;
698s  assert (harmmean (x, [3 2]), m, 4e-14);
698s  m(2,3) = 13.06617961315406;
698s  assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
698s ***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
698s ***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
698s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
698s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
698s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
698s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
698s ***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
698s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
698s 8 tests, 8 passed, 0 known failure, 0 skipped
698s [inst/gmdistribution.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/gmdistribution.m
698s ***** test
698s  mu = eye(2);
698s  Sigma = eye(2);
698s  GM = gmdistribution (mu, Sigma);
698s  density = GM.pdf ([0 0; 1 1]);
698s  assert (density(1) - density(2), 0, 1e-6);
698s 
698s  [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
698s  assert (idx, [1; 2]);
698s  [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
698s  assert (nlogl - nlogl2, 0, 1e-6);
698s  [idx3,nlogl3,P3] = cluster (GM, eye(2));
698s  assert (P - P3, zeros (2), 1e-6);
698s  [idx4,nlogl4] = cluster (GM, eye(2));
698s  assert (size (nlogl4), [1 1]);
698s  idx5 = cluster (GM, eye(2));
698s  assert (idx - idx5, zeros (2,1));
698s 
698s  D = GM.mahal ([1;0]);
698s  assert (D - M(1,:), zeros (1,2), 1e-6);
698s 
698s  P = GM.posterior ([0 1]);
698s  assert (P - P2(2,:), zeros (1,2), 1e-6);
698s 
698s  R = GM.random(20);
698s  assert (size(R), [20, 2]);
698s 
698s  R = GM.random();
698s  assert (size(R), [1, 2]);
698s 1 test, 1 passed, 0 known failure, 0 skipped
698s [inst/ranksum.m]
698s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/ranksum.m
698s ***** test
698s  mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
698s             32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
698s             32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
698s  [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
698s  assert (p, 0.004329004329004329, 1e-14);
698s  assert (h, true);
698s  assert (stats.ranksum, 21.5);
698s ***** test
698s  year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
698s           49 47 50 60 59 60 62 61 71]';
698s  year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
698s           54 50 53 62 62 62 72 60 67]';
698s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
698s  assert (p, 0.1270832752950605, 1e-14);
698s  assert (h, false);
698s  assert (stats.ranksum, 837.5);
698s  assert (stats.zval, -1.140287483634606, 1e-14);
698s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
698s                        "method", "exact");
698s  assert (p, 0.127343916432862, 1e-14);
698s  assert (h, false);
698s  assert (stats.ranksum, 837.5);
716s 2 tests, 2 passed, 0 known failure, 0 skipped
716s [inst/gscatter.m]
716s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/gscatter.m
716s ***** demo
716s  load fisheriris;
716s  X = meas(:,3:4);
716s  cidcs = kmeans (X, 3, "Replicates", 5);
716s  gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
716s  title ("Fisher's iris data");
716s ***** shared visibility_setting
716s  visibility_setting = get (0, "DefaultFigureVisible");
716s ***** test
716s  hf = figure ("visible", "off");
716s  unwind_protect
716s    load fisheriris;
716s    X = meas(:,3:4);
716s    cidcs = kmeans (X, 3, "Replicates", 5);
716s    gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
716s    title ("Fisher's iris data");
716s  unwind_protect_cleanup
716s    close (hf);
716s  end_unwind_protect
716s warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
716s ***** error gscatter ();
716s ***** error gscatter ([1]);
716s ***** error gscatter ([1], [2]);
716s ***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
716s ***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
716s ***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
716s ***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
716s ***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
716s 9 tests, 9 passed, 0 known failure, 0 skipped
716s [inst/sampsizepwr.m]
716s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/sampsizepwr.m
716s ***** demo
716s  ## Compute the mean closest to 100 that can be determined to be
716s  ## significantly different from 100 using a t-test with a sample size
716s  ## of 60 and a power of 0.8.
716s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
716s  disp (mu1);
716s ***** demo
716s  ## Compute the sample sizes required to distinguish mu0 = 100 from
716s  ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
716s  ## smaller sample sizes of 1.5 and a power of 0.6.
716s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
716s ***** demo
716s  ## Compute the sample size N required to distinguish p=.26 from p=.2
716s  ## with a binomial test.  The result is approximate, so make a plot to
716s  ## see if any smaller N values also have the required power of 0.6.
716s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
716s  nn = 1:250;
716s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
716s  Nexact = min (nn(pwr >= 0.6));
716s  plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
716s  grid on
716s ***** demo
716s  ## The company must test 52 bottles to detect the difference between a mean
716s  ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
716s  ## to visualize how the sample size affects the power of the test.
716s 
716s  nout = sampsizepwr('t',[100 5],102,0.80);
716s  nn = 1:100;
716s  pwrout = sampsizepwr('t',[100 5],102,[],nn);
716s 
716s  figure;
716s  plot (nn, pwrout, "b-", nout, 0.8, "ro")
716s  title ("Power versus Sample Size")
716s  xlabel ("Sample Size")
716s  ylabel ("Power")
716s ***** error<sampsizepwr: test type must be a non-empty string.> ...
716s  out = sampsizepwr ([], [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: test type must be a non-empty string.> ...
716s  out = sampsizepwr (3, [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: test type must be a non-empty string.> ...
716s  out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: invalid test type.> ...
716s  out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: parameters must be numeric.> ...
716s  out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("z", 100, [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("t", 100, [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("t2", 60, [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
716s  out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
716s  [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
716s  [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
716s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
716s  [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
716s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
716s  [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
716s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
716s  [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("var", 0, [], 0.8, 60);
716s ***** error<sampsizepwr: negative or zero variance.> ...
716s  out = sampsizepwr ("var", -5, [], 0.8, 60);
716s ***** error<sampsizepwr: out of range probability.> ...
716s  out = sampsizepwr ("p", 0, [], 0.8, 60);
716s ***** error<sampsizepwr: out of range probability.> ...
716s  out = sampsizepwr ("p", 1.2, [], 0.8, 60);
716s ***** error<sampsizepwr: out of range regression coefficient.> ...
716s  out = sampsizepwr ("r", -1.5, [], 0.8, 60);
716s ***** error<sampsizepwr: out of range regression coefficient.> ...
716s  out = sampsizepwr ("r", -1, [], 0.8, 60);
716s ***** error<sampsizepwr: out of range regression coefficient.> ...
716s  out = sampsizepwr ("r", 1.2, [], 0.8, 60);
716s ***** error<sampsizepwr: regression coefficient must not be 0.> ...
716s  out = sampsizepwr ("r", 0, [], 0.8, 60);
716s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
716s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
716s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
716s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
716s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
716s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
716s ***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
716s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
716s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
716s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
716s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
716s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
716s  out = sampsizepwr ("z", [100, 5], [], [], 60);
716s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
716s  out = sampsizepwr ("z", [100, 5], 110, [], []);
716s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
716s  out = sampsizepwr ("z", [100, 5], [], 0.8, []);
716s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
716s  out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
716s ***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
716s  out = sampsizepwr ("z", [100, 5], "mu", [], 60);
716s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
716s  out = sampsizepwr ("var", 5, -1, [], 60);
716s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
716s  out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
716s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
716s  out = sampsizepwr ("r", 0.8, 1.2, [], 60);
716s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
716s  out = sampsizepwr ("r", 0.8, -1.2, [], 60);
716s ***** error<sampsizepwr: invalid value for POWER.> ...
716s  out = sampsizepwr ("z", [100, 5], 110, 1.2);
716s ***** error<sampsizepwr: invalid value for POWER.> ...
716s  out = sampsizepwr ("z", [100, 5], 110, 0);
716s ***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
716s  out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
716s ***** error<sampsizepwr: input arguments size mismatch.> ...
716s  out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
716s ***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
716s  out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
716s ***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
716s  out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
716s ***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
716s  out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
716s ***** warning<sampsizepwr: approximate N.> ...
716s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
717s ***** warning<sampsizepwr: approximate N.> ...
717s  Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
717s ***** test
717s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
717s  assert (mu1, 103.67704316, 1e-8);
718s ***** test
718s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
718s  assert (N1, 9);
718s  assert (N2, 14);
719s ***** test
719s  nn = 1:250;
719s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
719s  pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
719s  assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
719s  pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
719s  assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
719s ***** test
719s  nout = sampsizepwr ("t", [100, 5], 102, 0.80);
719s  assert (nout, 52);
719s ***** test
719s  power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
719s  assert (power, 0.5797373588621888, 1e-14);
719s ***** test
719s  nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
719s  assert (nout, 18);
719s ***** test
719s  p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
719s  assert (p1out, 25.65317979360237, 2e-14);
720s ***** test
720s  pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
720s  assert (pwr, 0.716504004686586, 1e-14);
720s ***** test
720s  n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
720s  assert (n, 11);
721s ***** test
721s  [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
721s  assert ([n1, n2], [8, 16]);
722s 68 tests, 68 passed, 0 known failure, 0 skipped
722s [inst/crosstab.m]
722s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/crosstab.m
722s ***** error crosstab ()
722s ***** error crosstab (1)
722s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
722s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
722s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
722s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
722s ***** test
722s  load carbig
722s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
722s  assert (table(2,3,1), 38);
722s  assert (labels{3,3}, "Japan");
722s ***** test
722s  load carbig
722s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
722s  assert (table(2,3,2), 17);
722s  assert (labels{1,3}, "USA");
722s ***** test
722s  x = [1, 1, 2, 3, 1];
722s  y = [1, 2, 5, 3, 1];
722s  t = crosstab (x, y);
722s  assert (t, [2, 1, 0, 0; 0, 0, 0, 1; 0, 0, 1, 0]);
722s ***** test
722s  x = [1, 1, 2, 3, 1];
722s  y = [1, 2, 3, 5, 1];
722s  t = crosstab (x, y);
722s  assert (t, [2, 1, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1]);
722s ***** test
722s  x1 = [1, 3, 7, 7, 8];
722s  x2 = [4, 2, 1, 1, 1];
722s  x3 = [6, 2, 6, 2, NaN];
722s  T1 = [0, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 0];
722s  T2 = [0, 0, 1; 0, 0, 0; 1, 0, 0; 0, 0, 0];
722s  T = zeros (4, 3, 2);
722s  T(:,:,1) = T1;
722s  T(:,:,2) = T2;
722s  t = crosstab (x1, x2, x3);
722s  assert (t, T);
722s ***** test
722s  x = [1, 2, NaN, 1];
722s  y = [1, 2, 3, NaN];
722s  t = crosstab (x, y);
722s  assert (t, [1, 0, 0; 0, 1, 0]);
722s 12 tests, 12 passed, 0 known failure, 0 skipped
722s [inst/cophenet.m]
722s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/cophenet.m
722s ***** demo
722s  randn ("seed", 5)  # for reproducibility
722s  X = randn (10,2);
722s  y = pdist (X);
722s  Z = linkage (y, "average");
722s  cophenet (Z, y)
722s ***** error<cophenet: function called with too few input arguments.> cophenet ()
722s ***** error<cophenet: function called with too few input arguments.> cophenet (1)
722s ***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
722s  cophenet (ones (2,2), 1)
722s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
722s  cophenet ([1 2 1], "a")
722s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
722s  cophenet ([1 2 1], [1 2])
722s 5 tests, 5 passed, 0 known failure, 0 skipped
722s [inst/mhsample.m]
722s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/mhsample.m
722s ***** demo
722s  ## Define function to sample
722s  d = 2;
722s  mu = [-1; 2];
722s  rand ("seed", 5)  # for reproducibility
722s  Sigma = rand (d);
722s  Sigma = (Sigma + Sigma');
722s  Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
722s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
722s  ## Inputs
722s  start = ones (1, 2);
722s  nsamples = 500;
722s  sym = true;
722s  K = 500;
722s  m = 10;
722s  rand ("seed", 8)  # for reproducibility
722s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
722s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
722s                             "symmetric", sym, "burnin", K, "thin", m);
722s  figure;
722s  hold on;
722s  plot (smpl(:, 1), smpl(:, 2), 'x');
722s  [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
722s  z = reshape (pdf ([x(:), y(:)]), size(x));
722s  mesh (x, y, z, "facecolor", "None");
722s  ## Using sample points to find the volume of half a sphere with radius of .5
722s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
722s  int = mean (f (smpl) ./ pdf (smpl));
722s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
722s  trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
722s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
722s  printf ("Monte Carlo integral error estimate %f\n", errest);
722s  printf ("The actual error %f\n", trueerr);
722s  mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
722s ***** demo
722s  ## Integrate truncated normal distribution to find normalization constant
722s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
722s  nsamples = 1e3;
722s  rand ("seed", 5)  # for reproducibility
722s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
722s  [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
722s                             "symmetric", true, "thin", 4);
722s  f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
722s  x = linspace (-3, 3, 1000);
722s  area(x, f(x));
722s  xlabel ('x');
722s  ylabel ('f(x)');
722s  int = mean (f (smpl) ./ pdf (smpl));
722s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
722s  trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
722s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
722s  printf ("Monte Carlo integral error estimate %f\n", errest);
722s  printf ("The actual error %f\n", trueerr);
722s ***** test
722s  nchain = 1e4;
722s  start = rand (nchain, 1);
722s  nsamples = 1e3;
722s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
722s  proppdf = @(x, y) 1/3;
722s  proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
722s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
722s                             "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
722s                             "burnin", 0);
722s  assert (mean (mean (smpl, 1), 3), 1, .01);
722s  assert (mean (var (smpl, 1), 3), 1, .01)
727s ***** error mhsample ();
727s ***** error mhsample (1);
727s ***** error mhsample (1, 1);
727s ***** error mhsample (1, 1, "pdf", @(x)x);
727s ***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
727s 6 tests, 6 passed, 0 known failure, 0 skipped
727s [inst/loadmodel.m]
727s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/loadmodel.m
727s ***** error<loadmodel: too few arguments.> loadmodel ()
727s ***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
727s  loadmodel ("fisheriris.mat")
727s ***** error<loadmodel: 'ClassificationModel' is not supported.> ...
727s  loadmodel ("fail_loadmodel.mdl")
727s ***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
727s  loadmodel ("fail_load_model.mdl")
727s 4 tests, 4 passed, 0 known failure, 0 skipped
727s [inst/fullfact.m]
727s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/fullfact.m
727s ***** demo
727s  ## Full factorial design with 3 ordinal variables
727s  fullfact ([2, 3, 4])
727s ***** error<fullfact: one input argument is required.> fullfact ();
727s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
727s  fullfact (Inf);
727s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
727s  fullfact (NaN);
727s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
727s  fullfact (ones (2));
727s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
727s  fullfact ([1, 2, NaN]);
727s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
727s  fullfact ([1, 2, Inf]);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact (2.5);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact (0);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact (-3);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact (3+2i);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([1, 2, -3]);
727s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([0, 1, 2]);
727s ***** test
727s  A = fullfact (1);
727s  assert (A, 1);
727s ***** test
727s  A = fullfact (2);
727s  assert (A, [1; 2]);
727s ***** test
727s ***** test
727s  A = fullfact (3);
727s  assert (A, [1; 2; 3]);
733s ***** test
733s  A = fullfact ([1, 2, 4]);
733s  A_out = [1, 1, 1; 1, 2, 1; 1, 1, 2; 1, 2, 2; ...
733s           1, 1, 3; 1, 2, 3; 1, 1, 4; 1, 2, 4];
733s  assert (A, A_out);
733s ***** test
733s  A = fullfact ([2, 2]);
733s  assert (A, [1, 1; 2, 1; 1, 2; 2, 2]);
733s ***** test
733s  A = fullfact ([2, 2, 4]);
733s  A_out = [1, 1, 1; 2, 1, 1; 1, 2, 1; 2, 2, 1; ...
733s           1, 1, 2; 2, 1, 2; 1, 2, 2; 2, 2, 2; ...
733s           1, 1, 3; 2, 1, 3; 1, 2, 3; 2, 2, 3; ...
733s           1, 1, 4; 2, 1, 4; 1, 2, 4; 2, 2, 4];
733s  assert (A, A_out);
733s ***** test
733s  A = fullfact ([3, 2, 4]);
733s  A_out = [1, 1, 1; 2, 1, 1; 3, 1, 1; 1, 2, 1; 2, 2, 1; 3, 2, 1; ...
733s           1, 1, 2; 2, 1, 2; 3, 1, 2; 1, 2, 2; 2, 2, 2; 3, 2, 2; ...
733s           1, 1, 3; 2, 1, 3; 3, 1, 3; 1, 2, 3; 2, 2, 3; 3, 2, 3; ...
733s           1, 1, 4; 2, 1, 4; 3, 1, 4; 1, 2, 4; 2, 2, 4; 3, 2, 4];
733s  assert (A, A_out);
733s ***** test
733s  A = fullfact ([4, 2]);
733s  assert (A, [1, 1; 2, 1; 3, 1; 4, 1; 1, 2; 2, 2; 3, 2; 4, 2]);
733s 21 tests, 21 passed, 0 known failure, 0 skipped
733s [inst/rmmissing.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/rmmissing.m
733s ***** assert (rmmissing ([1,NaN,3]), [1,3])
733s ***** assert (rmmissing ('abcd f'), 'abcdf')
733s ***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
733s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
733s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
733s ***** assert (rmmissing ([1,2;NaN,2]), [1,2])
733s ***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
733s ***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
733s ***** test
733s  x = [1:6];
733s  x([2,4]) = NaN;
733s  [~, idx] = rmmissing (x);
733s  assert (idx, logical ([0, 1, 0, 1, 0, 0]));
733s  assert (class(idx), 'logical');
733s  x = reshape (x, [2, 3]);
733s  [~, idx] = rmmissing (x);
733s  assert (idx, logical ([0; 1]));
733s  assert (class(idx), 'logical');
733s  [~, idx] = rmmissing (x, 2);
733s  assert (idx, logical ([1, 1, 0]));
733s  assert (class(idx), 'logical');
733s  [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
733s  assert (idx, logical ([0; 1]));
733s  assert (class(idx), 'logical');
733s  [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
733s  assert (idx, logical ([0, 0, 0]));
733s  assert (class(idx), 'logical');
733s ***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
733s ***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
733s ***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
733s ***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
733s ***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
733s autopkgtest [09:00:59]: test command1: -----------------------]
733s ***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
733s ***** assert (rmmissing ([]), [])
733s ***** assert (rmmissing (ones (1,0)), ones (1,0))
733s ***** assert (rmmissing (ones (1,0), 1), ones (1,0))
733s ***** assert (rmmissing (ones (1,0), 2), ones (1,0))
733s ***** assert (rmmissing (ones (0,1)), ones (0,1))
733s ***** assert (rmmissing (ones (0,1), 1), ones (0,1))
733s ***** assert (rmmissing (ones (0,1), 2), ones (0,1))
733s ***** error <input dimension> rmmissing (ones (0,1,2))
733s ***** error rmmissing ()
733s ***** error <input dimension> rmmissing (ones(2,2,2))
733s ***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
733s ***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
733s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
733s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
733s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
733s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
733s 31 tests, 31 passed, 0 known failure, 0 skipped
733s [inst/mnrfit.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/mnrfit.m
733s ***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
733s ***** error<mnrfit: Predictors must be numeric.> ...
733s  mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
733s ***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
733s  mnrfit (ones (50, 4, 2), ones (50, 1))
733s ***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
733s  mnrfit (ones (50, 4), ones (50, 1, 3))
733s ***** error<mnrfit: Y must have the same number of rows as X.> ...
733s  mnrfit (ones (50, 4), ones (45,1))
733s ***** error<mnrfit: Response labels must be a character array, a cell vector> ...
733s  mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
733s ***** error<mnrfit: optional arguments must be in pairs.> ...
733s  mnrfit (ones (5, 4), ones (5, 1), "model")
733s ***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
733s  mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
733s ***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
733s  mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
733s ***** error<mnrfit: Y must contain positive integer category numbers.> ...
733s  mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
733s ***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
733s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
733s ***** error<mnrfit: fitting hierarchical responses not supported.> ...
733s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
733s ***** error<mnrfit: model type not recognised.> ...
733s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
733s 13 tests, 13 passed, 0 known failure, 0 skipped
733s [inst/datasample.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/datasample.m
733s ***** error datasample();
733s ***** error datasample(1);
733s ***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
733s ***** error <k must be a positive integer scalar> datasample([1 2], -1);
733s ***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
733s ***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
733s ***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
733s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
733s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
733s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
733s ***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
733s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
733s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
733s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
733s ***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
733s ***** test
733s  dat = randn (10, 4);
733s  assert (size (datasample (dat, 3, 1)), [3 4]);
733s ***** test
733s  dat = randn (10, 4);
733s  assert (size (datasample (dat, 3, 2)), [10 3]);
733s 17 tests, 17 passed, 0 known failure, 0 skipped
733s [inst/dendrogram.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/dendrogram.m
733s ***** demo
733s  ## simple dendrogram
733s  y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
733s  y(:,3) = 1:5;
733s  dendrogram (y);
733s  title ("simple dendrogram");
733s ***** demo
733s  ## another simple dendrogram
733s  v = 2 * rand (30, 1) - 1;
733s  d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
733s  y = linkage (squareform (d, "tovector"));
733s  dendrogram (y);
733s  title ("another simple dendrogram");
733s ***** demo
733s  ## collapsed tree, find all the leaves of node 5
733s  X = randn (60, 2);
733s  D = pdist (X);
733s  y = linkage (D, "average");
733s  subplot (2, 1, 1);
733s  title ("original tree");
733s  dendrogram (y, 0);
733s  subplot (2, 1, 2);
733s  title ("collapsed tree");
733s  [~, t] = dendrogram (y, 20);
733s  find(t == 5)
733s ***** demo
733s  ## optimal leaf order
733s  X = randn (30, 2);
733s  D = pdist (X);
733s  y = linkage (D, "average");
733s  order = optimalleaforder (y, D);
733s  subplot (2, 1, 1);
733s  title ("original leaf order");
733s  dendrogram (y);
733s  subplot (2, 1, 2);
733s  title ("optimal leaf order");
733s  dendrogram (y, "Reorder", order);
733s ***** demo
733s  ## horizontal orientation and labels
733s  X = randn (8, 2);
733s  D = pdist (X);
733s  L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
733s       "Sneezy"; "Dopey"];
733s  y = linkage (D, "average");
733s  dendrogram (y, "Orientation", "left", "Labels", L);
733s  title ("horizontal orientation and labels");
733s ***** shared visibility_setting
733s  visibility_setting = get (0, "DefaultFigureVisible");
733s ***** test
733s  hf = figure ("visible", "off");
733s  unwind_protect
733s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
733s    y(:,3) = 1:5;
733s    dendrogram (y);
733s  unwind_protect_cleanup
733s    close (hf);
733s  end_unwind_protect
733s ***** test
733s  hf = figure ("visible", "off");
733s  unwind_protect
733s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
733s    y(:,3) = 1:5;
733s    dendrogram (y);
733s  unwind_protect_cleanup
733s    close (hf);
733s  end_unwind_protect
733s ***** test
733s  hf = figure ("visible", "off");
733s  unwind_protect
733s    v = 2 * rand (30, 1) - 1;
733s    d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
733s    y = linkage (squareform (d, "tovector"));
733s    dendrogram (y);
733s  unwind_protect_cleanup
733s    close (hf);
733s  end_unwind_protect
733s ***** test
733s  hf = figure ("visible", "off");
733s  unwind_protect
733s    X = randn (30, 2);
733s    D = pdist (X);
733s    y = linkage (D, "average");
733s    order = optimalleaforder (y, D);
733s    subplot (2, 1, 1);
733s    title ("original leaf order");
733s    dendrogram (y);
733s    subplot (2, 1, 2);
733s    title ("optimal leaf order");
733s    dendrogram (y, "Reorder", order);
733s  unwind_protect_cleanup
733s    close (hf);
733s  end_unwind_protect
733s ***** error dendrogram ();
733s ***** error <tree must be .*> dendrogram (ones (2, 2), 1);
733s ***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
733s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
733s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
733s  fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
733s 9 tests, 9 passed, 0 known failure, 0 skipped
733s [inst/vartest.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/vartest.m
733s ***** error<vartest: too few input arguments.> vartest ();
733s ***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
733s ***** error<vartest: invalid value for alpha.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 0);
733s ***** error<vartest: invalid value for alpha.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
733s ***** error<vartest: invalid value for alpha.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", "val");
733s ***** error<vartest: invalid value for tail.>  ...
733s  vartest ([1, 2, 3, 4], 1, "tail", "val");
733s ***** error<vartest: invalid value for tail.>  ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
733s ***** error<vartest: invalid value for operating dimension.> ...
733s  vartest ([1, 2, 3, 4], 1, "dim", 3);
733s ***** error<vartest: invalid value for operating dimension.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
733s ***** error<vartest: invalid name for optional arguments.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
733s ***** error<vartest: optional arguments must be in name/value pairs.> ...
733s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
733s ***** test
733s  load carsmall
733s  [h, pval, ci] = vartest (MPG, 7^2);
733s  assert (h, 1);
733s  assert (pval, 0.04335086742174443, 1e-14);
733s  assert (ci, [49.397; 88.039], 1e-3);
733s ***** test
733s  load carsmall
733s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "left");
733s  assert (h, 0);
733s  assert (pval, 0.978324566289128, 1e-14);
733s  assert (ci, [0; 83.685], 1e-3);
733s ***** test
733s  load carsmall
733s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "right");
733s  assert (h, 1);
733s  assert (pval, 0.021675433710872, 1e-14);
733s  assert (ci, [51.543; Inf], 1e-3);
733s 14 tests, 14 passed, 0 known failure, 0 skipped
733s [inst/hmmgenerate.m]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/inst/hmmgenerate.m
733s ***** test
733s  len = 25;
733s  transprob = [0.8, 0.2; 0.4, 0.6];
733s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
733s  [sequence, states] = hmmgenerate (len, transprob, outprob);
733s  assert (length (sequence), len);
733s  assert (length (states), len);
733s  assert (min (sequence) >= 1);
733s  assert (max (sequence) <= columns (outprob));
733s  assert (min (states) >= 1);
733s  assert (max (states) <= rows (transprob));
733s ***** test
733s  len = 25;
733s  transprob = [0.8, 0.2; 0.4, 0.6];
733s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
733s  symbols = {"A", "B", "C"};
733s  statenames = {"One", "Two"};
733s  [sequence, states] = hmmgenerate (len, transprob, outprob, ...
733s                       "symbols", symbols, "statenames", statenames);
733s  assert (length (sequence), len);
733s  assert (length (states), len);
733s  assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
733s                                   strcmp (sequence, "C") == ones (1, len));
733s  assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
733s 2 tests, 2 passed, 0 known failure, 0 skipped
733s Checking C++ files ...
733s [src/libsvmread.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/libsvmread.cc
733s ***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
733s ***** error <libsvmread: wrong number of input or output arguments.> ...
733s  D = libsvmread ("filename");
733s ***** test
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s  assert (size (L), [270, 1]);
733s  assert (size (D), [270, 13]);
733s ***** test
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s  assert (issparse (L), false);
733s  assert (issparse (D), true);
733s 4 tests, 4 passed, 0 known failure, 0 skipped
733s [src/svmtrain.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/svmtrain.cc
733s ***** test
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s  model = svmtrain(L, D, '-c 1 -g 0.07');
733s  [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
733s  assert (isstruct (model), true);
733s  assert (isfield (model, "Parameters"), true);
733s  assert (model.totalSV, 130);
733s  assert (model.nr_class, 2);
733s  assert (size (model.Label), [2, 1]);
733s ***** shared L, D
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s ***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
733s ***** error <svmtrain: label vector and instance matrix must be double.> ...
733s  model = svmtrain (single (L), D);
733s ***** error <svmtrain: wrong number of input arguments.> ...
733s  model = svmtrain (L, D, "", "");
733s 4 tests, 4 passed, 0 known failure, 0 skipped
733s [src/editDistance.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/editDistance.cc
733s ***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
733s ***** error <editDistance: too many output arguments.> ...
733s  [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
733s ***** error <editDistance: too many output arguments.> ...
733s  [C, IA] = editDistance ({"AS","SD","AD"});
733s ***** error <editDistance: minDist must be a scalar value.> ...
733s  d = editDistance ({"AS","SD","AD"}, [1, 2]);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ({"AS","SD","AD"}, -2);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ({"AS","SD","AD"}, 1.25);
733s ***** error <editDistance: minDist must be a scalar value.> ...
733s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
733s ***** error <editDistance: minDist must be a scalar value.> ...
733s  d = editDistance ("string1", "string2", [1, 2]);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ("string1", "string2", -2);
733s ***** error <editDistance: minDist must be a nonnegative integer.> ...
733s  d = editDistance ("string1", "string2", 1.25);
733s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
733s  d = editDistance ({{"string1", "string2"}, 2});
733s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
733s  d = editDistance ({{"string1", "string2"}, 2}, 2);
733s ***** error <editDistance: STR1 must be a cellstr.> ...
733s  d = editDistance ([1, 2, 3]);
733s ***** error <editDistance: STR1 must be a cellstr.> ...
733s  d = editDistance (["AS","SD","AD","AS"]);
733s ***** error <editDistance: STR1 must be a cellstr.> ...
733s  d = editDistance (["AS","SD","AD"], 2);
733s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
733s  d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
733s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
733s  d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
733s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
733s  d = editDistance ([1,2,3], {"AS","AS","AD"});
733s ***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
733s  d = editDistance ({1,2,3}, {"AS","SD","AD"});
733s ***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
733s  d = editDistance ({"AS","SD","AD"}, {1,2,3});
733s ***** error <editDistance: cellstr input arguments size mismatch.> ...
733s  d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
733s ***** test
733s  d = editDistance ({"AS","SD","AD"});
733s  assert (d, [2; 1; 1]);
733s  assert (class (d), "double");
733s ***** test
733s  C = editDistance ({"AS","SD","AD"}, 1);
733s  assert (iscellstr (C), true);
733s  assert (C, {"AS";"SD"});
733s ***** test
733s  [C, IA] = editDistance ({"AS","SD","AD"}, 1);
733s  assert (class (IA), "double");
733s  assert (IA, [1;2]);
733s ***** test
733s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
733s  [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
733s  assert (class (IA), "double");
733s  assert (A(IA), C);
733s ***** test
733s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
733s  [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
733s  assert (class (IA), "cell");
733s  assert (C, {"ASS"; "FDE"; "OPA"});
733s  assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
733s  assert (A(IA{2}), {"FDE"; "EDS"});
733s  assert (A(IA{3}), {"OPA"});
733s ***** test
733s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
733s  [C, IA, IC] = editDistance (A, 2);
733s  assert (class (IA), "double");
733s  assert (A(IA), C);
733s  assert (IC, [1; 1; 3; 1; 5]);
733s ***** test
733s  d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
733s  assert (d, [0; 1; 2]);
733s  assert (class (d), "double");
733s ***** test
733s  d = editDistance ({"AS","SD","AD"}, {"AS"});
733s  assert (d, [0; 2; 1]);
733s  assert (class (d), "double");
733s ***** test
733s  d = editDistance ({"AS"}, {"AS","SD","AD"});
733s  assert (d, [0; 2; 1]);
733s  assert (class (d), "double");
733s ***** test
733s  b = editDistance ("Octave", "octave");
733s  assert (b, 1);
733s  assert (class (b), "double");
733s 33 tests, 33 passed, 0 known failure, 0 skipped
733s [src/fcnntrain.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/fcnntrain.cc
733s ***** shared X, Y, MODEL
733s  load fisheriris
733s  X = meas;
733s  Y = grp2idx (species);
733s ***** error <fcnntrain: too few input arguments.> ...
733s  model = fcnntrain (X, Y);
733s ***** error <fcnntrain: too many output arguments.> ...
733s  [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: X must be a real numeric matrix.> ...
733s  fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: X must be a real numeric matrix.> ...
733s  fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: X cannot be empty.> ...
733s  fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
733s  fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
733s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
733s  fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: Y cannot be empty.> ...
733s  fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: X and Y must have the same number of rows.> ...
733s  fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: labels in Y must be positive integers.> ...
733s  fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
733s  fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
733s  fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: cannot have a layer of zero size.> ...
733s  fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: invalid 'Activations' code.> ...
733s  fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: invalid 'Activations' code.> ...
733s  fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
733s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
733s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
733s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
733s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
733s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
733s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
733s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
733s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
733s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
733s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
733s 33 tests, 33 passed, 0 known failure, 0 skipped
733s [src/libsvmwrite.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/libsvmwrite.cc
733s ***** shared L, D
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s ***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
733s ***** error <libsvmwrite: length of label vector does not match instances.> ...
733s  libsvmwrite (tempname (), [L;L], D);
733s ***** error <libsvmwrite: wrong number of output arguments.> ...
733s  OUT = libsvmwrite (tempname (), L, D);
733s ***** error <libsvmwrite: label vector and instance matrix must be double.> ...
733s  libsvmwrite (tempname (), single (L), D);
733s ***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
733s ***** error <libsvmwrite: instance_matrix must be sparse.> ...
733s  libsvmwrite (tempname (), L, full (D));
733s ***** error <libsvmwrite: wrong number of input arguments.> ...
733s  libsvmwrite (tempname (), L, D, D);
733s 7 tests, 7 passed, 0 known failure, 0 skipped
733s [src/fcnnpredict.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/fcnnpredict.cc
733s ***** shared X, Y, MODEL
733s  load fisheriris
733s  X = meas;
733s  Y = grp2idx (species);
733s  MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
733s ***** test
733s  [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
733s  assert (numel (Y_pred), numel (Y));
733s  assert (isequal (size (Y_pred), size (Y)), true);
733s  assert (columns (Y_scores), numel (unique (Y)));
733s  assert (rows (Y_scores), numel (Y));
733s ***** error <fcnnpredict: too few input arguments.> ...
733s  fcnnpredict (MODEL);
733s ***** error <fcnnpredict: too many output arguments.> ...
733s  [Q, W, E] = fcnnpredict (MODEL, X);
733s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
733s  fcnnpredict (1, X);
733s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
733s  fcnnpredict (struct ("L", {1, 2, 3}), X);
733s ***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
733s  fcnnpredict (struct ("L", 1), X);
733s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", 1), X);
733s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {1}), X);
733s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
733s ***** error <fcnnpredict: model does not have an 'Activations' field.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
733s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
733s                       "Activations", [2]), X);
733s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
733s                       "Activations", [2; 2]), X);
733s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
733s                       "Activations", {{2, 2}}), X);
733s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
733s                       "Activations", {{"sigmoid", "softmax"}}), X);
733s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
733s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
733s                       "Activations", "sigmoid"), X);
733s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
733s  fcnnpredict (MODEL, complex (X));
733s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
733s  fcnnpredict (MODEL, {1, 2, 3, 4});
733s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
733s  fcnnpredict (MODEL, "asd");
733s ***** error <fcnnpredict: XC cannot be empty.> ...
733s  fcnnpredict (MODEL, []);
733s ***** error <fcnnpredict: the features in XC do not match the trained model.> ...
733s  fcnnpredict (MODEL, X(:,[1:3]));
733s 20 tests, 20 passed, 0 known failure, 0 skipped
733s [src/svmpredict.cc]
733s >>>>> /tmp/autopkgtest.HPv1lw/build.Fbp/src/src/svmpredict.cc
733s ***** test
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s  model = svmtrain (L, D, '-c 1 -g 0.07');
733s  [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
733s  assert (size (predict_label), size (dec_values));
733s  assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
733s  assert (dec_values(1), 1.225836001973273, 1e-14);
733s  assert (dec_values(2), -0.3212992933043805, 1e-14);
733s  assert (predict_label(1), 1);
733s ***** shared L, D, model
733s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
733s  model = svmtrain (L, D, '-c 1 -g 0.07');
733s ***** error <svmpredict: wrong number of output arguments.> ...
733s  [p, a] = svmpredict (L, D, model);
733s ***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
733s ***** error <svmpredict: label vector and instance matrix must be double.> ...
733s  p = svmpredict (single (L), D, model);
733s ***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
733s 5 tests, 5 passed, 0 known failure, 0 skipped
733s Done running the unit tests.
733s Summary: 11023 tests, 11021 passed, 1 known failures, 1 skipped
734s command1             PASS
734s autopkgtest [09:01:01]: test command1:  - - - - - - - - - - results - - - - - - - - - -
735s autopkgtest [09:01:02]: @@@@@@@@@@@@@@@@@@@@ summary
735s command1             PASS