diff options
58 files changed, 758 insertions, 540 deletions
diff --git a/.appveyor.yml b/.appveyor.yml index a257debc..9ff8f157 100644 --- a/.appveyor.yml +++ b/.appveyor.yml @@ -48,9 +48,9 @@ install: - python --version - pip --version - python -m pip install --user --upgrade pip - - python -m pip install --user -r .github/build-requirements.txt + - python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt # No PyKeOps on windows, let's workaround this one. - - for /F "tokens=*" %%A in (.github/test-requirements.txt) do python -m pip install --user %%A + - for /F "tokens=*" %%A in (ext/gudhi-deploy/test-requirements.txt) do python -m pip install --user %%A build_script: - mkdir build diff --git a/.circleci/config.yml b/.circleci/config.yml index 285a66a5..7fa9ae05 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1,7 +1,11 @@ version: 2.0 jobs: + +### With all third parties + examples: docker: + # cf. https://github.com/GUDHI/gudhi-deploy/blob/main/Dockerfile_for_circleci_image - image: gudhi/ci_for_gudhi:latest steps: - checkout @@ -95,10 +99,143 @@ jobs: path: /tmp/doxygen destination: doxygen + +### With all third parties, except CGAL and Eigen + + examples_without_cgal_eigen: + docker: + # cf. https://github.com/GUDHI/gudhi-deploy/blob/main/Dockerfile_for_circleci_image_without_cgal + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test examples without cgal and eigen + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=ON -DWITH_GUDHI_TEST=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + tests_without_cgal_eigen: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test unitary tests without cgal and eigen + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + utils_without_cgal_eigen: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test utilities without cgal and eigen + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_TEST=OFF -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + python_without_cgal_eigen: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test python module without cgal and eigen + command: | + git submodule init + git submodule update + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 .. + cd src/python + python3 setup.py build_ext --inplace + ctest --output-on-failure + + +### With all third parties, except CGAL + + examples_without_cgal: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test examples without cgal + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DEIGEN3_INCLUDE_DIR=/eigen-3.3.9 -DWITH_GUDHI_EXAMPLE=ON -DWITH_GUDHI_TEST=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + tests_without_cgal: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test unitary tests without cgal + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DEIGEN3_INCLUDE_DIR=/eigen-3.3.9 -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + utils_without_cgal: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test utilities without cgal + command: | + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DEIGEN3_INCLUDE_DIR=/eigen-3.3.9 -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_TEST=OFF -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=OFF .. + make all + ctest --output-on-failure + + python_without_cgal: + docker: + - image: gudhi/ci_for_gudhi_wo_cgal:latest + steps: + - checkout + - run: + name: Build and test python module without cgal + command: | + git submodule init + git submodule update + mkdir build + cd build + cmake -DCMAKE_BUILD_TYPE=Release -DEIGEN3_INCLUDE_DIR=/eigen-3.3.9 -DWITH_GUDHI_EXAMPLE=OFF -DWITH_GUDHI_UTILITIES=OFF -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 .. + cd src/python + python3 setup.py build_ext --inplace + ctest --output-on-failure + workflows: version: 2 build: jobs: + - examples_without_cgal_eigen + - tests_without_cgal_eigen + - utils_without_cgal_eigen + - python_without_cgal_eigen + - examples_without_cgal + - tests_without_cgal + - utils_without_cgal + - python_without_cgal - examples - tests - utils diff --git a/.github/for_maintainers/new_gudhi_version_creation.md b/.github/for_maintainers/new_gudhi_version_creation.md index 4de81b8a..aadfae7d 100644 --- a/.github/for_maintainers/new_gudhi_version_creation.md +++ b/.github/for_maintainers/new_gudhi_version_creation.md @@ -90,7 +90,8 @@ ln -s @GUDHI_VERSION@ latest ## Pip package -The pip package construction shall be started on release creation, you just have to check [gudhi github actions](https://github.com/GUDHI/gudhi-devel/actions) results. +The pip package construction shall be started on release creation, you just have to check +[gudhi github actions](https://github.com/GUDHI/gudhi-devel/actions) results. The version number must be conform to [pep440](https://www.python.org/dev/peps/pep-0440/#pre-releases) ## Conda package @@ -105,28 +106,20 @@ If you need to update conda tools (conda-build, conda-smithy, ...), add a commen ## Docker image -You have to modify the `Dockerfile_gudhi_installation` at the root of this repository in order to use the last release, cf. lines: +You have to modify the +[Dockerfile_gudhi_installation](https://github.com/GUDHI/gudhi-deploy/blob/main/Dockerfile_for_gudhi_installation) +in gudhi-deploy repository in order to use the last release, cf. lines: ``` ... -RUN curl -LO "https://github.com/GUDHI/gudhi-devel/releases/download/tags%2Fgudhi-release-@GUDHI_VERSION@/gudhi.@GUDHI_VERSION@.tar.gz" \ -&& tar xf gudhi.@GUDHI_VERSION@.tar.gz \ -&& cd gudhi.@GUDHI_VERSION@ \ +ARG GUDHI_VERSION="3.X.X" ... ``` -Build and push images to docker hub: -``` -docker build -f Dockerfile_gudhi_installation -t gudhi/latest_gudhi_version:@GUDHI_VERSION@ . -docker run --rm -it gudhi/latest_gudhi_version:@GUDHI_VERSION@ -``` - -***[Check there are no error with utils and python version]*** +After pushing the changes the docker image build will be automatically performed for +[latest_gudhi_version](https://hub.docker.com/repository/docker/gudhi/latest_gudhi_version) +docker image on docker hub. -``` -docker tag gudhi/latest_gudhi_version:@GUDHI_VERSION@ gudhi/latest_gudhi_version:latest -docker push gudhi/latest_gudhi_version:latest -docker push gudhi/latest_gudhi_version:@GUDHI_VERSION@ -``` +***[Check there are no error]*** ## Mail sending Send version mail to the following lists : diff --git a/.github/for_maintainers/tests_strategy.md b/.github/for_maintainers/tests_strategy.md new file mode 100644 index 00000000..9c181740 --- /dev/null +++ b/.github/for_maintainers/tests_strategy.md @@ -0,0 +1,90 @@ +# Tests strategy + +This document tries to sum up the tests strategy that has been put in place for gudhi continuous integration. + +The aim is to help maintainers to anticipate third parties modifications, updates. + +## Builds + +### Linux + +As all the third parties are already installed (thanks to docker), the compilations has been seperated by categories to be parallelized: + +* examples (C++) +* tests (C++) +* utils (C++) +* doxygen (C++ documentation that is available in the artefacts) +* python (including documentation and code coverage that are available in the artefacts) + +(cf. `.circleci/config.yml`) + +These build categories are done with and without CGAL, and, with and without Eigen to be sure the users won't be annoyed if a third party is missing. + +With CGAL and with Eigen builds are performed inside the docker image `gudhi/ci_for_gudhi` based on `Dockerfile_for_circleci_image` file. +Without CGAL, and, with or without Eigen builds are performed inside the docker image `gudhi/ci_for_gudhi_wo_cgal` based on `Dockerfile_for_circleci_image_without_cgal` file. + +#### Update docker images + +C++ third parties installation are done thanks to apt on Ubuntu latest LTS. + +Docker images need to be rebuild and push each time `.github/build-requirements`, `.github/test-requirements`, when a new third party is added, when a new CGAL version improves gudhi performances, ... + +```bash +docker build -f Dockerfile_for_circleci_image -t gudhi/ci_for_gudhi:latest . +docker build -f Dockerfile_for_circleci_image_without_cgal -t gudhi/ci_for_gudhi_wo_cgal:latest . +docker login # requires some specific rights on https://hub.docker.com/u/gudhi/repository/docker/gudhi +docker push gudhi/ci_for_gudhi:latest +docker push gudhi/ci_for_gudhi_wo_cgal:latest +``` + +### Windows + +The compilations has been seperated by categories to be parallelized, but I don't know why builds are not run in parallel: + +* examples (C++) +* tests (C++) +* utils (C++) +* python + +Doxygen (C++) is not tested. +(cf. `.appveyor.yml`) + +C++ third parties installation are done thanks to [vcpkg](https://github.com/microsoft/vcpkg/). +In case of installation issue, check in [vcpkg issues](https://github.com/microsoft/vcpkg/issues). + +### OSx + +The compilations has been seperated by categories to be parallelized: + +* examples (C++) +* tests (C++) +* utils (C++) +* python +* Doxygen (C++) + +(cf. `azure-pipelines.yml`) + +C++ third parties installation are done thanks to [brew](https://formulae.brew.sh/formula/). +In case of installation issue, check in formula issues. + +## Pip packaging + +Pip packaging is done in 2 parts: + +* on push and pull requests, the wheels are built (pip package dry-run) +* on releases, the wheels are built and sent to pypi.org (package) + +Only the Linux pip package is based on a docker image (`gudhi/pip_for_gudhi` based on `Dockerfile_for_pip` file) to make it faster. + +### Update docker image + +C++ third parties installation are done thanks to yum on an image based on `quay.io/pypa/manylinux2014_x86_64`. + +Docker image need to be rebuild and push each time `.github/build-requirements`, when a new third party is added, when a new CGAL version improves gudhi performances, ... +As `.github/test-requirements` is not installed, no need to rebuild image when this file is modified. + +```bash +docker build -f Dockerfile_for_pip -t gudhi/pip_for_gudhi:latest . +docker login # requires some specific rights on https://hub.docker.com/u/gudhi/repository/docker/gudhi +docker push gudhi/pip_for_gudhi:latest +``` diff --git a/.github/how_to_use_github_to_contribute_to_gudhi.md b/.github/how_to_use_github_to_contribute_to_gudhi.md index 747ca39b..738c1ce9 100644 --- a/.github/how_to_use_github_to_contribute_to_gudhi.md +++ b/.github/how_to_use_github_to_contribute_to_gudhi.md @@ -33,6 +33,9 @@ Hera, used for Wasserstein distance, is available on an external git repository. git submodule update --init ``` +[gudhi-deploy](https://github.com/GUDHI/gudhi-deploy) is used for Continuous Integration python +requirements and will also be downloaded by the above command. + ## Configuring a remote for a fork ```bash git remote add upstream https://github.com/GUDHI/gudhi-devel.git diff --git a/.github/next_release.md b/.github/next_release.md index 190f8408..26143b0e 100644 --- a/.github/next_release.md +++ b/.github/next_release.md @@ -1,19 +1,19 @@ -We are pleased to announce the release 3.4.0 of the GUDHI library. +We are pleased to announce the release 3.5.0 of the GUDHI library. -As a major new feature, the GUDHI library now offers dD weighted alpha complex, ... +As a major new feature, the GUDHI library now offers ... -We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.4.0.tar.gz). +We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz). -Below is a list of changes made since GUDHI 3.3.0: +Below is a list of changes made since GUDHI 3.4.0: -- [Alpha complex](https://gudhi.inria.fr/doc/latest/group__alpha__complex.html) - - the C++ weighted version for alpha complex is now available in dimension D. +- [Module](link) + - ... - [Module](link) - ... - Miscellaneous - - The [list of bugs that were solved since GUDHI-3.3.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.4.0+is%3Aclosed) is available on GitHub. + - The [list of bugs that were solved since GUDHI-3.4.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.5.0+is%3Aclosed) is available on GitHub. All modules are distributed under the terms of the MIT license. However, there are still GPL dependencies for many modules. We invite you to check our [license dedicated web page](https://gudhi.inria.fr/licensing/) for further details. diff --git a/.github/test-requirements.txt b/.github/test-requirements.txt index 688a2a11..d0803574 100644 --- a/.github/test-requirements.txt +++ b/.github/test-requirements.txt @@ -1,7 +1,7 @@ pytest pytest-cov sphinx -sphinxcontrib-bibtex +sphinxcontrib-bibtex==1.0.0 sphinx-paramlinks matplotlib scipy diff --git a/.github/workflows/pip-build-linux.yml b/.github/workflows/pip-build-linux.yml index 14fdc8d2..726ed0d5 100644 --- a/.github/workflows/pip-build-linux.yml +++ b/.github/workflows/pip-build-linux.yml @@ -6,16 +6,17 @@ jobs: build: name: build pip wheels runs-on: ubuntu-latest + # cf. https://github.com/GUDHI/gudhi-deploy/blob/main/Dockerfile_for_pip container: gudhi/pip_for_gudhi steps: - uses: actions/checkout@v1 with: submodules: true - - name: Build wheels for Python 3.8 + - name: Build wheels for Python 3.9 run: | - mkdir build_38 - cd build_38 - cmake -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=$PYTHON38/bin/python .. + mkdir build_39 + cd build_39 + cmake -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=$PYTHON39/bin/python .. cd src/python - $PYTHON38/bin/python setup.py bdist_wheel + $PYTHON39/bin/python setup.py bdist_wheel auditwheel repair dist/*.whl
\ No newline at end of file diff --git a/.github/workflows/pip-build-osx.yml b/.github/workflows/pip-build-osx.yml index 15b8880a..732e26af 100644 --- a/.github/workflows/pip-build-osx.yml +++ b/.github/workflows/pip-build-osx.yml @@ -8,7 +8,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: ['3.8'] + python-version: ['3.9'] name: Build wheels for Python ${{ matrix.python-version }} steps: - uses: actions/checkout@v1 @@ -22,7 +22,7 @@ jobs: run: | brew update || true brew install boost eigen gmp mpfr cgal || true - python -m pip install --user -r .github/build-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt python -m pip install --user twine delocate - name: Build python wheel run: | diff --git a/.github/workflows/pip-build-windows.yml b/.github/workflows/pip-build-windows.yml index 995d52dd..d07e8c46 100644 --- a/.github/workflows/pip-build-windows.yml +++ b/.github/workflows/pip-build-windows.yml @@ -8,7 +8,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: ['3.8'] + python-version: ['3.9'] name: Build wheels for Python ${{ matrix.python-version }} steps: - uses: actions/checkout@v1 @@ -24,7 +24,7 @@ jobs: vcpkg upgrade --no-dry-run type c:/vcpkg/ports/cgal/portfile.cmake vcpkg install eigen3 cgal --triplet x64-windows - python -m pip install --user -r .github/build-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt python -m pip list - name: Build python wheel run: | diff --git a/.github/workflows/pip-packaging-linux.yml b/.github/workflows/pip-packaging-linux.yml index bd524af9..95e8f034 100644 --- a/.github/workflows/pip-packaging-linux.yml +++ b/.github/workflows/pip-packaging-linux.yml @@ -8,6 +8,7 @@ jobs: build: name: build pip wheels runs-on: ubuntu-latest + # cf. https://github.com/GUDHI/gudhi-deploy/blob/main/Dockerfile_for_pip container: gudhi/pip_for_gudhi steps: - uses: actions/checkout@v1 @@ -45,12 +46,21 @@ jobs: cd src/python $PYTHON38/bin/python setup.py bdist_wheel auditwheel repair dist/*.whl + - name: Build wheels for Python 3.9 + run: | + mkdir build_39 + cd build_39 + cmake -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=$PYTHON39/bin/python .. + cd src/python + $PYTHON39/bin/python setup.py bdist_wheel + auditwheel repair dist/*.whl - name: Publish on PyPi env: TWINE_USERNAME: __token__ TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | - $PYTHON38/bin/python -m twine upload build_35/src/python/wheelhouse/* - $PYTHON38/bin/python -m twine upload build_36/src/python/wheelhouse/* - $PYTHON38/bin/python -m twine upload build_37/src/python/wheelhouse/* - $PYTHON38/bin/python -m twine upload build_38/src/python/wheelhouse/*
\ No newline at end of file + $PYTHON39/bin/python -m twine upload build_35/src/python/wheelhouse/* + $PYTHON39/bin/python -m twine upload build_36/src/python/wheelhouse/* + $PYTHON39/bin/python -m twine upload build_37/src/python/wheelhouse/* + $PYTHON39/bin/python -m twine upload build_38/src/python/wheelhouse/* + $PYTHON39/bin/python -m twine upload build_39/src/python/wheelhouse/*
\ No newline at end of file diff --git a/.github/workflows/pip-packaging-osx.yml b/.github/workflows/pip-packaging-osx.yml index c94369ac..56309b88 100644 --- a/.github/workflows/pip-packaging-osx.yml +++ b/.github/workflows/pip-packaging-osx.yml @@ -10,7 +10,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: ['3.5', '3.6', '3.7', '3.8'] + python-version: ['3.5', '3.6', '3.7', '3.8', '3.9'] name: Build wheels for Python ${{ matrix.python-version }} steps: - uses: actions/checkout@v1 @@ -24,7 +24,7 @@ jobs: run: | brew update || true brew install boost eigen gmp mpfr cgal || true - python -m pip install --user -r .github/build-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt python -m pip install --user twine delocate - name: Build python wheel run: | diff --git a/.github/workflows/pip-packaging-windows.yml b/.github/workflows/pip-packaging-windows.yml index 8f4ab6e7..a428eaba 100644 --- a/.github/workflows/pip-packaging-windows.yml +++ b/.github/workflows/pip-packaging-windows.yml @@ -10,7 +10,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: ['3.5', '3.6', '3.7', '3.8'] + python-version: ['3.5', '3.6', '3.7', '3.8', '3.9'] name: Build wheels for Python ${{ matrix.python-version }} steps: - uses: actions/checkout@v1 @@ -26,7 +26,7 @@ jobs: vcpkg upgrade --no-dry-run type c:/vcpkg/ports/cgal/portfile.cmake vcpkg install eigen3 cgal --triplet x64-windows - python -m pip install --user -r .github/build-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt python -m pip install --user twine python -m pip list - name: Build python wheel diff --git a/.gitmodules b/.gitmodules index f70c570d..2aa8ad96 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,6 @@ [submodule "ext/hera"] path = ext/hera url = https://github.com/grey-narn/hera.git +[submodule "ext/gudhi-deploy"] + path = ext/gudhi-deploy + url = https://github.com/GUDHI/gudhi-deploy diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt index 5f1eaacf..db9b243b 100644 --- a/CMakeGUDHIVersion.txt +++ b/CMakeGUDHIVersion.txt @@ -1,8 +1,8 @@ # Must be conform to pep440 - https://www.python.org/dev/peps/pep-0440/#pre-releases set (GUDHI_MAJOR_VERSION 3) -set (GUDHI_MINOR_VERSION 4) +set (GUDHI_MINOR_VERSION 5) # GUDHI_PATCH_VERSION can be 'ZaN' for Alpha release, 'ZbN' for Beta release, 'ZrcN' for release candidate or 'Z' for a final release. -set (GUDHI_PATCH_VERSION 0a0) +set (GUDHI_PATCH_VERSION 0rc1) set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION}) message(STATUS "GUDHI version : ${GUDHI_VERSION}") diff --git a/Dockerfile_for_circleci_image b/Dockerfile_for_circleci_image deleted file mode 100644 index ec1b8ff8..00000000 --- a/Dockerfile_for_circleci_image +++ /dev/null @@ -1,69 +0,0 @@ -FROM ubuntu:20.04 - -# Update and upgrade distribution -RUN apt-get update && \ - apt-get upgrade -y - -# Tools necessary for installing and configuring Ubuntu -RUN apt-get install -y \ - apt-utils \ - locales \ - tzdata - -# Timezone -RUN echo "Europe/Paris" | tee /etc/timezone && \ - ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ - dpkg-reconfigure -f noninteractive tzdata - -# Locale with UTF-8 support -RUN echo en_US.UTF-8 UTF-8 >> /etc/locale.gen && \ - locale-gen && \ - update-locale LC_ALL=en_US.UTF-8 LANG=en_US.UTF-8 -ENV LANG en_US.UTF-8 -ENV LANGUAGE en_US:en -ENV LC_ALL en_US.UTF-8 - -# Update again -RUN apt-get update - -# Required for Gudhi compilation -RUN apt-get install -y make \ - git \ - g++ \ - cmake \ - graphviz \ - perl \ - texlive-full \ - biber \ - doxygen \ - libboost-all-dev \ - libeigen3-dev \ - libgmp3-dev \ - libmpfr-dev \ - libtbb-dev \ - locales \ - python3 \ - python3-pip \ - python3-tk \ - python3-grpcio \ - libfreetype6-dev \ - pkg-config \ - curl - -RUN curl -LO "https://github.com/CGAL/cgal/releases/download/v5.1/CGAL-5.1.tar.xz" \ - && tar xf CGAL-5.1.tar.xz \ - && mkdir build \ - && cd build \ - && cmake -DCMAKE_BUILD_TYPE=Release ../CGAL-5.1/ \ - && make install \ - && cd .. \ - && rm -rf build CGAL-5.1 - -ADD .github/build-requirements.txt / -ADD .github/test-requirements.txt / - -RUN pip3 install -r build-requirements.txt -RUN pip3 --no-cache-dir install -r test-requirements.txt - -# apt clean up -RUN apt autoremove && rm -rf /var/lib/apt/lists/* diff --git a/Dockerfile_for_pip b/Dockerfile_for_pip deleted file mode 100644 index 98668a04..00000000 --- a/Dockerfile_for_pip +++ /dev/null @@ -1,50 +0,0 @@ -FROM quay.io/pypa/manylinux2014_x86_64 - -RUN yum -y update && yum -y install \ - wget \ - zlib-devel \ - eigen3-devel \ - mpfr-devel \ - gmp-devel \ - devtoolset-8 \ - && yum clean all - -RUN mkdir -p /opt/cmake \ - && wget https://github.com/Kitware/CMake/releases/download/v3.16.2/cmake-3.16.2-Linux-x86_64.sh \ - && sh cmake-3.16.2-Linux-x86_64.sh --skip-license --prefix=/opt/cmake \ - && rm -f cmake-3.16.2-Linux-x86_64.sh - -# yum install boost-devel installs boost 1.53 and copy is the only way to install headers only boost -RUN wget https://dl.bintray.com/boostorg/release/1.73.0/source/boost_1_73_0.tar.gz \ - && tar xf boost_1_73_0.tar.gz \ - && cd boost_1_73_0 \ - && ./bootstrap.sh \ - && ls \ - && cp -r boost /usr/local/include/ \ - && cd .. \ - && rm -rf boost - -RUN wget https://github.com/CGAL/cgal/releases/download/v5.1/CGAL-5.1.tar.xz \ - && tar xf CGAL-5.1.tar.xz \ - && mkdir build \ - && cd build \ - && /opt/cmake/bin/cmake -DCMAKE_BUILD_TYPE=Release ../CGAL-5.1/ \ - && make install \ - && cd .. \ - && rm -rf build CGAL-5.1 - -ADD .github/build-requirements.txt / - -RUN /opt/python/cp35-cp35m/bin/pip install -r build-requirements.txt \ - && /opt/python/cp36-cp36m/bin/pip install -r build-requirements.txt\ - && /opt/python/cp37-cp37m/bin/pip install -r build-requirements.txt\ - && /opt/python/cp38-cp38/bin/pip install -r build-requirements.txt\ - && /opt/python/cp38-cp38/bin/pip install twine - -ENV PYTHON35="/opt/python/cp35-cp35m/" -ENV PYTHON36="/opt/python/cp36-cp36m/" -ENV PYTHON37="/opt/python/cp37-cp37m/" -ENV PYTHON38="/opt/python/cp38-cp38/" - -ENV PATH="/opt/cmake/bin:${PATH}" -ENV PATH="/opt/rh/devtoolset-8/root/usr/bin:${PATH}" diff --git a/Dockerfile_gudhi_installation b/Dockerfile_gudhi_installation deleted file mode 100644 index ebd21f8d..00000000 --- a/Dockerfile_gudhi_installation +++ /dev/null @@ -1,80 +0,0 @@ -FROM ubuntu:20.04 - -# Update and upgrade distribution -RUN apt-get update && \ - apt-get upgrade -y - -# Tools necessary for installing and configuring Ubuntu -RUN apt-get install -y \ - apt-utils \ - locales \ - tzdata - -# Timezone -RUN echo "Europe/Paris" | tee /etc/timezone && \ - ln -fs /usr/share/zoneinfo/Europe/Paris /etc/localtime && \ - dpkg-reconfigure -f noninteractive tzdata - -# Locale with UTF-8 support -RUN echo en_US.UTF-8 UTF-8 >> /etc/locale.gen && \ - locale-gen && \ - update-locale LC_ALL=en_US.UTF-8 LANG=en_US.UTF-8 -ENV LANG en_US.UTF-8 -ENV LANGUAGE en_US:en -ENV LC_ALL en_US.UTF-8 - -# Update again -RUN apt-get update - -# Required for Gudhi compilation -RUN apt-get install -y make \ - g++ \ - cmake \ - graphviz \ - perl \ - texlive-bibtex-extra \ - biber \ - libboost-all-dev \ - libeigen3-dev \ - libgmp3-dev \ - libmpfr-dev \ - libtbb-dev \ - libcgal-dev \ - locales \ - python3 \ - python3-pip \ - python3-pytest \ - python3-tk \ - python3-pybind11 \ - libfreetype6-dev \ - pkg-config \ - curl - -RUN curl -LO "https://github.com/CGAL/cgal/releases/download/v5.1/CGAL-5.1.tar.xz" \ - && tar xf CGAL-5.1.tar.xz \ - && mkdir build \ - && cd build \ - && cmake -DCMAKE_BUILD_TYPE=Release ../CGAL-5.1/ \ - && make install \ - && cd .. \ - && rm -rf build CGAL-5.1 - -RUN pip3 install \ - numpy \ - matplotlib \ - scipy \ - Cython \ - POT \ - scikit-learn - -# apt clean up -RUN apt autoremove && rm -rf /var/lib/apt/lists/* - -RUN curl -LO "https://github.com/GUDHI/gudhi-devel/releases/download/tags%2Fgudhi-release-3.3.0/gudhi.3.3.0.tar.gz" \ -&& tar xf gudhi.3.3.0.tar.gz \ -&& cd gudhi.3.3.0 \ -&& mkdir build && cd build && cmake -DCMAKE_BUILD_TYPE=Release -DWITH_GUDHI_PYTHON=OFF -DPython_ADDITIONAL_VERSIONS=3 .. \ -&& make all test install \ -&& cmake -DWITH_GUDHI_PYTHON=ON . \ -&& cd python \ -&& python3 setup.py install diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 8e88cab5..6c194f2a 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -19,15 +19,15 @@ jobs: - bash: | source activate gudhi_build_env + git submodule update --init sudo conda install --yes --quiet --name gudhi_build_env python=$(pythonVersion) - python -m pip install --user -r .github/build-requirements.txt - python -m pip install --user -r .github/test-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/build-requirements.txt + python -m pip install --user -r ext/gudhi-deploy/test-requirements.txt brew update || true brew install graphviz doxygen boost eigen gmp mpfr tbb cgal || true displayName: 'Install build dependencies' - bash: | source activate gudhi_build_env - git submodule update --init mkdir build cd build cmake -DCMAKE_BUILD_TYPE:STRING=$(cmakeBuildType) -DWITH_GUDHI_TEST=ON -DWITH_GUDHI_UTILITIES=ON -DWITH_GUDHI_PYTHON=ON -DPython_ADDITIONAL_VERSIONS=3 .. diff --git a/ext/gudhi-deploy b/ext/gudhi-deploy new file mode 160000 +Subproject f71c3749c4d57afcb9747fe99a8cfbb8aa960b6 diff --git a/src/Alpha_complex/test/CMakeLists.txt b/src/Alpha_complex/test/CMakeLists.txt index db5d840f..0595ca92 100644 --- a/src/Alpha_complex/test/CMakeLists.txt +++ b/src/Alpha_complex/test/CMakeLists.txt @@ -59,4 +59,18 @@ if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 5.1.0) endif() gudhi_add_boost_test(Weighted_alpha_complex_test_unit) + add_executable ( Weighted_alpha_complex_non_visible_points_test_unit Weighted_alpha_complex_non_visible_points_unit_test.cpp ) + target_link_libraries(Weighted_alpha_complex_non_visible_points_test_unit ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(Weighted_alpha_complex_non_visible_points_test_unit ${TBB_LIBRARIES}) + endif() + gudhi_add_boost_test(Weighted_alpha_complex_non_visible_points_test_unit) + + add_executable ( Zero_weighted_alpha_complex_test_unit Zero_weighted_alpha_complex_unit_test.cpp ) + target_link_libraries(Zero_weighted_alpha_complex_test_unit ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(Zero_weighted_alpha_complex_test_unit ${TBB_LIBRARIES}) + endif() + gudhi_add_boost_test(Zero_weighted_alpha_complex_test_unit) + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 5.1.0)
\ No newline at end of file diff --git a/src/Alpha_complex/test/Weighted_alpha_complex_non_visible_points_unit_test.cpp b/src/Alpha_complex/test/Weighted_alpha_complex_non_visible_points_unit_test.cpp new file mode 100644 index 00000000..dd83c1da --- /dev/null +++ b/src/Alpha_complex/test/Weighted_alpha_complex_non_visible_points_unit_test.cpp @@ -0,0 +1,60 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "weighted_alpha_complex_non_visible_points" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +#include <CGAL/Epick_d.h> +#include <CGAL/Epeck_d.h> + +#include <vector> + +#include <gudhi/Alpha_complex.h> +#include <gudhi/Simplex_tree.h> + + +using list_of_1d_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epeck_d< CGAL::Dimension_tag<1>>, + CGAL::Epick_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epick_d< CGAL::Dimension_tag<1>> + >; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Weighted_alpha_complex_non_visible_points, Kernel, list_of_1d_kernel_variants) { + // check that for 2 closed weighted 1-d points, one with a high weight to hide the second one with a small weight, + // that the point with a small weight has the same high filtration value than the edge formed by the 2 points + using Point_d = typename Kernel::Point_d; + std::vector<Point_d> points; + std::vector<double> p1 {0.}; + points.emplace_back(p1.begin(), p1.end()); + // closed enough points + std::vector<double> p2 {0.1}; + points.emplace_back(p2.begin(), p2.end()); + std::vector<typename Kernel::FT> weights {100., 0.01}; + + Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex(points, weights); + Gudhi::Simplex_tree<> stree; + BOOST_CHECK(alpha_complex.create_complex(stree)); + + std::clog << "Iterator on weighted alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] " << std::endl; + } + + BOOST_CHECK(stree.filtration(stree.find({0})) == -100.); + BOOST_CHECK(stree.filtration(stree.find({1})) == stree.filtration(stree.find({0, 1}))); + BOOST_CHECK(stree.filtration(stree.find({1})) > 100000); +}
\ No newline at end of file diff --git a/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp index d267276c..875704ee 100644 --- a/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp +++ b/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp @@ -13,10 +13,8 @@ #include <boost/test/unit_test.hpp> #include <boost/mpl/list.hpp> -#include <CGAL/Epick_d.h> #include <CGAL/Epeck_d.h> -#include <cmath> // float comparison #include <vector> #include <random> #include <array> @@ -25,69 +23,6 @@ #include <gudhi/Alpha_complex.h> #include <gudhi/Alpha_complex_3d.h> #include <gudhi/Simplex_tree.h> -#include <gudhi/Unitary_tests_utils.h> - -using list_of_exact_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, - CGAL::Epeck_d< CGAL::Dimension_tag<4> > - > ; - -BOOST_AUTO_TEST_CASE_TEMPLATE(Zero_weighted_alpha_complex, Kernel, list_of_exact_kernel_variants) { - // Check that in exact mode for static dimension 4 the code for dD unweighted and for dD weighted with all weights - // 0 give exactly the same simplex tree (simplices and filtration values). - - // Random points construction - using Point_d = typename Kernel::Point_d; - std::vector<Point_d> points; - std::uniform_real_distribution<double> rd_pts(-10., 10.); - std::random_device rand_dev; - std::mt19937 rand_engine(rand_dev()); - for (int idx = 0; idx < 20; idx++) { - std::vector<double> point {rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine)}; - points.emplace_back(point.begin(), point.end()); - } - - // Alpha complex from points - Gudhi::alpha_complex::Alpha_complex<Kernel, false> alpha_complex_from_points(points); - Gudhi::Simplex_tree<> simplex; - Gudhi::Simplex_tree<>::Filtration_value infty = std::numeric_limits<Gudhi::Simplex_tree<>::Filtration_value>::infinity(); - BOOST_CHECK(alpha_complex_from_points.create_complex(simplex, infty, true)); - std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" - << std::endl; - for (auto f_simplex : simplex.filtration_simplex_range()) { - std::clog << " ( "; - for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { - std::clog << vertex << " "; - } - std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] " << std::endl; - } - - // Alpha complex from zero weighted points - std::vector<typename Kernel::FT> weights(20, 0.); - Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex_from_zero_weighted_points(points, weights); - Gudhi::Simplex_tree<> zw_simplex; - BOOST_CHECK(alpha_complex_from_zero_weighted_points.create_complex(zw_simplex, infty, true)); - - std::clog << "Iterator on zero weighted alpha complex simplices in the filtration order, with [filtration value]:" - << std::endl; - for (auto f_simplex : zw_simplex.filtration_simplex_range()) { - std::clog << " ( "; - for (auto vertex : zw_simplex.simplex_vertex_range(f_simplex)) { - std::clog << vertex << " "; - } - std::clog << ") -> " << "[" << zw_simplex.filtration(f_simplex) << "] " << std::endl; - } - - BOOST_CHECK(zw_simplex == simplex); -} - -template <typename Point_d> -bool cgal_3d_point_sort (Point_d a,Point_d b) { - if (a[0] != b[0]) - return a[0] < b[0]; - if (a[1] != b[1]) - return a[1] < b[1]; - return a[2] < b[2]; -} BOOST_AUTO_TEST_CASE(Weighted_alpha_complex_3d_comparison) { // check that for random weighted 3d points in safe mode the 3D and dD codes give the same result with some tolerance @@ -189,41 +124,4 @@ BOOST_AUTO_TEST_CASE(Weighted_alpha_complex_3d_comparison) { } ++dD_itr; } -} - -using list_of_1d_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, - CGAL::Epeck_d< CGAL::Dimension_tag<1>>, - CGAL::Epick_d< CGAL::Dynamic_dimension_tag >, - CGAL::Epick_d< CGAL::Dimension_tag<1>> - >; - -BOOST_AUTO_TEST_CASE_TEMPLATE(Weighted_alpha_complex_non_visible_points, Kernel, list_of_1d_kernel_variants) { - // check that for 2 closed weighted 1-d points, one with a high weight to hide the second one with a small weight, - // that the point with a small weight has the same high filtration value than the edge formed by the 2 points - using Point_d = typename Kernel::Point_d; - std::vector<Point_d> points; - std::vector<double> p1 {0.}; - points.emplace_back(p1.begin(), p1.end()); - // closed enough points - std::vector<double> p2 {0.1}; - points.emplace_back(p2.begin(), p2.end()); - std::vector<typename Kernel::FT> weights {100., 0.01}; - - Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex(points, weights); - Gudhi::Simplex_tree<> stree; - BOOST_CHECK(alpha_complex.create_complex(stree)); - - std::clog << "Iterator on weighted alpha complex simplices in the filtration order, with [filtration value]:" - << std::endl; - for (auto f_simplex : stree.filtration_simplex_range()) { - std::clog << " ( "; - for (auto vertex : stree.simplex_vertex_range(f_simplex)) { - std::clog << vertex << " "; - } - std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] " << std::endl; - } - - BOOST_CHECK(stree.filtration(stree.find({0})) == -100.); - BOOST_CHECK(stree.filtration(stree.find({1})) == stree.filtration(stree.find({0, 1}))); - BOOST_CHECK(stree.filtration(stree.find({1})) > 100000); }
\ No newline at end of file diff --git a/src/Alpha_complex/test/Zero_weighted_alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Zero_weighted_alpha_complex_unit_test.cpp new file mode 100644 index 00000000..b7df07c7 --- /dev/null +++ b/src/Alpha_complex/test/Zero_weighted_alpha_complex_unit_test.cpp @@ -0,0 +1,77 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "zero_weighted_alpha_complex" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +#include <CGAL/Epeck_d.h> + +#include <vector> +#include <random> +#include <cmath> // for std::fabs + +#include <gudhi/Alpha_complex.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Unitary_tests_utils.h> + +using list_of_exact_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epeck_d< CGAL::Dimension_tag<4> > + > ; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Zero_weighted_alpha_complex, Kernel, list_of_exact_kernel_variants) { + // Check that in exact mode for static dimension 4 the code for dD unweighted and for dD weighted with all weights + // 0 give exactly the same simplex tree (simplices and filtration values). + + // Random points construction + using Point_d = typename Kernel::Point_d; + std::vector<Point_d> points; + std::uniform_real_distribution<double> rd_pts(-10., 10.); + std::random_device rand_dev; + std::mt19937 rand_engine(rand_dev()); + for (int idx = 0; idx < 20; idx++) { + std::vector<double> point {rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine)}; + points.emplace_back(point.begin(), point.end()); + } + + // Alpha complex from points + Gudhi::alpha_complex::Alpha_complex<Kernel, false> alpha_complex_from_points(points); + Gudhi::Simplex_tree<> simplex; + Gudhi::Simplex_tree<>::Filtration_value infty = std::numeric_limits<Gudhi::Simplex_tree<>::Filtration_value>::infinity(); + BOOST_CHECK(alpha_complex_from_points.create_complex(simplex, infty, true)); + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : simplex.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] " << std::endl; + } + + // Alpha complex from zero weighted points + std::vector<typename Kernel::FT> weights(20, 0.); + Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex_from_zero_weighted_points(points, weights); + Gudhi::Simplex_tree<> zw_simplex; + BOOST_CHECK(alpha_complex_from_zero_weighted_points.create_complex(zw_simplex, infty, true)); + + std::clog << "Iterator on zero weighted alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : zw_simplex.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : zw_simplex.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << zw_simplex.filtration(f_simplex) << "] " << std::endl; + } + + BOOST_CHECK(zw_simplex == simplex); +}
\ No newline at end of file diff --git a/src/Rips_complex/include/gudhi/Sparse_rips_complex.h b/src/Rips_complex/include/gudhi/Sparse_rips_complex.h index 1b250818..a5501004 100644 --- a/src/Rips_complex/include/gudhi/Sparse_rips_complex.h +++ b/src/Rips_complex/include/gudhi/Sparse_rips_complex.h @@ -67,8 +67,7 @@ class Sparse_rips_complex { : epsilon_(epsilon) { GUDHI_CHECK(epsilon > 0, "epsilon must be positive"); auto dist_fun = [&](Vertex_handle i, Vertex_handle j) { return distance(points[i], points[j]); }; - Ker<decltype(dist_fun)> kernel(dist_fun); - subsampling::choose_n_farthest_points(kernel, boost::irange<Vertex_handle>(0, boost::size(points)), -1, -1, + subsampling::choose_n_farthest_points(dist_fun, boost::irange<Vertex_handle>(0, boost::size(points)), -1, -1, std::back_inserter(sorted_points), std::back_inserter(params)); compute_sparse_graph(dist_fun, epsilon, mini, maxi); } @@ -128,17 +127,6 @@ class Sparse_rips_complex { } private: - // choose_n_farthest_points wants the distance function in this form... - template <class Distance> - struct Ker { - typedef std::size_t Point_d; // index into point range - Ker(Distance& d) : dist(d) {} - // Despite the name, this is not squared... - typedef Distance Squared_distance_d; - Squared_distance_d& squared_distance_d_object() const { return dist; } - Distance& dist; - }; - // PointRange must be random access. template <typename Distance> void compute_sparse_graph(Distance& dist, double epsilon, Filtration_value mini, Filtration_value maxi) { diff --git a/src/Spatial_searching/include/gudhi/Kd_tree_search.h b/src/Spatial_searching/include/gudhi/Kd_tree_search.h index 87969dd9..a50a8537 100644 --- a/src/Spatial_searching/include/gudhi/Kd_tree_search.h +++ b/src/Spatial_searching/include/gudhi/Kd_tree_search.h @@ -12,11 +12,12 @@ #ifndef KD_TREE_SEARCH_H_ #define KD_TREE_SEARCH_H_ +#include <gudhi/Debug_utils.h> + #include <CGAL/Orthogonal_k_neighbor_search.h> #include <CGAL/Orthogonal_incremental_neighbor_search.h> #include <CGAL/Search_traits.h> #include <CGAL/Search_traits_adapter.h> -#include <CGAL/Fuzzy_sphere.h> #include <CGAL/property_map.h> #include <CGAL/version.h> // for CGAL_VERSION_NR @@ -40,7 +41,6 @@ namespace Gudhi { namespace spatial_searching { - /** * \class Kd_tree_search Kd_tree_search.h gudhi/Kd_tree_search.h * \brief Spatial tree data structure to perform (approximate) nearest and furthest neighbor search. @@ -83,7 +83,8 @@ class Kd_tree_search { typedef CGAL::Search_traits< FT, Point, typename Traits::Cartesian_const_iterator_d, - typename Traits::Construct_cartesian_const_iterator_d> Traits_base; + typename Traits::Construct_cartesian_const_iterator_d, + typename Traits::Dimension> Traits_base; typedef CGAL::Search_traits_adapter< std::ptrdiff_t, @@ -110,7 +111,76 @@ class Kd_tree_search { /// of a point P and `second` is the squared distance between P and the query point. typedef Incremental_neighbor_search INS_range; - typedef CGAL::Fuzzy_sphere<STraits> Fuzzy_sphere; + // Because CGAL::Fuzzy_sphere takes the radius and not its square + struct Sphere_for_kdtree_search + { + typedef typename Traits::Point_d Point_d; + typedef typename Traits::FT FT; + typedef typename Traits::Dimension D; + typedef D Dimension; + + private: + STraits traits; + Point_d c; + FT sqradmin, sqradmax; + bool use_max; + + public: + // `prefer_max` means that we prefer outputting more points at squared distance between r2min and r2max, + // while `!prefer_max` means we prefer fewer. + Sphere_for_kdtree_search(Point_d const& c_, FT const& r2min, FT const& r2max, bool prefer_max=true, STraits const& traits_ = {}) + : traits(traits_), c(c_), sqradmin(r2min), sqradmax(r2max), use_max(prefer_max) + { GUDHI_CHECK(r2min >= 0 && r2max >= r2min, "0 <= r2min <= r2max"); } + + bool contains(std::ptrdiff_t i) const { + const Point_d& p = get(traits.point_property_map(), i); + auto ccci = traits.construct_cartesian_const_iterator_d_object(); + return contains_point_given_as_coordinates(ccci(p), ccci(p, 0)); + } + + template <typename Coord_iterator> + bool contains_point_given_as_coordinates(Coord_iterator pi, Coord_iterator CGAL_UNUSED) const { + FT distance = 0; + auto ccci = traits.construct_cartesian_const_iterator_d_object(); + auto ci = ccci(c); + auto ce = ccci(c, 0); + FT const& limit = use_max ? sqradmax : sqradmin; + while (ci != ce) { + distance += CGAL::square(*pi++ - *ci++); + // I think Clément advised to check the distance at every step instead of + // just at the end, especially when the dimension becomes large. Distance + // isn't part of the concept anyway. + if (distance > limit) return false; + } + return true; + } + + bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT, D> const& rect) const { + auto ccci = traits.construct_cartesian_const_iterator_d_object(); + FT distance = 0; + auto ci = ccci(c); + auto ce = ccci(c, 0); + for (int i = 0; ci != ce; ++i, ++ci) { + distance += CGAL::square(CGAL::max<FT>(CGAL::max<FT>(*ci - rect.max_coord(i), rect.min_coord(i) - *ci), 0 )); + if (distance > sqradmin) return false; + } + return true; + } + + + bool outer_range_contains(CGAL::Kd_tree_rectangle<FT, D> const& rect) const { + auto ccci = traits.construct_cartesian_const_iterator_d_object(); + FT distance = 0; + auto ci = ccci(c); + auto ce = ccci(c, 0); + for (int i = 0; ci != ce; ++i, ++ci) { + distance += CGAL::square(CGAL::max<FT>(*ci - rect.min_coord(i), rect.max_coord(i) - *ci)); + if (distance > sqradmax) return false; + } + return true; + } + }; + /// \brief Constructor /// @param[in] points Const reference to the point range. This range /// is not copied, so it should not be destroyed or modified afterwards. @@ -266,10 +336,26 @@ class Kd_tree_search { /// @param[in] eps Approximation factor. template <typename OutputIterator> void all_near_neighbors(Point const& p, - FT radius, + FT const& radius, OutputIterator it, FT eps = FT(0)) const { - m_tree.search(it, Fuzzy_sphere(p, radius, eps, m_tree.traits())); + all_near_neighbors2(p, CGAL::square(radius - eps), CGAL::square(radius + eps), it); + } + + /// \brief Search for all the neighbors in a ball. This is similar to `all_near_neighbors` but takes directly + /// the square of the minimum distance below which points must be considered neighbors and square of the + /// maximum distance above which they cannot be. + /// @param[in] p The query point. + /// @param[in] sq_radius_min The square of the minimum search radius + /// @param[in] sq_radius_max The square of the maximum search radius + /// @param[out] it The points that lie inside the sphere of center `p` and squared radius `sq_radius`. + /// Note: `it` is used this way: `*it++ = each_point`. + template <typename OutputIterator> + void all_near_neighbors2(Point const& p, + FT const& sq_radius_min, + FT const& sq_radius_max, + OutputIterator it) const { + m_tree.search(it, Sphere_for_kdtree_search(p, sq_radius_min, sq_radius_max, true, m_tree.traits())); } int tree_depth() const { diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt index dfac055c..f4a23d22 100644 --- a/src/Subsampling/example/CMakeLists.txt +++ b/src/Subsampling/example/CMakeLists.txt @@ -3,7 +3,6 @@ project(Subsampling_examples) if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) add_executable(Subsampling_example_pick_n_random_points example_pick_n_random_points.cpp) add_executable(Subsampling_example_choose_n_farthest_points example_choose_n_farthest_points.cpp) - add_executable(Subsampling_example_custom_kernel example_custom_kernel.cpp) add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp) target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY}) @@ -13,5 +12,6 @@ if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) COMMAND $<TARGET_FILE:Subsampling_example_choose_n_farthest_points>) add_test(NAME Subsampling_example_sparsify_point_set COMMAND $<TARGET_FILE:Subsampling_example_sparsify_point_set>) - endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + +add_executable(Subsampling_example_custom_distance example_custom_distance.cpp) diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp index 27cf5d4e..e8b3ce2d 100644 --- a/src/Subsampling/example/example_choose_n_farthest_points.cpp +++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp @@ -20,7 +20,7 @@ int main(void) { K k; std::vector<Point_d> results; - Gudhi::subsampling::choose_n_farthest_points(k, points, 100, + Gudhi::subsampling::choose_n_farthest_points(k.squared_distance_d_object(), points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(results)); std::clog << "Before sparsification: " << points.size() << " points.\n"; diff --git a/src/Subsampling/example/example_custom_distance.cpp b/src/Subsampling/example/example_custom_distance.cpp new file mode 100644 index 00000000..3325b12d --- /dev/null +++ b/src/Subsampling/example/example_custom_distance.cpp @@ -0,0 +1,44 @@ +#include <gudhi/choose_n_farthest_points.h> + +#include <iostream> +#include <vector> +#include <iterator> + + +typedef unsigned Point; + +/* The class Distance contains a distance function defined on the set of points {0, 1, 2, 3} + * and computes a distance according to the matrix: + * 0 1 2 4 + * 1 0 4 2 + * 2 4 0 1 + * 4 2 1 0 + */ +class Distance { + private: + std::vector<std::vector<double>> matrix_; + + public: + Distance() { + matrix_.push_back({0, 1, 2, 4}); + matrix_.push_back({1, 0, 4, 2}); + matrix_.push_back({2, 4, 0, 1}); + matrix_.push_back({4, 2, 1, 0}); + } + + double operator()(Point p1, Point p2) const { + return matrix_[p1][p2]; + } +}; + +int main(void) { + std::vector<Point> points = {0, 1, 2, 3}; + std::vector<Point> results; + + Gudhi::subsampling::choose_n_farthest_points(Distance(), points, 2, + Gudhi::subsampling::random_starting_point, + std::back_inserter(results)); + std::clog << "Before sparsification: " << points.size() << " points.\n"; + std::clog << "After sparsification: " << results.size() << " points.\n"; + std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n"; +} diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp deleted file mode 100644 index 535bf42a..00000000 --- a/src/Subsampling/example/example_custom_kernel.cpp +++ /dev/null @@ -1,63 +0,0 @@ -#include <gudhi/choose_n_farthest_points.h> - -#include <iostream> -#include <vector> -#include <iterator> - - -/* The class Kernel contains a distance function defined on the set of points {0, 1, 2, 3} - * and computes a distance according to the matrix: - * 0 1 2 4 - * 1 0 4 2 - * 2 4 0 1 - * 4 2 1 0 - */ -class Kernel { - public: - typedef double FT; - typedef unsigned Point_d; - - // Class Squared_distance_d - class Squared_distance_d { - private: - std::vector<std::vector<FT>> matrix_; - - public: - Squared_distance_d() { - matrix_.push_back(std::vector<FT>({0, 1, 2, 4})); - matrix_.push_back(std::vector<FT>({1, 0, 4, 2})); - matrix_.push_back(std::vector<FT>({2, 4, 0, 1})); - matrix_.push_back(std::vector<FT>({4, 2, 1, 0})); - } - - FT operator()(Point_d p1, Point_d p2) { - return matrix_[p1][p2]; - } - }; - - // Constructor - Kernel() {} - - // Object of type Squared_distance_d - Squared_distance_d squared_distance_d_object() const { - return Squared_distance_d(); - } -}; - -int main(void) { - typedef Kernel K; - typedef typename K::Point_d Point_d; - - K k; - std::vector<Point_d> points = {0, 1, 2, 3}; - std::vector<Point_d> results; - - Gudhi::subsampling::choose_n_farthest_points(k, points, 2, - Gudhi::subsampling::random_starting_point, - std::back_inserter(results)); - std::clog << "Before sparsification: " << points.size() << " points.\n"; - std::clog << "After sparsification: " << results.size() << " points.\n"; - std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n"; - - return 0; -} diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h index b70af8a0..e6347d96 100644 --- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h +++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h @@ -38,33 +38,35 @@ enum : std::size_t { * \ingroup subsampling * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the * current chosen point set to the subsampling. - * The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, with a random landmark. - * \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the - * concept <a target="_blank" - * href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> (despite the name, taken from CGAL, this can be any kind of metric or proximity measure). - * It must also contain a public member `squared_distance_d_object()` that returns an object of this type. - * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access - * via `operator[]` and the points should be stored contiguously in memory. - * \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d. - * \tparam DistanceOutputIterator Output iterator for distances. - * \details It chooses `final_size` points from a random access range + * \details + * The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, + * with a random landmark. + * It chooses `final_size` points from a random access range * `input_pts` (or the number of distinct points if `final_size` is larger) * and outputs them in the output iterator `output_it`. It also * outputs the distance from each of those points to the set of previous * points in `dist_it`. - * @param[in] k A kernel object. - * @param[in] input_pts Const reference to the input points. + * \tparam Distance must provide an operator() that takes 2 points (value type of the range) + * and returns their distance (or some more general proximity measure) as a `double`. + * \tparam Point_range Random access range of points. + * \tparam PointOutputIterator Output iterator whose value type is the point type. + * \tparam DistanceOutputIterator Output iterator for distances. + * @param[in] dist A distance function. + * @param[in] input_pts The input points. * @param[in] final_size The size of the subsample to compute. * @param[in] starting_point The seed in the farthest point algorithm. * @param[out] output_it The output iterator for points. * @param[out] dist_it The optional output iterator for distances. + * + * \warning Older versions of this function took a CGAL kernel as argument. Users need to replace `k` with + * `k.squared_distance_d_object()` in the first argument of every call to `choose_n_farthest_points`. * */ -template < typename Kernel, +template < typename Distance, typename Point_range, typename PointOutputIterator, typename DistanceOutputIterator = Null_output_iterator> -void choose_n_farthest_points(Kernel const &k, +void choose_n_farthest_points(Distance dist, Point_range const &input_pts, std::size_t final_size, std::size_t starting_point, @@ -86,9 +88,9 @@ void choose_n_farthest_points(Kernel const &k, starting_point = dis(gen); } - typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); - std::size_t current_number_of_landmarks = 0; // counter for landmarks + static_assert(std::numeric_limits<double>::has_infinity, "the number type needs to support infinity()"); + // FIXME: don't hard-code the type as double. For Epeck_d, we also want to handle types that do not have an infinity. const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry) std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts @@ -100,7 +102,7 @@ void choose_n_farthest_points(Kernel const &k, *dist_it++ = dist_to_L[curr_max_w]; std::size_t i = 0; for (auto&& p : input_pts) { - double curr_dist = sqdist(p, input_pts[curr_max_w]); + double curr_dist = dist(p, input_pts[curr_max_w]); if (curr_dist < dist_to_L[i]) dist_to_L[i] = curr_dist; ++i; diff --git a/src/Subsampling/include/gudhi/pick_n_random_points.h b/src/Subsampling/include/gudhi/pick_n_random_points.h index a67b2b84..e4246c29 100644 --- a/src/Subsampling/include/gudhi/pick_n_random_points.h +++ b/src/Subsampling/include/gudhi/pick_n_random_points.h @@ -11,7 +11,9 @@ #ifndef PICK_N_RANDOM_POINTS_H_ #define PICK_N_RANDOM_POINTS_H_ -#include <gudhi/Clock.h> +#ifdef GUDHI_SUBSAMPLING_PROFILING +# include <gudhi/Clock.h> +#endif #include <boost/range/size.hpp> @@ -44,6 +46,12 @@ void pick_n_random_points(Point_container const &points, Gudhi::Clock t; #endif + std::random_device rd; + std::mt19937 g(rd()); + +#if __cplusplus >= 201703L + std::sample(std::begin(points), std::end(points), output_it, final_size, g); +#else std::size_t nbP = boost::size(points); if (final_size > nbP) final_size = nbP; @@ -51,14 +59,12 @@ void pick_n_random_points(Point_container const &points, std::vector<int> landmarks(nbP); std::iota(landmarks.begin(), landmarks.end(), 0); - std::random_device rd; - std::mt19937 g(rd()); - std::shuffle(landmarks.begin(), landmarks.end(), g); landmarks.resize(final_size); for (int l : landmarks) *output_it++ = points[l]; +#endif #ifdef GUDHI_SUBSAMPLING_PROFILING t.end(); diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h index b30cec80..4571b8f3 100644 --- a/src/Subsampling/include/gudhi/sparsify_point_set.h +++ b/src/Subsampling/include/gudhi/sparsify_point_set.h @@ -11,6 +11,13 @@ #ifndef SPARSIFY_POINT_SET_H_ #define SPARSIFY_POINT_SET_H_ +#include <boost/version.hpp> +#if BOOST_VERSION < 106600 +# include <boost/function_output_iterator.hpp> +#else +# include <boost/iterator/function_output_iterator.hpp> +#endif + #include <gudhi/Kd_tree_search.h> #ifdef GUDHI_SUBSAMPLING_PROFILING #include <gudhi/Clock.h> @@ -27,7 +34,7 @@ namespace subsampling { * \ingroup subsampling * \brief Outputs a subset of the input points so that the * squared distance between any two points - * is greater than or equal to `min_squared_dist`. + * is greater than `min_squared_dist`. * * \tparam Kernel must be a model of the <a target="_blank" * href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a> @@ -63,29 +70,15 @@ sparsify_point_set( // Parse the input points, and add them if they are not too close to // the other points std::size_t pt_idx = 0; - for (typename Point_range::const_iterator it_pt = input_pts.begin(); - it_pt != input_pts.end(); - ++it_pt, ++pt_idx) { - if (dropped_points[pt_idx]) + for (auto const& pt : input_pts) { + if (dropped_points[pt_idx++]) continue; - *output_it++ = *it_pt; - - auto ins_range = points_ds.incremental_nearest_neighbors(*it_pt); + *output_it++ = pt; // If another point Q is closer that min_squared_dist, mark Q to be dropped - for (auto const& neighbor : ins_range) { - std::size_t neighbor_point_idx = neighbor.first; - // If the neighbor is too close, we drop the neighbor - if (neighbor.second < min_squared_dist) { - // N.B.: If neighbor_point_idx < pt_idx, - // dropped_points[neighbor_point_idx] is already true but adding a - // test doesn't make things faster, so why bother? - dropped_points[neighbor_point_idx] = true; - } else { - break; - } - } + auto drop = [&dropped_points] (std::ptrdiff_t neighbor_point_idx) { dropped_points[neighbor_point_idx] = true; }; + points_ds.all_near_neighbors2(pt, min_squared_dist, min_squared_dist, boost::make_function_output_iterator(std::ref(drop))); } #ifdef GUDHI_SUBSAMPLING_PROFILING diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp index b318d58e..94793295 100644 --- a/src/Subsampling/test/test_choose_n_farthest_points.cpp +++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp @@ -44,7 +44,8 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point, Kernel, list_of_tested landmarks.clear(); Kernel k; - Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); + auto d = k.squared_distance_d_object(); + Gudhi::subsampling::choose_n_farthest_points(d, points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); BOOST_CHECK(landmarks.size() == 100); for (auto landmark : landmarks) @@ -61,32 +62,33 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of std::vector< FT > distances; landmarks.clear(); Kernel k; + auto d = k.squared_distance_d_object(); // Choose -1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); landmarks.clear(); distances.clear(); // Choose 0 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); landmarks.clear(); distances.clear(); // Choose 1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); landmarks.clear(); distances.clear(); std::vector<FT> point({0.0, 0.0, 0.0, 0.0}); points.emplace_back(point.begin(), point.end()); // Choose -1 farthest points in a one point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 1 && distances.size() == 1); BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); landmarks.clear(); distances.clear(); // Choose 0 farthest points in a one point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0 && distances.size() == 0); landmarks.clear(); distances.clear(); // Choose 1 farthest points in a one point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 1 && distances.size() == 1); BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); landmarks.clear(); distances.clear(); @@ -94,7 +96,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of std::vector<FT> point2({1.0, 0.0, 0.0, 0.0}); points.emplace_back(point2.begin(), point2.end()); // Choose all farthest points among 2 points - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 2 && distances.size() == 2); BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); BOOST_CHECK(distances[1] == 1); @@ -102,7 +104,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of // Ignore duplicated points points.emplace_back(point.begin(), point.end()); - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + Gudhi::subsampling::choose_n_farthest_points(d, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 2 && distances.size() == 2); BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); BOOST_CHECK(distances[1] == 1); diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h index f007bdd5..f3491f91 100644 --- a/src/Tangential_complex/include/gudhi/Tangential_complex.h +++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h @@ -954,7 +954,11 @@ class Tangential_complex { // Triangulation's traits functor & objects typename Tr_traits::Compute_weight_d point_weight = local_tr_traits.compute_weight_d_object(); +#if CGAL_VERSION_NR < 1050200000 typename Tr_traits::Power_center_d power_center = local_tr_traits.power_center_d_object(); +#else + typename Tr_traits::Construct_power_sphere_d power_center = local_tr_traits.construct_power_sphere_d_object(); +#endif //*************************************************** // Build a minimal triangulation in the tangent space @@ -1100,7 +1104,11 @@ class Tangential_complex { std::size_t closest_pt_index = updated_pts_ds.k_nearest_neighbors(center_point, 1, false).begin()->first; typename K::Construct_weighted_point_d k_constr_wp = m_k.construct_weighted_point_d_object(); +#if CGAL_VERSION_NR < 1050200000 typename K::Power_distance_d k_power_dist = m_k.power_distance_d_object(); +#else + typename K::Compute_power_product_d k_power_dist = m_k.compute_power_product_d_object(); +#endif // Construct a weighted point equivalent to the star sphere Weighted_point star_sphere = k_constr_wp(compute_perturbed_point(i), m_squared_star_spheres_radii_incl_margin[i]); diff --git a/src/Witness_complex/doc/Witness_complex_doc.h b/src/Witness_complex/doc/Witness_complex_doc.h index 62203054..202f4539 100644 --- a/src/Witness_complex/doc/Witness_complex_doc.h +++ b/src/Witness_complex/doc/Witness_complex_doc.h @@ -92,11 +92,11 @@ int main(int argc, char * const argv[]) { // Choose landmarks (one can choose either of the two methods below) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), point_vector, nbL, + Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex - Witness_complex witness_complex(landmarks, - point_vector); + Witness_complex witness_complex(landmarks, point_vector); witness_complex.create_complex(simplex_tree, alpha2, lim_dim); } diff --git a/src/Witness_complex/example/example_strong_witness_complex_off.cpp b/src/Witness_complex/example/example_strong_witness_complex_off.cpp index 583a04ab..2bb135bf 100644 --- a/src/Witness_complex/example/example_strong_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_strong_witness_complex_off.cpp @@ -43,7 +43,8 @@ int main(int argc, char* const argv[]) { // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), point_vector, + nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex diff --git a/src/Witness_complex/example/example_witness_complex_off.cpp b/src/Witness_complex/example/example_witness_complex_off.cpp index 3635da78..e1384c73 100644 --- a/src/Witness_complex/example/example_witness_complex_off.cpp +++ b/src/Witness_complex/example/example_witness_complex_off.cpp @@ -47,7 +47,8 @@ int main(int argc, char * const argv[]) { // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), point_vector, nbL, + Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex start = clock(); diff --git a/src/Witness_complex/example/example_witness_complex_sphere.cpp b/src/Witness_complex/example/example_witness_complex_sphere.cpp index 78d5db4f..12a56de4 100644 --- a/src/Witness_complex/example/example_witness_complex_sphere.cpp +++ b/src/Witness_complex/example/example_witness_complex_sphere.cpp @@ -53,7 +53,7 @@ int main(int argc, char* const argv[]) { // Choose landmarks start = clock(); // Gudhi::subsampling::pick_n_random_points(point_vector, number_of_landmarks, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, number_of_landmarks, + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), point_vector, number_of_landmarks, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); diff --git a/src/Witness_complex/utilities/strong_witness_persistence.cpp b/src/Witness_complex/utilities/strong_witness_persistence.cpp index 1f61c77c..614de0d4 100644 --- a/src/Witness_complex/utilities/strong_witness_persistence.cpp +++ b/src/Witness_complex/utilities/strong_witness_persistence.cpp @@ -61,7 +61,8 @@ int main(int argc, char* argv[]) { // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), witnesses, nbL, Gudhi::subsampling::random_starting_point, + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), witnesses, nbL, + Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex diff --git a/src/Witness_complex/utilities/weak_witness_persistence.cpp b/src/Witness_complex/utilities/weak_witness_persistence.cpp index 93050af5..5ea31d6b 100644 --- a/src/Witness_complex/utilities/weak_witness_persistence.cpp +++ b/src/Witness_complex/utilities/weak_witness_persistence.cpp @@ -61,7 +61,8 @@ int main(int argc, char* argv[]) { // Choose landmarks (decomment one of the following two lines) // Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); - Gudhi::subsampling::choose_n_farthest_points(K(), witnesses, nbL, Gudhi::subsampling::random_starting_point, + Gudhi::subsampling::choose_n_farthest_points(K().squared_distance_d_object(), witnesses, nbL, + Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks)); // Compute witness complex diff --git a/src/common/doc/examples.h b/src/common/doc/examples.h index c19b3444..474f8699 100644 --- a/src/common/doc/examples.h +++ b/src/common/doc/examples.h @@ -42,7 +42,7 @@ * @example Persistence_representations/persistence_landscape.cpp * @example Tangential_complex/example_basic.cpp * @example Tangential_complex/example_with_perturb.cpp - * @example Subsampling/example_custom_kernel.cpp + * @example Subsampling/example_custom_distance.cpp * @example Subsampling/example_choose_n_farthest_points.cpp * @example Subsampling/example_sparsify_point_set.cpp * @example Subsampling/example_pick_n_random_points.cpp diff --git a/src/common/doc/installation.h b/src/common/doc/installation.h index 9df1c2f0..c2e63a24 100644 --- a/src/common/doc/installation.h +++ b/src/common/doc/installation.h @@ -113,8 +113,6 @@ make doxygen * Spatial_searching/example_spatial_searching.cpp</a> * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html"> * Subsampling/example_choose_n_farthest_points.cpp</a> - * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html"> - * Subsampling/example_custom_kernel.cpp</a> * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html"> * Subsampling/example_pick_n_random_points.cpp</a> * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html"> @@ -153,8 +151,6 @@ make doxygen * Spatial_searching/example_spatial_searching.cpp</a> * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html"> * Subsampling/example_choose_n_farthest_points.cpp</a> - * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html"> - * Subsampling/example_custom_kernel.cpp</a> * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html"> * Subsampling/example_pick_n_random_points.cpp</a> * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html"> diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index 56b6876c..5c1402a6 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -133,6 +133,10 @@ if(PYTHONINTERP_FOUND) add_gudhi_debug_info("Eigen3 version ${EIGEN3_VERSION}") # No problem, even if no CGAL found set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ") + set(GUDHI_PYTHON_EXTRA_COMPILE_ARGS "${GUDHI_PYTHON_EXTRA_COMPILE_ARGS}'-DGUDHI_USE_EIGEN3', ") + set(GUDHI_USE_EIGEN3 "True") + else (EIGEN3_FOUND) + set(GUDHI_USE_EIGEN3 "False") endif (EIGEN3_FOUND) set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}'off_reader', ") @@ -504,7 +508,7 @@ if(PYTHONINTERP_FOUND) endif() # Representations - if(SKLEARN_FOUND AND MATPLOTLIB_FOUND) + if(SKLEARN_FOUND AND MATPLOTLIB_FOUND AND OT_FOUND AND NOT CGAL_VERSION VERSION_LESS 4.11.0) add_gudhi_py_test(test_representations) endif() diff --git a/src/python/doc/conf.py b/src/python/doc/conf.py index 3cc5d1d6..b06baf9c 100755 --- a/src/python/doc/conf.py +++ b/src/python/doc/conf.py @@ -44,6 +44,8 @@ extensions = [ 'sphinx_paramlinks', ] +bibtex_bibfiles = ['../../biblio/bibliography.bib'] + todo_include_todos = True # plot option : do not show hyperlinks (Source code, png, hires.png, pdf) plot_html_show_source_link = False diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py index c4a71a7a..2801576e 100755 --- a/src/python/example/diagram_vectorizations_distances_kernels.py +++ b/src/python/example/diagram_vectorizations_distances_kernels.py @@ -5,11 +5,11 @@ import numpy as np from sklearn.kernel_approximation import RBFSampler from sklearn.preprocessing import MinMaxScaler -from gudhi.representations import DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ +from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ - SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel, WassersteinDistance + SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance) D1 = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]]) @@ -93,14 +93,21 @@ print("SW distance is " + str(sW(D1, D2))) SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.) print("SW kernel is " + str(SW(D1, D2))) -W = WassersteinDistance(order=2, internal_p=2, mode="pot") -print("Wasserstein distance (POT) is " + str(W(D1, D2))) +try: + W = WassersteinDistance(order=2, internal_p=2, mode="pot") + print("Wasserstein distance (POT) is " + str(W(D1, D2))) +except ImportError: + print("WassersteinDistance (POT) is not available, you may be missing pot.") W = WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001) print("Wasserstein distance (hera) is " + str(W(D1, D2))) -W = BottleneckDistance(epsilon=.001) -print("Bottleneck distance is " + str(W(D1, D2))) +try: + from gudhi.representations import BottleneckDistance + W = BottleneckDistance(epsilon=.001) + print("Bottleneck distance is " + str(W(D1, D2))) +except ImportError: + print("BottleneckDistance is not available, you may be missing CGAL.") PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.) print("PF kernel is " + str(PF(D1, D2))) diff --git a/src/python/gudhi/__init__.py.in b/src/python/gudhi/__init__.py.in index 79e12fbc..3043201a 100644 --- a/src/python/gudhi/__init__.py.in +++ b/src/python/gudhi/__init__.py.in @@ -23,6 +23,10 @@ __all__ = [@GUDHI_PYTHON_MODULES@ @GUDHI_PYTHON_MODULES_EXTRA@] __available_modules = '' __missing_modules = '' +# For unitary tests purpose +# could use "if 'collapse_edges' in gudhi.__all__" when collapse edges will have a python module +__GUDHI_USE_EIGEN3 = @GUDHI_USE_EIGEN3@ + # Try to import * from gudhi.__module_name for default modules. # Extra modules require an explicit import by the user (mostly because of # unusual dependencies, but also to avoid cluttering namespace gudhi and diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index cdcb1fde..84bc99a2 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -605,18 +605,19 @@ class Atol(BaseEstimator, TransformerMixin): >>> b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) >>> c = np.array([[3, 2, -1], [1, 2, -1]]) >>> atol_vectoriser = Atol(quantiser=KMeans(n_clusters=2, random_state=202006)) - >>> atol_vectoriser.fit(X=[a, b, c]).centers - array([[ 2. , 0.66666667, 3.33333333], - [ 2.6 , 2.8 , -0.4 ]]) + >>> atol_vectoriser.fit(X=[a, b, c]).centers # doctest: +SKIP + >>> # array([[ 2. , 0.66666667, 3.33333333], + >>> # [ 2.6 , 2.8 , -0.4 ]]) >>> atol_vectoriser(a) - array([1.18168665, 0.42375966]) + >>> # array([1.18168665, 0.42375966]) # doctest: +SKIP >>> atol_vectoriser(c) - array([0.02062512, 1.25157463]) - >>> atol_vectoriser.transform(X=[a, b, c]) - array([[1.18168665, 0.42375966], - [0.29861028, 1.06330156], - [0.02062512, 1.25157463]]) + >>> # array([0.02062512, 1.25157463]) # doctest: +SKIP + >>> atol_vectoriser.transform(X=[a, b, c]) # doctest: +SKIP + >>> # array([[1.18168665, 0.42375966], + >>> # [0.29861028, 1.06330156], + >>> # [0.02062512, 1.25157463]]) """ + # Note the example above must be up to date with the one in tests called test_atol_doc def __init__(self, quantiser, weighting_method="cloud", contrast="gaussian"): """ Constructor for the Atol measure vectorisation class. diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx index 72e82c79..c3470292 100644 --- a/src/python/gudhi/rips_complex.pyx +++ b/src/python/gudhi/rips_complex.pyx @@ -49,13 +49,13 @@ cdef class RipsComplex: :type max_edge_length: float :param points: A list of points in d-Dimension. - :type points: list of list of double + :type points: list of list of float Or :param distance_matrix: A distance matrix (full square or lower triangular). - :type points: list of list of double + :type points: list of list of float And in both cases @@ -89,10 +89,10 @@ cdef class RipsComplex: def create_simplex_tree(self, max_dimension=1): """ - :param max_dimension: graph expansion for rips until this given maximal + :param max_dimension: graph expansion for Rips until this given maximal dimension. :type max_dimension: int - :returns: A simplex tree created from the Delaunay Triangulation. + :returns: A simplex tree encoding the Vietoris–Rips filtration. :rtype: SimplexTree """ stree = SimplexTree() diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 3c4cbed3..000323af 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -63,7 +63,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": bool make_filtration_non_decreasing() nogil void compute_extended_filtration() nogil vector[vector[pair[int, pair[double, double]]]] compute_extended_persistence_subdiagrams(vector[pair[int, pair[double, double]]] dgm, double min_persistence) nogil - Simplex_tree_interface_full_featured* collapse_edges(int nb_collapse_iteration) nogil + Simplex_tree_interface_full_featured* collapse_edges(int nb_collapse_iteration) nogil except + void reset_filtration(double filtration, int dimension) nogil # Iterators over Simplex tree pair[vector[int], double] get_simplex_and_filtration(Simplex_tree_simplex_handle f_simplex) nogil diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx index cdd2e87b..d7991417 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -627,6 +627,9 @@ cdef class SimplexTree: :param nb_iterations: The number of edge collapse iterations to perform. Default is 1. :type nb_iterations: int + + :note: collapse_edges method requires `Eigen <installation.html#eigen>`_ >= 3.1.0 and an exception is thrown + if this method is not available. """ # Backup old pointer cdef Simplex_tree_interface_full_featured* ptr = self.get_ptr() diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index b11d07e5..46f32335 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -105,7 +105,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): """Outputs a subset of the input points so that the squared distance - between any two points is greater than or equal to min_squared_dist. + between any two points is greater than min_squared_dist. :param points: The input point set. :type points: Iterable[Iterable[float]] diff --git a/src/python/include/Alpha_complex_factory.h b/src/python/include/Alpha_complex_factory.h index d699ad9b..3405fdd6 100644 --- a/src/python/include/Alpha_complex_factory.h +++ b/src/python/include/Alpha_complex_factory.h @@ -48,11 +48,14 @@ static CgalPointType pt_cython_to_cgal(std::vector<double> const& vec) { class Abstract_alpha_complex { public: virtual std::vector<double> get_point(int vh) = 0; + virtual bool create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, bool default_filtration_value) = 0; + + virtual ~Abstract_alpha_complex() = default; }; -class Exact_Alphacomplex_dD : public Abstract_alpha_complex { +class Exact_Alphacomplex_dD final : public Abstract_alpha_complex { private: using Kernel = CGAL::Epeck_d<CGAL::Dynamic_dimension_tag>; using Point = typename Kernel::Point_d; @@ -78,7 +81,7 @@ class Exact_Alphacomplex_dD : public Abstract_alpha_complex { Alpha_complex<Kernel> alpha_complex_; }; -class Inexact_Alphacomplex_dD : public Abstract_alpha_complex { +class Inexact_Alphacomplex_dD final : public Abstract_alpha_complex { private: using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; using Point = typename Kernel::Point_d; @@ -104,7 +107,7 @@ class Inexact_Alphacomplex_dD : public Abstract_alpha_complex { }; template <complexity Complexity> -class Alphacomplex_3D : public Abstract_alpha_complex { +class Alphacomplex_3D final : public Abstract_alpha_complex { private: using Point = typename Alpha_complex_3d<Complexity, false, false>::Bare_point_3; diff --git a/src/python/include/Simplex_tree_interface.h b/src/python/include/Simplex_tree_interface.h index baff3850..629f6083 100644 --- a/src/python/include/Simplex_tree_interface.h +++ b/src/python/include/Simplex_tree_interface.h @@ -15,7 +15,9 @@ #include <gudhi/distance_functions.h> #include <gudhi/Simplex_tree.h> #include <gudhi/Points_off_io.h> +#ifdef GUDHI_USE_EIGEN3 #include <gudhi/Flag_complex_edge_collapser.h> +#endif #include <iostream> #include <vector> @@ -162,6 +164,7 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { } Simplex_tree_interface* collapse_edges(int nb_collapse_iteration) { +#ifdef GUDHI_USE_EIGEN3 using Filtered_edge = std::tuple<Vertex_handle, Vertex_handle, Filtration_value>; std::vector<Filtered_edge> edges; for (Simplex_handle sh : Base::skeleton_simplex_range(1)) { @@ -187,6 +190,9 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { collapsed_stree_ptr->insert({std::get<0>(remaining_edge), std::get<1>(remaining_edge)}, std::get<2>(remaining_edge)); } return collapsed_stree_ptr; +#else + throw std::runtime_error("Unable to collapse edges as it requires Eigen3 >= 3.1.0."); +#endif } // Iterator over the simplex tree diff --git a/src/python/include/Subsampling_interface.h b/src/python/include/Subsampling_interface.h index cdda851f..6aee7231 100644 --- a/src/python/include/Subsampling_interface.h +++ b/src/python/include/Subsampling_interface.h @@ -11,6 +11,7 @@ #ifndef INCLUDE_SUBSAMPLING_INTERFACE_H_ #define INCLUDE_SUBSAMPLING_INTERFACE_H_ +#include <gudhi/distance_functions.h> #include <gudhi/choose_n_farthest_points.h> #include <gudhi/pick_n_random_points.h> #include <gudhi/sparsify_point_set.h> @@ -27,14 +28,13 @@ namespace subsampling { using Subsampling_dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; using Subsampling_point_d = Subsampling_dynamic_kernel::Point_d; -using Subsampling_ft = Subsampling_dynamic_kernel::FT; // ------ choose_n_farthest_points ------ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, unsigned nb_points) { std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks)); + choose_n_farthest_points(Euclidean_distance(), points, nb_points, + random_starting_point, std::back_inserter(landmarks)); return landmarks; } @@ -42,8 +42,8 @@ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, unsigned nb_points, unsigned starting_point) { std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, starting_point, std::back_inserter(landmarks)); + choose_n_farthest_points(Euclidean_distance(), points, nb_points, + starting_point, std::back_inserter(landmarks)); return landmarks; } diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index 43c914f3..cda1a15b 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -46,6 +46,32 @@ def test_multiple(): assert d1 == pytest.approx(d2, rel=0.02) +# Test sorted values as points order can be inverted, and sorted test is not documentation-friendly +# Note the test below must be up to date with the Atol class documentation +def test_atol_doc(): + a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]]) + b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) + c = np.array([[3, 2, -1], [1, 2, -1]]) + + atol_vectoriser = Atol(quantiser=KMeans(n_clusters=2, random_state=202006)) + # Atol will do + # X = np.concatenate([a,b,c]) + # kmeans = KMeans(n_clusters=2, random_state=202006).fit(X) + # kmeans.labels_ will be : array([1, 0, 1, 0, 0, 1, 0, 0]) + first_cluster = np.asarray([a[0], a[2], b[2]]) + second_cluster = np.asarray([a[1], b[0], b[2], c[0], c[1]]) + + # Check the center of the first_cluster and second_cluster are in Atol centers + centers = atol_vectoriser.fit(X=[a, b, c]).centers + np.isclose(centers, first_cluster.mean(axis=0)).all(1).any() + np.isclose(centers, second_cluster.mean(axis=0)).all(1).any() + + vectorization = atol_vectoriser.transform(X=[a, b, c]) + assert np.allclose(vectorization[0], atol_vectoriser(a)) + assert np.allclose(vectorization[1], atol_vectoriser(b)) + assert np.allclose(vectorization[2], atol_vectoriser(c)) + + def test_dummy_atol(): a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]]) b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py index 3b23fa0b..a3eacaa9 100755 --- a/src/python/test/test_simplex_tree.py +++ b/src/python/test/test_simplex_tree.py @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from gudhi import SimplexTree +from gudhi import SimplexTree, __GUDHI_USE_EIGEN3 import pytest __author__ = "Vincent Rouvreau" @@ -353,11 +353,16 @@ def test_collapse_edges(): assert st.num_simplices() == 10 - st.collapse_edges() - assert st.num_simplices() == 9 - assert st.find([1, 3]) == False - for simplex in st.get_skeleton(0): - assert simplex[1] == 1. + if __GUDHI_USE_EIGEN3: + st.collapse_edges() + assert st.num_simplices() == 9 + assert st.find([1, 3]) == False + for simplex in st.get_skeleton(0): + assert simplex[1] == 1. + else: + # If no Eigen3, collapse_edges throws an exception + with pytest.raises(RuntimeError): + st.collapse_edges() def test_reset_filtration(): st = SimplexTree() diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py index 31f64e32..4019852e 100755 --- a/src/python/test/test_subsampling.py +++ b/src/python/test/test_subsampling.py @@ -141,12 +141,16 @@ def test_simple_sparsify_points(): # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == [] # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == [] assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.0) == point_set - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.0) == [ + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.999) == point_set + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.001) == [ [0, 1], [1, 0], ] - assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.01) == [[0, 1]] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.999) == [ + [0, 1], + [1, 0], + ] + assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]] assert ( len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0)) @@ -157,11 +161,11 @@ def test_simple_sparsify_points(): == 5 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1)) == 4 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=90.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9)) == 3 ) assert ( @@ -169,7 +173,7 @@ def test_simple_sparsify_points(): == 2 ) assert ( - len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.0)) + len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9)) == 2 ) assert ( |