diff options
| author | Gard Spreemann <gspreemann@gmail.com> | 2018-02-02 14:05:27 +0100 |
|---|---|---|
| committer | Gard Spreemann <gspreemann@gmail.com> | 2018-02-02 14:05:27 +0100 |
| commit | df9caafd97960a610906a79cf40ba52e40c6cf48 (patch) | |
| tree | a9c0b0330239e6438510432b0ba78f35799224d9 | |
| parent | 5bb200877fac0090032b9d56b782d2ae361efa9b (diff) | |
| parent | 8e51c74d8855a641c006765ee246402e218828f3 (diff) | |
Merge branch 'dfsg/latest' into debian/sid
194 files changed, 33737 insertions, 1854 deletions
diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt index bfef1590..5e71f7eb 100644 --- a/CMakeGUDHIVersion.txt +++ b/CMakeGUDHIVersion.txt @@ -1,6 +1,6 @@ set (GUDHI_MAJOR_VERSION 2) -set (GUDHI_MINOR_VERSION 0) -set (GUDHI_PATCH_VERSION 1) +set (GUDHI_MINOR_VERSION 1) +set (GUDHI_PATCH_VERSION 0) set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION}) message(STATUS "GUDHI version : ${GUDHI_VERSION}") diff --git a/CMakeLists.txt b/CMakeLists.txt index 795005b1..94587044 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -16,6 +16,7 @@ add_gudhi_module(Bitmap_cubical_complex) add_gudhi_module(Bottleneck_distance) add_gudhi_module(Contraction) add_gudhi_module(Hasse_complex) +add_gudhi_module(Persistence_representations) add_gudhi_module(Persistent_cohomology) add_gudhi_module(Rips_complex) add_gudhi_module(Simplex_tree) @@ -24,6 +25,7 @@ add_gudhi_module(Spatial_searching) add_gudhi_module(Subsampling) add_gudhi_module(Tangential_complex) add_gudhi_module(Witness_complex) +add_gudhi_module(Nerve_GIC) message("++ GUDHI_MODULES list is:\"${GUDHI_MODULES}\"") @@ -38,7 +38,7 @@ PROJECT_NAME = "GUDHI" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER = "2.0.1" +PROJECT_NUMBER = "2.1.0" # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a @@ -801,7 +801,7 @@ EXCLUDE_SYMLINKS = NO # Note that the wildcards are matched against the file with absolute path, so to # exclude all test directories for example use the pattern */test/* -EXCLUDE_PATTERNS = +EXCLUDE_PATTERNS = */utilities/*/*.md # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the @@ -819,7 +819,8 @@ EXCLUDE_SYMBOLS = # command). EXAMPLE_PATH = biblio/ \ - example/ + example/ \ + utilities/ # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and @@ -851,7 +852,9 @@ IMAGE_PATH = doc/Skeleton_blocker/ \ doc/Subsampling/ \ doc/Spatial_searching/ \ doc/Tangential_complex/ \ - doc/Bottleneck_distance/ + doc/Bottleneck_distance/ \ + doc/Nerve_GIC/ \ + doc/Persistence_representations/ # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program diff --git a/GudhUI/CMakeLists.txt b/GudhUI/CMakeLists.txt index 374195d0..2503a03e 100644 --- a/GudhUI/CMakeLists.txt +++ b/GudhUI/CMakeLists.txt @@ -1,37 +1,41 @@ cmake_minimum_required(VERSION 2.8) project(GudhUI) -find_package(Qt5 COMPONENTS Widgets Xml OpenGL) -find_package(QGLViewer) +# Need to find OpenGL first as find_package(Qt5) tries to #include"GL/gl.h" on some platforms find_package(OpenGL) -if ( CGAL_FOUND AND Qt5_FOUND AND OPENGL_FOUND AND QGLVIEWER_FOUND AND NOT CGAL_VERSION VERSION_EQUAL 4.8.0) - - set(CMAKE_AUTOMOC ON) - set(CMAKE_AUTOUIC ON) - set(CMAKE_INCLUDE_CURRENT_DIR ON) - - SET(Boost_USE_STATIC_LIBS ON) - SET(Boost_USE_MULTITHREAD OFF) - include_directories (${QGLVIEWER_INCLUDE_DIR}) - - add_executable ( GudhUI - gui/gudhui.cpp - gui/MainWindow.cpp - gui/Menu_k_nearest_neighbors.cpp - gui/Menu_uniform_neighbors.cpp - gui/Menu_edge_contraction.cpp - gui/Menu_persistence.cpp - view/Viewer_instructor.cpp - view/Viewer.cpp - ) - target_link_libraries( GudhUI Qt5::Widgets Qt5::Xml Qt5::OpenGL ) - target_link_libraries( GudhUI ${QGLVIEWER_LIBRARIES} ) - target_link_libraries( GudhUI ${OPENGL_gl_LIBRARY} ${OPENGL_glu_LIBRARY} ) - if (TBB_FOUND) - target_link_libraries( GudhUI ${TBB_LIBRARIES}) +if (OPENGL_FOUND) + find_package(Qt5 COMPONENTS Widgets Xml OpenGL) + find_package(QGLViewer) + + if ( CGAL_FOUND AND Qt5_FOUND AND QGLVIEWER_FOUND AND NOT CGAL_VERSION VERSION_EQUAL 4.8.0) + + set(CMAKE_AUTOMOC ON) + set(CMAKE_AUTOUIC ON) + set(CMAKE_INCLUDE_CURRENT_DIR ON) + + SET(Boost_USE_STATIC_LIBS ON) + SET(Boost_USE_MULTITHREAD OFF) + include_directories (${QGLVIEWER_INCLUDE_DIR}) + + add_executable ( GudhUI + gui/gudhui.cpp + gui/MainWindow.cpp + gui/Menu_k_nearest_neighbors.cpp + gui/Menu_uniform_neighbors.cpp + gui/Menu_edge_contraction.cpp + gui/Menu_persistence.cpp + view/Viewer_instructor.cpp + view/Viewer.cpp + ) + target_link_libraries( GudhUI Qt5::Widgets Qt5::Xml Qt5::OpenGL ) + target_link_libraries( GudhUI ${QGLVIEWER_LIBRARIES} ) + target_link_libraries( GudhUI ${OPENGL_gl_LIBRARY} ${OPENGL_glu_LIBRARY} ) + if (TBB_FOUND) + target_link_libraries( GudhUI ${TBB_LIBRARIES}) + endif() + + install(TARGETS GudhUI DESTINATION bin) + endif() - - install(TARGETS GudhUI DESTINATION bin) - -endif() +endif(OPENGL_FOUND)
\ No newline at end of file diff --git a/biblio/bibliography.bib b/biblio/bibliography.bib index 29fc5650..b101cb76 100644 --- a/biblio/bibliography.bib +++ b/biblio/bibliography.bib @@ -6,6 +6,30 @@ year = {2014}, } +@article{Carriere17c, + author = {Carri\`ere, Mathieu and Michel, Bertrand and Oudot, Steve}, + title = {{Statistical Analysis and Parameter Selection for Mapper}}, + journal = {CoRR}, + volume = {abs/1706.00204}, + year = {2017} +} + +@inproceedings{Dey13, + author = {Dey, Tamal and Fan, Fengtao and Wang, Yusu}, + title = {Graph Induced Complex on Point Data}, + booktitle = {Proceedings of the Twenty-ninth Annual Symposium on Computational Geometry}, + year = {2013}, + pages = {107--116}, +} + +@article{Carriere16, + title={{Structure and Stability of the 1-Dimensional Mapper}}, + author={Carri\`ere, Mathieu and Oudot, Steve}, + journal={CoRR}, + volume= {abs/1511.05823}, + year={2015} +} + @inproceedings{zigzag_reflection, author = {Jean-Daniel Boissonnat and Cl\'ement Maria and Steve Oudot}, title = {Zigzag Persistent Homology Algorithm via Reflections}, @@ -979,3 +1003,72 @@ language={English} pages={157-166}, year={2004} } + +@ARTICLE{bubenik_landscapes_2015, + author = {P. Bubenik}, + title = {Statistical topological data analysis using persistence landscapes.}, + journal = {Journal of Machine Learning Research}, + year = {2015} +} + +@ARTICLE{bubenik_dlotko_landscapes_2016, + author = {P. Bubenik and P. Dlotko}, + title = {A persistence landscapes toolbox for topological statistics.}, + journal = {Journal of Symbolic Computation.}, + year = {2016} +} + +@ARTICLE{Fasy_Kim_Lecci_Maria_tda, + author = {B. Fasy and J. Kim and F. Lecci and C. Maria}, + title = {Introduction to the R package TDA.}, + journal = {arXiv:1411.1830.}, + year = {2016} +} + + +@ARTICLE{Ferri_Frosini_comparision_sheme_1, + author = {P. Donatini and P. Frosini and A. Lovato}, + title = {Size functions for signature recognition.}, + journal = {Proceedings of SPIE, Vision Geometry VII, vol. 3454}, + year = {1998} +} + + +@ARTICLE{Ferri_Frosini_comparision_sheme_2, + author = {M. Ferri and P. Frosini and A. Lovato and C. Zambelli}, + title = {Point selection: A new comparison scheme for size functions (With an application to monogram recognition).}, + journal = {Proceedings Third Asian Conference on Computer Vision, Lecture Notes in Computer Science 1351.}, + year = {1998} +} + +@ARTICLE{Persistence_Images_2017, + author = {H. Adams and S. Chepushtanova and T. Emerson and E. Hanson and M. Kirby and F. Motta and R. Neville and C. Peterson and P. Shipman and L. Ziegelmeier}, + title = {Persistence Images: A Stable Vector Representation of Persistent Homology.}, + journal = {Journal of Machine Learning Research}, + year = {2017} +} + + +@ARTICLE{Kusano_Fukumizu_Hiraoka_PWGK, + author = {G. Kusano and K. Fukumizu and Y. Hiraoka}, + title = {Persistence weighted Gaussian kernel for topological data analysis.}, + journal = {ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48}, + year = {2016} +} + +@ARTICLE{Reininghaus_Huber_ALL_PSSK, + author = {J. Reininghaus and S. Huber and U. Bauer and R. Kwitt}, + title = {A Stable Multi-Scale Kernel for Topological Machine Learning.}, + journal = {Proc. 2015 IEEE Conf. Comp. Vision & Pat. Rec. (CVPR '15)}, + year = {2015} +} + +@ARTICLE{Carriere_Oudot_Ovsjanikov_top_signatures_3d, + author = {M. Carrière and S. Oudot and M. Ovsjanikov}, + title = {Stable Topological Signatures for Points on 3D Shapes.}, + journal = {Proc. Sympos. on Geometry Processing}, + year = {2015} +} + + + diff --git a/biblio/how_to_cite_gudhi.bib b/biblio/how_to_cite_gudhi.bib index 59c05a5b..5994124a 100644 --- a/biblio/how_to_cite_gudhi.bib +++ b/biblio/how_to_cite_gudhi.bib @@ -122,3 +122,21 @@ , url = "http://gudhi.gforge.inria.fr/python/latest/" , year = 2016 } + +@incollection{gudhi:CoverComplex +, author = "Mathieu Carri\`ere" +, title = "Cover complex" +, publisher = "{GUDHI Editorial Board}" +, booktitle = "{GUDHI} User and Reference Manual" +, url = "http://gudhi.gforge.inria.fr/doc/latest/group__cover__complex.html" +, year = 2017 +} + +@incollection{gudhi:PersistenceRepresentations +, author = "Pawel Dlotko" +, title = "Persistence representations" +, publisher = "{GUDHI Editorial Board}" +, booktitle = "{GUDHI} User and Reference Manual" +, url = "http://gudhi.gforge.inria.fr/doc/latest/group___persistence__representations.html" +, year = 2017 +} diff --git a/cmake/modules/GUDHI_doxygen_target.cmake b/cmake/modules/GUDHI_doxygen_target.cmake index d2cb952d..f3e2d9f5 100644 --- a/cmake/modules/GUDHI_doxygen_target.cmake +++ b/cmake/modules/GUDHI_doxygen_target.cmake @@ -3,6 +3,11 @@ find_package(Doxygen) if(DOXYGEN_FOUND) # configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY) + #starting from cmake 3.9 the usage of DOXYGEN_EXECUTABLE is deprecated + if(TARGET Doxygen::doxygen) + get_property(DOXYGEN_EXECUTABLE TARGET Doxygen::doxygen PROPERTY IMPORTED_LOCATION) + endif() + add_custom_target(doxygen ${DOXYGEN_EXECUTABLE} ${GUDHI_USER_VERSION_DIR}/Doxyfile WORKING_DIRECTORY ${GUDHI_USER_VERSION_DIR} DEPENDS ${GUDHI_USER_VERSION_DIR}/Doxyfile ${GUDHI_DOXYGEN_DEPENDENCY} diff --git a/cmake/modules/GUDHI_test_coverage.cmake b/cmake/modules/GUDHI_test_coverage.cmake index ce171a0e..bea5b2d6 100644 --- a/cmake/modules/GUDHI_test_coverage.cmake +++ b/cmake/modules/GUDHI_test_coverage.cmake @@ -8,7 +8,19 @@ if (GPROF_PATH) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pg") endif() +if (DEBUG_TRACES) + # Make CTest more verbose with DEBUG_TRACES - no XML output + set(GUDHI_UT_LOG_LEVEL "--log_level=all") + set(GUDHI_UT_REPORT_LEVEL "--report_level=detailed") +else() + set(GUDHI_UT_LOG_FORMAT "--log_format=XML") + set(GUDHI_UT_LOG_SINK "--log_sink=${CMAKE_BINARY_DIR}/${unitary_test}_UT.xml") + set(GUDHI_UT_LOG_LEVEL "--log_level=test_suite") + set(GUDHI_UT_REPORT_LEVEL "--report_level=no") +endif() + function(gudhi_add_coverage_test unitary_test) add_test(NAME ${unitary_test} COMMAND $<TARGET_FILE:${unitary_test}> - "--log_format=XML" "--log_sink=${CMAKE_BINARY_DIR}/${unitary_test}_UT.xml" "--log_level=test_suite" "--report_level=no") + ${GUDHI_UT_LOG_FORMAT} ${GUDHI_UT_LOG_SINK} + ${GUDHI_UT_LOG_LEVEL} ${GUDHI_UT_REPORT_LEVEL}) endfunction() diff --git a/cmake/modules/GUDHI_third_party_libraries.cmake b/cmake/modules/GUDHI_third_party_libraries.cmake index f2bbafdc..419c2581 100644 --- a/cmake/modules/GUDHI_third_party_libraries.cmake +++ b/cmake/modules/GUDHI_third_party_libraries.cmake @@ -1,6 +1,6 @@ # This files manage third party libraries required by GUDHI -find_package(Boost REQUIRED COMPONENTS system filesystem unit_test_framework program_options thread) +find_package(Boost 1.48.0 REQUIRED COMPONENTS system filesystem unit_test_framework program_options thread) if(NOT Boost_FOUND) message(FATAL_ERROR "NOTICE: This program requires Boost and will not be compiled.") @@ -54,10 +54,12 @@ if(CGAL_FOUND) endforeach(CGAL_INCLUDE_DIR ${CGAL_INCLUDE_DIRS}) endif(NOT CGAL_VERSION VERSION_GREATER 4.9.0) - # For dev version - include_directories(BEFORE "src/common/include/gudhi_patches") - # For user version - include_directories(BEFORE "include/gudhi_patches") + if (NOT CGAL_VERSION VERSION_GREATER 4.11.0) + # For dev version + include_directories(BEFORE "src/common/include/gudhi_patches") + # For user version + include_directories(BEFORE "include/gudhi_patches") + endif (NOT CGAL_VERSION VERSION_GREATER 4.11.0) endif() endif() @@ -83,7 +85,6 @@ FIND_PROGRAM( GCOVR_PATH gcovr ) if (GCOVR_PATH) message("gcovr found in ${GCOVR_PATH}") endif() -# Required programs for unitary tests purpose FIND_PROGRAM( GPROF_PATH gprof ) if (GPROF_PATH) message("gprof found in ${GPROF_PATH}") @@ -92,6 +93,10 @@ FIND_PROGRAM( DIFF_PATH diff ) if (DIFF_PATH) message("diff found in ${DIFF_PATH}") endif() +FIND_PROGRAM( GNUPLOT_PATH gnuplot ) +if (GNUPLOT_PATH) + message("gnuplot found in ${GNUPLOT_PATH}") +endif() # BOOST ISSUE result_of vs C++11 add_definitions(-DBOOST_RESULT_OF_USE_DECLTYPE) diff --git a/cmake/modules/GUDHI_user_version_target.cmake b/cmake/modules/GUDHI_user_version_target.cmake index cff64ad2..4abc2574 100644 --- a/cmake/modules/GUDHI_user_version_target.cmake +++ b/cmake/modules/GUDHI_user_version_target.cmake @@ -48,7 +48,11 @@ if (NOT CMAKE_VERSION VERSION_LESS 2.8.11) copy_directory ${CMAKE_SOURCE_DIR}/src/GudhUI ${GUDHI_USER_VERSION_DIR}/GudhUI) set(GUDHI_DIRECTORIES "doc;example;concept;utilities") - set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi;include/gudhi_patches") + if (NOT CGAL_VERSION VERSION_GREATER 4.11.0) + set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi;include/gudhi_patches") + else () + set(GUDHI_INCLUDE_DIRECTORIES "include/gudhi") + endif () foreach(GUDHI_MODULE ${GUDHI_MODULES_FULL_LIST}) foreach(GUDHI_DIRECTORY ${GUDHI_DIRECTORIES}) diff --git a/concept/Persistence_representations/Real_valued_topological_data.h b/concept/Persistence_representations/Real_valued_topological_data.h new file mode 100644 index 00000000..1d3595ad --- /dev/null +++ b/concept/Persistence_representations/Real_valued_topological_data.h @@ -0,0 +1,51 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef CONCEPT_REAL_VALUED_TOPOLOGICAL_DATA_H_ +#define CONCEPT_REAL_VALUED_TOPOLOGICAL_DATA_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \brief The concept Real_valued_topological_data describes the requirements + * for a type to implement a container that allows computations of its projections to R. + */ +class Real_valued_topological_data { + public: + /** +* Typically there are various ways data can be projected to R. This function gives us the number of functions for +* vectorization provided by a given class. + **/ + size_t number_of_projections_to_R(); + /** +* This is a function to compute the projection from this container to reals. The parameter of a function have to +* be between 0 and the value returned by number_of_projections_to_R(). +**/ + double project_to_R(size_t number_of_projection); +}; + +} // namespace Persistence_representations + +} // namespace Gudhi + +#endif // CONCEPT_REAL_VALUED_TOPOLOGICAL_DATA_H_ diff --git a/concept/Persistence_representations/Topological_data_with_averages.h b/concept/Persistence_representations/Topological_data_with_averages.h new file mode 100644 index 00000000..9ebd105f --- /dev/null +++ b/concept/Persistence_representations/Topological_data_with_averages.h @@ -0,0 +1,44 @@ + +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef CONCEPT_TOPOLOGICAL_DATA_WITH_AVERAGES_H_ +#define CONCEPT_TOPOLOGICAL_DATA_WITH_AVERAGES_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \brief The concept Topological_data_with_averages describes the requirements + * for a type to implement a container that allows computations of averages. + * Note that the average object after being computed is stored in *this. + */ +class Topological_data_with_averages { + public: + void compute_average(const std::vector<Topological_data_with_averages*>& to_average); +}; + +} // namespace Persistence_representations + +} // namespace Gudhi + +#endif // CONCEPT_TOPOLOGICAL_DATA_WITH_AVERAGES_H_ diff --git a/concept/Persistence_representations/Topological_data_with_distances.h b/concept/Persistence_representations/Topological_data_with_distances.h new file mode 100644 index 00000000..a6a62307 --- /dev/null +++ b/concept/Persistence_representations/Topological_data_with_distances.h @@ -0,0 +1,49 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef CONCEPT_TOPOLOGICAL_DATA_WITH_DISTANCES_H_ +#define CONCEPT_TOPOLOGICAL_DATA_WITH_DISTANCES_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \brief The concept Topological_data_with_distances describes the requirements + * for a type to implement a container that allows computations of distance to another contained of that type. + * \details + * The second parameter of the distance function allow to declare power of a distance. The exact meaning of that + * number will be different for different distances. A few examples are given below: + * In case of p-Wasserstein distance, the power is equal to p. power = std::limit<double>::max() for bottleneck + * distance. + * + * In case of L^p landscape distance, the power is equal to p. s + */ +class Topological_data_with_distances { + public: + double distance(const Topological_data_with_distances& second, double power = 1); +}; + +} // namespace Persistence_representations + +} // namespace Gudhi + +#endif // CONCEPT_TOPOLOGICAL_DATA_WITH_DISTANCES_H_ diff --git a/concept/Persistence_representations/Topological_data_with_scalar_product.h b/concept/Persistence_representations/Topological_data_with_scalar_product.h new file mode 100644 index 00000000..2e506ca9 --- /dev/null +++ b/concept/Persistence_representations/Topological_data_with_scalar_product.h @@ -0,0 +1,42 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef CONCEPT_TOPOLOGICAL_DATA_WITH_SCALAR_PRODUCT_H_ +#define CONCEPT_TOPOLOGICAL_DATA_WITH_SCALAR_PRODUCT_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \brief The concept Topological_data_with_scalar_product describes the requirements + * for a type to implement a container that allows computations of scalar products. + */ +class Topological_data_with_scalar_product { + public: + double compute_scalar_product(const Topological_data_with_scalar_product& second); +}; + +} // namespace Persistence_representations + +} // namespace Gudhi + +#endif // CONCEPT_TOPOLOGICAL_DATA_WITH_SCALAR_PRODUCT_H_ diff --git a/concept/Persistence_representations/Vectorized_topological_data.h b/concept/Persistence_representations/Vectorized_topological_data.h new file mode 100644 index 00000000..b6b5b109 --- /dev/null +++ b/concept/Persistence_representations/Vectorized_topological_data.h @@ -0,0 +1,51 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef CONCEPT_VECTORIZED_TOPOLOGICAL_DATA_H_ +#define CONCEPT_VECTORIZED_TOPOLOGICAL_DATA_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \brief The concept Vectorized_topological_data describes the requirements + * for a type to implement a container that allows vectorization. + */ +class Vectorized_topological_data { + public: + /** + * There are various ways data can be vectorized. This function give us the number of functions for vectorization + *provided by a given class. + **/ + size_t number_of_vectorize_functions(); + /** + * This is a function to vectorize given container. The parameter of a function have to be between 0 and the value + *returned by number_of_vectorize_functions(). + **/ + std::vector<double> vectorize(int number_of_function); +}; + +} // namespace Persistence_representations + +} // namespace Gudhi + +#endif // CONCEPT_VECTORIZED_TOPOLOGICAL_DATA_H_ diff --git a/cython/cython/periodic_cubical_complex.pyx b/cython/cython/periodic_cubical_complex.pyx index 581c7b69..3025f125 100644 --- a/cython/cython/periodic_cubical_complex.pyx +++ b/cython/cython/periodic_cubical_complex.pyx @@ -33,7 +33,7 @@ __license__ = "GPL v3" cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": cdef cppclass Periodic_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>": - Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells) + Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions) Periodic_cubical_complex_base_interface(string perseus_file) int num_simplices() int dimension() @@ -58,7 +58,7 @@ cdef class PeriodicCubicalComplex: # Fake constructor that does nothing but documenting the constructor def __init__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): + periodic_dimensions=None, perseus_file=''): """PeriodicCubicalComplex constructor from dimensions and top_dimensional_cells or from a Perseus-style file name. @@ -66,6 +66,8 @@ cdef class PeriodicCubicalComplex: :type dimensions: list of int :param top_dimensional_cells: A list of cells filtration values. :type top_dimensional_cells: list of double + :param periodic_dimensions: A list of top dimensional cells periodicity value. + :type periodic_dimensions: list of boolean Or @@ -75,10 +77,10 @@ cdef class PeriodicCubicalComplex: # The real cython constructor def __cinit__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): - if (dimensions is not None) and (top_dimensional_cells is not None) and (perseus_file is ''): - self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells) - elif (dimensions is None) and (top_dimensional_cells is None) and (perseus_file is not ''): + periodic_dimensions=None, perseus_file=''): + if (dimensions is not None) and (top_dimensional_cells is not None) and (periodic_dimensions is not None) and (perseus_file is ''): + self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells, periodic_dimensions) + elif (dimensions is None) and (top_dimensional_cells is None) and (periodic_dimensions is None) and (perseus_file is not ''): if os.path.isfile(perseus_file): self.thisptr = new Periodic_cubical_complex_base_interface(str.encode(perseus_file)) else: diff --git a/cython/cython/simplex_tree.pyx b/cython/cython/simplex_tree.pyx index 45487158..0cb575d2 100644 --- a/cython/cython/simplex_tree.pyx +++ b/cython/cython/simplex_tree.pyx @@ -37,11 +37,13 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": Simplex_tree() double simplex_filtration(vector[int] simplex) + void assign_simplex_filtration(vector[int] simplex, double filtration) void initialize_filtration() int num_vertices() int num_simplices() void set_dimension(int dimension) int dimension() + int upper_bound_dimension() bint find_simplex(vector[int] simplex) bint insert_simplex_and_subfaces(vector[int] simplex, double filtration) @@ -50,8 +52,9 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": vector[pair[vector[int], double]] get_star(vector[int] simplex) vector[pair[vector[int], double]] get_cofaces(vector[int] simplex, int dimension) - void remove_maximal_simplex(vector[int] simplex) void expansion(int max_dim) + void remove_maximal_simplex(vector[int] simplex) + bool prune_above_filtration(double filtration) cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": @@ -103,8 +106,8 @@ cdef class SimplexTree: return self.pcohptr != NULL def filtration(self, simplex): - """This function returns the simplicial complex filtration value for a - given N-simplex. + """This function returns the filtration value for a given N-simplex in + this simplicial complex, or +infinity if it is not in the complex. :param simplex: The N-simplex, represented by a list of vertex. :type simplex: list of int. @@ -113,15 +116,31 @@ cdef class SimplexTree: """ return self.thisptr.simplex_filtration(simplex) + def assign_filtration(self, simplex, filtration): + """This function assigns the simplicial complex filtration value for a + given N-simplex. + + :param simplex: The N-simplex, represented by a list of vertex. + :type simplex: list of int. + :param filtration: The simplicial complex filtration value. + :type filtration: float + """ + self.thisptr.assign_simplex_filtration(simplex, filtration) + def initialize_filtration(self): """This function initializes and sorts the simplicial complex filtration vector. .. note:: - This function must be launched before persistence, betti_numbers, - persistent_betti_numbers or get_filtration after inserting or - removing simplices. + This function must be launched before + :func:`persistence()<gudhi.SimplexTree.persistence>`, + :func:`betti_numbers()<gudhi.SimplexTree.betti_numbers>`, + :func:`persistent_betti_numbers()<gudhi.SimplexTree.persistent_betti_numbers>`, + or :func:`get_filtration()<gudhi.SimplexTree.get_filtration>` + after :func:`inserting<gudhi.SimplexTree.insert>` or + :func:`removing<gudhi.SimplexTree.remove_maximal_simplex>` + simplices. """ self.thisptr.initialize_filtration() @@ -148,21 +167,42 @@ cdef class SimplexTree: :returns: the simplicial complex dimension. :rtype: int + + .. note:: + + This function is not constant time because it can recompute + dimension if required (can be triggered by + :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + or + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + methods). """ return self.thisptr.dimension() - def set_dimension(self, dimension): - """This function sets the dimension of the simplicial complex. + def upper_bound_dimension(self): + """This function returns a valid dimension upper bound of the + simplicial complex. - insert and remove_maximal_simplex functions do not update dimension - value of the `SimplexTree`. + :returns: an upper bound on the dimension of the simplicial complex. + :rtype: int + """ + return self.thisptr.upper_bound_dimension() - `AlphaComplex`, `RipsComplex`, `TangentialComplex` and `WitnessComplex` - automatically sets the correct dimension in their `create_simplex_tree` - functions. + def set_dimension(self, dimension): + """This function sets the dimension of the simplicial complex. :param dimension: The new dimension value. :type dimension: int. + + .. note:: + + This function must be used with caution because it disables + dimension recomputation when required + (this recomputation can be triggered by + :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` + or + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + ). """ self.thisptr.set_dimension(<int>dimension) @@ -182,14 +222,17 @@ cdef class SimplexTree: def insert(self, simplex, filtration=0.0): """This function inserts the given N-simplex and its subfaces with the - given filtration value (default value is '0.0'). + given filtration value (default value is '0.0'). If some of those + simplices are already present with a higher filtration value, their + filtration value is lowered. :param simplex: The N-simplex to insert, represented by a list of vertex. :type simplex: list of int. :param filtration: The filtration value of the simplex. :type filtration: float. - :returns: true if the simplex was found, false otherwise. + :returns: true if the simplex was not yet in the complex, false + otherwise (whatever its original filtration value). :rtype: bool """ cdef vector[int] csimplex @@ -286,9 +329,57 @@ cdef class SimplexTree: :param simplex: The N-simplex, represented by a list of vertex. :type simplex: list of int. + + .. note:: + + Be aware that removing is shifting data in a flat_map + (:func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` to be done). + + .. note:: + + The dimension of the simplicial complex may be lower after calling + remove_maximal_simplex than it was before. However, + :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + method will return the old value, which + remains a valid upper bound. If you care, you can call + :func:`dimension()<gudhi.SimplexTree.dimension>` + to recompute the exact dimension. """ self.thisptr.remove_maximal_simplex(simplex) + def prune_above_filtration(self, filtration): + """Prune above filtration value given as parameter. + + :param filtration: Maximum threshold value. + :type filtration: float. + :returns: The filtration modification information. + :rtype: bint + + + .. note:: + + Some simplex tree functions require the filtration to be valid. + prune_above_filtration function is not launching + :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` + but returns the filtration modification + information. If the complex has changed , please call + :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` + to recompute it. + + .. note:: + + Note that the dimension of the simplicial complex may be lower + after calling + :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` + than it was before. However, + :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` + will return the old value, which remains a + valid upper bound. If you care, you can call + :func:`dimension()<gudhi.SimplexTree.dimension>` + method to recompute the exact dimension. + """ + return self.thisptr.prune_above_filtration(filtration) + def expansion(self, max_dim): """Expands the Simplex_tree containing only its one skeleton until dimension max_dim. @@ -312,7 +403,7 @@ cdef class SimplexTree: """This function returns the persistence of the simplicial complex. :param homology_coeff_field: The homology coefficient field. Must be a - prime number + prime number. Default value is 11. :type homology_coeff_field: int. :param min_persistence: The minimum persistence value to take into account (strictly greater than min_persistence). Default value is @@ -336,8 +427,9 @@ cdef class SimplexTree: :returns: The Betti numbers ([B0, B1, ..., Bn]). :rtype: list of int - :note: betti_numbers function requires persistence function to be - launched first. + :note: betti_numbers function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. """ cdef vector[int] bn_result if self.pcohptr != NULL: @@ -361,7 +453,8 @@ cdef class SimplexTree: :returns: The persistent Betti numbers ([B0, B1, ..., Bn]). :rtype: list of int - :note: persistent_betti_numbers function requires persistence + :note: persistent_betti_numbers function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` function to be launched first. """ cdef vector[int] pbn_result @@ -381,8 +474,9 @@ cdef class SimplexTree: :returns: The persistence intervals. :rtype: list of pair of float - :note: intervals_in_dim function requires persistence function to be - launched first. + :note: intervals_in_dim function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. """ cdef vector[pair[double,double]] intervals_result if self.pcohptr != NULL: @@ -399,8 +493,9 @@ cdef class SimplexTree: :param persistence_file: The specific dimension. :type persistence_file: string. - :note: intervals_in_dim function requires persistence function to be - launched first. + :note: intervals_in_dim function requires + :func:`persistence()<gudhi.SimplexTree.persistence>` + function to be launched first. """ if self.pcohptr != NULL: if persistence_file != '': diff --git a/cython/doc/_templates/layout.html b/cython/doc/_templates/layout.html index 243f33c6..8e4eba40 100644 --- a/cython/doc/_templates/layout.html +++ b/cython/doc/_templates/layout.html @@ -198,6 +198,8 @@ <ul class="dropdown"> <li><a href="http://gudhi.gforge.inria.fr/licensing/">Licensing</a></li> <li><a href="https://gforge.inria.fr/frs/?group_id=3865" target="_blank">Get the sources</a></li> + <li><a href="https://gforge.inria.fr/frs/download.php/file/37113/GUDHI_2.0.0_OSX_UTILS.beta.tar.gz" target="_blank">Utils for Mac OSx</a></li> + <li><a href="https://gforge.inria.fr/frs/download.php/file/37112/GUDHI_2.0.0_WIN64_UTILS.beta.zip" target="_blank">Utils for Win x64</a></li> </ul> </li> <li class="divider"></li> @@ -208,6 +210,8 @@ <li><a href="http://gudhi.gforge.inria.fr/doc/latest/installation.html">C++ installation manual</a></li> <li><a href="http://gudhi.gforge.inria.fr/python/latest/">Python documentation</a></li> <li><a href="http://gudhi.gforge.inria.fr/python/latest/installation.html">Python installation manual</a></li> + <li><a href="http://gudhi.gforge.inria.fr/utils/">Utilities</a></li> + <li><a href="http://bertrand.michel.perso.math.cnrs.fr/Enseignements/TDA-Gudhi-Python.html" target="_blank">Tutorial</a></li> </ul> </li> <li class="divider"></li> @@ -255,7 +259,7 @@ {%- if hasdoc('copyright') %} {% trans path=pathto('copyright'), copyright=copyright|e %}© <a href="{{ path }}">Copyright</a> {{ copyright }}.{% endtrans %} {%- else %} - {% trans copyright=copyright|e %}© Copyright {{ copyright }}.{% endtrans %} + {% trans copyright=copyright|e %} {{ copyright }}.{% endtrans %} {%- endif %} {%- endif %} {%- if last_updated %} diff --git a/cython/doc/conf.py b/cython/doc/conf.py index 19a880d4..a13c9751 100755 --- a/cython/doc/conf.py +++ b/cython/doc/conf.py @@ -62,7 +62,7 @@ import gudhi # General information about the project. project = gudhi.__name__ -copyright = gudhi.__copyright__ +copyright = gudhi.__copyright__ + ' - ' + gudhi.__license__ # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -158,7 +158,7 @@ html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' +html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. diff --git a/cython/doc/cubical_complex_user.rst b/cython/doc/cubical_complex_user.rst index 2bfac62a..34598f02 100644 --- a/cython/doc/cubical_complex_user.rst +++ b/cython/doc/cubical_complex_user.rst @@ -81,23 +81,7 @@ filtration to all cubes. There are a number of constructors that can be used to who want to use the code directly. They can be found in the :doc:`cubical_complex_ref`. Currently one input from a text file is used. It uses a format used already in `Perseus software <http://www.sas.upenn.edu/~vnanda/perseus/>`_ by Vidit Nanda. -Below we are providing a description of the format. The first line contains a number d begin the dimension of the -bitmap (2 in the example below). Next d lines are the numbers of top dimensional cubes in each dimensions (3 and 3 -in the example below). Next, in lexicographical order, the filtration of top dimensional cubes is given (1 4 6 8 -20 4 7 6 5 in the example below). - -.. figure:: - ../../doc/Bitmap_cubical_complex/exampleBitmap.png - :alt: Example of a input data. - :figclass: align-center - - Example of a input data. - -The input file for the following complex is: - -.. literalinclude:: ../../data/bitmap/cubicalcomplexdoc.txt - -.. centered:: ../../data/bitmap/cubicalcomplexdoc.txt +The file format is described here: :doc:`Perseus <fileformats>`. .. testcode:: @@ -124,15 +108,9 @@ Imposing periodic boundary conditions in the direction i, means that the left an :math:`\mathcal{K}` are considered the same. In particular, if for a bitmap :math:`\mathcal{K}` periodic boundary conditions are imposed in all directions, then complex :math:`\mathcal{K}` became n-dimensional torus. One can use various constructors from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical -complex with periodic boundary conditions. One can also use Perseus style input files. To indicate periodic boundary -conditions in a given direction, then number of top dimensional cells in this direction have to be multiplied by -1. -For instance: - -.. literalinclude:: ../../data/bitmap/periodiccubicalcomplexdoc.txt - -.. centered:: ../../data/bitmap/periodiccubicalcomplexdoc.txt +complex with periodic boundary conditions. -Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. +One can also use Perseus style input files (see :doc:`Perseus <fileformats>`) for the specific periodic case: .. testcode:: @@ -149,6 +127,23 @@ the program output is: Periodic cubical complex is of dimension 2 - 42 simplices. +Or it can be defined as follows: + +.. testcode:: + + from gudhi import PeriodicCubicalComplex as pcc + periodic_cc = pcc(dimensions=[3,3], + top_dimensional_cells= [0, 0, 0, 0, 1, 0, 0, 0, 0], + periodic_dimensions=[True, False]) + result_str = 'Periodic cubical complex is of dimension ' + repr(periodic_cc.dimension()) + ' - ' + \ + repr(periodic_cc.num_simplices()) + ' simplices.' + print(result_str) + +the program output is: + +.. testoutput:: + + Periodic cubical complex is of dimension 2 - 42 simplices. Examples. --------- diff --git a/cython/doc/fileformats.rst b/cython/doc/fileformats.rst index 156ef4e4..4f0b6f6d 100644 --- a/cython/doc/fileformats.rst +++ b/cython/doc/fileformats.rst @@ -23,7 +23,7 @@ Here is a simple sample file:: 3 34.2 34.974 4 3. inf -Other sample files can be found in the data/persistence_diagram folder. +Other sample files can be found in the `data/persistence_diagram` folder. Such files can be generated with :meth:`gudhi.SimplexTree.write_persistence_diagram`, read with @@ -31,3 +31,56 @@ Such files can be generated with :meth:`gudhi.read_persistence_intervals_in_dimension` and displayed with :meth:`gudhi.plot_persistence_barcode` or :meth:`gudhi.plot_persistence_diagram`. + +Iso-cuboid +********** + +Such a file describes an iso-oriented cuboid with diagonal opposite vertices +(min_x, min_y, min_z,...) and (max_x, max_y, max_z, ...). The format is:: + + min_x min_y [min_z ...] + max_x max_y [max_z ...] + +Here is a simple sample file in the 3D case:: + + -1. -1. -1. + 1. 1. 1. + + +Perseus +******* + +This file format is the format used by the +`Perseus software <http://www.sas.upenn.edu/~vnanda/perseus/>`_ by Vidit Nanda. +The first line contains a number d begin the dimension of the bitmap (2 in the +example below). Next d lines are the numbers of top dimensional cubes in each +dimensions (3 and 3 in the example below). Next, in lexicographical order, the +filtration of top dimensional cubes is given (1 4 6 8 20 4 7 6 5 in the example +below). + +.. figure:: + ../../doc/Bitmap_cubical_complex/exampleBitmap.png + :alt: Example of a input data. + :figclass: align-center + + Example of a input data. + +The input file for the following complex is: + +.. literalinclude:: ../../data/bitmap/cubicalcomplexdoc.txt + +.. centered:: ../../data/bitmap/cubicalcomplexdoc.txt + +To indicate periodic boundary conditions in a given direction, then number of +top dimensional cells in this direction have to be multiplied by -1. For +instance: + +.. literalinclude:: ../../data/bitmap/periodiccubicalcomplexdoc.txt + +.. centered:: ../../data/bitmap/periodiccubicalcomplexdoc.txt + + +Indicate that we have imposed periodic boundary conditions in the direction x, +but not in the direction y. + +Other sample files can be found in the `data/bitmap` folder. diff --git a/cython/doc/python3-sphinx-build.py b/cython/doc/python3-sphinx-build.py index 44b94169..84d158cf 100755 --- a/cython/doc/python3-sphinx-build.py +++ b/cython/doc/python3-sphinx-build.py @@ -1,4 +1,4 @@ -#!/usr/bin/python3 +#!/usr/bin/env python3 """ Emulate sphinx-build for python3 diff --git a/cython/doc/witness_complex_user.rst b/cython/doc/witness_complex_user.rst index 29413269..99be5185 100644 --- a/cython/doc/witness_complex_user.rst +++ b/cython/doc/witness_complex_user.rst @@ -121,7 +121,7 @@ Example2: Computing persistence using strong relaxed witness complex Here is an example of constructing a strong witness complex filtration and computing persistence on it: -* :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` +* :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` Bibliography ============ diff --git a/cython/example/simplex_tree_example.py b/cython/example/simplex_tree_example.py index 831d9da8..51a60e73 100755 --- a/cython/example/simplex_tree_example.py +++ b/cython/example/simplex_tree_example.py @@ -48,8 +48,6 @@ if st.insert([0, 1, 2], filtration=4.0): else: print("Not inserted...") -# FIXME: Remove this line -st.set_dimension(3) print("dimension=", st.dimension()) st.initialize_filtration() diff --git a/cython/include/Cubical_complex_interface.h b/cython/include/Cubical_complex_interface.h index 7c0148f1..fad92c2c 100644 --- a/cython/include/Cubical_complex_interface.h +++ b/cython/include/Cubical_complex_interface.h @@ -43,6 +43,12 @@ class Cubical_complex_interface : public Bitmap_cubical_complex<CubicalComplexOp : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells) { } + Cubical_complex_interface(const std::vector<unsigned>& dimensions, + const std::vector<double>& top_dimensional_cells, + const std::vector<bool>& periodic_dimensions) + : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells, periodic_dimensions) { + } + Cubical_complex_interface(const std::string& perseus_file) : Bitmap_cubical_complex<CubicalComplexOptions>(perseus_file.c_str()) { } diff --git a/cython/include/Simplex_tree_interface.h b/cython/include/Simplex_tree_interface.h index 09e7e992..54a4f824 100644 --- a/cython/include/Simplex_tree_interface.h +++ b/cython/include/Simplex_tree_interface.h @@ -52,6 +52,10 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { return (Base::find(vh) != Base::null_simplex()); } + void assign_simplex_filtration(const Simplex& vh, Filtration_value filtration) { + Base::assign_filtration(Base::find(vh), filtration); + } + bool insert(const Simplex& simplex, Filtration_value filtration = 0) { Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); return (result.second); diff --git a/cython/include/Tangential_complex_interface.h b/cython/include/Tangential_complex_interface.h index 5e9dc0e4..0c3a510e 100644 --- a/cython/include/Tangential_complex_interface.h +++ b/cython/include/Tangential_complex_interface.h @@ -105,9 +105,7 @@ class Tangential_complex_interface { } void create_simplex_tree(Simplex_tree<>* simplex_tree) { - int max_dim = tangential_complex_->create_complex<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>(*simplex_tree); - // FIXME - simplex_tree->set_dimension(max_dim); + tangential_complex_->create_complex<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>(*simplex_tree); simplex_tree->initialize_filtration(); } diff --git a/cython/setup.py.in b/cython/setup.py.in index fefa36bb..c767e93d 100644 --- a/cython/setup.py.in +++ b/cython/setup.py.in @@ -23,7 +23,7 @@ from Cython.Build import cythonize along with this program. If not, see <http://www.gnu.org/licenses/>. """ -__author__ = "Vincent Rouvreau" +__author__ = "GUDHI Editorial Board" __copyright__ = "Copyright (C) 2016 INRIA" __license__ = "GPL v3" @@ -41,7 +41,7 @@ gudhi = Extension( setup( name = 'gudhi', - author='Vincent Rouvreau', + author='GUDHI Editorial Board', author_email='gudhi-contact@lists.gforge.inria.fr', version='@GUDHI_VERSION@', url='http://gudhi.gforge.inria.fr/', diff --git a/cython/test/test_cubical_complex.py b/cython/test/test_cubical_complex.py index 9a365823..0e81554d 100755 --- a/cython/test/test_cubical_complex.py +++ b/cython/test/test_cubical_complex.py @@ -62,17 +62,17 @@ def test_dimension_or_perseus_file_constructor(): assert cub.__is_defined() == False assert cub.__is_persistence_defined() == False -def test_dimension_constructor(): +def test_dimension_simple_constructor(): cub = CubicalComplex(dimensions=[3, 3], top_dimensional_cells = [1,2,3,4,5,6,7,8,9]) assert cub.__is_defined() == True assert cub.__is_persistence_defined() == False - assert cub.persistence() == [(1, (0.0, 100.0)), (0, (0.0, float('inf')))] + assert cub.persistence() == [(0, (1.0, float('inf')))] assert cub.__is_persistence_defined() == True - assert cub.betti_numbers() == [1, 0] - assert cub.persistent_betti_numbers(0, 1000) == [0, 0] + assert cub.betti_numbers() == [1, 0, 0] + assert cub.persistent_betti_numbers(0, 1000) == [0, 0, 0] -def test_dimension_constructor(): +def test_dimension_file_constructor(): # Create test file test_file = open('CubicalOneSphere.txt', 'w') test_file.write('2\n3\n3\n0\n0\n0\n0\n100\n0\n0\n0\n0\n') diff --git a/cython/test/test_simplex_tree.py b/cython/test/test_simplex_tree.py index 4d452d7d..6dec5d94 100755 --- a/cython/test/test_simplex_tree.py +++ b/cython/test/test_simplex_tree.py @@ -34,9 +34,13 @@ def test_insertion(): # insert test assert st.insert([0, 1]) == True + + assert st.dimension() == 1 + assert st.insert([0, 1, 2], filtration=4.0) == True - # FIXME: Remove this line - st.set_dimension(2) + + assert st.dimension() == 2 + assert st.num_simplices() == 7 assert st.num_vertices() == 3 @@ -86,8 +90,9 @@ def test_insertion(): assert st.find([2]) == True st.initialize_filtration() - assert st.persistence() == [(1, (4.0, float('inf'))), (0, (0.0, float('inf')))] + assert st.persistence(persistence_dim_max = True) == [(1, (4.0, float('inf'))), (0, (0.0, float('inf')))] assert st.__is_persistence_defined() == True + assert st.betti_numbers() == [1, 1] assert st.persistent_betti_numbers(-0.1, 10000.0) == [0, 0] assert st.persistent_betti_numbers(0.0, 10000.0) == [1, 0] @@ -129,3 +134,30 @@ def test_expansion(): ([1, 2], 0.5), ([0, 1, 2], 0.5), ([1, 2, 3], 0.5), ([5], 0.6), ([6], 0.6), ([5, 6], 0.6), ([4], 0.7), ([2, 4], 0.7), ([0, 3], 0.8), ([0, 1, 3], 0.8), ([0, 2, 3], 0.8), ([0, 1, 2, 3], 0.8), ([4, 6], 0.9), ([3, 6], 1.0)] + +def test_automatic_dimension(): + st = SimplexTree() + assert st.__is_defined() == True + assert st.__is_persistence_defined() == False + + # insert test + assert st.insert([0,1,3], filtration=0.5) == True + assert st.insert([0,1,2], filtration=1.) == True + + assert st.num_vertices() == 4 + assert st.num_simplices() == 11 + + assert st.dimension() == 2 + assert st.upper_bound_dimension() == 2 + + assert st.prune_above_filtration(0.6) == True + assert st.dimension() == 2 + assert st.upper_bound_dimension() == 2 + + st.assign_filtration([0, 1, 3], 0.7) + assert st.filtration([0, 1, 3]) == 0.7 + + st.remove_maximal_simplex([0, 1, 3]) + assert st.upper_bound_dimension() == 2 + assert st.dimension() == 1 + assert st.upper_bound_dimension() == 1 diff --git a/data/points/KleinBottle5D.off b/data/points/KleinBottle5D.off new file mode 100644 index 00000000..b578593c --- /dev/null +++ b/data/points/KleinBottle5D.off @@ -0,0 +1,15879 @@ +nOFF +5 +15876 0 0 +3 0 0 0 1 +3.05 0 0.09983 0 0.9988 +3.1 0 0.1987 0 0.995 +3.149 0 0.2955 0 0.9888 +3.199 0 0.3894 0 0.9801 +3.247 0 0.4794 0 0.9689 +3.296 0 0.5646 0 0.9553 +3.343 0 0.6442 0 0.9394 +3.389 0 0.7174 0 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index cf1a946a..a08663ca 100644 --- a/doc/Alpha_complex/Intro_alpha_complex.h +++ b/doc/Alpha_complex/Intro_alpha_complex.h @@ -31,7 +31,7 @@ namespace alpha_complex { /** \defgroup alpha_complex Alpha complex * * \author Vincent Rouvreau - * + * * @{ * * \section definition Definition @@ -195,8 +195,6 @@ namespace alpha_complex { * * \include Alpha_complex/alphaoffreader_for_doc_32.txt * - * \copyright GNU General Public License v3. - * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim */ /** @} */ // end defgroup alpha_complex diff --git a/doc/Bitmap_cubical_complex/Gudhi_Cubical_Complex_doc.h b/doc/Bitmap_cubical_complex/Gudhi_Cubical_Complex_doc.h index 5963caa3..a5d7b60f 100644 --- a/doc/Bitmap_cubical_complex/Gudhi_Cubical_Complex_doc.h +++ b/doc/Bitmap_cubical_complex/Gudhi_Cubical_Complex_doc.h @@ -63,7 +63,7 @@ namespace cubical_complex { * For further details and theory of cubical complexes, please consult \cite kaczynski2004computational as well as the * following paper \cite peikert2012topological . * - * \section cubicalcomplexdatastructure Data structure. + * \section cubicalcomplexdatastructure Data structure * * The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in * \f$\mathbb{R}^n\f$. This extra assumption allows for a memory efficient way of storing cubical complexes in a form @@ -85,37 +85,14 @@ namespace cubical_complex { * present in the product that gives the cube \f$C\f$. In a similar way, we can compute boundary and the coboundary of * each cube. Further details can be found in the literature. * - * \section inputformat Input Format. + * \section inputformat Input Format * * In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star * filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users * who want to use the code directly. They can be found in the \a Bitmap_cubical_complex class. * Currently one input from a text file is used. It uses a format used already in Perseus software - * (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. - * Below we are providing a description of the format. The first line contains a number d begin the dimension of the - * bitmap (2 in the example below). Next d lines are the numbers of top dimensional cubes in each dimensions (3 and 3 - * in the example below). Next, in lexicographical order, the filtration of top dimensional cubes is given (1 4 6 8 - * 20 4 7 6 5 in the example below). - * - * - * \image html "exampleBitmap.png" "Example of a input data." - * - * The input file for the following complex is: - * \verbatim -2 -3 -3 -1 -4 -6 -8 -20 -4 -7 -6 -5 -\endverbatim - + * (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. The file format is described here: \ref FileFormatsPerseus. + * * \section PeriodicBoundaryConditions Periodic boundary conditions * Often one would like to impose periodic boundary conditions to the cubical complex. Let \f$ I_1\times ... \times * I_n \f$ be a box that is decomposed with a cubical complex \f$ \mathcal{K} \f$. Imposing periodic boundary @@ -123,30 +100,11 @@ namespace cubical_complex { * considered the same. In particular, if for a bitmap \f$ \mathcal{K} \f$ periodic boundary conditions are imposed * in all directions, then complex \f$ \mathcal{K} \f$ became n-dimensional torus. One can use various constructors * from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical complex with periodic - * boundary conditions. One can also use Perseus style input files. To indicate periodic boundary conditions in a - * given direction, then number of top dimensional cells in this direction have to be multiplied by -1. For instance: - - *\verbatim -2 --3 -3 -1 -4 -6 -8 -20 -4 -7 -6 -5 -\endverbatim - - * Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. - + * boundary conditions. One can also use Perseus style input files (see \ref FileFormatsPerseus). + * * \section BitmapExamples Examples - * End user programs are available in example/Bitmap_cubical_complex folder. + * End user programs are available in example/Bitmap_cubical_complex and utilities/Bitmap_cubical_complex folders. * - * \copyright GNU General Public License v3. */ /** @} */ // end defgroup cubical_complex diff --git a/doc/Nerve_GIC/COPYRIGHT b/doc/Nerve_GIC/COPYRIGHT new file mode 100644 index 00000000..0c36a526 --- /dev/null +++ b/doc/Nerve_GIC/COPYRIGHT @@ -0,0 +1,19 @@ +The files of this directory are part of the Gudhi Library. The Gudhi library +(Geometric Understanding in Higher Dimensions) is a generic C++ library for +computational topology. + +Author(s): Mathieu Carrière + +Copyright (C) 2017 INRIA + +This program is free software: you can redistribute it and/or modify it under +the terms of the GNU General Public License as published by the Free Software +Foundation, either version 3 of the License, or (at your option) any later +version. + +This program is distributed in the hope that it will be useful, but WITHOUT +ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along with +this program. If not, see <http://www.gnu.org/licenses/>. diff --git a/doc/Nerve_GIC/GIC.jpg b/doc/Nerve_GIC/GIC.jpg Binary files differnew file mode 100644 index 00000000..cb1b9b7f --- /dev/null +++ b/doc/Nerve_GIC/GIC.jpg diff --git a/doc/Nerve_GIC/GIC.pdf b/doc/Nerve_GIC/GIC.pdf Binary files differnew file mode 100644 index 00000000..30525745 --- /dev/null +++ b/doc/Nerve_GIC/GIC.pdf diff --git a/doc/Nerve_GIC/Intro_graph_induced_complex.h b/doc/Nerve_GIC/Intro_graph_induced_complex.h new file mode 100644 index 00000000..2b648425 --- /dev/null +++ b/doc/Nerve_GIC/Intro_graph_induced_complex.h @@ -0,0 +1,186 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carriere + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ +#define DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ + +namespace Gudhi { + +namespace cover_complex { + +/** \defgroup cover_complex Cover complex + * + * \author Mathieu Carrière + * + * @{ + * + * Visualizations of the simplicial complexes can be done with either + * neato (from <a target="_blank" href="http://www.graphviz.org/">graphviz</a>), + * <a target="_blank" href="http://www.geomview.org/">geomview</a>, + * <a target="_blank" href="https://github.com/MLWave/kepler-mapper">KeplerMapper</a>. + * Input point clouds are assumed to be + * <a target="_blank" href="http://www.geomview.org/docs/html/OFF.html">OFF files</a>. + * + * \section covers Covers + * + * Nerves and Graph Induced Complexes require a cover C of the input point cloud P, + * that is a set of subsets of P whose union is P itself. + * Very often, this cover is obtained from the preimage of a family of intervals covering + * the image of some scalar-valued function f defined on P. This family is parameterized + * by its resolution, which can be either the number or the length of the intervals, + * and its gain, which is the overlap percentage between consecutive intervals (ordered by their first values). + * + * \section nerves Nerves + * + * \subsection nervedefinition Nerve definition + * + * Assume you are given a cover C of your point cloud P. Then, the Nerve of this cover + * is the simplicial complex that has one k-simplex per k-fold intersection of cover elements. + * See also <a target="_blank" href="https://en.wikipedia.org/wiki/Nerve_of_a_covering"> Wikipedia </a>. + * + * \image html "nerve.png" "Nerve of a double torus" + * + * \subsection nerveexample Example + * + * This example builds the Nerve of a point cloud sampled on a 3D human shape (human.off). + * The cover C comes from the preimages of intervals (10 intervals with gain 0.3) + * covering the height function (coordinate 2), + * which are then refined into their connected components using the triangulation of the .OFF file. + * + * \include Nerve_GIC/Nerve.cpp + * + * When launching: + * + * \code $> ./Nerve ../../data/points/human.off 2 10 0.3 -v + * \endcode + * + * the program output is: + * + * \include Nerve_GIC/Nerve.txt + * + * The program also writes a file ../../data/points/human_sc.txt. The first three lines in this file are the location + * of the input point cloud and the function used to compute the cover. + * The fourth line contains the number of vertices nv and edges ne of the Nerve. + * The next nv lines represent the vertices. Each line contains the vertex ID, + * the number of data points it contains, and their average color function value. + * Finally, the next ne lines represent the edges, characterized by the ID of their vertices. + * + * Using KeplerMapper, one can obtain the following visualization: + * + * \image html "nervevisu.jpg" "Visualization with KeplerMapper" + * + * \section gic Graph Induced Complexes (GIC) + * + * \subsection gicdefinition GIC definition + * + * Again, assume you are given a cover C of your point cloud P. Moreover, assume + * you are also given a graph G built on top of P. Then, for any clique in G + * whose nodes all belong to different elements of C, the GIC includes a corresponding + * simplex, whose dimension is the number of nodes in the clique minus one. + * See \cite Dey13 for more details. + * + * \image html "GIC.jpg" "GIC of a point cloud." + * + * \subsection gicexamplevor Example with cover from Voronoï + * + * This example builds the GIC of a point cloud sampled on a 3D human shape (human.off). + * We randomly subsampled 100 points in the point cloud, which act as seeds of + * a geodesic Voronoï diagram. Each cell of the diagram is then an element of C. + * The graph G (used to compute both the geodesics for Voronoï and the GIC) + * comes from the triangulation of the human shape. Note that the resulting simplicial complex is in dimension 3 + * in this example. + * + * \include Nerve_GIC/VoronoiGIC.cpp + * + * When launching: + * + * \code $> ./VoronoiGIC ../../data/points/human.off 700 -v + * \endcode + * + * the program outputs SC.off. Using e.g. + * + * \code $> geomview ../../data/points/human_sc.off + * \endcode + * + * one can obtain the following visualization: + * + * \image html "gicvoronoivisu.jpg" "Visualization with Geomview" + * + * \subsection functionalGICdefinition Functional GIC + * + * If one restricts to the cliques in G whose nodes all belong to preimages of consecutive + * intervals (assuming the cover of the height function is minimal, i.e. no more than + * two intervals can intersect at a time), the GIC is of dimension one, i.e. a graph. + * We call this graph the functional GIC. See \cite Carriere16 for more details. + * + * \subsection functionalGICexample Example + * + * Functional GIC comes with automatic selection of the Rips threshold, + * the resolution and the gain of the function cover. See \cite Carriere17c for more details. In this example, + * we compute the functional GIC of a Klein bottle embedded in R^5, + * where the graph G comes from a Rips complex with automatic threshold, + * and the cover C comes from the preimages of intervals covering the first coordinate, + * with automatic resolution and gain. Note that automatic threshold, resolution and gain + * can be computed as well for the Nerve. + * + * \include Nerve_GIC/CoordGIC.cpp + * + * When launching: + * + * \code $> ./CoordGIC ../../data/points/KleinBottle5D.off 0 -v + * \endcode + * + * the program outputs SC.dot. Using e.g. + * + * \code $> neato SC.dot -Tpdf -o SC.pdf + * \endcode + * + * one can obtain the following visualization: + * + * \image html "coordGICvisu2.jpg" "Visualization with Neato" + * + * where nodes are colored by the filter function values and, for each node, the first number is its ID + * and the second is the number of data points that its contain. + * + * We also provide an example on a set of 72 pictures taken around the same object (lucky_cat.off). + * The function is now the first eigenfunction given by PCA, whose values + * are written in a file (lucky_cat_PCA1). Threshold, resolution and gain are automatically selected as before. + * + * \include Nerve_GIC/FuncGIC.cpp + * + * When launching: + * + * \code $> ./FuncGIC ../../data/points/COIL_database/lucky_cat.off ../../data/points/COIL_database/lucky_cat_PCA1 -v + * \endcode + * + * the program outputs again SC.dot which gives the following visualization after using neato: + * + * \image html "funcGICvisu.jpg" "Visualization with neato" + * + */ +/** @} */ // end defgroup cover_complex + +} // namespace cover_complex + +} // namespace Gudhi + +#endif // DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_ diff --git a/doc/Nerve_GIC/coordGICvisu.pdf b/doc/Nerve_GIC/coordGICvisu.pdf Binary files differnew file mode 100644 index 00000000..313aa1b5 --- /dev/null +++ b/doc/Nerve_GIC/coordGICvisu.pdf diff --git a/doc/Nerve_GIC/coordGICvisu2.jpg b/doc/Nerve_GIC/coordGICvisu2.jpg Binary files differnew file mode 100644 index 00000000..046feb2a --- /dev/null +++ b/doc/Nerve_GIC/coordGICvisu2.jpg diff --git a/doc/Nerve_GIC/funcGICvisu.jpg b/doc/Nerve_GIC/funcGICvisu.jpg Binary files differnew file mode 100644 index 00000000..36b64dde --- /dev/null +++ b/doc/Nerve_GIC/funcGICvisu.jpg diff --git a/doc/Nerve_GIC/funcGICvisu.pdf b/doc/Nerve_GIC/funcGICvisu.pdf Binary files differnew file mode 100644 index 00000000..d7456cd3 --- /dev/null +++ b/doc/Nerve_GIC/funcGICvisu.pdf diff --git a/doc/Nerve_GIC/gicvisu.jpg b/doc/Nerve_GIC/gicvisu.jpg Binary files differnew file mode 100644 index 00000000..576dae47 --- /dev/null +++ b/doc/Nerve_GIC/gicvisu.jpg diff --git a/doc/Nerve_GIC/gicvoronoivisu.jpg b/doc/Nerve_GIC/gicvoronoivisu.jpg Binary files differnew file mode 100644 index 00000000..cd86c411 --- /dev/null +++ b/doc/Nerve_GIC/gicvoronoivisu.jpg diff --git a/doc/Nerve_GIC/nerve.png b/doc/Nerve_GIC/nerve.png Binary files differnew file mode 100644 index 00000000..b66da4a4 --- /dev/null +++ b/doc/Nerve_GIC/nerve.png diff --git a/doc/Nerve_GIC/nervevisu.jpg b/doc/Nerve_GIC/nervevisu.jpg Binary files differnew file mode 100644 index 00000000..67ae1d7e --- /dev/null +++ b/doc/Nerve_GIC/nervevisu.jpg diff --git a/doc/Persistence_representations/Persistence_representations_doc.h b/doc/Persistence_representations/Persistence_representations_doc.h new file mode 100644 index 00000000..38bd3a21 --- /dev/null +++ b/doc/Persistence_representations/Persistence_representations_doc.h @@ -0,0 +1,259 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA Sophia-Saclay (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef DOC_GUDHI_STAT_H_ +#define DOC_GUDHI_STAT_H_ + +namespace Gudhi { + +namespace Persistence_representations { + +/** \defgroup Persistence_representations Persistence representations + * + * \author Pawel Dlotko + * + * @{ + *\section Persistence_representations_idea Idea + + In order to perform most of the statistical tests and machine learning algorithms on a data one need to be able to + perform only a very limited number of operations on them. Let us fix a representation of + data of a type A. To perform most of the statistical and machine learning operations one need to be able to compute + average of objects of type A (so that the averaged object is also of a type A), to + compute distance between objects of a type A, to vectorize object of a type A and to compute scalar product of a pair + objects of a type A. + + To put this statement into a context, let us assume we have two collections \f$ c_1,\ldots,c_n\f$ and + \f$d_1,...,d_n\f$ of objects of a type A. We want to verify if the average of those two collections + are different by performing a permutation test. + First of all, we compute averages of those two collections: C average of \f$ c_1,\ldots,c_n \f$ and D average of + \f$d_1,\ldots,d_n\f$. Note that both C and D are of a type A. Then we compute \f$d(C,D)\f$, + a distance between C and D. + Later we put the two collections into one bin: + \f[B = \{ c_1,...,c_n,d_1,...,d_n \}\f] + Then we shuffle B, and we divide the shuffled version of B into two classes: \f$B_1\f$ and \f$B_2\f$ (in this case, of + the same cardinality). Then we compute averages \f$\hat{B_1}\f$ and \f$\hat{B_2}\f$ + of elements in \f$B_1\f$ and \f$B_2\f$. Note that again, \f$\hat{B_1}\f$ and \f$\hat{B_2}\f$ are of a type A. + Then we compute their distance \f$d(\hat{B_1},\hat{B_2})\f$. The procedure of shuffling and dividing the set \f$B\f$ + is repeated \f$N\f$ times (where \f$N\f$ is reasonably large number). + Then the p-value of a statement that the averages of \f$c_1,...,c_n\f$ and \f$d_1,...,d_n\f$ is approximated by the + number of times \f$d(\hat{B_1},\hat{B_2}) > d(C,D)\f$ divided by \f$N\f$. + + The permutation test reminded above can be performed for any type A which can be averaged, and which allows for + computations of distances. + + The Persistence\_representations contains a collection of various representations of persistent homology that + implements various concepts described below: + + \li Concept of a representation of persistence that allows averaging (so that the average object is of the same type). + \li Concept of representation of persistence that allows computations of distances. + \li Concept of representation of persistence that allows computations of scalar products. + \li Concept of representation of persistence that allows vectorization. + \li Concept of representation of persistence that allows computations of real-valued characteristics of objects. + + + At the moment an implementation of the following representations of persistence are available (further details of + those representations will be discussed later): + + \li Exact persistence landscapes (allow averaging, computation of distances, scalar products, vectorizations and real + value characteristics). + \li Persistence landscapes on a grid (allow averaging, computation of distances scalar products, vectorizations and + real value characteristics). + \li Persistence heat maps – various representations where one put some weighted or not Gaussian kernel for each point + of diagram (allow averaging, computation of distances, scalar products, + vectorizations and real value characteristics). + \li Persistence vectors (allow averaging, computation of distances, scalar products, vectorizations and real value + characteristics). + \li Persistence diagrams / barcodes (allow computation of distances, vectorizations and real value characteristics). + + + Note that at the while functionalities like averaging, distances and scalar products are fixed, there is no canonical + way of vectorizing and computing real valued characteristics of objects. Therefore the + vectorizations and computation of real value characteristics procedures are quite likely to evolve in the furthering + versions of the library. + + The main aim of this implementation is to be able to implement various statistical methods, both on the level of C++ + and on the level of python. The methods will operate on the functionalities offered + by concepts. That means that the statistical and ML methods will be able to operate on any representation that + implement the required concept (including the ones that are not in the library at the moment). + That gives provides a framework, that is very easy to extend, for topological statistics. + + Below we are discussing the representations which are currently implemented in Persistence\_representations package: + + \section sec_persistence_landscapes Persistence Landscapes + <b>Reference manual:</b> \ref Gudhi::Persistence_representations::Persistence_landscape <br> + Persistence landscapes were originally proposed by Bubenik in \cite bubenik_landscapes_2015. Efficient algorithms to + compute them rigorously were proposed by Bubenik and Dlotko in \cite bubenik_dlotko_landscapes_2016. The idea of + persistence landscapes is shortly summarized in below. + + To begin with, suppose we are given a point \f$(b,d) \in \mathbb{R}^2\f$ in a + persistence diagram. With this point, we associate a piecewise + linear function \f$f_{(b,d)} : \mathbb{R} \rightarrow [0,\infty)\f$, which is + defined as + + \f[f_{(b,d)}(x) = + \left\{ \begin{array}{ccl} + 0 & \mbox{ if } & x \not\in (b, d) \; , \\ + x - b & \mbox{ if } & x \in \left( b, \frac{b+d}{2} + \right] \; , \\ + d - x & \mbox{ if } & x \in \left(\frac{b+d}{2}, + d \right) \; . + \end{array} \right. + \f] + + A persistence landscape of the birth-death + pairs \f$(b_i , d_i)\f$, where \f$i = 1,\ldots,m\f$, which constitute the given + persistence diagram is the sequence of functions \f$\lambda_k : \mathbb{R} \rightarrow [0,\infty)\f$ for \f$k \in + \mathbb{N}\f$, where \f$\lambda_k(x)\f$ + denotes the \f$k^{\rm th}\f$ largest value of the numbers \f$f_{(b_i,d_i)}(x)\f$, + for \f$i = 1, \ldots, m\f$, and we define \f$\lambda_k(x) = 0\f$ if \f$k > m\f$. + Equivalently, this sequence of functions can be combined into a single + function \f$L : \mathbb{N} \times \mathbb{R} \to [0,\infty)\f$ of two + variables, if we define \f$L(k,t) = \lambda_k(t)\f$. + + The detailed description of algorithms used to compute persistence landscapes can be found in + \cite bubenik_dlotko_landscapes_2016. + Note that this implementation provides exact representation of landscapes. That have many advantages, but also a few + drawbacks. For instance, as discussed + in \cite bubenik_dlotko_landscapes_2016, the exact representation of landscape may be of quadratic size with respect + to the input persistence diagram. It may therefore happen + that, for very large diagrams, using this representation may be memory--prohibitive. In such a case, there are two + possible ways to proceed: + + \li Use non exact representation on a grid described in the Section \ref sec_landscapes_on_grid. + \li Compute just a number of initial nonzero landscapes. This option is available from C++ level as a last parameter of + the constructor of persistence landscape (set by default to std::numeric_limits<size_t>::max()). + + + + \section sec_landscapes_on_grid Persistence Landscapes on a grid + <b>Reference manual:</b> \ref Gudhi::Persistence_representations::Persistence_landscape_on_grid <br> + This is an alternative, not--exact, representation of persistence landscapes defined in the Section \ref + sec_persistence_landscapes. Unlike in the Section \ref sec_persistence_landscapes we build a + representation of persistence landscape by sampling its values on a finite, equally distributed grid of points. + Since, the persistence landscapes that originate from persistence diagrams have slope \f$1\f$ or \f$-1\f$, we have an + estimate of a region between the grid points where the landscape cab be located. + That allows to estimate an error make when performing various operations on landscape. Note that for average + landscapes the slope is in range \f$[-1,1]\f$ and similar estimate can be used. + + Due to a lack of rigorous description of the algorithms to deal with this non--rigorous representation of persistence + landscapes in the literature, we are providing a short discussion of them in below. + + Let us assume that we want to compute persistence landscape on a interval \f$[x,y]\f$. Let us assume that we want to + use \f$N\f$ grid points for that purpose. + Then we will sample the persistence landscape on points \f$x_1 = x , x_2 = x + \frac{y-x}{N}, \ldots , x_{N} = y\f$. + Persistence landscapes are represented as a vector of + vectors of real numbers. Assume that i-th vector consist of \f$n_i\f$ numbers sorted from larger to smaller. They + represent the values of the functions + \f$\lambda_1,\ldots,\lambda_{n_i}\f$ ,\f$\lambda_{n_i+1}\f$ and the functions with larger indices are then zero + functions) on the i-th point of a grid, i.e. \f$x + i \frac{y-x}{N}\f$. + + When averaging two persistence landscapes represented by a grid we need to make sure that they are defined in a + compatible grids. I.e. the intervals \f$[x,y]\f$ on which they are defined are + the same, and the numbers of grid points \f$N\f$ are the same in both cases. If this is the case, we simply compute + point-wise averages of the entries of corresponding + vectors (In this whole section we assume that if one vector of numbers is shorter than another, we extend the shorter + one with zeros so that they have the same length.) + + Computations of distances between two persistence landscapes on a grid is not much different than in the rigorous + case. In this case, we sum up the distances between the same levels of + corresponding landscapes. For fixed level, we approximate the landscapes between the corresponding constitutive + points of landscapes by linear functions, and compute the \f$L^p\f$ distance between them. + + Similarly as in case of distance, when computing the scalar product of two persistence landscapes on a grid, we sum up + the scalar products of corresponding levels of landscapes. For each level, + we assume that the persistence landscape on a grid between two grid points is approximated by linear function. + Therefore to compute scalar product of two corresponding levels of landscapes, + we sum up the integrals of products of line segments for every pair of constitutive grid points. + + Note that for this representation we need to specify a few parameters: + + \li Begin and end point of a grid -- the interval \f$[x,y]\f$ (real numbers). + \li Number of points in a grid (positive integer \f$N\f$). + + + Note that the same representation is used in TDA R-package \cite Fasy_Kim_Lecci_Maria_tda. + + \section sec_persistence_heat_maps Persistence heat maps + <b>Reference manual:</b> \ref Gudhi::Persistence_representations::Persistence_heat_maps <br> + This is a general class of discrete structures which are based on idea of placing a kernel in the points of + persistence diagrams. + This idea appeared in work by many authors over the last 15 years. As far as we know this idea was firstly described + in the work of Bologna group in \cite Ferri_Frosini_comparision_sheme_1 and \cite Ferri_Frosini_comparision_sheme_2. + Later it has been described by Colorado State University group in \cite Persistence_Images_2017. The presented paper + in the first time provide a discussion of stability of the representation. + Also, the same ideas are used in construction of two recent kernels used for machine learning: + \cite Kusano_Fukumizu_Hiraoka_PWGK and \cite Reininghaus_Huber_ALL_PSSK. Both the kernel's construction uses + interesting ideas to ensure stability of the representation with respect to Wasserstein metric. In the kernel + presented in \cite Kusano_Fukumizu_Hiraoka_PWGK, a scaling function is used to multiply the Gaussian kernel in the + way that the points close to diagonal got low weight and consequently do not have a big influence on the resulting + distribution. In \cite Reininghaus_Huber_ALL_PSSK for every point \f$(b,d)\f$ two Gaussian kernels + are added: first, with a weight 1 in a point \f$(b,d)\f$, and the second, with the weight -1 for a point \f$(b,d)\f$. + In both cases, the representations are stable with respect to 1-Wasserstein distance. + + In Persistence\_representations package we currently implement a discretization of the distributions described above. + The base of this implementation is 2-dimensional array of pixels. Each pixel have assigned a real value which + is a sum of values of distributions induced by each point of the persistence diagram. At the moment we compute the + sum of values on a center of a pixels. It can be easily extended to any other function + (like for instance sum of integrals of the intermediate distribution on a pixel). + + The parameters that determine the structure are the following: + + \li A positive integer k determining the size of the kernel we used (we always assume that the kernels are square). + \li A filter: in practice a square matrix of a size \f$2k+1 \times 2k+1\f$. By default, this is a discretization of + N(0,1) kernel. + \li The box \f$[x_0,x_1]\times [y_0,y_1]\f$ bounding the domain of the persistence image. + \li Scaling function. Each Gaussian kernel at point \f$(p,q)\f$ gets multiplied by the value of this function at the + point \f$(p,q)\f$. + \li A boolean value determining if the space below diagonal should be erased or not. To be precise: when points close + to diagonal are given then sometimes the kernel have support that reaches the region + below the diagonal. If the value of this parameter is true, then the values below diagonal can be erased. + + + \section sec_persistence_vectors Persistence vectors + <b>Reference manual:</b> \ref Gudhi::Persistence_representations::Vector_distances_in_diagram <br> + This is a representation of persistent homology in a form of a vector which was designed for an application in 3d + graphic in \cite Carriere_Oudot_Ovsjanikov_top_signatures_3d. Below we provide a short description of this + representation. + + Given a persistence diagram \f$D = \{ (b_i,d_i) \}\f$, for every pair of birth--death points \f$(b_1,d_1)\f$ and + \f$(b_2,d_2)\f$ we compute the following three distances: + + \li \f$d( (b_1,d_1) , (b_2,d_2) )\f$. + \li \f$d( (b_1,d_1) , (\frac{b_1,d_1}{2},\frac{b_1,d_1}{2}) )\f$. + \li \f$d( (b_2,d_2) , (\frac{b_2,d_2}{2},\frac{b_2,d_2}{2}) )\f$. + + We pick the smallest of those and add it to a vector. The obtained vector of numbers is then sorted in decreasing + order. This way we obtain a persistence vector representing the diagram. + + Given two persistence vectors, the computation of distances, averages and scalar products is straightforward. Average + is simply a coordinate-wise average of a collection of vectors. In this section we + assume that the vectors are extended by zeros if they are of a different size. To compute distances we compute + absolute value of differences between coordinates. A scalar product is a sum of products of + values at the corresponding positions of two vectors. + + */ +/** @} */ // end defgroup Persistence_representations + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // Persistence_representations diff --git a/doc/Persistence_representations/average_landscape.png b/doc/Persistence_representations/average_landscape.png Binary files differnew file mode 100644 index 00000000..ea59926b --- /dev/null +++ b/doc/Persistence_representations/average_landscape.png diff --git a/doc/Persistent_cohomology/Intro_persistent_cohomology.h b/doc/Persistent_cohomology/Intro_persistent_cohomology.h index 6400116b..4dbe82c7 100644 --- a/doc/Persistent_cohomology/Intro_persistent_cohomology.h +++ b/doc/Persistent_cohomology/Intro_persistent_cohomology.h @@ -143,8 +143,8 @@ namespace persistent_cohomology { We provide several example files: run these examples with -h for details on their use, and read the README file. -\li <a href="_persistent_cohomology_2rips_persistence_8cpp-example.html"> -Persistent_cohomology/rips_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence +\li <a href="_rips_complex_2rips_persistence_8cpp-example.html"> +Rips_complex/rips_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence diagram. \code $> ./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3 \endcode \code The complex contains 177838 simplices @@ -158,44 +158,44 @@ diagram. Persistent_cohomology/rips_multifield_persistence.cpp</a> computes the Rips complex of a point cloud and outputs its persistence diagram with a family of field coefficients. -\li <a href="_persistent_cohomology_2rips_distance_matrix_persistence_8cpp-example.html"> -Persistent_cohomology/rips_distance_matrix_persistence.cpp</a> computes the Rips complex of a distance matrix and +\li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html"> +Rips_complex/rips_distance_matrix_persistence.cpp</a> computes the Rips complex of a distance matrix and outputs its persistence diagram. -\li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. -\code $> ./alpha_complex_3d_persistence ../../data/points/tore3D_300.off 2 0.45 \endcode +\code $> ./alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45 \endcode \code Simplex_tree dim: 3 2 0 0 inf 2 1 0.0682162 1.0001 2 1 0.0934117 1.00003 2 2 0.56444 1.03938 \endcode -\li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. Here, as CGAL computes the exact values, it is slower, but it is necessary when points are on a grid for instance. -\code $> ./exact_alpha_complex_3d_persistence ../../data/points/sphere3D_pts_on_grid.off 2 0.1 \endcode +\code $> ./exact_alpha_complex_3d_persistence ../../data/points/sphere3D_pts_on_grid.off -p 2 -m 0.1 \endcode \code Simplex_tree dim: 3 2 0 0 inf 2 2 0.0002 0.2028 \endcode -\li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the weighted alpha complex on points sampling from an OFF file and a weights file. \code $> ./weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off -../../data/points/tore3D_300.weights 2 0.45 \endcode +../../data/points/tore3D_300.weights -p 2 -m 0.45 \endcode \code Simplex_tree dim: 3 2 0 -1 inf 2 1 -0.931784 0.000103311 2 1 -0.906588 2.60165e-05 2 2 -0.43556 0.0393798 \endcode -\li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> -Persistent_cohomology/alpha_complex_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> +Alpha_complex/alpha_complex_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/p\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. \code $> ./alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off \endcode \code Alpha complex is of dimension 3 - 9273 simplices - 300 vertices. @@ -205,11 +205,13 @@ Simplex_tree dim: 3 2 1 0.0934117 1.00003 2 2 0.56444 1.03938 \endcode -\li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> -Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> +Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the periodic alpha complex on points sampling from an OFF file. +The second parameter is a \ref FileFormatsIsoCuboid file with coordinates of the periodic cuboid. +Note that the lengths of the sides of the periodic cuboid have to be the same. \code $> ./periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off -../../data/points/iso_cuboid_3_in_0_1.txt 3 1.0 \endcode +../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0 \endcode \code Periodic Delaunay computed. Simplex_tree dim: 3 3 0 0 inf @@ -221,11 +223,31 @@ Simplex_tree dim: 3 3 2 0.005 inf 3 3 0.0075 inf \endcode +\li <a href="_persistent_cohomology_2weighted_periodic_alpha_complex_3d_persistence_8cpp-example.html"> +Persistent_cohomology/weighted_periodic_alpha_complex_3d_persistence.cpp</a> computes the persistent homology with +\f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the periodic alpha complex on weighted points from an OFF file. The +additional parameters of this program are:<br> +(a) The file with the weights of points. The file consist of a sequence of numbers (as many as points). +Note that the weight of each single point have to be bounded by 1/64 times the square of the cuboid edge length.<br> +(b) A \ref FileFormatsIsoCuboid file with coordinates of the periodic cuboid. +Note that the lengths of the sides of the periodic cuboid have to be the same.<br> +\code $> ./weighted_periodic_alpha_complex_3d_persistence ../../data/points/shifted_sphere.off +../../data/points/shifted_sphere.weights ../../data/points/iso_cuboid_3_in_0_10.txt 3 1.0 \endcode +\code Weighted Periodic Delaunay computed. +Simplex_tree dim: 3 +3 0 -0.0001 inf +3 1 16.0264 inf +3 1 16.0273 inf +3 1 16.0303 inf +3 2 36.8635 inf +3 2 36.8704 inf +3 2 36.8838 inf +3 3 58.6783 inf \endcode + \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html"> Persistent_cohomology/plain_homology.cpp</a> computes the plain homology of a simple simplicial complex without filtration values. - \copyright GNU General Public License v3. */ } // namespace persistent_cohomology diff --git a/doc/Rips_complex/Intro_rips_complex.h b/doc/Rips_complex/Intro_rips_complex.h index 124dfec9..8c517516 100644 --- a/doc/Rips_complex/Intro_rips_complex.h +++ b/doc/Rips_complex/Intro_rips_complex.h @@ -146,8 +146,6 @@ namespace rips_complex { * * \include Rips_complex/full_skeleton_rips_for_doc.txt * - * \copyright GNU General Public License v3. - * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim */ /** @} */ // end defgroup rips_complex diff --git a/doc/Simplex_tree/Intro_simplex_tree.h b/doc/Simplex_tree/Intro_simplex_tree.h index f5b72ff6..6b80d1c9 100644 --- a/doc/Simplex_tree/Intro_simplex_tree.h +++ b/doc/Simplex_tree/Intro_simplex_tree.h @@ -67,16 +67,18 @@ Information of the Simplex Tree: Number of vertices = 10 Number of simplices = 98 \endcode * * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> - * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - Simplex tree is computed and displayed from a 3D alpha - * complex (Requires CGAL, GMP and GMPXX to be installed) - * + * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - Simplex tree is computed and displayed + * from a 3D alpha complex (Requires CGAL, GMP and GMPXX to be installed). * + * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html"> + * Simplex_tree/graph_expansion_with_blocker.cpp</a> - Simple simplex tree construction from a one-skeleton graph with + * a simple blocker expansion method. + * * \subsection filteredcomplexeshassecomplex Hasse complex * The second one is the Hasse_complex. The Hasse complex is a data structure representing explicitly all co-dimension * 1 incidence relations in a complex. It is consequently faster when accessing the boundary of a simplex, but is less * compact and harder to construct from scratch. * - * \copyright GNU General Public License v3. * @} */ diff --git a/doc/Spatial_searching/Intro_spatial_searching.h b/doc/Spatial_searching/Intro_spatial_searching.h index 1ee5e92e..52ed65e4 100644 --- a/doc/Spatial_searching/Intro_spatial_searching.h +++ b/doc/Spatial_searching/Intro_spatial_searching.h @@ -50,8 +50,6 @@ namespace spatial_searching { * * \include Spatial_searching/example_spatial_searching.cpp * - * \copyright GNU General Public License v3. - * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim */ /** @} */ // end defgroup spatial_searching diff --git a/doc/Subsampling/Intro_subsampling.h b/doc/Subsampling/Intro_subsampling.h index c84616dd..ab9cdc37 100644 --- a/doc/Subsampling/Intro_subsampling.h +++ b/doc/Subsampling/Intro_subsampling.h @@ -58,8 +58,6 @@ namespace subsampling { * This example outputs a subset of 100 points picked randomly. * * \include Subsampling/example_pick_n_random_points.cpp - * \copyright GNU General Public License v3. - * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim */ /** @} */ // end defgroup subsampling diff --git a/doc/Tangential_complex/Intro_tangential_complex.h b/doc/Tangential_complex/Intro_tangential_complex.h index 3d687c1d..00e00c52 100644 --- a/doc/Tangential_complex/Intro_tangential_complex.h +++ b/doc/Tangential_complex/Intro_tangential_complex.h @@ -107,8 +107,6 @@ dimensions are known at compile-time. \include Tangential_complex/example_with_perturb.cpp -\copyright GNU General Public License v3. -\verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim */ /** @} */ // end defgroup tangential_complex diff --git a/doc/Witness_complex/Witness_complex_doc.h b/doc/Witness_complex/Witness_complex_doc.h index 171a185f..62203054 100644 --- a/doc/Witness_complex/Witness_complex_doc.h +++ b/doc/Witness_complex/Witness_complex_doc.h @@ -90,8 +90,9 @@ int main(int argc, char * const argv[]) { Gudhi::Points_off_reader<Point_d> off_reader(file_name); point_vector = Point_vector(off_reader.get_point_cloud()); - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + // 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)); // Compute witness complex Witness_complex witness_complex(landmarks, @@ -107,10 +108,14 @@ int main(int argc, char * const argv[]) { Here is an example of constructing a strong witness complex filtration and computing persistence on it: - \include Witness_complex/example_strong_witness_persistence.cpp + \include Witness_complex/strong_witness_persistence.cpp - \copyright GNU General Public License v3. + \section witnessexample3 Example3: Computing relaxed witness complex persistence from a distance matrix + In this example we compute the relaxed witness complex persistence from a given matrix of closest landmarks to each witness. + Each landmark is given as the couple (index, distance). + + \include Witness_complex/example_nearest_landmark_table.cpp */ diff --git a/doc/common/examples.h b/doc/common/examples.h new file mode 100644 index 00000000..40f202c7 --- /dev/null +++ b/doc/common/examples.h @@ -0,0 +1,99 @@ +// List of GUDHI examples - Doxygen needs at least a file tag to analyse comments +// In user_version, `find . -name "*.cpp"` in example and utilities folders +/*! @file Examples + * @example Alpha_complex/Alpha_complex_from_off.cpp + * @example Alpha_complex/Alpha_complex_from_points.cpp + * @example Bottleneck_distance/bottleneck_basic_example.cpp + * @example Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp + * @example Witness_complex/example_nearest_landmark_table.cpp + * @example Witness_complex/example_witness_complex_off.cpp + * @example Witness_complex/example_witness_complex_sphere.cpp + * @example Witness_complex/example_strong_witness_complex_off.cpp + * @example Simplex_tree/mini_simplex_tree.cpp + * @example Simplex_tree/graph_expansion_with_blocker.cpp + * @example Simplex_tree/simple_simplex_tree.cpp + * @example Simplex_tree/simplex_tree_from_cliques_of_graph.cpp + * @example Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp + * @example Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp + * @example Persistent_cohomology/plain_homology.cpp + * @example Persistent_cohomology/persistence_from_file.cpp + * @example Persistent_cohomology/rips_persistence_step_by_step.cpp + * @example Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp + * @example Persistent_cohomology/custom_persistence_sort.cpp + * @example Persistent_cohomology/persistence_from_simple_simplex_tree.cpp + * @example Persistent_cohomology/rips_multifield_persistence.cpp + * @example Skeleton_blocker/Skeleton_blocker_from_simplices.cpp + * @example Skeleton_blocker/Skeleton_blocker_iteration.cpp + * @example Skeleton_blocker/Skeleton_blocker_link.cpp + * @example Contraction/Garland_heckbert.cpp + * @example Contraction/Rips_contraction.cpp + * @example Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp + * @example common/example_CGAL_3D_points_off_reader.cpp + * @example common/example_vector_double_points_off_reader.cpp + * @example common/example_CGAL_points_off_reader.cpp + * @example Rips_complex/example_one_skeleton_rips_from_distance_matrix.cpp + * @example Rips_complex/example_one_skeleton_rips_from_points.cpp + * @example Rips_complex/example_rips_complex_from_csv_distance_matrix_file.cpp + * @example Rips_complex/example_rips_complex_from_off_file.cpp + * @example Persistence_representations/persistence_intervals.cpp + * @example Persistence_representations/persistence_vectors.cpp + * @example Persistence_representations/persistence_heat_maps.cpp + * @example Persistence_representations/persistence_landscape_on_grid.cpp + * @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_choose_n_farthest_points.cpp + * @example Subsampling/example_sparsify_point_set.cpp + * @example Subsampling/example_pick_n_random_points.cpp + * @example Nerve_GIC/CoordGIC.cpp + * @example Nerve_GIC/Nerve.cpp + * @example Nerve_GIC/FuncGIC.cpp + * @example Nerve_GIC/VoronoiGIC.cpp + * @example Spatial_searching/example_spatial_searching.cpp + * @example Alpha_complex/alpha_complex_3d_persistence.cpp + * @example Alpha_complex/alpha_complex_persistence.cpp + * @example Alpha_complex/weighted_periodic_alpha_complex_3d_persistence.cpp + * @example Alpha_complex/weighted_alpha_complex_3d_persistence.cpp + * @example Alpha_complex/periodic_alpha_complex_3d_persistence.cpp + * @example Alpha_complex/exact_alpha_complex_3d_persistence.cpp + * @example Bottleneck_distance/bottleneck_distance.cpp + * @example Witness_complex/weak_witness_persistence.cpp + * @example Witness_complex/strong_witness_persistence.cpp + * @example Bitmap_cubical_complex/cubical_complex_persistence.cpp + * @example Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp + * @example common/off_file_from_shape_generator.cpp + * @example Rips_complex/rips_distance_matrix_persistence.cpp + * @example Rips_complex/rips_persistence.cpp + * @example Persistence_representations/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp + * @example Persistence_representations/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp + * @example Persistence_representations/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp + * @example Persistence_representations/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp + * @example Persistence_representations/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp + * @example Persistence_representations/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp + * @example Persistence_representations/persistence_intervals/compute_number_of_dominant_intervals.cpp + * @example Persistence_representations/persistence_intervals/plot_persistence_Betti_numbers.cpp + * @example Persistence_representations/persistence_intervals/plot_persistence_intervals.cpp + * @example Persistence_representations/persistence_intervals/plot_histogram_of_intervals_lengths.cpp + * @example Persistence_representations/persistence_intervals/compute_bottleneck_distance.cpp + * @example Persistence_representations/persistence_heat_maps/create_pssk.cpp + * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp + * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp + * @example Persistence_representations/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp + * @example Persistence_representations/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp + * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp + * @example Persistence_representations/persistence_heat_maps/average_persistence_heat_maps.cpp + * @example Persistence_representations/persistence_heat_maps/plot_persistence_heat_map.cpp + * @example Persistence_representations/persistence_heat_maps/create_persistence_heat_maps.cpp + * @example Persistence_representations/persistence_vectors/plot_persistence_vectors.cpp + * @example Persistence_representations/persistence_vectors/compute_distance_of_persistence_vectors.cpp + * @example Persistence_representations/persistence_vectors/average_persistence_vectors.cpp + * @example Persistence_representations/persistence_vectors/create_persistence_vectors.cpp + * @example Persistence_representations/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp + * @example Persistence_representations/persistence_landscapes/average_landscapes.cpp + * @example Persistence_representations/persistence_landscapes/compute_scalar_product_of_landscapes.cpp + * @example Persistence_representations/persistence_landscapes/create_landscapes.cpp + * @example Persistence_representations/persistence_landscapes/compute_distance_of_landscapes.cpp + * @example Persistence_representations/persistence_landscapes/plot_landscapes.cpp + */ + diff --git a/doc/common/file_formats.h b/doc/common/file_formats.h index d715aa4d..c60ed15a 100644 --- a/doc/common/file_formats.h +++ b/doc/common/file_formats.h @@ -53,6 +53,72 @@ namespace Gudhi { Such files can be generated with `Gudhi::persistent_cohomology::Persistent_cohomology::output_diagram()` and read with `Gudhi::read_persistence_intervals_and_dimension()`, `Gudhi::read_persistence_intervals_grouped_by_dimension()` or `Gudhi::read_persistence_intervals_in_dimension()`. + + + \section FileFormatsIsoCuboid Iso-cuboid + + Such a file describes an iso-oriented cuboid with diagonal opposite vertices (min_x, min_y, min_z,...) and (max_x, max_y, max_z, ...). The format is:<br> + \verbatim + min_x min_y [min_z ...] + max_x max_y [max_z ...] + \endverbatim + + Here is a simple sample file in the 3D case: + \verbatim + -1. -1. -1. + 1. 1. 1. + \endverbatim + + + \section FileFormatsPerseus Perseus + + This file format is the format used by the Perseus software + (http://www.sas.upenn.edu/~vnanda/perseus/) by Vidit Nanda. + The first line contains a number d begin the dimension of the + bitmap (2 in the example below). Next d lines are the numbers of top dimensional cubes in each dimensions (3 and 3 + in the example below). Next, in lexicographical order, the filtration of top dimensional cubes is given (1 4 6 8 + 20 4 7 6 5 in the example below). + + \image html "exampleBitmap.png" "Example of a input data." + + The input file for the following complex is: + \verbatim + 2 + 3 + 3 + 1 + 4 + 6 + 8 + 20 + 4 + 7 + 6 + 5 + \endverbatim + + To indicate periodic boundary conditions in a + given direction, then number of top dimensional cells in this direction have to be multiplied by -1. For instance: + + \verbatim + 2 + -3 + 3 + 1 + 4 + 6 + 8 + 20 + 4 + 7 + 6 + 5 + \endverbatim + + Indicate that we have imposed periodic boundary conditions in the direction x, but not in the direction y. + + Other sample files can be found in the `data/bitmap` folder. + */ } // namespace Gudhi diff --git a/doc/common/footer.html b/doc/common/footer.html index 7b4cdc5c..a557922b 100644 --- a/doc/common/footer.html +++ b/doc/common/footer.html @@ -6,24 +6,18 @@ <!--BEGIN PROJECT_NAME--> $projectname <!--BEGIN PROJECT_NUMBER--> Version $projectnumber<!--END PROJECT_NUMBER--> <!--BEGIN PROJECT_BRIEF--> - $projectbrief<!--END PROJECT_BRIEF--> +<!--BEGIN PROJECT_BRIEF--> - Copyright : GPL v3<!--END PROJECT_BRIEF--> <!--END PROJECT_NAME--> </td> <td class="network-entypo"> <!--BEGIN GENERATE_TREEVIEW--> $generatedby <a href="http://www.doxygen.org/index.html"> - <img class="footer" src="$relpath^doxygen.png" alt="doxygen"/></a> $doxygenversion + Doxygen</a> $doxygenversion <!--END GENERATE_TREEVIEW--> </td> </tr> </table> -<!--BEGIN !GENERATE_TREEVIEW--> -<hr class="footer"/><address class="footer"><small> tralala -$generatedby  <a href="http://www.doxygen.org/index.html"> -<img class="footer" src="$relpath^doxygen.png" alt="doxygen"/> -</a> $doxygenversion -</small></address> -<!--END !GENERATE_TREEVIEW--> </body> </html> diff --git a/doc/common/header.html b/doc/common/header.html index 53b5c0a2..9c514381 100644 --- a/doc/common/header.html +++ b/doc/common/header.html @@ -56,6 +56,8 @@ $extrastylesheet <ul class="dropdown"> <li><a href="http://gudhi.gforge.inria.fr/licensing/">Licensing</a></li> <li><a href="https://gforge.inria.fr/frs/?group_id=3865" target="_blank">Get the sources</a></li> + <li><a href="https://gforge.inria.fr/frs/download.php/file/37113/GUDHI_2.0.0_OSX_UTILS.beta.tar.gz" target="_blank">Utils for Mac OSx</a></li> + <li><a href="https://gforge.inria.fr/frs/download.php/file/37112/GUDHI_2.0.0_WIN64_UTILS.beta.zip" target="_blank">Utils for Win x64</a></li> </ul> </li> <li class="divider"></li> @@ -66,6 +68,8 @@ $extrastylesheet <li><a href="http://gudhi.gforge.inria.fr/doc/latest/installation.html">C++ installation manual</a></li> <li><a href="http://gudhi.gforge.inria.fr/python/latest/">Python documentation</a></li> <li><a href="http://gudhi.gforge.inria.fr/python/latest/installation.html">Python installation manual</a></li> + <li><a href="http://gudhi.gforge.inria.fr/utils/">Utilities</a></li> + <li><a href="http://bertrand.michel.perso.math.cnrs.fr/Enseignements/TDA-Gudhi-Python.html" target="_blank">Tutorial</a></li> </ul> </li> <li class="divider"></li> diff --git a/doc/common/installation.h b/doc/common/installation.h new file mode 100644 index 00000000..25675cc5 --- /dev/null +++ b/doc/common/installation.h @@ -0,0 +1,263 @@ +/*! \page installation GUDHI installation + * \tableofcontents + * As GUDHI is a header only library, there is no need to install the library. + * + * Examples of GUDHI headers inclusion can be found in \ref demos. + * + * \section compiling Compiling + * The library uses c++11 and requires <a target="_blank" href="http://www.boost.org/">Boost</a> with version 1.48.0 or + * more recent. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2015. + * + * \subsection demos Demos and examples + * To build the demos and examples, run the following commands in a terminal: +\verbatim cd /path-to-gudhi/ +mkdir build +cd build/ +cmake .. +make \endverbatim + * A list of examples is available <a href="examples.html">here</a>. + * + * \subsection testsuites Test suites + * To test your build, run the following command in a terminal: + * \verbatim make test \endverbatim + * + * \subsection documentationgeneration Documentation + * To generate the documentation, <a target="_blank" href="http://www.doxygen.org/">Doxygen</a> is required. + * Run the following command in a terminal: +\verbatim +make doxygen +# Documentation will be generated in the folder YYYY-MM-DD-hh-mm-ss_GUDHI_X.Y.Z/doc/html/ +# You can customize the directory name by calling `cmake -DUSER_VERSION_DIR=/my/custom/folder` +\endverbatim + * + * \section optionallibrary Optional third-party library + * \subsection gmp GMP + * The multi-field persistent homology algorithm requires GMP which is a free library for arbitrary-precision + * arithmetic, operating on signed integers, rational numbers, and floating point numbers. + * + * The following example requires the <a target="_blank" href="http://gmplib.org/">GNU Multiple Precision Arithmetic + * Library</a> (GMP) and will not be built if GMP is not installed: + * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html"> + * Persistent_cohomology/rips_multifield_persistence.cpp</a> + * + * Having GMP version 4.2 or higher installed is recommended. + * + * \subsection cgal CGAL + * The \ref alpha_complex data structure, \ref bottleneck_distance, and few examples requires CGAL, which is a C++ + * library which provides easy access to efficient and reliable geometric algorithms. + * + * \note There is no need to install CGAL, you can just <CODE>cmake . && make</CODE> CGAL (or even + * <CODE>cmake -DCGAL_HEADER_ONLY=ON .</CODE> for CGAL version ≥ 4.8.0), thereafter you will be able to compile + * GUDHI by calling <CODE>cmake -DCGAL_DIR=/your/path/to/CGAL-X.Y .. && make</CODE> + * + * Having CGAL version 4.4.0 or higher installed is recommended. The procedure to install this library according to + * your operating system is detailed here http://doc.cgal.org/latest/Manual/installation.html + * + * The following examples/utilities require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms + * Library</a> (CGAL \cite cgal:eb-15b) and will not be built if CGAL is not installed: + * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> + * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> + * + * The following examples/utilities require CGAL version ≥ 4.6.0: + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_strong_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html"> + * Witness_complex/example_witness_complex_sphere.cpp</a> + * + * The following example requires CGAL version ≥ 4.7.0: + * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_off.cpp</a> + * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_points.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> + * Persistent_cohomology/custom_persistence_sort.cpp</a> + * + * The following example requires CGAL version ≥ 4.8.1: + * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html"> + * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> + * \li <a href="_bottleneck_distance_2bottleneck_basic_example_8cpp-example.html"> + * Bottleneck_distance/bottleneck_basic_example.cpp</a> + * \li <a href="_bottleneck_distance_2bottleneck_read_file_8cpp-example.html"> + * Bottleneck_distance/bottleneck_distance.cpp</a> + * \li <a href="_nerve__g_i_c_2_coord_g_i_c_8cpp-example.html"> + * Nerve_GIC/CoordGIC.cpp</a> + * \li <a href="_nerve__g_i_c_2_func_g_i_c_8cpp-example.html"> + * Nerve_GIC/FuncGIC.cpp</a> + * \li <a href="_nerve__g_i_c_2_nerve_8cpp-example.html"> + * Nerve_GIC/Nerve.cpp</a> + * \li <a href="_nerve__g_i_c_2_voronoi_g_i_c_8cpp-example.html"> + * Nerve_GIC/VoronoiGIC.cpp</a> + * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> + * 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"> + * Subsampling/example_sparsify_point_set.cpp</a> + * \li <a href="_tangential_complex_2example_basic_8cpp-example.html"> + * Tangential_complex/example_basic.cpp</a> + * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html"> + * Tangential_complex/example_with_perturb.cpp</a> + * + * \subsection eigen3 Eigen3 + * The \ref alpha_complex data structure and few examples requires + * <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> is a C++ template library for linear algebra: + * matrices, vectors, numerical solvers, and related algorithms. + * + * The following examples/utilities require the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be + * built if Eigen3 is not installed: + * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_off.cpp</a> + * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_points.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html"> + * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> + * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> + * Persistent_cohomology/custom_persistence_sort.cpp</a> + * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> + * 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"> + * Subsampling/example_sparsify_point_set.cpp</a> + * \li <a href="_tangential_complex_2example_basic_8cpp-example.html"> + * Tangential_complex/example_basic.cpp</a> + * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html"> + * Tangential_complex/example_with_perturb.cpp</a> + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_strong_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html"> + * Witness_complex/example_witness_complex_off.cpp</a> + * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html"> + * Witness_complex/example_witness_complex_sphere.cpp</a> + * + * \subsection tbb Threading Building Blocks + * <a target="_blank" href="https://www.threadingbuildingblocks.org/">Intel® TBB</a> lets you easily write parallel + * C++ programs that take full advantage of multicore performance, that are portable and composable, and that have + * future-proof scalability. + * + * Having Intel® TBB installed is recommended to parallelize and accelerate some GUDHI computations. + * + * The following examples/utilities are using Intel® TBB if installed: + * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_off.cpp</a> + * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> + * Alpha_complex/Alpha_complex_from_points.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html"> + * Alpha_complex/alpha_complex_persistence.cpp</a> + * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> + * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_8cpp-example.html"> + * Bitmap_cubical_complex/cubical_complex_persistence.cpp</a> + * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_periodic_boundary_conditions_8cpp-example.html"> + * Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp</a> + * \li <a href="_bitmap_cubical_complex_2_random_bitmap_cubical_complex_8cpp-example.html"> + * Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp</a> + * \li <a href="_nerve__g_i_c_2_coord_g_i_c_8cpp-example.html"> + * Nerve_GIC/CoordGIC.cpp</a> + * \li <a href="_nerve__g_i_c_2_func_g_i_c_8cpp-example.html"> + * Nerve_GIC/FuncGIC.cpp</a> + * \li <a href="_nerve__g_i_c_2_nerve_8cpp-example.html"> + * Nerve_GIC/Nerve.cpp</a> + * \li <a href="_nerve__g_i_c_2_voronoi_g_i_c_8cpp-example.html"> + * Nerve_GIC/VoronoiGIC.cpp</a> + * \li <a href="_simplex_tree_2simple_simplex_tree_8cpp-example.html"> + * Simplex_tree/simple_simplex_tree.cpp</a> + * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> + * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> + * \li <a href="_simplex_tree_2simplex_tree_from_cliques_of_graph_8cpp-example.html"> + * Simplex_tree/simplex_tree_from_cliques_of_graph.cpp</a> + * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html"> + * Simplex_tree/graph_expansion_with_blocker.cpp</a> + * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> + * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> + * Persistent_cohomology/alpha_complex_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2rips_persistence_via_boundary_matrix_8cpp-example.html"> + * Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp</a> + * \li <a href="_persistent_cohomology_2persistence_from_file_8cpp-example.html"> + * Persistent_cohomology/persistence_from_file.cpp</a> + * \li <a href="_persistent_cohomology_2persistence_from_simple_simplex_tree_8cpp-example.html"> + * Persistent_cohomology/persistence_from_simple_simplex_tree.cpp</a> + * \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html"> + * Persistent_cohomology/plain_homology.cpp</a> + * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html"> + * Persistent_cohomology/rips_multifield_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html"> + * Persistent_cohomology/rips_persistence_step_by_step.cpp</a> + * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> + * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> + * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> + * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> + * Persistent_cohomology/custom_persistence_sort.cpp</a> + * \li <a href="_rips_complex_2example_one_skeleton_rips_from_points_8cpp-example.html"> + * Rips_complex/example_one_skeleton_rips_from_points.cpp</a> + * \li <a href="_rips_complex_2example_rips_complex_from_off_file_8cpp-example.html"> + * Rips_complex/example_rips_complex_from_off_file.cpp</a> + * \li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html"> + * Rips_complex/rips_distance_matrix_persistence.cpp</a> + * \li <a href="_rips_complex_2rips_persistence_8cpp-example.html"> + * Rips_complex/rips_persistence.cpp</a> + * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html"> + * Witness_complex/strong_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html"> + * Witness_complex/weak_witness_persistence.cpp</a> + * \li <a href="_witness_complex_2example_nearest_landmark_table_8cpp-example.html"> + * Witness_complex/example_nearest_landmark_table.cpp</a> + * + * \section Contributions Bug reports and contributions + * Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to: + * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim + * + * GUDHI is open to external contributions. If you want to join our development team, please contact us. + * +*/ + +/*! \page Citation Acknowledging the GUDHI library + * We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use + * of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages. + * Feel free to contact us in case you have any question or remark on this topic. + * + * We provide \ref GudhiBibtex entries for the modules of the User and Reference Manual, as well as for publications + * directly related to the GUDHI library. + * \section GudhiBibtex GUDHI bibtex + * \verbinclude biblio/how_to_cite_gudhi.bib +*/ diff --git a/doc/common/main_page.h b/doc/common/main_page.h index 1a7994a5..b3e9ea03 100644 --- a/doc/common/main_page.h +++ b/doc/common/main_page.h @@ -93,6 +93,25 @@ </td> </tr> </table> + \subsection CoverComplexDataStructure Cover Complexes + \image html "gicvisu.jpg" "Graph Induced Complex of a point cloud." +<table border="0"> + <tr> + <td width="25%"> + <b>Author:</b> Mathieu Carrière<br> + <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Copyright:</b> GPL v3<br> + <b>Requires:</b> \ref cgal ≥ 4.8.1 + </td> + <td width="75%"> + Nerves and Graph Induced Complexes are cover complexes, i.e. simplicial complexes that provably contain + topological information about the input data. They can be computed with a cover of the + data, that comes i.e. from the preimage of a family of intervals covering the image + of a scalar-valued function defined on the data. <br> + <b>User manual:</b> \ref cover_complex - <b>Reference manual:</b> Gudhi::cover_complex::Cover_complex + </td> + </tr> +</table> \subsection SkeletonBlockerDataStructure Skeleton blocker \image html "ds_representation.png" "Skeleton blocker representation" <table border="0"> @@ -152,6 +171,7 @@ </table> \section Toolbox Toolbox + \subsection BottleneckDistanceToolbox Bottleneck distance \image html "perturb_pd.png" "Bottleneck distance is the length of the longest edge" <table border="0"> @@ -211,276 +231,23 @@ <b>User manual:</b> \ref persistent_cohomology - <b>Reference manual:</b> Gudhi::persistent_cohomology::Persistent_cohomology </td> </tr> +</table> + \subsection PersistenceRepresentationsToolbox Persistence representations + \image html "average_landscape.png" "Persistence representations" +<table border="0"> + <tr> + <td width="25%"> + <b>Author:</b> Pawel Dlotko<br> + <b>Introduced in:</b> GUDHI 2.1.0<br> + <b>Copyright:</b> GPL v3<br> + </td> + <td width="75%"> + It contains implementation of various representations of persistence diagrams; diagrams themselves, persistence + landscapes (rigorous and grid version), persistence heath maps, vectors and others. It implements basic + functionalities which are neccessary to use persistence in statistics and machine learning.<br> + <b>User manual:</b> \ref Persistence_representations + </td> + </tr> </table> + */ - -/*! \page installation GUDHI installation - * \tableofcontents - * As GUDHI is a header only library, there is no need to install the library. - * - * Examples of GUDHI headers inclusion can be found in \ref demos. - * - * \section compiling Compiling - * The library uses c++11 and requires <a target="_blank" href="http://www.boost.org/">Boost</a> with version 1.48.0 or - * more recent. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2015. - * - * \subsection demos Demos and examples - * To build the demos and examples, run the following commands in a terminal: -\verbatim cd /path-to-gudhi/ -mkdir build -cd build/ -cmake .. -make \endverbatim - * A list of examples is available <a href="examples.html">here</a>. - * - * \subsection testsuites Test suites - * To test your build, run the following command in a terminal: - * \verbatim make test \endverbatim - * - * \subsection documentationgeneration Documentation - * To generate the documentation, <a target="_blank" href="http://www.doxygen.org/">Doxygen</a> is required. - * Run the following command in a terminal: -\verbatim -make doxygen -# Documentation will be generated in the folder YYYY-MM-DD-hh-mm-ss_GUDHI_X.Y.Z/doc/html/ -# You can customize the directory name by calling `cmake -DUSER_VERSION_DIR=/my/custom/folder` -\endverbatim - * - * \section optionallibrary Optional third-party library - * \subsection gmp GMP - * The multi-field persistent homology algorithm requires GMP which is a free library for arbitrary-precision - * arithmetic, operating on signed integers, rational numbers, and floating point numbers. - * - * The following example requires the <a target="_blank" href="http://gmplib.org/">GNU Multiple Precision Arithmetic - * Library</a> (GMP) and will not be built if GMP is not installed: - * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_multifield_persistence.cpp</a> - * - * Having GMP version 4.2 or higher installed is recommended. - * - * \subsection cgal CGAL - * The \ref alpha_complex data structure, \ref bottleneck_distance, and few examples requires CGAL, which is a C++ - * library which provides easy access to efficient and reliable geometric algorithms. - * - * \note There is no need to install CGAL, you can just <CODE>cmake . && make</CODE> CGAL (or even - * <CODE>cmake -DCGAL_HEADER_ONLY=ON .</CODE> for CGAL version ≥ 4.8.0), thereafter you will be able to compile - * GUDHI by calling <CODE>cmake -DCGAL_DIR=/your/path/to/CGAL-X.Y .. && make</CODE> - * - * Having CGAL version 4.4.0 or higher installed is recommended. The procedure to install this library according to - * your operating system is detailed here http://doc.cgal.org/latest/Manual/installation.html - * - * The following examples require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms - * Library</a> (CGAL \cite cgal:eb-15b) and will not be built if CGAL is not installed: - * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> - * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - * - * The following example requires CGAL version ≥ 4.6.0: - * \li <a href="_witness_complex_2witness_complex_sphere_8cpp-example.html"> - * Witness_complex/witness_complex_sphere.cpp</a> - * - * The following example requires CGAL version ≥ 4.7.0: - * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_off.cpp</a> - * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_points.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> - * Persistent_cohomology/custom_persistence_sort.cpp</a> - * - * The following example requires CGAL version ≥ 4.8.1: - * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html"> - * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> - * \li <a href="_bottleneck_distance_2bottleneck_basic_example_8cpp-example.html"> - * Bottleneck_distance/bottleneck_basic_example.cpp</a> - * \li <a href="_bottleneck_distance_2bottleneck_read_file_example_8cpp-example.html"> - * Bottleneck_distance/bottleneck_read_file_example.cpp</a> - * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> - * 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"> - * Subsampling/example_sparsify_point_set.cpp</a> - * \li <a href="_tangential_complex_2example_basic_8cpp-example.html"> - * Tangential_complex/example_basic.cpp</a> - * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html"> - * Tangential_complex/example_with_perturb.cpp</a> - * - * \subsection eigen3 Eigen3 - * The \ref alpha_complex data structure and few examples requires - * <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> is a C++ template library for linear algebra: - * matrices, vectors, numerical solvers, and related algorithms. - * - * The following example requires the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be - * built if Eigen3 is not installed: - * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_off.cpp</a> - * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_points.cpp</a> - * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html"> - * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> - * Persistent_cohomology/custom_persistence_sort.cpp</a> - * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html"> - * 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"> - * Subsampling/example_sparsify_point_set.cpp</a> - * \li <a href="_tangential_complex_2example_basic_8cpp-example.html"> - * Tangential_complex/example_basic.cpp</a> - * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html"> - * Tangential_complex/example_with_perturb.cpp</a> - * - * \subsection tbb Threading Building Blocks - * <a target="_blank" href="https://www.threadingbuildingblocks.org/">Intel® TBB</a> lets you easily write parallel - * C++ programs that take full advantage of multicore performance, that are portable and composable, and that have - * future-proof scalability. - * - * Having Intel® TBB installed is recommended to parallelize and accelerate some GUDHI computations. - * - * The following examples are using Intel® TBB if installed: - * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_off.cpp</a> - * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html"> - * Alpha_complex/Alpha_complex_from_points.cpp</a> - * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_8cpp-example.html"> - * Bitmap_cubical_complex/Bitmap_cubical_complex.cpp</a> - * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_periodic_boundary_conditions_8cpp-example.html"> - * Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp</a> - * \li <a href="_bitmap_cubical_complex_2_random_bitmap_cubical_complex_8cpp-example.html"> - * Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_simplex_tree_2simple_simplex_tree_8cpp-example.html"> - * Simplex_tree/simple_simplex_tree.cpp</a> - * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html"> - * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a> - * \li <a href="_simplex_tree_2simplex_tree_from_cliques_of_graph_8cpp-example.html"> - * Simplex_tree/simplex_tree_from_cliques_of_graph.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html"> - * Persistent_cohomology/alpha_complex_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2rips_persistence_via_boundary_matrix_8cpp-example.html"> - * Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp</a> - * \li <a href="_persistent_cohomology_2persistence_from_file_8cpp-example.html"> - * Persistent_cohomology/persistence_from_file.cpp</a> - * \li <a href="_persistent_cohomology_2persistence_from_simple_simplex_tree_8cpp-example.html"> - * Persistent_cohomology/persistence_from_simple_simplex_tree.cpp</a> - * \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html"> - * Persistent_cohomology/plain_homology.cpp</a> - * \li <a href="_persistent_cohomology_2rips_distance_matrix_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_distance_matrix_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_multifield_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2rips_persistence_8cpp-example.html"> - * Persistent_cohomology/rips_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html"> - * Persistent_cohomology/rips_persistence_step_by_step.cpp</a> - * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2periodic_alpha_complex_3d_persistence_8cpp-example.html"> - * Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp</a> - * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html"> - * Persistent_cohomology/custom_persistence_sort.cpp</a> - * \li <a href="_rips_complex_2example_one_skeleton_rips_from_points_8cpp-example.html"> - * Rips_complex/example_one_skeleton_rips_from_points.cpp</a> - * \li <a href="_rips_complex_2example_rips_complex_from_off_file_8cpp-example.html"> - * Rips_complex/example_rips_complex_from_off_file.cpp</a> - * - * \section Contributions Bug reports and contributions - * Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to: - * \verbatim Contact: gudhi-users@lists.gforge.inria.fr \endverbatim - * - * GUDHI is open to external contributions. If you want to join our development team, please contact us. - * -*/ - -/*! \page Citation Acknowledging the GUDHI library - * We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use - * of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages. - * Feel free to contact us in case you have any question or remark on this topic. - * - * We provide \ref GudhiBibtex entries for the modules of the User and Reference Manual, as well as for publications - * directly related to the GUDHI library. - * \section GudhiBibtex GUDHI bibtex - * \verbinclude biblio/how_to_cite_gudhi.bib -*/ - -// List of GUDHI examples - Doxygen needs at least a file tag to analyse comments -/*! @file Examples - * @example Alpha_complex/Alpha_complex_from_off.cpp - * @example Alpha_complex/Alpha_complex_from_points.cpp - * @example Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp - * @example Bottleneck_distance/bottleneck_basic_example.cpp - * @example Bottleneck_distance/bottleneck_read_file_example.cpp - * @example Bitmap_cubical_complex/Bitmap_cubical_complex.cpp - * @example Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp - * @example Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp - * @example common/example_CGAL_3D_points_off_reader.cpp - * @example common/example_CGAL_points_off_reader.cpp - * @example Contraction/Garland_heckbert.cpp - * @example Contraction/Rips_contraction.cpp - * @example Persistent_cohomology/alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/alpha_complex_persistence.cpp - * @example Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp - * @example Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp - * @example Persistent_cohomology/persistence_from_file.cpp - * @example Persistent_cohomology/persistence_from_simple_simplex_tree.cpp - * @example Persistent_cohomology/plain_homology.cpp - * @example Persistent_cohomology/rips_multifield_persistence.cpp - * @example Persistent_cohomology/rips_distance_matrix_persistence.cpp - * @example Persistent_cohomology/rips_persistence.cpp - * @example Persistent_cohomology/custom_persistence_sort.cpp - * @example Persistent_cohomology/rips_persistence_step_by_step.cpp - * @example Rips_complex/example_one_skeleton_rips_from_points.cpp - * @example Rips_complex/example_rips_complex_from_off_file.cpp - * @example Simplex_tree/mini_simplex_tree.cpp - * @example Simplex_tree/simple_simplex_tree.cpp - * @example Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp - * @example Simplex_tree/simplex_tree_from_cliques_of_graph.cpp - * @example Skeleton_blocker/Skeleton_blocker_from_simplices.cpp - * @example Skeleton_blocker/Skeleton_blocker_iteration.cpp - * @example Skeleton_blocker/Skeleton_blocker_link.cpp - * @example Spatial_searching/example_spatial_searching.cpp - * @example Subsampling/example_choose_n_farthest_points.cpp - * @example Subsampling/example_custom_kernel.cpp - * @example Subsampling/example_pick_n_random_points.cpp - * @example Subsampling/example_sparsify_point_set.cpp - * @example Tangential_complex/example_basic.cpp - * @example Tangential_complex/example_with_perturb.cpp - * @example Witness_complex/example_nearest_landmark_table.cpp - * @example Witness_complex/example_strong_witness_complex_off.cpp - * @example Witness_complex/example_strong_witness_persistence.cpp - * @example Witness_complex/example_witness_complex_off.cpp - * @example Witness_complex/example_witness_complex_persistence.cpp - * @example Witness_complex/example_witness_complex_sphere.cpp - */ - diff --git a/example/Bitmap_cubical_complex/CMakeLists.txt b/example/Bitmap_cubical_complex/CMakeLists.txt index a0401619..99304aa4 100644 --- a/example/Bitmap_cubical_complex/CMakeLists.txt +++ b/example/Bitmap_cubical_complex/CMakeLists.txt @@ -1,17 +1,6 @@ cmake_minimum_required(VERSION 2.6) project(Bitmap_cubical_complex_examples) -add_executable ( Bitmap_cubical_complex Bitmap_cubical_complex.cpp ) -if (TBB_FOUND) - target_link_libraries(Bitmap_cubical_complex ${TBB_LIBRARIES}) -endif() - -add_test(NAME Bitmap_cubical_complex_example_persistence_one_sphere COMMAND $<TARGET_FILE:Bitmap_cubical_complex> - "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalOneSphere.txt") - -add_test(NAME Bitmap_cubical_complex_example_persistence_two_sphere COMMAND $<TARGET_FILE:Bitmap_cubical_complex> - "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt") - add_executable ( Random_bitmap_cubical_complex Random_bitmap_cubical_complex.cpp ) if (TBB_FOUND) target_link_libraries(Random_bitmap_cubical_complex ${TBB_LIBRARIES}) @@ -19,19 +8,4 @@ endif() add_test(NAME Bitmap_cubical_complex_example_random COMMAND $<TARGET_FILE:Random_bitmap_cubical_complex> "2" "100" "100") -add_executable ( Bitmap_cubical_complex_periodic_boundary_conditions Bitmap_cubical_complex_periodic_boundary_conditions.cpp ) -if (TBB_FOUND) - target_link_libraries(Bitmap_cubical_complex_periodic_boundary_conditions ${TBB_LIBRARIES}) -endif() - -add_test(NAME Bitmap_cubical_complex_example_periodic_boundary_conditions_2d_torus - COMMAND $<TARGET_FILE:Bitmap_cubical_complex_periodic_boundary_conditions> - "${CMAKE_SOURCE_DIR}/data/bitmap/2d_torus.txt") - -add_test(NAME Bitmap_cubical_complex_example_periodic_boundary_conditions_3d_torus - COMMAND $<TARGET_FILE:Bitmap_cubical_complex_periodic_boundary_conditions> - "${CMAKE_SOURCE_DIR}/data/bitmap/3d_torus.txt") - -install(TARGETS Bitmap_cubical_complex DESTINATION bin) install(TARGETS Random_bitmap_cubical_complex DESTINATION bin) -install(TARGETS Bitmap_cubical_complex_periodic_boundary_conditions DESTINATION bin) diff --git a/example/Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp b/example/Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp index 16ad65a0..f70558f2 100644 --- a/example/Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp +++ b/example/Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - // for persistence algorithm #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> @@ -34,10 +33,11 @@ int main(int argc, char** argv) { srand(time(0)); - std::cout << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << - "complexes. The first parameter of the program is the dimension D of the bitmap. The next D parameters are " << - "number of top dimensional cubes in each dimension of the bitmap. The program will create random cubical " << - "complex of that sizes and compute persistent homology of it." << std::endl; + std::cout + << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " + << "complexes. The first parameter of the program is the dimension D of the bitmap. The next D parameters are " + << "number of top dimensional cubes in each dimension of the bitmap. The program will create random cubical " + << "complex of that sizes and compute persistent homology of it." << std::endl; int p = 2; double min_persistence = 0; @@ -47,16 +47,16 @@ int main(int argc, char** argv) { return 1; } - size_t dimensionOfBitmap = (size_t) atoi(argv[1]); - std::vector< unsigned > sizes; + size_t dimensionOfBitmap = (size_t)atoi(argv[1]); + std::vector<unsigned> sizes; size_t multipliers = 1; for (size_t dim = 0; dim != dimensionOfBitmap; ++dim) { - unsigned sizeInThisDimension = (unsigned) atoi(argv[2 + dim]); + unsigned sizeInThisDimension = (unsigned)atoi(argv[2 + dim]); sizes.push_back(sizeInThisDimension); multipliers *= sizeInThisDimension; } - std::vector< double > data; + std::vector<double> data; for (size_t i = 0; i != multipliers; ++i) { data.push_back(rand() / static_cast<double>(RAND_MAX)); } @@ -80,4 +80,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/example/Bottleneck_distance/CMakeLists.txt b/example/Bottleneck_distance/CMakeLists.txt index eac617db..6095d6eb 100644 --- a/example/Bottleneck_distance/CMakeLists.txt +++ b/example/Bottleneck_distance/CMakeLists.txt @@ -2,37 +2,21 @@ cmake_minimum_required(VERSION 2.6) project(Bottleneck_distance_examples) if (NOT CGAL_VERSION VERSION_LESS 4.8.1) - add_executable (bottleneck_read_file_example bottleneck_read_file_example.cpp) add_executable (bottleneck_basic_example bottleneck_basic_example.cpp) + add_executable (alpha_rips_persistence_bottleneck_distance alpha_rips_persistence_bottleneck_distance.cpp) + target_link_libraries(alpha_rips_persistence_bottleneck_distance ${Boost_PROGRAM_OPTIONS_LIBRARY}) if (TBB_FOUND) - target_link_libraries(bottleneck_read_file_example ${TBB_LIBRARIES}) + target_link_libraries(alpha_rips_persistence_bottleneck_distance ${TBB_LIBRARIES}) target_link_libraries(bottleneck_basic_example ${TBB_LIBRARIES}) endif(TBB_FOUND) - + add_test(NAME Bottleneck_distance_example_basic COMMAND $<TARGET_FILE:bottleneck_basic_example>) - - add_test(NAME Bottleneck_read_file_example - COMMAND $<TARGET_FILE:bottleneck_read_file_example> - "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers" "${CMAKE_SOURCE_DIR}/data/persistence_diagram/second.pers") - - install(TARGETS bottleneck_read_file_example DESTINATION bin) - install(TARGETS bottleneck_basic_example DESTINATION bin) - -endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) - -# Eigen3 and CGAL > 4.7.0 is required for alpha complex -# CGAL > 4.8.1 is required for bottleneck distance => -if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - add_executable (alpha_rips_persistence_bottleneck_distance alpha_rips_persistence_bottleneck_distance.cpp) - target_link_libraries(alpha_rips_persistence_bottleneck_distance ${Boost_PROGRAM_OPTIONS_LIBRARY}) - add_test(NAME Bottleneck_distance_example_alpha_rips_persistence_bottleneck - COMMAND $<TARGET_FILE:alpha_rips_persistence_bottleneck_distance> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.15" "-m" "0.12" "-d" "3" "-p" "3") + COMMAND $<TARGET_FILE:alpha_rips_persistence_bottleneck_distance> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.15" "-m" "0.12" "-d" "3" "-p" "3") + install(TARGETS bottleneck_basic_example DESTINATION bin) install(TARGETS alpha_rips_persistence_bottleneck_distance DESTINATION bin) - if (TBB_FOUND) - target_link_libraries(alpha_rips_persistence_bottleneck_distance ${TBB_LIBRARIES}) - endif(TBB_FOUND) -endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + +endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/example/Bottleneck_distance/README b/example/Bottleneck_distance/README new file mode 100644 index 00000000..01bcd74a --- /dev/null +++ b/example/Bottleneck_distance/README @@ -0,0 +1,19 @@ +# Bottleneck_distance #
+
+## `alpha_rips_persistence_bottleneck_distance` ##
+This program computes the persistent homology with coefficient field Z/pZ of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+Usage:
+`alpha_rips_persistence_bottleneck_distance [options] <OFF input file>`
+
+Allowed options:
+
+* `-h [ --help ]` Produce help message
+* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction.`
+* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute.`
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
diff --git a/example/Contraction/CMakeLists.txt b/example/Contraction/CMakeLists.txt index 83594c0e..a92d1685 100644 --- a/example/Contraction/CMakeLists.txt +++ b/example/Contraction/CMakeLists.txt @@ -1,9 +1,10 @@ cmake_minimum_required(VERSION 2.6) project(Contraction_examples) - add_executable(RipsContraction Rips_contraction.cpp) + add_executable(GarlandHeckbert Garland_heckbert.cpp) +target_link_libraries(GarlandHeckbert ${Boost_TIMER_LIBRARY}) add_test(NAME Contraction_example_tore3D_0.2 COMMAND $<TARGET_FILE:RipsContraction> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "0.2") diff --git a/example/Contraction/Garland_heckbert.cpp b/example/Contraction/Garland_heckbert.cpp index f0cde95e..2b0dc973 100644 --- a/example/Contraction/Garland_heckbert.cpp +++ b/example/Contraction/Garland_heckbert.cpp @@ -29,7 +29,6 @@ #include <gudhi/Edge_contraction.h> #include <gudhi/Skeleton_blocker.h> #include <gudhi/Off_reader.h> -#include <gudhi/Clock.h> #include <iostream> @@ -165,8 +164,6 @@ int main(int argc, char *argv[]) { int num_contractions = atoi(argv[3]); - Gudhi::Clock contraction_chrono("Time to simplify and enumerate simplices"); - // constructs the contractor object with Garland Heckbert policies. Complex_contractor contractor(complex, new GH_cost(complex), @@ -182,8 +179,6 @@ int main(int argc, char *argv[]) { complex.num_edges() << " edges and " << complex.num_triangles() << " triangles." << std::endl; - std::cout << contraction_chrono; - // write simplified complex Gudhi::skeleton_blocker::Skeleton_blocker_off_writer<Complex> off_writer(argv[2], complex); diff --git a/example/Nerve_GIC/CMakeLists.txt b/example/Nerve_GIC/CMakeLists.txt new file mode 100644 index 00000000..f2626927 --- /dev/null +++ b/example/Nerve_GIC/CMakeLists.txt @@ -0,0 +1,26 @@ +cmake_minimum_required(VERSION 2.6) +project(Nerve_GIC_examples) + +if (NOT CGAL_VERSION VERSION_LESS 4.8.1) + + add_executable ( CoordGIC CoordGIC.cpp ) + add_executable ( FuncGIC FuncGIC.cpp ) + + if (TBB_FOUND) + target_link_libraries(CoordGIC ${TBB_LIBRARIES}) + target_link_libraries(FuncGIC ${TBB_LIBRARIES}) + endif() + + # Copy files for not to pollute sources when testing + file(COPY "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + file(COPY "${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat.off" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + file(COPY "${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat_PCA1" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + + add_test(NAME Nerve_GIC_example_CoordGIC COMMAND $<TARGET_FILE:CoordGIC> + "tore3D_1307.off" "0") + + add_test(NAME Nerve_GIC_example_FuncGIC COMMAND $<TARGET_FILE:FuncGIC> + "lucky_cat.off" + "lucky_cat_PCA1") + +endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/example/Nerve_GIC/CoordGIC.cpp b/example/Nerve_GIC/CoordGIC.cpp new file mode 100644 index 00000000..c92cf235 --- /dev/null +++ b/example/Nerve_GIC/CoordGIC.cpp @@ -0,0 +1,93 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off coordinate [-v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 2 -v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int coord = atoi(argv[2]); + bool verb = 0; + if (argc == 4) verb = 1; + + // ----------------------------------------- + // Init of a functional GIC from an OFF file + // ----------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_coordinate(coord); + GIC.set_function_from_coordinate(coord); + + GIC.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); + GIC.set_automatic_resolution(); + GIC.set_gain(); + GIC.set_cover_from_function(); + + GIC.find_simplices(); + + GIC.plot_DOT(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // -------------------------------------------- + // Display information about the functional GIC + // -------------------------------------------- + + if (verb) { + std::cout << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on functional GIC simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/example/Nerve_GIC/FuncGIC.cpp b/example/Nerve_GIC/FuncGIC.cpp new file mode 100644 index 00000000..cb0f0d63 --- /dev/null +++ b/example/Nerve_GIC/FuncGIC.cpp @@ -0,0 +1,94 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off function [-v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/COIL_database/lucky_cat.off " + "../../data/points/COIL_database/lucky_cat_PCA1 -v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + std::string func_file_name = argv[2]; + bool verb = 0; + if (argc == 4) verb = 1; + + // ----------------------------------------- + // Init of a functional GIC from an OFF file + // ----------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_file(func_file_name); + GIC.set_function_from_file(func_file_name); + + GIC.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); + GIC.set_automatic_resolution(); + GIC.set_gain(); + GIC.set_cover_from_function(); + + GIC.find_simplices(); + + GIC.plot_DOT(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // -------------------------------------------- + // Display information about the functional GIC + // -------------------------------------------- + + if (verb) { + std::cout << "Functional GIC is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on functional GIC simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/example/Persistence_representations/CMakeLists.txt b/example/Persistence_representations/CMakeLists.txt new file mode 100644 index 00000000..eb3258f8 --- /dev/null +++ b/example/Persistence_representations/CMakeLists.txt @@ -0,0 +1,27 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_representations_example) + +add_executable ( Persistence_representations_example_landscape_on_grid persistence_landscape_on_grid.cpp ) +add_test(NAME Persistence_representations_example_landscape_on_grid + COMMAND $<TARGET_FILE:Persistence_representations_example_landscape_on_grid>) + +add_executable ( Persistence_representations_example_landscape persistence_landscape.cpp ) +add_test(NAME Persistence_representations_example_landscape + COMMAND $<TARGET_FILE:Persistence_representations_example_landscape>) + +add_executable ( Persistence_representations_example_intervals persistence_intervals.cpp ) +add_test(NAME Persistence_representations_example_intervals + COMMAND $<TARGET_FILE:Persistence_representations_example_intervals> + "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers") + +add_executable ( Persistence_representations_example_vectors persistence_vectors.cpp ) +add_test(NAME Persistence_representations_example_vectors + COMMAND $<TARGET_FILE:Persistence_representations_example_vectors>) + +add_executable ( Persistence_representations_example_heat_maps persistence_heat_maps.cpp ) +add_test(NAME Persistence_representations_example_heat_maps + COMMAND $<TARGET_FILE:Persistence_representations_example_heat_maps>) + + + + diff --git a/example/Persistence_representations/persistence_heat_maps.cpp b/example/Persistence_representations/persistence_heat_maps.cpp new file mode 100644 index 00000000..2a472ac6 --- /dev/null +++ b/example/Persistence_representations/persistence_heat_maps.cpp @@ -0,0 +1,80 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <vector> +#include <utility> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + // create two simple vectors with birth--death pairs: + + std::vector<std::pair<double, double> > persistence1; + std::vector<std::pair<double, double> > persistence2; + + persistence1.push_back(std::make_pair(1, 2)); + persistence1.push_back(std::make_pair(6, 8)); + persistence1.push_back(std::make_pair(0, 4)); + persistence1.push_back(std::make_pair(3, 8)); + + persistence2.push_back(std::make_pair(2, 9)); + persistence2.push_back(std::make_pair(1, 6)); + persistence2.push_back(std::make_pair(3, 5)); + persistence2.push_back(std::make_pair(6, 10)); + + // over here we define a function we sill put on a top on every birth--death pair in the persistence interval. It can + // be anything. Over here we will use standard Gaussian + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(5, 1); + + // creating two heat maps. + Persistence_heat_maps hm1(persistence1, filter, false, 20, 0, 11); + Persistence_heat_maps hm2(persistence2, filter, false, 20, 0, 11); + + std::vector<Persistence_heat_maps*> vector_of_maps; + vector_of_maps.push_back(&hm1); + vector_of_maps.push_back(&hm2); + + // compute median/mean of a vector of heat maps: + Persistence_heat_maps mean; + mean.compute_mean(vector_of_maps); + Persistence_heat_maps median; + median.compute_median(vector_of_maps); + + // to compute L^1 distance between hm1 and hm2: + std::cout << "The L^1 distance is : " << hm1.distance(hm2, 1) << std::endl; + + // to average of hm1 and hm2: + std::vector<Persistence_heat_maps*> to_average; + to_average.push_back(&hm1); + to_average.push_back(&hm2); + Persistence_heat_maps av; + av.compute_average(to_average); + + // to compute scalar product of hm1 and hm2: + std::cout << "Scalar product is : " << hm1.compute_scalar_product(hm2) << std::endl; + + return 0; +} diff --git a/example/Persistence_representations/persistence_intervals.cpp b/example/Persistence_representations/persistence_intervals.cpp new file mode 100644 index 00000000..c1ceb458 --- /dev/null +++ b/example/Persistence_representations/persistence_intervals.cpp @@ -0,0 +1,89 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <utility> +#include <vector> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + if (argc != 2) { + std::cout << "To run this program, please provide the name of a file with persistence diagram \n"; + return 1; + } + + Persistence_intervals p(argv[1]); + std::pair<double, double> min_max_ = p.get_x_range(); + std::cout << "Birth-death range : " << min_max_.first << " " << min_max_.second << std::endl; + + std::vector<double> dominant_ten_intervals_length = p.length_of_dominant_intervals(10); + std::cout << "Length of ten dominant intervals : " << std::endl; + for (size_t i = 0; i != dominant_ten_intervals_length.size(); ++i) { + std::cout << dominant_ten_intervals_length[i] << std::endl; + } + + std::vector<std::pair<double, double> > ten_dominant_intervals = p.dominant_intervals(10); + std::cout << "Here are the dominant intervals : " << std::endl; + for (size_t i = 0; i != ten_dominant_intervals.size(); ++i) { + std::cout << "( " << ten_dominant_intervals[i].first << "," << ten_dominant_intervals[i].second << std::endl; + } + + std::vector<size_t> histogram = p.histogram_of_lengths(10); + std::cout << "Here is the histogram of barcode's length : " << std::endl; + for (size_t i = 0; i != histogram.size(); ++i) { + std::cout << histogram[i] << " "; + } + std::cout << std::endl; + + std::vector<size_t> cumulative_histogram = p.cumulative_histogram_of_lengths(10); + std::cout << "Cumulative histogram : " << std::endl; + for (size_t i = 0; i != cumulative_histogram.size(); ++i) { + std::cout << cumulative_histogram[i] << " "; + } + std::cout << std::endl; + + std::vector<double> char_funct_diag = p.characteristic_function_of_diagram(min_max_.first, min_max_.second); + std::cout << "Characteristic function of diagram : " << std::endl; + for (size_t i = 0; i != char_funct_diag.size(); ++i) { + std::cout << char_funct_diag[i] << " "; + } + std::cout << std::endl; + + std::vector<double> cumul_char_funct_diag = + p.cumulative_characteristic_function_of_diagram(min_max_.first, min_max_.second); + std::cout << "Cumulative characteristic function of diagram : " << std::endl; + for (size_t i = 0; i != cumul_char_funct_diag.size(); ++i) { + std::cout << cumul_char_funct_diag[i] << " "; + } + std::cout << std::endl; + + std::cout << "Persistence Betti numbers \n"; + std::vector<std::pair<double, size_t> > pbns = p.compute_persistent_betti_numbers(); + for (size_t i = 0; i != pbns.size(); ++i) { + std::cout << pbns[i].first << " " << pbns[i].second << std::endl; + } + + return 0; +} diff --git a/example/Persistence_representations/persistence_landscape.cpp b/example/Persistence_representations/persistence_landscape.cpp new file mode 100644 index 00000000..400a9ae1 --- /dev/null +++ b/example/Persistence_representations/persistence_landscape.cpp @@ -0,0 +1,86 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <vector> +#include <utility> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + // create two simple vectors with birth--death pairs: + + std::vector<std::pair<double, double> > persistence1; + std::vector<std::pair<double, double> > persistence2; + + persistence1.push_back(std::make_pair(1, 2)); + persistence1.push_back(std::make_pair(6, 8)); + persistence1.push_back(std::make_pair(0, 4)); + persistence1.push_back(std::make_pair(3, 8)); + + persistence2.push_back(std::make_pair(2, 9)); + persistence2.push_back(std::make_pair(1, 6)); + persistence2.push_back(std::make_pair(3, 5)); + persistence2.push_back(std::make_pair(6, 10)); + + // create two persistence landscapes based on persistence1 and persistence2: + Persistence_landscape l1(persistence1); + Persistence_landscape l2(persistence2); + + // This is how to compute integral of landscapes: + std::cout << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; + std::cout << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; + + // And here how to write landscapes to stream: + std::cout << "l1 : " << l1 << std::endl; + std::cout << "l2 : " << l2 << std::endl; + + // Arithmetic operations on landscapes: + Persistence_landscape sum = l1 + l2; + std::cout << "sum : " << sum << std::endl; + + // here are the maxima of the functions: + std::cout << "Maximum of l1 : " << l1.compute_maximum() << std::endl; + std::cout << "Maximum of l2 : " << l2.compute_maximum() << std::endl; + + // here are the norms of landscapes: + std::cout << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; + std::cout << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; + + // here is the average of landscapes: + Persistence_landscape average; + average.compute_average({&l1, &l2}); + std::cout << "average : " << average << std::endl; + + // here is the distance of landscapes: + std::cout << "Distance : " << l1.distance(l2) << std::endl; + + // here is the scalar product of landscapes: + std::cout << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; + + // here is how to create a file which is suitable for visualization via gnuplot: + average.plot("average_landscape"); + + return 0; +} diff --git a/example/Persistence_representations/persistence_landscape_on_grid.cpp b/example/Persistence_representations/persistence_landscape_on_grid.cpp new file mode 100644 index 00000000..b201b397 --- /dev/null +++ b/example/Persistence_representations/persistence_landscape_on_grid.cpp @@ -0,0 +1,82 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <utility> +#include <vector> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + // create two simple vectors with birth--death pairs: + + std::vector<std::pair<double, double> > persistence1; + std::vector<std::pair<double, double> > persistence2; + + persistence1.push_back(std::make_pair(1, 2)); + persistence1.push_back(std::make_pair(6, 8)); + persistence1.push_back(std::make_pair(0, 4)); + persistence1.push_back(std::make_pair(3, 8)); + + persistence2.push_back(std::make_pair(2, 9)); + persistence2.push_back(std::make_pair(1, 6)); + persistence2.push_back(std::make_pair(3, 5)); + persistence2.push_back(std::make_pair(6, 10)); + + // create two persistence landscapes based on persistence1 and persistence2: + Persistence_landscape_on_grid l1(persistence1, 0, 11, 20); + Persistence_landscape_on_grid l2(persistence2, 0, 11, 20); + + // This is how to compute integral of landscapes: + std::cout << "Integral of the first landscape : " << l1.compute_integral_of_landscape() << std::endl; + std::cout << "Integral of the second landscape : " << l2.compute_integral_of_landscape() << std::endl; + + // And here how to write landscapes to stream: + std::cout << "l1 : " << l1 << std::endl; + std::cout << "l2 : " << l2 << std::endl; + + // here are the maxima of the functions: + std::cout << "Maximum of l1 : " << l1.compute_maximum() << std::endl; + std::cout << "Maximum of l2 : " << l2.compute_maximum() << std::endl; + + // here are the norms of landscapes: + std::cout << "L^1 Norm of l1 : " << l1.compute_norm_of_landscape(1.) << std::endl; + std::cout << "L^1 Norm of l2 : " << l2.compute_norm_of_landscape(1.) << std::endl; + + // here is the average of landscapes: + Persistence_landscape_on_grid average; + average.compute_average({&l1, &l2}); + std::cout << "average : " << average << std::endl; + + // here is the distance of landscapes: + std::cout << "Distance : " << l1.distance(l2) << std::endl; + + // here is the scalar product of landscapes: + std::cout << "Scalar product : " << l1.compute_scalar_product(l2) << std::endl; + + // here is how to create a file which is suitable for visualization via gnuplot: + average.plot("average_landscape"); + + return 0; +} diff --git a/example/Persistence_representations/persistence_vectors.cpp b/example/Persistence_representations/persistence_vectors.cpp new file mode 100644 index 00000000..834ae644 --- /dev/null +++ b/example/Persistence_representations/persistence_vectors.cpp @@ -0,0 +1,74 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <vector> +#include <cmath> +#include <iomanip> +#include <limits> +#include <utility> + +using Vector_distances_in_diagram = + Gudhi::Persistence_representations::Vector_distances_in_diagram<Gudhi::Euclidean_distance>; + +int main(int argc, char** argv) { + // create two simple vectors with birth--death pairs: + + std::vector<std::pair<double, double> > persistence1; + std::vector<std::pair<double, double> > persistence2; + + persistence1.push_back(std::make_pair(1, 2)); + persistence1.push_back(std::make_pair(6, 8)); + persistence1.push_back(std::make_pair(0, 4)); + persistence1.push_back(std::make_pair(3, 8)); + + persistence2.push_back(std::make_pair(2, 9)); + persistence2.push_back(std::make_pair(1, 6)); + persistence2.push_back(std::make_pair(3, 5)); + persistence2.push_back(std::make_pair(6, 10)); + + // create two persistence vectors based on persistence1 and persistence2: + Vector_distances_in_diagram v1(persistence1, std::numeric_limits<size_t>::max()); + Vector_distances_in_diagram v2(persistence2, std::numeric_limits<size_t>::max()); + + // writing to a stream: + std::cout << "v1 : " << v1 << std::endl; + std::cout << "v2 : " << v2 << std::endl; + + // averages: + Vector_distances_in_diagram average; + average.compute_average({&v1, &v2}); + std::cout << "Average : " << average << std::endl; + + // computations of distances: + std::cout << "l^1 distance : " << v1.distance(v2) << std::endl; + + // computations of scalar product: + std::cout << "Scalar product of l1 and l2 : " << v1.compute_scalar_product(v2) << std::endl; + + // create a file with a gnuplot script: + v1.plot("plot_of_vector_representation"); + + return 0; +} diff --git a/example/Persistent_cohomology/CMakeLists.txt b/example/Persistent_cohomology/CMakeLists.txt index f47de4c3..18e2913b 100644 --- a/example/Persistent_cohomology/CMakeLists.txt +++ b/example/Persistent_cohomology/CMakeLists.txt @@ -5,12 +5,6 @@ add_executable(plain_homology plain_homology.cpp) add_executable(persistence_from_simple_simplex_tree persistence_from_simple_simplex_tree.cpp) -add_executable(rips_distance_matrix_persistence rips_distance_matrix_persistence.cpp) -target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - -add_executable(rips_persistence rips_persistence.cpp) -target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - add_executable(rips_persistence_step_by_step rips_persistence_step_by_step.cpp) target_link_libraries(rips_persistence_step_by_step ${Boost_PROGRAM_OPTIONS_LIBRARY}) @@ -23,8 +17,6 @@ target_link_libraries(persistence_from_file ${Boost_PROGRAM_OPTIONS_LIBRARY}) if (TBB_FOUND) target_link_libraries(plain_homology ${TBB_LIBRARIES}) target_link_libraries(persistence_from_simple_simplex_tree ${TBB_LIBRARIES}) - target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) - target_link_libraries(rips_persistence ${TBB_LIBRARIES}) target_link_libraries(rips_persistence_step_by_step ${TBB_LIBRARIES}) target_link_libraries(rips_persistence_via_boundary_matrix ${TBB_LIBRARIES}) target_link_libraries(persistence_from_file ${TBB_LIBRARIES}) @@ -33,10 +25,6 @@ endif() add_test(NAME Persistent_cohomology_example_plain_homology COMMAND $<TARGET_FILE:plain_homology>) add_test(NAME Persistent_cohomology_example_from_simple_simplex_tree COMMAND $<TARGET_FILE:persistence_from_simple_simplex_tree> "1" "0") -add_test(NAME Persistent_cohomology_example_from_rips_distance_matrix COMMAND $<TARGET_FILE:rips_distance_matrix_persistence> - "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" "-r" "1.0" "-d" "3" "-p" "3" "-m" "0") -add_test(NAME Persistent_cohomology_example_from_rips_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") add_test(NAME Persistent_cohomology_example_from_rips_step_by_step_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence_step_by_step> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") add_test(NAME Persistent_cohomology_example_via_boundary_matrix COMMAND $<TARGET_FILE:rips_persistence_via_boundary_matrix> @@ -48,8 +36,6 @@ add_test(NAME Persistent_cohomology_example_from_file_3_3_100 COMMAND $<TARGET_F install(TARGETS plain_homology DESTINATION bin) install(TARGETS persistence_from_simple_simplex_tree DESTINATION bin) -install(TARGETS rips_distance_matrix_persistence DESTINATION bin) -install(TARGETS rips_persistence DESTINATION bin) install(TARGETS rips_persistence_step_by_step DESTINATION bin) install(TARGETS rips_persistence_via_boundary_matrix DESTINATION bin) install(TARGETS persistence_from_file DESTINATION bin) @@ -69,53 +55,15 @@ if(GMP_FOUND) endif(GMP_FOUND) if(CGAL_FOUND) - add_executable(alpha_complex_3d_persistence alpha_complex_3d_persistence.cpp) - target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(exact_alpha_complex_3d_persistence exact_alpha_complex_3d_persistence.cpp) - target_link_libraries(exact_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(weighted_alpha_complex_3d_persistence weighted_alpha_complex_3d_persistence.cpp) - target_link_libraries(weighted_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - - if (TBB_FOUND) - target_link_libraries(alpha_complex_3d_persistence ${TBB_LIBRARIES}) - target_link_libraries(exact_alpha_complex_3d_persistence ${TBB_LIBRARIES}) - target_link_libraries(weighted_alpha_complex_3d_persistence ${TBB_LIBRARIES}) - endif(TBB_FOUND) - add_test(NAME Persistent_cohomology_example_alpha_complex_3d COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "2" "0.45") - add_test(NAME Persistent_cohomology_example_exact_alpha_complex_3d COMMAND $<TARGET_FILE:exact_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "2" "0.45") - add_test(NAME Persistent_cohomology_example_weighted_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.weights" "2" "0.45") - - install(TARGETS alpha_complex_3d_persistence DESTINATION bin) - install(TARGETS exact_alpha_complex_3d_persistence DESTINATION bin) - install(TARGETS weighted_alpha_complex_3d_persistence DESTINATION bin) - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) - add_executable (alpha_complex_persistence alpha_complex_persistence.cpp) - target_link_libraries(alpha_complex_persistence - ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - add_executable(periodic_alpha_complex_3d_persistence periodic_alpha_complex_3d_persistence.cpp) - target_link_libraries(periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY}) - add_executable(custom_persistence_sort custom_persistence_sort.cpp) target_link_libraries(custom_persistence_sort ${CGAL_LIBRARY}) if (TBB_FOUND) - target_link_libraries(alpha_complex_persistence ${TBB_LIBRARIES}) - target_link_libraries(periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) target_link_libraries(custom_persistence_sort ${TBB_LIBRARIES}) endif(TBB_FOUND) - add_test(NAME Persistent_cohomology_example_alpha_complex COMMAND $<TARGET_FILE:alpha_complex_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") - add_test(NAME Persistent_cohomology_example_periodic_alpha_complex_3d COMMAND $<TARGET_FILE:periodic_alpha_complex_3d_persistence> - "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "2" "0") add_test(NAME Persistent_cohomology_example_custom_persistence_sort COMMAND $<TARGET_FILE:custom_persistence_sort>) - install(TARGETS alpha_complex_persistence DESTINATION bin) - install(TARGETS periodic_alpha_complex_3d_persistence DESTINATION bin) install(TARGETS custom_persistence_sort DESTINATION bin) endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) diff --git a/example/Persistent_cohomology/README b/example/Persistent_cohomology/README index 794b94ae..f39d9584 100644 --- a/example/Persistent_cohomology/README +++ b/example/Persistent_cohomology/README @@ -1,43 +1,14 @@ -To build the example, run in a Terminal: +To build the examples, run in a Terminal: -cd /path-to-example/ +cd /path-to-examples/ cmake . make *********************************************************************************************************************** Example of use of RIPS: -Computation of the persistent homology with Z/2Z coefficients of the Rips complex on points -sampling a Klein bottle: - -./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2 - -output: -2 0 0 inf -2 1 0.0983494 inf -2 1 0.104347 inf -2 2 0.138335 inf - - -Every line is of this format: p1*...*pr dim b d -where - p1*...*pr is the product of prime numbers pi such that the homology feature exists in homology with Z/piZ coefficients. - dim is the dimension of the homological feature, - b and d are respectively the birth and death of the feature and - - - -with Z/3Z coefficients: - -./rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3 - -output: -3 0 0 inf -3 1 0.0983494 inf -3 1 0.104347 inf -3 2 0.138335 inf - -and the computation with Z/2Z and Z/3Z coefficients simultaneously: +Computation of the persistent homology with Z/2Z and Z/3Z coefficients simultaneously of the Rips complex +on points sampling a 3D torus: ./rips_multifield_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.12 -d 3 -p 2 -q 3 @@ -53,7 +24,13 @@ output: 6 0 0 0.12047 6 0 0 0.120414 -and finally the computation with all Z/pZ for 2 <= p <= 71 (20 first prime numbers): +Every line is of this format: p1*...*pr dim b d +where + p1*...*pr is the product of prime numbers pi such that the homology feature exists in homology with Z/piZ coefficients. + dim is the dimension of the homological feature, + b and d are respectively the birth and death of the feature and + +and the computation with all Z/pZ for 2 <= p <= 71 (20 first prime numbers): ./rips_multifield_persistence ../../data/points/Kl.off -r 0.25 -m 0.5 -d 3 -p 2 -q 71 @@ -70,82 +47,6 @@ output: 557940830126698960967415390 0 0 0.120414 *********************************************************************************************************************** -Example of use of ALPHA: - -For a more verbose mode, please run cmake with option "DEBUG_TRACES=TRUE" and recompile the programs. - -1) 3D special case ------------------- -Computation of the persistent homology with Z/2Z coefficients of the alpha complex on points -sampling a torus 3D: - -./alpha_complex_3d_persistence ../../data/points/tore3D_300.off 2 0.45 - -output: -Simplex_tree dim: 3 -2 0 0 inf -2 1 0.0682162 1.0001 -2 1 0.0934117 1.00003 -2 2 0.56444 1.03938 - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 2 -Betti numbers[2] = 1 - -N.B.: - alpha_complex_3d_persistence accepts only OFF files in 3D dimension. - - filtration values are alpha square values - -2) d-Dimension case -------------------- -Computation of the persistent homology with Z/2Z coefficients of the alpha complex on points -sampling a torus 3D: - -./alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off - -output: -Alpha complex is of dimension 3 - 9273 simplices - 300 vertices. -Simplex_tree dim: 3 -2 0 0 inf -2 1 0.0682162 1.0001 -2 1 0.0934117 1.00003 -2 2 0.56444 1.03938 - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 2 -Betti numbers[2] = 1 - -N.B.: - alpha_complex_persistence accepts OFF files in d-Dimension. - - filtration values are alpha square values - -3) 3D periodic special case ---------------------------- -./periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt 3 1.0 - -output: -Periodic Delaunay computed. -Simplex_tree dim: 3 -3 0 0 inf -3 1 0.0025 inf -3 1 0.0025 inf -3 1 0.0025 inf -3 2 0.005 inf -3 2 0.005 inf -3 2 0.005 inf -3 3 0.0075 inf - -Here we retrieve expected Betti numbers on a tore 3D: -Betti numbers[0] = 1 -Betti numbers[1] = 3 -Betti numbers[2] = 3 -Betti numbers[3] = 1 - -N.B.: - periodic_alpha_complex_3d_persistence accepts only OFF files in 3D dimension. In this example, the periodic cube -is hard coded to { x = [0,1]; y = [0,1]; z = [0,1] } - - filtration values are alpha square values - -*********************************************************************************************************************** Example of use of PLAIN HOMOLOGY: This example computes the plain homology of the following simplicial complex without filtration values: diff --git a/example/Persistent_cohomology/persistence_from_simple_simplex_tree.cpp b/example/Persistent_cohomology/persistence_from_simple_simplex_tree.cpp index 8214d66a..8ef479d4 100644 --- a/example/Persistent_cohomology/persistence_from_simple_simplex_tree.cpp +++ b/example/Persistent_cohomology/persistence_from_simple_simplex_tree.cpp @@ -142,7 +142,6 @@ int main(int argc, char * const argv[]) { /* An edge [11,6] */ /* An edge [10,12,2] */ - st.set_dimension(2); std::cout << "The complex contains " << st.num_simplices() << " simplices - " << st.num_vertices() << " vertices " << std::endl; diff --git a/example/Persistent_cohomology/plain_homology.cpp b/example/Persistent_cohomology/plain_homology.cpp index 50f692f2..a5ae09c8 100644 --- a/example/Persistent_cohomology/plain_homology.cpp +++ b/example/Persistent_cohomology/plain_homology.cpp @@ -64,8 +64,6 @@ int main() { st.insert_simplex_and_subfaces(edge03); st.insert_simplex(edge13); st.insert_simplex(vertex4); - // FIXME: Remove this line - st.set_dimension(2); // Sort the simplices in the order of the filtration st.initialize_filtration(); diff --git a/example/Persistent_cohomology/rips_persistence_step_by_step.cpp b/example/Persistent_cohomology/rips_persistence_step_by_step.cpp index 554eeba6..c1de0ef8 100644 --- a/example/Persistent_cohomology/rips_persistence_step_by_step.cpp +++ b/example/Persistent_cohomology/rips_persistence_step_by_step.cpp @@ -45,14 +45,7 @@ using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Vertex_handle = Simplex_tree::Vertex_handle; using Filtration_value = Simplex_tree::Filtration_value; -using Graph_t = boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS -, boost::property < vertex_filtration_t, Filtration_value > -, boost::property < edge_filtration_t, Filtration_value > ->; -using Edge_t = std::pair< Vertex_handle, Vertex_handle >; - -template< typename InputPointRange, typename Distance > -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold, Distance distance); +using Proximity_graph = Gudhi::Proximity_graph<Simplex_tree>; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp >; @@ -81,8 +74,9 @@ int main(int argc, char * argv[]) { Points_off_reader off_reader(off_file_points); // Compute the proximity graph of the points - Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold - , Gudhi::Euclidean_distance()); + Proximity_graph prox_graph = Gudhi::compute_proximity_graph<Simplex_tree>(off_reader.get_point_cloud(), + threshold, + Gudhi::Euclidean_distance()); // Construct the Rips complex in a Simplex Tree Simplex_tree st; @@ -170,48 +164,3 @@ void program_options(int argc, char * argv[] std::abort(); } } - -/** Output the proximity graph of the points. - * - * If points contains n elements, the proximity graph is the graph - * with n vertices, and an edge [u,v] iff the distance function between - * points u and v is smaller than threshold. - * - * The type PointCloud furnishes .begin() and .end() methods, that return - * iterators with value_type Point. - */ -template< typename InputPointRange, typename Distance > -Graph_t compute_proximity_graph(InputPointRange &points, Filtration_value threshold, Distance distance) { - std::vector< Edge_t > edges; - std::vector< Filtration_value > edges_fil; - - Vertex_handle idx_u, idx_v; - Filtration_value fil; - idx_u = 0; - for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { - idx_v = idx_u + 1; - for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { - fil = distance(*it_u, *it_v); - if (fil <= threshold) { - edges.emplace_back(idx_u, idx_v); - edges_fil.push_back(fil); - } - } - ++idx_u; - } - - Graph_t skel_graph(edges.begin() - , edges.end() - , edges_fil.begin() - , idx_u); // number of points labeled from 0 to idx_u-1 - - auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph); - - boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; - for (std::tie(vi, vi_end) = boost::vertices(skel_graph); - vi != vi_end; ++vi) { - boost::put(vertex_prop, *vi, 0.); - } - - return skel_graph; -} diff --git a/example/Simplex_tree/CMakeLists.txt b/example/Simplex_tree/CMakeLists.txt index e22cc92c..b33b2d05 100644 --- a/example/Simplex_tree/CMakeLists.txt +++ b/example/Simplex_tree/CMakeLists.txt @@ -35,4 +35,21 @@ if(GMP_FOUND AND CGAL_FOUND) install(TARGETS Simplex_tree_example_alpha_shapes_3_from_off DESTINATION bin) + add_executable ( Simplex_tree_example_cech_complex_cgal_mini_sphere_3d cech_complex_cgal_mini_sphere_3d.cpp ) + target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${Boost_PROGRAM_OPTIONS_LIBRARY} ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_cech_complex_cgal_mini_sphere_3d ${TBB_LIBRARIES}) + endif() + add_test(NAME Simplex_tree_example_cech_complex_cgal_mini_sphere_3d COMMAND $<TARGET_FILE:Simplex_tree_example_cech_complex_cgal_mini_sphere_3d> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" -r 0.3 -d 3) + + install(TARGETS Simplex_tree_example_alpha_shapes_3_from_off DESTINATION bin) endif() + +add_executable ( Simplex_tree_example_graph_expansion_with_blocker graph_expansion_with_blocker.cpp ) +if (TBB_FOUND) + target_link_libraries(Simplex_tree_example_graph_expansion_with_blocker ${TBB_LIBRARIES}) +endif() +add_test(NAME Simplex_tree_example_graph_expansion_with_blocker COMMAND $<TARGET_FILE:Simplex_tree_example_graph_expansion_with_blocker>) + +install(TARGETS Simplex_tree_example_graph_expansion_with_blocker DESTINATION bin) diff --git a/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp b/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp new file mode 100644 index 00000000..9bd51106 --- /dev/null +++ b/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp @@ -0,0 +1,221 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Clément Maria + * + * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/distance_functions.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Points_off_io.h> + +#include <CGAL/Epick_d.h> +#include <CGAL/Min_sphere_of_spheres_d.h> +#include <CGAL/Min_sphere_of_points_d_traits_d.h> + +#include <boost/program_options.hpp> + +#include <string> +#include <vector> +#include <limits> // infinity +#include <utility> // for pair +#include <map> + +// ------------------------------------------------------------------------------- +// cech_complex_cgal_mini_sphere_3d is an example of each step that is required to +// build a Cech over a Simplex_tree. Please refer to cech_persistence to see +// how to do the same thing with the Cech_complex wrapper for less detailed +// steps. +// ------------------------------------------------------------------------------- + +// Types definition +using Simplex_tree = Gudhi::Simplex_tree<>; +using Vertex_handle = Simplex_tree::Vertex_handle; +using Simplex_handle = Simplex_tree::Simplex_handle; +using Filtration_value = Simplex_tree::Filtration_value; +using Siblings = Simplex_tree::Siblings; +using Graph_t = boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS, + boost::property<Gudhi::vertex_filtration_t, Filtration_value>, + boost::property<Gudhi::edge_filtration_t, Filtration_value> >; +using Edge_t = std::pair<Vertex_handle, Vertex_handle>; + +using Kernel = CGAL::Epick_d<CGAL::Dimension_tag<3> >; +using Point = Kernel::Point_d; +using Traits = CGAL::Min_sphere_of_points_d_traits_d<Kernel, Filtration_value, 3>; +using Min_sphere = CGAL::Min_sphere_of_spheres_d<Traits>; + +using Points_off_reader = Gudhi::Points_off_reader<Point>; + +class Cech_blocker { + public: + bool operator()(Simplex_handle sh) { + std::vector<Point> points; +#if DEBUG_TRACES + std::cout << "Cech_blocker on ["; +#endif // DEBUG_TRACES + for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) { + points.push_back(point_cloud_[vertex]); +#if DEBUG_TRACES + std::cout << vertex << ", "; +#endif // DEBUG_TRACES + } + Min_sphere ms(points.begin(), points.end()); + Filtration_value radius = ms.radius(); +#if DEBUG_TRACES + std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; +#endif // DEBUG_TRACES + simplex_tree_.assign_filtration(sh, radius); + return (radius > threshold_); + } + Cech_blocker(Simplex_tree& simplex_tree, Filtration_value threshold, const std::vector<Point>& point_cloud) + : simplex_tree_(simplex_tree), threshold_(threshold), point_cloud_(point_cloud) {} + + private: + Simplex_tree simplex_tree_; + Filtration_value threshold_; + std::vector<Point> point_cloud_; +}; + +template <typename InputPointRange> +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold); + +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max); + +int main(int argc, char* argv[]) { + std::string off_file_points; + Filtration_value threshold; + int dim_max; + + program_options(argc, argv, off_file_points, threshold, dim_max); + + // Extract the points from the file filepoints + Points_off_reader off_reader(off_file_points); + + // Compute the proximity graph of the points + Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold); + + // Min_sphere sph1(off_reader.get_point_cloud()[0], off_reader.get_point_cloud()[1], off_reader.get_point_cloud()[2]); + // Construct the Rips complex in a Simplex Tree + Simplex_tree st; + // insert the proximity graph in the simplex tree + st.insert_graph(prox_graph); + // expand the graph until dimension dim_max + st.expansion_with_blockers(dim_max, Cech_blocker(st, threshold, off_reader.get_point_cloud())); + + std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; + std::cout << " and has dimension " << st.dimension() << " \n"; + + // Sort the simplices in the order of the filtration + st.initialize_filtration(); + +#if DEBUG_TRACES + std::cout << "********************************************************************\n"; + // Display the Simplex_tree - Can not be done in the middle of 2 inserts + std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << " " + << "[" << st.filtration(f_simplex) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << static_cast<int>(vertex) << " "; + } + std::cout << std::endl; + } +#endif // DEBUG_TRACES + return 0; +} + +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of an OFF file containing a 3d point set.\n"); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "max-edge-length,r", + po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal length of an edge for the Cech complex construction.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Cech complex we want to compute."); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Construct a Cech complex defined on a set of input points.\n \n"; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} + +/** Output the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph + * with n vertices, and an edge [u,v] iff the distance function between + * points u and v is smaller than threshold. + * + * The type PointCloud furnishes .begin() and .end() methods, that return + * iterators with value_type Point. + */ +template <typename InputPointRange> +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold) { + std::vector<Edge_t> edges; + std::vector<Filtration_value> edges_fil; + + Kernel k; + Vertex_handle idx_u, idx_v; + Filtration_value fil; + idx_u = 0; + for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { + idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { + fil = k.squared_distance_d_object()(*it_u, *it_v); + // For Cech Complex, threshold is a radius (distance /2) + fil = std::sqrt(fil) / 2.; + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + ++idx_u; + } + + Graph_t skel_graph(edges.begin(), edges.end(), edges_fil.begin(), + idx_u); // number of points labeled from 0 to idx_u-1 + + auto vertex_prop = boost::get(Gudhi::vertex_filtration_t(), skel_graph); + + boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; + for (std::tie(vi, vi_end) = boost::vertices(skel_graph); vi != vi_end; ++vi) { + boost::put(vertex_prop, *vi, 0.); + } + + return skel_graph; +} diff --git a/example/Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp b/example/Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp index ff2eebcb..d8289ba9 100644 --- a/example/Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp +++ b/example/Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp @@ -28,6 +28,8 @@ #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> #include <CGAL/Delaunay_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> +#include <CGAL/Alpha_shape_vertex_base_3.h> +#include <CGAL/Alpha_shape_cell_base_3.h> #include <CGAL/iterator.h> #include <fstream> diff --git a/example/Simplex_tree/graph_expansion_with_blocker.cpp b/example/Simplex_tree/graph_expansion_with_blocker.cpp new file mode 100644 index 00000000..0d458cbd --- /dev/null +++ b/example/Simplex_tree/graph_expansion_with_blocker.cpp @@ -0,0 +1,77 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2014 + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Simplex_tree.h> + +#include <iostream> + +using Simplex_tree = Gudhi::Simplex_tree<>; +using Simplex_handle = Simplex_tree::Simplex_handle; + +int main(int argc, char* const argv[]) { + // Construct the Simplex Tree with a 1-skeleton graph example + Simplex_tree simplexTree; + + simplexTree.insert_simplex({0, 1}, 0.); + simplexTree.insert_simplex({0, 2}, 1.); + simplexTree.insert_simplex({0, 3}, 2.); + simplexTree.insert_simplex({1, 2}, 3.); + simplexTree.insert_simplex({1, 3}, 4.); + simplexTree.insert_simplex({2, 3}, 5.); + simplexTree.insert_simplex({2, 4}, 6.); + simplexTree.insert_simplex({3, 6}, 7.); + simplexTree.insert_simplex({4, 5}, 8.); + simplexTree.insert_simplex({4, 6}, 9.); + simplexTree.insert_simplex({5, 6}, 10.); + simplexTree.insert_simplex({6}, 10.); + + simplexTree.expansion_with_blockers(3, [&](Simplex_handle sh) { + bool result = false; + std::cout << "Blocker on ["; + // User can loop on the vertices from the given simplex_handle i.e. + for (auto vertex : simplexTree.simplex_vertex_range(sh)) { + // We block the expansion, if the vertex '6' is in the given list of vertices + if (vertex == 6) result = true; + std::cout << vertex << ", "; + } + std::cout << "] ( " << simplexTree.filtration(sh); + // User can re-assign a new filtration value directly in the blocker (default is the maximal value of boudaries) + simplexTree.assign_filtration(sh, simplexTree.filtration(sh) + 1.); + + std::cout << " + 1. ) = " << result << std::endl; + + return result; + }); + + std::cout << "********************************************************************\n"; + std::cout << "* The complex contains " << simplexTree.num_simplices() << " simplices"; + std::cout << " - dimension " << simplexTree.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : simplexTree.filtration_simplex_range()) { + std::cout << " " + << "[" << simplexTree.filtration(f_simplex) << "] "; + for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + return 0; +} diff --git a/example/Simplex_tree/mini_simplex_tree.cpp b/example/Simplex_tree/mini_simplex_tree.cpp index ad99df23..19e45361 100644 --- a/example/Simplex_tree/mini_simplex_tree.cpp +++ b/example/Simplex_tree/mini_simplex_tree.cpp @@ -52,8 +52,6 @@ int main() { auto edge03 = {0, 3}; st.insert_simplex_and_subfaces(triangle012); st.insert_simplex_and_subfaces(edge03); - // FIXME: Remove this line - st.set_dimension(2); auto edge02 = {0, 2}; ST::Simplex_handle e = st.find(edge02); diff --git a/example/Simplex_tree/simple_simplex_tree.cpp b/example/Simplex_tree/simple_simplex_tree.cpp index d8318f03..828977c2 100644 --- a/example/Simplex_tree/simple_simplex_tree.cpp +++ b/example/Simplex_tree/simple_simplex_tree.cpp @@ -30,10 +30,10 @@ using Simplex_tree = Gudhi::Simplex_tree<>; using Vertex_handle = Simplex_tree::Vertex_handle; using Filtration_value = Simplex_tree::Filtration_value; -using typeVectorVertex = std::vector< Vertex_handle >; -using typePairSimplexBool = std::pair< Simplex_tree::Simplex_handle, bool >; +using typeVectorVertex = std::vector<Vertex_handle>; +using typePairSimplexBool = std::pair<Simplex_tree::Simplex_handle, bool>; -int main(int argc, char * const argv[]) { +int main(int argc, char* const argv[]) { const Filtration_value FIRST_FILTRATION_VALUE = 0.1; const Filtration_value SECOND_FILTRATION_VALUE = 0.2; const Filtration_value THIRD_FILTRATION_VALUE = 0.3; @@ -54,7 +54,7 @@ int main(int argc, char * const argv[]) { // ++ FIRST std::cout << " * INSERT 0" << std::endl; - typeVectorVertex firstSimplexVector = { 0 }; + typeVectorVertex firstSimplexVector = {0}; typePairSimplexBool returnValue = simplexTree.insert_simplex(firstSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); @@ -66,9 +66,8 @@ int main(int argc, char * const argv[]) { // ++ SECOND std::cout << " * INSERT 1" << std::endl; - typeVectorVertex secondSimplexVector = { 1 }; - returnValue = - simplexTree.insert_simplex(secondSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); + typeVectorVertex secondSimplexVector = {1}; + returnValue = simplexTree.insert_simplex(secondSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + 1 INSERTED" << std::endl; @@ -78,9 +77,8 @@ int main(int argc, char * const argv[]) { // ++ THIRD std::cout << " * INSERT (0,1)" << std::endl; - typeVectorVertex thirdSimplexVector = { 0, 1 }; - returnValue = - simplexTree.insert_simplex(thirdSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); + typeVectorVertex thirdSimplexVector = {0, 1}; + returnValue = simplexTree.insert_simplex(thirdSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (0,1) INSERTED" << std::endl; @@ -90,9 +88,8 @@ int main(int argc, char * const argv[]) { // ++ FOURTH std::cout << " * INSERT 2" << std::endl; - typeVectorVertex fourthSimplexVector = { 2 }; - returnValue = - simplexTree.insert_simplex(fourthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); + typeVectorVertex fourthSimplexVector = {2}; + returnValue = simplexTree.insert_simplex(fourthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + 2 INSERTED" << std::endl; @@ -102,9 +99,8 @@ int main(int argc, char * const argv[]) { // ++ FIFTH std::cout << " * INSERT (2,0)" << std::endl; - typeVectorVertex fifthSimplexVector = { 2, 0 }; - returnValue = - simplexTree.insert_simplex(fifthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); + typeVectorVertex fifthSimplexVector = {2, 0}; + returnValue = simplexTree.insert_simplex(fifthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (2,0) INSERTED" << std::endl; @@ -114,9 +110,8 @@ int main(int argc, char * const argv[]) { // ++ SIXTH std::cout << " * INSERT (2,1)" << std::endl; - typeVectorVertex sixthSimplexVector = { 2, 1 }; - returnValue = - simplexTree.insert_simplex(sixthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); + typeVectorVertex sixthSimplexVector = {2, 1}; + returnValue = simplexTree.insert_simplex(sixthSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (2,1) INSERTED" << std::endl; @@ -126,9 +121,8 @@ int main(int argc, char * const argv[]) { // ++ SEVENTH std::cout << " * INSERT (2,1,0)" << std::endl; - typeVectorVertex seventhSimplexVector = { 2, 1, 0 }; - returnValue = - simplexTree.insert_simplex(seventhSimplexVector, Filtration_value(THIRD_FILTRATION_VALUE)); + typeVectorVertex seventhSimplexVector = {2, 1, 0}; + returnValue = simplexTree.insert_simplex(seventhSimplexVector, Filtration_value(THIRD_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (2,1,0) INSERTED" << std::endl; @@ -138,9 +132,8 @@ int main(int argc, char * const argv[]) { // ++ EIGHTH std::cout << " * INSERT 3" << std::endl; - typeVectorVertex eighthSimplexVector = { 3 }; - returnValue = - simplexTree.insert_simplex(eighthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); + typeVectorVertex eighthSimplexVector = {3}; + returnValue = simplexTree.insert_simplex(eighthSimplexVector, Filtration_value(FIRST_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + 3 INSERTED" << std::endl; @@ -150,9 +143,8 @@ int main(int argc, char * const argv[]) { // ++ NINETH std::cout << " * INSERT (3,0)" << std::endl; - typeVectorVertex ninethSimplexVector = { 3, 0 }; - returnValue = - simplexTree.insert_simplex(ninethSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); + typeVectorVertex ninethSimplexVector = {3, 0}; + returnValue = simplexTree.insert_simplex(ninethSimplexVector, Filtration_value(SECOND_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (3,0) INSERTED" << std::endl; @@ -162,7 +154,7 @@ int main(int argc, char * const argv[]) { // ++ TENTH std::cout << " * INSERT 0 (already inserted)" << std::endl; - typeVectorVertex tenthSimplexVector = { 0 }; + typeVectorVertex tenthSimplexVector = {0}; // With a different filtration value returnValue = simplexTree.insert_simplex(tenthSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); @@ -174,9 +166,8 @@ int main(int argc, char * const argv[]) { // ++ ELEVENTH std::cout << " * INSERT (2,1,0) (already inserted)" << std::endl; - typeVectorVertex eleventhSimplexVector = { 2, 1, 0 }; - returnValue = - simplexTree.insert_simplex(eleventhSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); + typeVectorVertex eleventhSimplexVector = {2, 1, 0}; + returnValue = simplexTree.insert_simplex(eleventhSimplexVector, Filtration_value(FOURTH_FILTRATION_VALUE)); if (returnValue.second == true) { std::cout << " + (2,1,0) INSERTED" << std::endl; @@ -185,7 +176,6 @@ int main(int argc, char * const argv[]) { } // ++ GENERAL VARIABLE SET - simplexTree.set_dimension(2); // Max dimension = 2 -> (2,1,0) std::cout << "********************************************************************\n"; // Display the Simplex_tree - Can not be done in the middle of 2 inserts @@ -193,10 +183,9 @@ int main(int argc, char * const argv[]) { std::cout << " - dimension " << simplexTree.dimension() << "\n"; std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; for (auto f_simplex : simplexTree.filtration_simplex_range()) { - std::cout << " " << "[" << simplexTree.filtration(f_simplex) << "] "; - for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) { - std::cout << static_cast<int>(vertex) << " "; - } + std::cout << " " + << "[" << simplexTree.filtration(f_simplex) << "] "; + for (auto vertex : simplexTree.simplex_vertex_range(f_simplex)) std::cout << "(" << vertex << ")"; std::cout << std::endl; } // [0.1] 0 @@ -219,7 +208,7 @@ int main(int argc, char * const argv[]) { else std::cout << "***- NO IT ISN'T\n"; - typeVectorVertex unknownSimplexVector = { 15 }; + typeVectorVertex unknownSimplexVector = {15}; simplexFound = simplexTree.find(unknownSimplexVector); std::cout << "**************IS THE SIMPLEX {15} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) @@ -234,7 +223,7 @@ int main(int argc, char * const argv[]) { else std::cout << "***- NO IT ISN'T\n"; - typeVectorVertex otherSimplexVector = { 1, 15 }; + typeVectorVertex otherSimplexVector = {1, 15}; simplexFound = simplexTree.find(otherSimplexVector); std::cout << "**************IS THE SIMPLEX {15,1} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) @@ -242,12 +231,38 @@ int main(int argc, char * const argv[]) { else std::cout << "***- NO IT ISN'T\n"; - typeVectorVertex invSimplexVector = { 1, 2, 0 }; + typeVectorVertex invSimplexVector = {1, 2, 0}; simplexFound = simplexTree.find(invSimplexVector); std::cout << "**************IS THE SIMPLEX {1,2,0} IN THE SIMPLEX TREE ?\n"; if (simplexFound != simplexTree.null_simplex()) std::cout << "***+ YES IT IS!\n"; else std::cout << "***- NO IT ISN'T\n"; + + simplexFound = simplexTree.find({0, 1}); + std::cout << "**************IS THE SIMPLEX {0,1} IN THE SIMPLEX TREE ?\n"; + if (simplexFound != simplexTree.null_simplex()) + std::cout << "***+ YES IT IS!\n"; + else + std::cout << "***- NO IT ISN'T\n"; + + std::cout << "**************COFACES OF {0,1} IN CODIMENSION 1 ARE\n"; + for (auto& simplex : simplexTree.cofaces_simplex_range(simplexTree.find({0, 1}), 1)) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + std::cout << "**************STARS OF {0,1} ARE\n"; + for (auto& simplex : simplexTree.star_simplex_range(simplexTree.find({0, 1}))) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + + std::cout << "**************BOUNDARIES OF {0,1,2} ARE\n"; + for (auto& simplex : simplexTree.boundary_simplex_range(simplexTree.find({0, 1, 2}))) { + for (auto vertex : simplexTree.simplex_vertex_range(simplex)) std::cout << "(" << vertex << ")"; + std::cout << std::endl; + } + return 0; } diff --git a/example/Witness_complex/CMakeLists.txt b/example/Witness_complex/CMakeLists.txt index cbc53902..a8231392 100644 --- a/example/Witness_complex/CMakeLists.txt +++ b/example/Witness_complex/CMakeLists.txt @@ -13,39 +13,23 @@ install(TARGETS Witness_complex_example_nearest_landmark_table DESTINATION bin) # CGAL and Eigen3 are required for Euclidean version of Witness if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) add_executable( Witness_complex_example_off example_witness_complex_off.cpp ) - add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) add_executable ( Witness_complex_example_sphere example_witness_complex_sphere.cpp ) - - add_executable ( Witness_complex_example_witness_persistence example_witness_complex_persistence.cpp ) - target_link_libraries(Witness_complex_example_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - add_executable ( Witness_complex_example_strong_witness_persistence example_strong_witness_persistence.cpp ) - target_link_libraries(Witness_complex_example_strong_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) - - if (TBB_FOUND) - target_link_libraries(Witness_complex_example_witness_persistence ${TBB_LIBRARIES}) - target_link_libraries(Witness_complex_example_strong_witness_persistence ${TBB_LIBRARIES}) - endif() + + add_executable( Witness_complex_example_strong_off example_strong_witness_complex_off.cpp ) + target_link_libraries(Witness_complex_example_strong_off) add_test(NAME Witness_complex_example_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_off> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "20" "1.0" "3") + add_test(NAME Witness_complex_example_test_sphere_10 + COMMAND $<TARGET_FILE:Witness_complex_example_sphere> "10") add_test(NAME Witness_complex_example_strong_off_test_torus COMMAND $<TARGET_FILE:Witness_complex_example_strong_off> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "20" "1.0" "3") - add_test(NAME Witness_complex_example_test_sphere_10 - COMMAND $<TARGET_FILE:Witness_complex_example_sphere> "10") - add_test(NAME Witness_complex_example_test_torus_persistence - COMMAND $<TARGET_FILE:Witness_complex_example_witness_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") - add_test(NAME Witness_complex_example_strong_test_torus_persistence - COMMAND $<TARGET_FILE:Witness_complex_example_strong_witness_persistence> - "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") - + install(TARGETS Witness_complex_example_off DESTINATION bin) - install(TARGETS Witness_complex_example_strong_off DESTINATION bin) install(TARGETS Witness_complex_example_sphere DESTINATION bin) - install(TARGETS Witness_complex_example_witness_persistence DESTINATION bin) - install(TARGETS Witness_complex_example_strong_witness_persistence DESTINATION bin) + install(TARGETS Witness_complex_example_strong_off DESTINATION bin) + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) diff --git a/example/Witness_complex/example_strong_witness_complex_off.cpp b/example/Witness_complex/example_strong_witness_complex_off.cpp index 0ee9ee90..346bef6d 100644 --- a/example/Witness_complex/example_strong_witness_complex_off.cpp +++ b/example/Witness_complex/example_strong_witness_complex_off.cpp @@ -23,6 +23,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_strong_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/Points_off_io.h> #include <CGAL/Epick_d.h> @@ -38,10 +39,9 @@ using Point_d = typename K::Point_d; using Witness_complex = Gudhi::witness_complex::Euclidean_strong_witness_complex<K>; using Point_vector = std::vector<Point_d>; -int main(int argc, char * const argv[]) { +int main(int argc, char* const argv[]) { if (argc != 5) { - std::cerr << "Usage: " << argv[0] - << " path_to_point_file number_of_landmarks max_squared_alpha limit_dimension\n"; + std::cerr << "Usage: " << argv[0] << " path_to_point_file number_of_landmarks max_squared_alpha limit_dimension\n"; return 0; } @@ -55,25 +55,25 @@ int main(int argc, char * const argv[]) { Point_vector point_vector, landmarks; Gudhi::Points_off_reader<Point_d> off_reader(file_name); if (!off_reader.is_valid()) { - std::cerr << "Strong witness complex - Unable to read file " << file_name << "\n"; - exit(-1); // ----- >> - } + std::cerr << "Strong witness complex - Unable to read file " << file_name << "\n"; + exit(-1); // ----- >> + } point_vector = Point_vector(off_reader.get_point_cloud()); std::cout << "Successfully read " << point_vector.size() << " points.\n"; std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + // 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)); // Compute witness complex start = clock(); - Witness_complex witness_complex(landmarks, - point_vector); + Witness_complex witness_complex(landmarks, point_vector); witness_complex.create_complex(simplex_tree, alpha2, lim_dim); end = clock(); - std::cout << "Strong witness complex took " - << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; + std::cout << "Strong witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; } diff --git a/example/Witness_complex/example_witness_complex_off.cpp b/example/Witness_complex/example_witness_complex_off.cpp index b36dac0d..be11c955 100644 --- a/example/Witness_complex/example_witness_complex_off.cpp +++ b/example/Witness_complex/example_witness_complex_off.cpp @@ -4,6 +4,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/Points_off_io.h> #include <CGAL/Epick_d.h> @@ -44,8 +45,9 @@ int main(int argc, char * const argv[]) { std::cout << "Successfully read " << point_vector.size() << " points.\n"; std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n"; - // Choose landmarks - Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks)); + // 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)); // Compute witness complex start = clock(); diff --git a/example/Witness_complex/example_witness_complex_sphere.cpp b/example/Witness_complex/example_witness_complex_sphere.cpp index 124fd99b..a6e9b11a 100644 --- a/example/Witness_complex/example_witness_complex_sphere.cpp +++ b/example/Witness_complex/example_witness_complex_sphere.cpp @@ -25,6 +25,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Euclidean_witness_complex.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <gudhi/reader_utils.h> #include <CGAL/Epick_d.h> @@ -41,27 +42,25 @@ /** Write a gnuplot readable file. * Data range is a random access range of pairs (arg, value) */ -template < typename Data_range > -void write_data(Data_range & data, std::string filename) { +template <typename Data_range> +void write_data(Data_range& data, std::string filename) { std::ofstream ofs(filename, std::ofstream::out); - for (auto entry : data) - ofs << entry.first << ", " << entry.second << "\n"; + for (auto entry : data) ofs << entry.first << ", " << entry.second << "\n"; ofs.close(); } -int main(int argc, char * const argv[]) { +int main(int argc, char* const argv[]) { using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; using Witness_complex = Gudhi::witness_complex::Euclidean_witness_complex<Kernel>; if (argc != 2) { - std::cerr << "Usage: " << argv[0] - << " number_of_landmarks \n"; + std::cerr << "Usage: " << argv[0] << " number_of_landmarks \n"; return 0; } int number_of_landmarks = atoi(argv[1]); - std::vector< std::pair<int, double> > l_time; + std::vector<std::pair<int, double> > l_time; // Generate points for (int nbP = 500; nbP < 10000; nbP += 500) { @@ -75,16 +74,17 @@ 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::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::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, 0); end = clock(); double time = static_cast<double>(end - start) / CLOCKS_PER_SEC; - std::cout << "Witness complex for " << number_of_landmarks << " landmarks took " - << time << " s. \n"; + std::cout << "Witness complex for " << number_of_landmarks << " landmarks took " << time << " s. \n"; std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; l_time.push_back(std::make_pair(nbP, time)); } diff --git a/include/gudhi/Alpha_complex.h b/include/gudhi/Alpha_complex.h index 1ff95c3d..63c6675c 100644 --- a/include/gudhi/Alpha_complex.h +++ b/include/gudhi/Alpha_complex.h @@ -175,7 +175,7 @@ class Alpha_complex { * * @return The number of vertices. */ - const std::size_t number_of_vertices() const { + std::size_t number_of_vertices() const { return vertex_handle_to_iterator_.size(); } @@ -268,8 +268,6 @@ class Alpha_complex { return false; // ----- >> } - complex.set_dimension(triangulation_->maximal_dimension()); - // -------------------------------------------------------------------------------------------- // Simplex_tree construction from loop on triangulation finite full cells list if (triangulation_->number_of_vertices() > 0) { diff --git a/include/gudhi/Bitmap_cubical_complex.h b/include/gudhi/Bitmap_cubical_complex.h index f395de65..969daba6 100644 --- a/include/gudhi/Bitmap_cubical_complex.h +++ b/include/gudhi/Bitmap_cubical_complex.h @@ -31,10 +31,11 @@ #endif #include <limits> -#include <utility> // for pair<> +#include <utility> // for pair<> #include <algorithm> // for sort #include <vector> #include <numeric> // for iota +#include <cstddef> namespace Gudhi { @@ -43,7 +44,8 @@ namespace cubical_complex { // global variable, was used just for debugging. const bool globalDbg = false; -template <typename T> class is_before_in_filtration; +template <typename T> +class is_before_in_filtration; /** * @brief Cubical complex represented as a bitmap. @@ -60,11 +62,10 @@ class Bitmap_cubical_complex : public T { //*********************************************// // Typedefs and typenames //*********************************************// - typedef size_t Simplex_key; + typedef std::size_t Simplex_key; typedef typename T::filtration_type Filtration_value; typedef Simplex_key Simplex_handle; - //*********************************************// // Constructors //*********************************************// @@ -77,12 +78,12 @@ class Bitmap_cubical_complex : public T { /** * Constructor form a Perseus-style file. **/ - Bitmap_cubical_complex(const char* perseus_style_file) : - T(perseus_style_file), key_associated_to_simplex(this->total_number_of_cells + 1) { + Bitmap_cubical_complex(const char* perseus_style_file) + : T(perseus_style_file), key_associated_to_simplex(this->total_number_of_cells + 1) { if (globalDbg) { std::cerr << "Bitmap_cubical_complex( const char* perseus_style_file )\n"; } - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -97,10 +98,9 @@ class Bitmap_cubical_complex : public T { * with filtration on top dimensional cells. **/ Bitmap_cubical_complex(const std::vector<unsigned>& dimensions, - const std::vector<Filtration_value>& top_dimensional_cells) : - T(dimensions, top_dimensional_cells), - key_associated_to_simplex(this->total_number_of_cells + 1) { - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + const std::vector<Filtration_value>& top_dimensional_cells) + : T(dimensions, top_dimensional_cells), key_associated_to_simplex(this->total_number_of_cells + 1) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -118,10 +118,10 @@ class Bitmap_cubical_complex : public T { **/ Bitmap_cubical_complex(const std::vector<unsigned>& dimensions, const std::vector<Filtration_value>& top_dimensional_cells, - std::vector< bool > directions_in_which_periodic_b_cond_are_to_be_imposed) : - T(dimensions, top_dimensional_cells, directions_in_which_periodic_b_cond_are_to_be_imposed), - key_associated_to_simplex(this->total_number_of_cells + 1) { - for (size_t i = 0; i != this->total_number_of_cells; ++i) { + std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed) + : T(dimensions, top_dimensional_cells, directions_in_which_periodic_b_cond_are_to_be_imposed), + key_associated_to_simplex(this->total_number_of_cells + 1) { + for (std::size_t i = 0; i != this->total_number_of_cells; ++i) { this->key_associated_to_simplex[i] = i; } // we initialize this only once, in each constructor, when the bitmap is constructed. @@ -142,9 +142,7 @@ class Bitmap_cubical_complex : public T { /** * Returns number of all cubes in the complex. **/ - size_t num_simplices()const { - return this->total_number_of_cells; - } + std::size_t num_simplices() const { return this->total_number_of_cells; } /** * Returns a Simplex_handle to a cube that do not exist in this complex. @@ -159,14 +157,12 @@ class Bitmap_cubical_complex : public T { /** * Returns dimension of the complex. **/ - inline size_t dimension()const { - return this->sizes.size(); - } + inline std::size_t dimension() const { return this->sizes.size(); } /** * Return dimension of a cell pointed by the Simplex_handle. **/ - inline unsigned dimension(Simplex_handle sh)const { + inline unsigned dimension(Simplex_handle sh) const { if (globalDbg) { std::cerr << "unsigned dimension(const Simplex_handle& sh)\n"; } @@ -199,7 +195,7 @@ class Bitmap_cubical_complex : public T { /** * Return the key of a cube pointed by the Simplex_handle. **/ - Simplex_key key(Simplex_handle sh)const { + Simplex_key key(Simplex_handle sh) const { if (globalDbg) { std::cerr << "Simplex_key key(const Simplex_handle& sh)\n"; } @@ -217,7 +213,7 @@ class Bitmap_cubical_complex : public T { std::cerr << "Simplex_handle simplex(Simplex_key key)\n"; } if (key != null_key()) { - return this->simplex_associated_to_key[ key ]; + return this->simplex_associated_to_key[key]; } return null_simplex(); } @@ -246,8 +242,8 @@ class Bitmap_cubical_complex : public T { /** * Boundary_simplex_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< Simplex_handle >::iterator Boundary_simplex_iterator; - typedef typename std::vector< Simplex_handle > Boundary_simplex_range; + typedef typename std::vector<Simplex_handle>::iterator Boundary_simplex_iterator; + typedef typename std::vector<Simplex_handle> Boundary_simplex_range; /** * Filtration_simplex_iterator class provides an iterator though the whole structure in the order of filtration. @@ -257,13 +253,13 @@ class Bitmap_cubical_complex : public T { **/ class Filtration_simplex_range; - class Filtration_simplex_iterator : std::iterator< std::input_iterator_tag, Simplex_handle > { + class Filtration_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> { // Iterator over all simplices of the complex in the order of the indexing scheme. // 'value_type' must be 'Simplex_handle'. public: - Filtration_simplex_iterator(Bitmap_cubical_complex* b) : b(b), position(0) { } + Filtration_simplex_iterator(Bitmap_cubical_complex* b) : b(b), position(0) {} - Filtration_simplex_iterator() : b(NULL), position(0) { } + Filtration_simplex_iterator() : b(NULL), position(0) {} Filtration_simplex_iterator operator++() { if (globalDbg) { @@ -288,14 +284,14 @@ class Bitmap_cubical_complex : public T { return (*this); } - bool operator==(const Filtration_simplex_iterator& rhs)const { + bool operator==(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator == ( const Filtration_simplex_iterator& rhs )\n"; } - return ( this->position == rhs.position); + return (this->position == rhs.position); } - bool operator!=(const Filtration_simplex_iterator& rhs)const { + bool operator!=(const Filtration_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator != ( const Filtration_simplex_iterator& rhs )\n"; } @@ -306,14 +302,14 @@ class Bitmap_cubical_complex : public T { if (globalDbg) { std::cerr << "Simplex_handle operator*()\n"; } - return this->b->simplex_associated_to_key[ this->position ]; + return this->b->simplex_associated_to_key[this->position]; } friend class Filtration_simplex_range; private: Bitmap_cubical_complex<T>* b; - size_t position; + std::size_t position; }; /** @@ -326,7 +322,7 @@ class Bitmap_cubical_complex : public T { typedef Filtration_simplex_iterator const_iterator; typedef Filtration_simplex_iterator iterator; - Filtration_simplex_range(Bitmap_cubical_complex<T>* b) : b(b) { } + Filtration_simplex_range(Bitmap_cubical_complex<T>* b) : b(b) {} Filtration_simplex_iterator begin() { if (globalDbg) { @@ -348,8 +344,6 @@ class Bitmap_cubical_complex : public T { Bitmap_cubical_complex<T>* b; }; - - //*********************************************// // Methods to access iterators from the container: @@ -357,9 +351,7 @@ class Bitmap_cubical_complex : public T { * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Boundary_simplex_range boundary_simplex_range(Simplex_handle sh) { - return this->get_boundary_of_a_cell(sh); - } + Boundary_simplex_range boundary_simplex_range(Simplex_handle sh) { return this->get_boundary_of_a_cell(sh); } /** * filtration_simplex_range creates an object of a Filtration_simplex_range class @@ -374,8 +366,6 @@ class Bitmap_cubical_complex : public T { } //*********************************************// - - //*********************************************// // Elements which are in Gudhi now, but I (and in all the cases I asked also Marc) do not understand why they are // there. @@ -390,43 +380,41 @@ class Bitmap_cubical_complex : public T { * Function needed for compatibility with Gudhi. Not useful for other purposes. **/ std::pair<Simplex_handle, Simplex_handle> endpoints(Simplex_handle sh) { - std::vector< size_t > bdry = this->get_boundary_of_a_cell(sh); + std::vector<std::size_t> bdry = this->get_boundary_of_a_cell(sh); if (globalDbg) { std::cerr << "std::pair<Simplex_handle, Simplex_handle> endpoints( Simplex_handle sh )\n"; std::cerr << "bdry.size() : " << bdry.size() << std::endl; } // this method returns two first elements from the boundary of sh. if (bdry.size() < 2) - throw("Error in endpoints in Bitmap_cubical_complex class. The cell have less than two elements in the " - "boundary."); + throw( + "Error in endpoints in Bitmap_cubical_complex class. The cell have less than two elements in the " + "boundary."); return std::make_pair(bdry[0], bdry[1]); } - /** * Class needed for compatibility with Gudhi. Not useful for other purposes. **/ class Skeleton_simplex_range; - class Skeleton_simplex_iterator : std::iterator< std::input_iterator_tag, Simplex_handle > { + class Skeleton_simplex_iterator : std::iterator<std::input_iterator_tag, Simplex_handle> { // Iterator over all simplices of the complex in the order of the indexing scheme. // 'value_type' must be 'Simplex_handle'. public: - Skeleton_simplex_iterator(Bitmap_cubical_complex* b, size_t d) : b(b), dimension(d) { + Skeleton_simplex_iterator(Bitmap_cubical_complex* b, std::size_t d) : b(b), dimension(d) { if (globalDbg) { - std::cerr << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , size_t d )\n"; + std::cerr << "Skeleton_simplex_iterator ( Bitmap_cubical_complex* b , std::size_t d )\n"; } // find the position of the first simplex of a dimension d this->position = 0; - while ( - (this->position != b->data.size()) && - (this->b->get_dimension_of_a_cell(this->position) != this->dimension) - ) { + while ((this->position != b->data.size()) && + (this->b->get_dimension_of_a_cell(this->position) != this->dimension)) { ++this->position; } } - Skeleton_simplex_iterator() : b(NULL), position(0), dimension(0) { } + Skeleton_simplex_iterator() : b(NULL), position(0), dimension(0) {} Skeleton_simplex_iterator operator++() { if (globalDbg) { @@ -434,10 +422,8 @@ class Bitmap_cubical_complex : public T { } // increment the position as long as you did not get to the next element of the dimension dimension. ++this->position; - while ( - (this->position != this->b->data.size()) && - (this->b->get_dimension_of_a_cell(this->position) != this->dimension) - ) { + while ((this->position != this->b->data.size()) && + (this->b->get_dimension_of_a_cell(this->position) != this->dimension)) { ++this->position; } return (*this); @@ -459,14 +445,14 @@ class Bitmap_cubical_complex : public T { return (*this); } - bool operator==(const Skeleton_simplex_iterator& rhs)const { + bool operator==(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator ==\n"; } - return ( this->position == rhs.position); + return (this->position == rhs.position); } - bool operator!=(const Skeleton_simplex_iterator& rhs)const { + bool operator!=(const Skeleton_simplex_iterator& rhs) const { if (globalDbg) { std::cerr << "bool operator != ( const Skeleton_simplex_iterator& rhs )\n"; } @@ -481,9 +467,10 @@ class Bitmap_cubical_complex : public T { } friend class Skeleton_simplex_range; + private: Bitmap_cubical_complex<T>* b; - size_t position; + std::size_t position; unsigned dimension; }; @@ -497,7 +484,7 @@ class Bitmap_cubical_complex : public T { typedef Skeleton_simplex_iterator const_iterator; typedef Skeleton_simplex_iterator iterator; - Skeleton_simplex_range(Bitmap_cubical_complex<T>* b, unsigned dimension) : b(b), dimension(dimension) { } + Skeleton_simplex_range(Bitmap_cubical_complex<T>* b, unsigned dimension) : b(b), dimension(dimension) {} Skeleton_simplex_iterator begin() { if (globalDbg) { @@ -533,8 +520,8 @@ class Bitmap_cubical_complex : public T { friend class is_before_in_filtration<T>; protected: - std::vector< size_t > key_associated_to_simplex; - std::vector< size_t > simplex_associated_to_key; + std::vector<std::size_t> key_associated_to_simplex; + std::vector<std::size_t> simplex_associated_to_key; }; // Bitmap_cubical_complex template <typename T> @@ -542,7 +529,7 @@ void Bitmap_cubical_complex<T>::initialize_simplex_associated_to_key() { if (globalDbg) { std::cerr << "void Bitmap_cubical_complex<T>::initialize_elements_ordered_according_to_filtration() \n"; } - this->simplex_associated_to_key = std::vector<size_t>(this->data.size()); + this->simplex_associated_to_key = std::vector<std::size_t>(this->data.size()); std::iota(std::begin(simplex_associated_to_key), std::end(simplex_associated_to_key), 0); #ifdef GUDHI_USE_TBB tbb::parallel_sort(simplex_associated_to_key.begin(), simplex_associated_to_key.end(), @@ -552,16 +539,15 @@ void Bitmap_cubical_complex<T>::initialize_simplex_associated_to_key() { #endif // we still need to deal here with a key_associated_to_simplex: - for ( size_t i = 0 ; i != simplex_associated_to_key.size() ; ++i ) { - this->key_associated_to_simplex[ simplex_associated_to_key[i] ] = i; + for (std::size_t i = 0; i != simplex_associated_to_key.size(); ++i) { + this->key_associated_to_simplex[simplex_associated_to_key[i]] = i; } } template <typename T> class is_before_in_filtration { public: - explicit is_before_in_filtration(Bitmap_cubical_complex<T> * CC) - : CC_(CC) { } + explicit is_before_in_filtration(Bitmap_cubical_complex<T>* CC) : CC_(CC) {} bool operator()(const typename Bitmap_cubical_complex<T>::Simplex_handle& sh1, const typename Bitmap_cubical_complex<T>::Simplex_handle& sh2) const { @@ -573,8 +559,8 @@ class is_before_in_filtration { return fil1 < fil2; } // in this case they are on the same filtration level, so the dimension decide. - size_t dim1 = CC_->get_dimension_of_a_cell(sh1); - size_t dim2 = CC_->get_dimension_of_a_cell(sh2); + std::size_t dim1 = CC_->get_dimension_of_a_cell(sh1); + std::size_t dim2 = CC_->get_dimension_of_a_cell(sh2); if (dim1 != dim2) { return dim1 < dim2; } diff --git a/include/gudhi/Bitmap_cubical_complex/counter.h b/include/gudhi/Bitmap_cubical_complex/counter.h index 4b072f10..705b68a0 100644 --- a/include/gudhi/Bitmap_cubical_complex/counter.h +++ b/include/gudhi/Bitmap_cubical_complex/counter.h @@ -25,6 +25,7 @@ #include <iostream> #include <vector> +#include <cstddef> namespace Gudhi { @@ -63,14 +64,14 @@ class counter { * If the value of the function is false, that means, that the counter have reached its end-value. **/ bool increment() { - size_t i = 0; + std::size_t i = 0; while ((i != this->end.size()) && (this->current[i] == this->end[i])) { ++i; } if (i == this->end.size())return false; ++this->current[i]; - for (size_t j = 0; j != i; ++j) { + for (std::size_t j = 0; j != i; ++j) { this->current[j] = this->begin[j]; } return true; @@ -80,7 +81,7 @@ class counter { * Function to check if we are at the end of counter. **/ bool isFinal() { - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if (this->current[i] == this->end[i])return true; } return false; @@ -93,7 +94,7 @@ class counter { **/ std::vector< unsigned > find_opposite(const std::vector< bool >& directionsForPeriodicBCond) { std::vector< unsigned > result; - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if ((this->current[i] == this->end[i]) && (directionsForPeriodicBCond[i] == true)) { result.push_back(this->begin[i]); } else { @@ -108,7 +109,7 @@ class counter { **/ std::vector< bool > directions_of_finals() { std::vector< bool > result; - for (size_t i = 0; i != this->current.size(); ++i) { + for (std::size_t i = 0; i != this->current.size(); ++i) { if (this->current[i] == this->end[i]) { result.push_back(true); } else { @@ -123,7 +124,7 @@ class counter { **/ friend std::ostream& operator<<(std::ostream& out, const counter& c) { // std::cerr << "c.current.size() : " << c.current.size() << endl; - for (size_t i = 0; i != c.current.size(); ++i) { + for (std::size_t i = 0; i != c.current.size(); ++i) { out << c.current[i] << " "; } return out; diff --git a/include/gudhi/Bitmap_cubical_complex_base.h b/include/gudhi/Bitmap_cubical_complex_base.h index 0442ac34..bf257be1 100644 --- a/include/gudhi/Bitmap_cubical_complex_base.h +++ b/include/gudhi/Bitmap_cubical_complex_base.h @@ -32,7 +32,9 @@ #include <algorithm> #include <iterator> #include <limits> -#include <utility> // for pair<> +#include <utility> +#include <stdexcept> +#include <cstddef> namespace Gudhi { @@ -65,8 +67,7 @@ class Bitmap_cubical_complex_base { /** *Default constructor **/ - Bitmap_cubical_complex_base() : - total_number_of_cells(0) { } + Bitmap_cubical_complex_base() : total_number_of_cells(0) {} /** * There are a few constructors of a Bitmap_cubical_complex_base class. * First one, that takes vector<unsigned>, creates an empty bitmap of a dimension equal @@ -90,7 +91,7 @@ class Bitmap_cubical_complex_base { /** * Destructor of the Bitmap_cubical_complex_base class. **/ - virtual ~Bitmap_cubical_complex_base() { } + virtual ~Bitmap_cubical_complex_base() {} /** * The functions get_boundary_of_a_cell, get_coboundary_of_a_cell, get_dimension_of_a_cell @@ -100,8 +101,10 @@ class Bitmap_cubical_complex_base { * non-negative integer, indicating a position of a cube in the data structure. * In the case of functions that compute (co)boundary, the output is a vector if non-negative integers pointing to * the positions of (co)boundary element of the input cell. + * The boundary elements are guaranteed to be returned so that the + * incidence coefficients of boundary elements are alternating. */ - virtual inline std::vector< size_t > get_boundary_of_a_cell(size_t cell)const; + virtual inline std::vector<std::size_t> get_boundary_of_a_cell(std::size_t cell) const; /** * The functions get_coboundary_of_a_cell, get_coboundary_of_a_cell, * get_dimension_of_a_cell and get_cell_data are the basic @@ -112,21 +115,81 @@ class Bitmap_cubical_complex_base { * In the case of functions that compute (co)boundary, the output is a vector if * non-negative integers pointing to the * positions of (co)boundary element of the input cell. + * Note that unlike in the case of boundary, over here the elements are + * not guaranteed to be returned with alternating incidence numbers. + * **/ - virtual inline std::vector< size_t > get_coboundary_of_a_cell(size_t cell)const; + virtual inline std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const; + /** - * In the case of get_dimension_of_a_cell function, the output is a non-negative integer - * indicating the dimension of a cell. - **/ - inline unsigned get_dimension_of_a_cell(size_t cell)const; + * This procedure compute incidence numbers between cubes. For a cube \f$A\f$ of + * dimension n and a cube \f$B \subset A\f$ of dimension n-1, an incidence + * between \f$A\f$ and \f$B\f$ is the integer with which \f$B\f$ appears in the boundary of \f$A\f$. + * Note that first parameter is a cube of dimension n, + * and the second parameter is an adjusted cube in dimension n-1. + * Given \f$A = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [b_{j},e_{j}] \times [b_{j+1},e_{j+1}] \times \ldots + *\times [b_{n},e_{n}] \f$ + * such that \f$ b_{j} \neq e_{j} \f$ + * and \f$B = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [a,a] \times [b_{j+1},e_{j+1}] \times \ldots \times + *[b_{n},e_{n}] \f$ + * where \f$ a = b_{j}\f$ or \f$ a = e_{j}\f$, the incidence between \f$A\f$ and \f$B\f$ + * computed by this procedure is given by formula: + * \f$ c\ (-1)^{\sum_{i=1}^{j-1} dim [b_{i},e_{i}]} \f$ + * Where \f$ dim [b_{i},e_{i}] = 0 \f$ if \f$ b_{i}=e_{i} \f$ and 1 in other case. + * c is -1 if \f$ a = b_{j}\f$ and 1 if \f$ a = e_{j}\f$. + * @exception std::logic_error In case when the cube \f$B\f$ is not n-1 + * dimensional face of a cube \f$A\f$. + **/ + virtual int compute_incidence_between_cells(std::size_t coface, std::size_t face) const { + // first get the counters for coface and face: + std::vector<unsigned> coface_counter = this->compute_counter_for_given_cell(coface); + std::vector<unsigned> face_counter = this->compute_counter_for_given_cell(face); + + // coface_counter and face_counter should agree at all positions except from one: + int number_of_position_in_which_counters_do_not_agree = -1; + std::size_t number_of_full_faces_that_comes_before = 0; + for (std::size_t i = 0; i != coface_counter.size(); ++i) { + if ((coface_counter[i] % 2 == 1) && (number_of_position_in_which_counters_do_not_agree == -1)) { + ++number_of_full_faces_that_comes_before; + } + if (coface_counter[i] != face_counter[i]) { + if (number_of_position_in_which_counters_do_not_agree != -1) { + std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + throw std::logic_error( + "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); + } + number_of_position_in_which_counters_do_not_agree = i; + } + } + + int incidence = 1; + if (number_of_full_faces_that_comes_before % 2) incidence = -1; + // if the face cell is on the right from coface cell: + if (coface_counter[number_of_position_in_which_counters_do_not_agree] + 1 == + face_counter[number_of_position_in_which_counters_do_not_agree]) { + incidence *= -1; + } + + return incidence; + } + + /** +* In the case of get_dimension_of_a_cell function, the output is a non-negative integer +* indicating the dimension of a cell. +* Note that unlike in the case of boundary, over here the elements are +* not guaranteed to be returned with alternating incidence numbers. +* To compute incidence between cells use compute_incidence_between_cells +* procedure +**/ + inline unsigned get_dimension_of_a_cell(std::size_t cell) const; + /** * In the case of get_cell_data, the output parameter is a reference to the value of a cube in a given position. * This allows reading and changing the value of filtration. Note that if the value of a filtration is changed, the * code do not check if we have a filtration or not. i.e. it do not check if the value of a filtration of a cell is * not smaller than the value of a filtration of its boundary and not greater than the value of its coboundary. **/ - inline T& get_cell_data(size_t cell); - + inline T& get_cell_data(std::size_t cell); /** * Typical input used to construct a baseBitmap class is a filtration given at the top dimensional cells. @@ -141,33 +204,29 @@ class Bitmap_cubical_complex_base { /** * Returns dimension of a complex. **/ - inline unsigned dimension()const { - return sizes.size(); - } + inline unsigned dimension() const { return sizes.size(); } /** * Returns number of all cubes in the data structure. **/ - inline unsigned size()const { - return this->data.size(); - } + inline unsigned size() const { return this->data.size(); } /** * Writing to stream operator. By using it we get the values T of cells in order in which they are stored in the * structure. This procedure is used for debugging purposes. **/ template <typename K> - friend std::ostream& operator<<(std::ostream & os, const Bitmap_cubical_complex_base<K>& b); + friend std::ostream& operator<<(std::ostream& os, const Bitmap_cubical_complex_base<K>& b); /** * Function that put the input data to bins. By putting data to bins we mean rounding them to a sequence of values * equally distributed in the range of data. * Sometimes if most of the cells have different birth-death times, the performance of the algorithms to compute * persistence gets worst. When dealing with this type of data, one may want to put different values on cells to - * some number of bins. The function put_data_to_bins( size_t number_of_bins ) is designed for that purpose. + * some number of bins. The function put_data_to_bins( std::size_t number_of_bins ) is designed for that purpose. * The parameter of the function is the number of bins (distinct values) we want to have in the cubical complex. **/ - void put_data_to_bins(size_t number_of_bins); + void put_data_to_bins(std::size_t number_of_bins); /** * Function that put the input data to bins. By putting data to bins we mean rounding them to a sequence of values @@ -184,7 +243,7 @@ class Bitmap_cubical_complex_base { /** * Functions to find min and max values of filtration. **/ - std::pair< T, T > min_max_filtration(); + std::pair<T, T> min_max_filtration(); // ITERATORS @@ -192,11 +251,9 @@ class Bitmap_cubical_complex_base { * @brief Iterator through all cells in the complex (in order they appear in the structure -- i.e. * in lexicographical order). **/ - class All_cells_iterator : std::iterator< std::input_iterator_tag, T > { + class All_cells_iterator : std::iterator<std::input_iterator_tag, T> { public: - All_cells_iterator() { - this->counter = 0; - } + All_cells_iterator() { this->counter = 0; } All_cells_iterator operator++() { // first find first element of the counter that can be increased: @@ -215,14 +272,12 @@ class Bitmap_cubical_complex_base { return *this; } - bool operator==(const All_cells_iterator& rhs)const { - if (this->counter != rhs.counter)return false; + bool operator==(const All_cells_iterator& rhs) const { + if (this->counter != rhs.counter) return false; return true; } - bool operator!=(const All_cells_iterator& rhs)const { - return !(*this == rhs); - } + bool operator!=(const All_cells_iterator& rhs) const { return !(*this == rhs); } /* * The operator * returns position of a cube in the structure of cubical complex. This position can be then used as @@ -231,12 +286,11 @@ class Bitmap_cubical_complex_base { * boundary and coboundary and dimension * and in function get_cell_data to get a filtration of a cell. */ - size_t operator*() { - return this->counter; - } + std::size_t operator*() { return this->counter; } friend class Bitmap_cubical_complex_base; + protected: - size_t counter; + std::size_t counter; }; /** @@ -261,71 +315,61 @@ class Bitmap_cubical_complex_base { **/ class All_cells_range { public: - All_cells_range(Bitmap_cubical_complex_base* b) : b(b) { } + All_cells_range(Bitmap_cubical_complex_base* b) : b(b) {} - All_cells_iterator begin() { - return b->all_cells_iterator_begin(); - } + All_cells_iterator begin() { return b->all_cells_iterator_begin(); } + + All_cells_iterator end() { return b->all_cells_iterator_end(); } - All_cells_iterator end() { - return b->all_cells_iterator_end(); - } private: Bitmap_cubical_complex_base<T>* b; }; - All_cells_range all_cells_range() { - return All_cells_range(this); - } - + All_cells_range all_cells_range() { return All_cells_range(this); } /** * Boundary_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< size_t >::const_iterator Boundary_iterator; - typedef typename std::vector< size_t > Boundary_range; + typedef typename std::vector<std::size_t>::const_iterator Boundary_iterator; + typedef typename std::vector<std::size_t> Boundary_range; /** * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Boundary_range boundary_range(size_t sh) { - return this->get_boundary_of_a_cell(sh); - } + Boundary_range boundary_range(std::size_t sh) { return this->get_boundary_of_a_cell(sh); } /** * Coboundary_range class provides ranges for boundary iterators. **/ - typedef typename std::vector< size_t >::const_iterator Coboundary_iterator; - typedef typename std::vector< size_t > Coboundary_range; + typedef typename std::vector<std::size_t>::const_iterator Coboundary_iterator; + typedef typename std::vector<std::size_t> Coboundary_range; /** * boundary_simplex_range creates an object of a Boundary_simplex_range class * that provides ranges for the Boundary_simplex_iterator. **/ - Coboundary_range coboundary_range(size_t sh) { - return this->get_coboundary_of_a_cell(sh); - } + Coboundary_range coboundary_range(std::size_t sh) { return this->get_coboundary_of_a_cell(sh); } /** * @brief Iterator through top dimensional cells of the complex. The cells appear in order they are stored * in the structure (i.e. in lexicographical order) **/ - class Top_dimensional_cells_iterator : std::iterator< std::input_iterator_tag, T > { + class Top_dimensional_cells_iterator : std::iterator<std::input_iterator_tag, T> { public: Top_dimensional_cells_iterator(Bitmap_cubical_complex_base& b) : b(b) { - this->counter = std::vector<size_t>(b.dimension()); + this->counter = std::vector<std::size_t>(b.dimension()); // std::fill( this->counter.begin() , this->counter.end() , 0 ); } Top_dimensional_cells_iterator operator++() { // first find first element of the counter that can be increased: - size_t dim = 0; - while ((dim != this->b.dimension()) && (this->counter[dim] == this->b.sizes[dim] - 1))++dim; + std::size_t dim = 0; + while ((dim != this->b.dimension()) && (this->counter[dim] == this->b.sizes[dim] - 1)) ++dim; if (dim != this->b.dimension()) { ++this->counter[dim]; - for (size_t i = 0; i != dim; ++i) { + for (std::size_t i = 0; i != dim; ++i) { this->counter[i] = 0; } } else { @@ -346,18 +390,16 @@ class Bitmap_cubical_complex_base { return *this; } - bool operator==(const Top_dimensional_cells_iterator& rhs)const { - if (&this->b != &rhs.b)return false; - if (this->counter.size() != rhs.counter.size())return false; - for (size_t i = 0; i != this->counter.size(); ++i) { - if (this->counter[i] != rhs.counter[i])return false; + bool operator==(const Top_dimensional_cells_iterator& rhs) const { + if (&this->b != &rhs.b) return false; + if (this->counter.size() != rhs.counter.size()) return false; + for (std::size_t i = 0; i != this->counter.size(); ++i) { + if (this->counter[i] != rhs.counter[i]) return false; } return true; } - bool operator!=(const Top_dimensional_cells_iterator& rhs)const { - return !(*this == rhs); - } + bool operator!=(const Top_dimensional_cells_iterator& rhs) const { return !(*this == rhs); } /* * The operator * returns position of a cube in the structure of cubical complex. This position can be then used as @@ -366,26 +408,25 @@ class Bitmap_cubical_complex_base { * boundary and coboundary and dimension * and in function get_cell_data to get a filtration of a cell. */ - size_t operator*() { - return this->compute_index_in_bitmap(); - } + std::size_t operator*() { return this->compute_index_in_bitmap(); } - size_t compute_index_in_bitmap()const { - size_t index = 0; - for (size_t i = 0; i != this->counter.size(); ++i) { + std::size_t compute_index_in_bitmap() const { + std::size_t index = 0; + for (std::size_t i = 0; i != this->counter.size(); ++i) { index += (2 * this->counter[i] + 1) * this->b.multipliers[i]; } return index; } - void print_counter()const { - for (size_t i = 0; i != this->counter.size(); ++i) { + void print_counter() const { + for (std::size_t i = 0; i != this->counter.size(); ++i) { std::cout << this->counter[i] << " "; } } friend class Bitmap_cubical_complex_base; + protected: - std::vector< size_t > counter; + std::vector<std::size_t> counter; Bitmap_cubical_complex_base& b; }; @@ -402,7 +443,7 @@ class Bitmap_cubical_complex_base { **/ Top_dimensional_cells_iterator top_dimensional_cells_iterator_end() { Top_dimensional_cells_iterator a(*this); - for (size_t i = 0; i != this->dimension(); ++i) { + for (std::size_t i = 0; i != this->dimension(); ++i) { a.counter[i] = this->sizes[i] - 1; } a.counter[0]++; @@ -414,32 +455,24 @@ class Bitmap_cubical_complex_base { **/ class Top_dimensional_cells_range { public: - Top_dimensional_cells_range(Bitmap_cubical_complex_base* b) : b(b) { } + Top_dimensional_cells_range(Bitmap_cubical_complex_base* b) : b(b) {} - Top_dimensional_cells_iterator begin() { - return b->top_dimensional_cells_iterator_begin(); - } + Top_dimensional_cells_iterator begin() { return b->top_dimensional_cells_iterator_begin(); } + + Top_dimensional_cells_iterator end() { return b->top_dimensional_cells_iterator_end(); } - Top_dimensional_cells_iterator end() { - return b->top_dimensional_cells_iterator_end(); - } private: Bitmap_cubical_complex_base<T>* b; }; - Top_dimensional_cells_range top_dimensional_cells_range() { - return Top_dimensional_cells_range(this); - } - + Top_dimensional_cells_range top_dimensional_cells_range() { return Top_dimensional_cells_range(this); } //****************************************************************************************************************// //****************************************************************************************************************// //****************************************************************************************************************// //****************************************************************************************************************// - inline size_t number_cells()const { - return this->total_number_of_cells; - } + inline std::size_t number_cells() const { return this->total_number_of_cells; } //****************************************************************************************************************// //****************************************************************************************************************// @@ -450,11 +483,11 @@ class Bitmap_cubical_complex_base { std::vector<unsigned> sizes; std::vector<unsigned> multipliers; std::vector<T> data; - size_t total_number_of_cells; + std::size_t total_number_of_cells; void set_up_containers(const std::vector<unsigned>& sizes) { unsigned multiplier = 1; - for (size_t i = 0; i != sizes.size(); ++i) { + for (std::size_t i = 0; i != sizes.size(); ++i) { this->sizes.push_back(sizes[i]); this->multipliers.push_back(multiplier); multiplier *= 2 * sizes[i] + 1; @@ -463,18 +496,18 @@ class Bitmap_cubical_complex_base { this->total_number_of_cells = multiplier; } - size_t compute_position_in_bitmap(const std::vector< unsigned >& counter) { - size_t position = 0; - for (size_t i = 0; i != this->multipliers.size(); ++i) { + std::size_t compute_position_in_bitmap(const std::vector<unsigned>& counter) { + std::size_t position = 0; + for (std::size_t i = 0; i != this->multipliers.size(); ++i) { position += this->multipliers[i] * counter[i]; } return position; } - std::vector<unsigned> compute_counter_for_given_cell(size_t cell)const { + std::vector<unsigned> compute_counter_for_given_cell(std::size_t cell) const { std::vector<unsigned> counter; counter.reserve(this->sizes.size()); - for (size_t dim = this->sizes.size(); dim != 0; --dim) { + for (std::size_t dim = this->sizes.size(); dim != 0; --dim) { counter.push_back(cell / this->multipliers[dim - 1]); cell = cell % this->multipliers[dim - 1]; } @@ -486,96 +519,94 @@ class Bitmap_cubical_complex_base { const std::vector<T>& top_dimensional_cells); Bitmap_cubical_complex_base(const char* perseus_style_file, std::vector<bool> directions); Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes, std::vector<bool> directions); - Bitmap_cubical_complex_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& top_dimensional_cells, + Bitmap_cubical_complex_base(const std::vector<unsigned>& dimensions, const std::vector<T>& top_dimensional_cells, std::vector<bool> directions); }; template <typename T> -void Bitmap_cubical_complex_base<T>::put_data_to_bins(size_t number_of_bins) { - bool bdg = false; +void Bitmap_cubical_complex_base<T>::put_data_to_bins(std::size_t number_of_bins) { + bool dbg = false; - std::pair< T, T > min_max = this->min_max_filtration(); - T dx = (min_max.second - min_max.first) / (T) number_of_bins; + std::pair<T, T> min_max = this->min_max_filtration(); + T dx = (min_max.second - min_max.first) / (T)number_of_bins; // now put the data into the appropriate bins: - for (size_t i = 0; i != this->data.size(); ++i) { - if (bdg) { + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (dbg) { std::cerr << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + dx * (this->data[i] - min_max.first) / number_of_bins; - if (bdg) { + if (dbg) { std::cerr << "After binning : " << this->data[i] << std::endl; - getchar(); } } } template <typename T> void Bitmap_cubical_complex_base<T>::put_data_to_bins(T diameter_of_bin) { - bool bdg = false; - std::pair< T, T > min_max = this->min_max_filtration(); + bool dbg = false; + std::pair<T, T> min_max = this->min_max_filtration(); - size_t number_of_bins = (min_max.second - min_max.first) / diameter_of_bin; + std::size_t number_of_bins = (min_max.second - min_max.first) / diameter_of_bin; // now put the data into the appropriate bins: - for (size_t i = 0; i != this->data.size(); ++i) { - if (bdg) { + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (dbg) { std::cerr << "Before binning : " << this->data[i] << std::endl; } this->data[i] = min_max.first + diameter_of_bin * (this->data[i] - min_max.first) / number_of_bins; - if (bdg) { + if (dbg) { std::cerr << "After binning : " << this->data[i] << std::endl; - getchar(); } } } template <typename T> -std::pair< T, T > Bitmap_cubical_complex_base<T>::min_max_filtration() { - std::pair< T, T > min_max(std::numeric_limits<T>::max(), std::numeric_limits<T>::min()); - for (size_t i = 0; i != this->data.size(); ++i) { - if (this->data[i] < min_max.first)min_max.first = this->data[i]; - if (this->data[i] > min_max.second)min_max.second = this->data[i]; +std::pair<T, T> Bitmap_cubical_complex_base<T>::min_max_filtration() { + std::pair<T, T> min_max(std::numeric_limits<T>::max(), std::numeric_limits<T>::min()); + for (std::size_t i = 0; i != this->data.size(); ++i) { + if (this->data[i] < min_max.first) min_max.first = this->data[i]; + if (this->data[i] > min_max.second) min_max.second = this->data[i]; } return min_max; } template <typename K> -std::ostream& operator<<(std::ostream & out, const Bitmap_cubical_complex_base<K>& b) { - for (typename Bitmap_cubical_complex_base<K>::all_cells_const_iterator - it = b.all_cells_const_begin(); it != b.all_cells_const_end(); ++it) { +std::ostream& operator<<(std::ostream& out, const Bitmap_cubical_complex_base<K>& b) { + for (typename Bitmap_cubical_complex_base<K>::all_cells_const_iterator it = b.all_cells_const_begin(); + it != b.all_cells_const_end(); ++it) { out << *it << " "; } return out; } template <typename T> -Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base -(const std::vector<unsigned>& sizes) { +Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes) { this->set_up_containers(sizes); } template <typename T> -void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells_list(const std::vector<unsigned>& sizes_in_following_directions, - const std::vector<T>& top_dimensional_cells) { +void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells_list( + const std::vector<unsigned>& sizes_in_following_directions, const std::vector<T>& top_dimensional_cells) { this->set_up_containers(sizes_in_following_directions); - size_t number_of_top_dimensional_elements = 1; - for (size_t i = 0; i != sizes_in_following_directions.size(); ++i) { + std::size_t number_of_top_dimensional_elements = 1; + for (std::size_t i = 0; i != sizes_in_following_directions.size(); ++i) { number_of_top_dimensional_elements *= sizes_in_following_directions[i]; } if (number_of_top_dimensional_elements != top_dimensional_cells.size()) { - std::cerr << "Error in constructor Bitmap_cubical_complex_base ( std::vector<size_t> sizes_in_following_directions" - << ", std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " - << "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector." - << std::endl; - throw("Error in constructor Bitmap_cubical_complex_base( std::vector<size_t> sizes_in_following_directions," - "std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " - "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector."); + std::cerr << "Error in constructor Bitmap_cubical_complex_base ( std::vector<std::size_t> " + << "sizes_in_following_directions, std::vector<T> top_dimensional_cells ). Number of top dimensional " + << "elements that follow from sizes_in_following_directions vector is different than the size of " + << "top_dimensional_cells vector." + << std::endl; + throw( + "Error in constructor Bitmap_cubical_complex_base( std::vector<std::size_t> sizes_in_following_directions," + "std::vector<T> top_dimensional_cells ). Number of top dimensional elements that follow from " + "sizes_in_following_directions vector is different than the size of top_dimensional_cells vector."); } Bitmap_cubical_complex_base<T>::Top_dimensional_cells_iterator it(*this); - size_t index = 0; + std::size_t index = 0; for (it = this->top_dimensional_cells_iterator_begin(); it != this->top_dimensional_cells_iterator_end(); ++it) { this->get_cell_data(*it) = top_dimensional_cells[index]; ++index; @@ -584,8 +615,8 @@ void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells } template <typename T> -Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base -(const std::vector<unsigned>& sizes_in_following_directions, const std::vector<T>& top_dimensional_cells) { +Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const std::vector<unsigned>& sizes_in_following_directions, + const std::vector<T>& top_dimensional_cells) { this->setup_bitmap_based_on_top_dimensional_cells_list(sizes_in_following_directions, top_dimensional_cells); } @@ -599,15 +630,17 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus if (dbg) { std::cerr << "dimensionOfData : " << dimensionOfData << std::endl; - getchar(); } std::vector<unsigned> sizes; sizes.reserve(dimensionOfData); - for (size_t i = 0; i != dimensionOfData; ++i) { + // all dimensions multiplied + std::size_t dimensions = 1; + for (std::size_t i = 0; i != dimensionOfData; ++i) { unsigned size_in_this_dimension; inFiltration >> size_in_this_dimension; sizes.push_back(size_in_this_dimension); + dimensions *= size_in_this_dimension; if (dbg) { std::cerr << "size_in_this_dimension : " << size_in_this_dimension << std::endl; } @@ -617,19 +650,20 @@ void Bitmap_cubical_complex_base<T>::read_perseus_style_file(const char* perseus Bitmap_cubical_complex_base<T>::Top_dimensional_cells_iterator it(*this); it = this->top_dimensional_cells_iterator_begin(); - while (!inFiltration.eof()) { - T filtrationLevel; - inFiltration >> filtrationLevel; + T filtrationLevel; + for (std::size_t i = 0; i < dimensions; ++i) { + if (!(inFiltration >> filtrationLevel) || (inFiltration.eof())) { + throw std::ios_base::failure("Bad Perseus file format."); + } if (dbg) { - std::cerr << "Cell of an index : " - << it.compute_index_in_bitmap() - << " and dimension: " - << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) - << " get the value : " << filtrationLevel << std::endl; + std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) + << " get the value : " << filtrationLevel << std::endl; } this->get_cell_data(*it) = filtrationLevel; ++it; } + inFiltration.close(); this->impose_lower_star_filtration(); } @@ -668,37 +702,44 @@ Bitmap_cubical_complex_base<T>::Bitmap_cubical_complex_base(const char* perseus_ } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_base<T>::get_boundary_of_a_cell(size_t cell)const { - std::vector< size_t > boundary_elements; +std::vector<std::size_t> Bitmap_cubical_complex_base<T>::get_boundary_of_a_cell(std::size_t cell) const { + std::vector<std::size_t> boundary_elements; // Speed traded of for memory. Check if it is better in practice. - boundary_elements.reserve(this->dimension()*2); + boundary_elements.reserve(this->dimension() * 2); - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::size_t sum_of_dimensions = 0; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; if (position % 2 == 1) { - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } + ++sum_of_dimensions; } cell1 = cell1 % this->multipliers[i - 1]; } + return boundary_elements; } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(size_t cell)const { +std::vector<std::size_t> Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(std::size_t cell) const { std::vector<unsigned> counter = this->compute_counter_for_given_cell(cell); - std::vector< size_t > coboundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> coboundary_elements; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; if (position % 2 == 0) { if ((cell > this->multipliers[i - 1]) && (counter[i - 1] != 0)) { coboundary_elements.push_back(cell - this->multipliers[i - 1]); } - if ( - (cell + this->multipliers[i - 1] < this->data.size()) && (counter[i - 1] != 2 * this->sizes[i - 1])) { + if ((cell + this->multipliers[i - 1] < this->data.size()) && (counter[i - 1] != 2 * this->sizes[i - 1])) { coboundary_elements.push_back(cell + this->multipliers[i - 1]); } } @@ -708,11 +749,11 @@ std::vector< size_t > Bitmap_cubical_complex_base<T>::get_coboundary_of_a_cell(s } template <typename T> -unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)const { +unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(std::size_t cell) const { bool dbg = false; if (dbg) std::cerr << "\n\n\n Computing position o a cell of an index : " << cell << std::endl; unsigned dimension = 0; - for (size_t i = this->multipliers.size(); i != 0; --i) { + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell / this->multipliers[i - 1]; if (dbg) { @@ -720,7 +761,6 @@ unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)con std::cerr << "cell : " << cell << std::endl; std::cerr << "position : " << position << std::endl; std::cerr << "multipliers[" << i - 1 << "] = " << this->multipliers[i - 1] << std::endl; - getchar(); } if (position % 2 == 1) { @@ -733,7 +773,7 @@ unsigned Bitmap_cubical_complex_base<T>::get_dimension_of_a_cell(size_t cell)con } template <typename T> -inline T& Bitmap_cubical_complex_base<T>::get_cell_data(size_t cell) { +inline T& Bitmap_cubical_complex_base<T>::get_cell_data(std::size_t cell) { return this->data[cell]; } @@ -744,12 +784,12 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { // this vector will be used to check which elements have already been taken care of in imposing lower star filtration std::vector<bool> is_this_cell_considered(this->data.size(), false); - size_t size_to_reserve = 1; - for (size_t i = 0; i != this->multipliers.size(); ++i) { - size_to_reserve *= (size_t) ((this->multipliers[i] - 1) / 2); + std::size_t size_to_reserve = 1; + for (std::size_t i = 0; i != this->multipliers.size(); ++i) { + size_to_reserve *= (std::size_t)((this->multipliers[i] - 1) / 2); } - std::vector<size_t> indices_to_consider; + std::vector<std::size_t> indices_to_consider; indices_to_consider.reserve(size_to_reserve); // we assume here that we already have a filtration on the top dimensional cells and // we have to extend it to lower ones. @@ -761,32 +801,29 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { while (indices_to_consider.size()) { if (dbg) { std::cerr << "indices_to_consider in this iteration \n"; - for (size_t i = 0; i != indices_to_consider.size(); ++i) { + for (std::size_t i = 0; i != indices_to_consider.size(); ++i) { std::cout << indices_to_consider[i] << " "; } - getchar(); } - std::vector<size_t> new_indices_to_consider; - for (size_t i = 0; i != indices_to_consider.size(); ++i) { - std::vector<size_t> bd = this->get_boundary_of_a_cell(indices_to_consider[i]); - for (size_t boundaryIt = 0; boundaryIt != bd.size(); ++boundaryIt) { + std::vector<std::size_t> new_indices_to_consider; + for (std::size_t i = 0; i != indices_to_consider.size(); ++i) { + std::vector<std::size_t> bd = this->get_boundary_of_a_cell(indices_to_consider[i]); + for (std::size_t boundaryIt = 0; boundaryIt != bd.size(); ++boundaryIt) { if (dbg) { - std::cerr << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[ bd[boundaryIt] ] - << " while of a cell: " << indices_to_consider[i] << " is: " << this->data[ indices_to_consider[i] ] - << std::endl; - getchar(); + std::cerr << "filtration of a cell : " << bd[boundaryIt] << " is : " << this->data[bd[boundaryIt]] + << " while of a cell: " << indices_to_consider[i] << " is: " << this->data[indices_to_consider[i]] + << std::endl; } - if (this->data[ bd[boundaryIt] ] > this->data[ indices_to_consider[i] ]) { - this->data[ bd[boundaryIt] ] = this->data[ indices_to_consider[i] ]; + if (this->data[bd[boundaryIt]] > this->data[indices_to_consider[i]]) { + this->data[bd[boundaryIt]] = this->data[indices_to_consider[i]]; if (dbg) { - std::cerr << "Setting the value of a cell : " << bd[boundaryIt] << " to : " - << this->data[ indices_to_consider[i] ] << std::endl; - getchar(); + std::cerr << "Setting the value of a cell : " << bd[boundaryIt] + << " to : " << this->data[indices_to_consider[i]] << std::endl; } } - if (is_this_cell_considered[ bd[boundaryIt] ] == false) { + if (is_this_cell_considered[bd[boundaryIt]] == false) { new_indices_to_consider.push_back(bd[boundaryIt]); - is_this_cell_considered[ bd[boundaryIt] ] = true; + is_this_cell_considered[bd[boundaryIt]] = true; } } } @@ -795,8 +832,8 @@ void Bitmap_cubical_complex_base<T>::impose_lower_star_filtration() { } template <typename T> -bool compareFirstElementsOfTuples(const std::pair< std::pair< T, size_t >, char >& first, - const std::pair< std::pair< T, size_t >, char >& second) { +bool compareFirstElementsOfTuples(const std::pair<std::pair<T, std::size_t>, char>& first, + const std::pair<std::pair<T, std::size_t>, char>& second) { if (first.first.first < second.first.first) { return true; } else { diff --git a/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h b/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h index c3cc93dd..4a0d1c74 100644 --- a/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h +++ b/include/gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h @@ -28,6 +28,8 @@ #include <cmath> #include <limits> // for numeric_limits<> #include <vector> +#include <stdexcept> +#include <cstddef> namespace Gudhi { @@ -41,7 +43,8 @@ namespace cubical_complex { /** * @brief Cubical complex with periodic boundary conditions represented as a bitmap. * @ingroup cubical_complex - * @details This is a class implementing a bitmap data structure with periodic boundary conditions. Most of the functions are + * @details This is a class implementing a bitmap data structure with periodic boundary conditions. Most of the + * functions are * identical to the functions from Bitmap_cubical_complex_base. * The ones that needed to be updated are the constructors and get_boundary_of_a_cell and get_coboundary_of_a_cell. */ @@ -53,7 +56,7 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c /** * Default constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base() { } + Bitmap_cubical_complex_periodic_boundary_conditions_base() {} /** * A constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class that takes the following * parameters: (1) vector with numbers of top dimensional cells in all dimensions and (2) vector of booleans. If @@ -61,8 +64,9 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c * imposed in this direction. In case of false, the periodic boundary conditions will not be imposed in the direction * i. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); + Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); /** * A constructor of Bitmap_cubical_complex_periodic_boundary_conditions_base class that takes the name of Perseus * style file as an input. Please consult the documentation about the specification of the file. @@ -75,9 +79,9 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c * value, that means that periodic boundary conditions are to be imposed in this direction. In case of false, the * periodic boundary conditions will not be imposed in the direction i. */ - Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector< bool >& directions_in_which_periodic_b_cond_are_to_be_imposed); + Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); /** * Destructor of the Bitmap_cubical_complex_periodic_boundary_conditions_base class. @@ -88,21 +92,81 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c /** * A version of a function that return boundary of a given cell for an object of * Bitmap_cubical_complex_periodic_boundary_conditions_base class. + * The boundary elements are guaranteed to be returned so that the + * incidence coefficients are alternating. */ - virtual std::vector< size_t > get_boundary_of_a_cell(size_t cell) const; + virtual std::vector<std::size_t> get_boundary_of_a_cell(std::size_t cell) const; /** * A version of a function that return coboundary of a given cell for an object of * Bitmap_cubical_complex_periodic_boundary_conditions_base class. + * Note that unlike in the case of boundary, over here the elements are + * not guaranteed to be returned with alternating incidence numbers. + * To compute incidence between cells use compute_incidence_between_cells + * procedure */ - virtual std::vector< size_t > get_coboundary_of_a_cell(size_t cell) const; + virtual std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const; + + /** + * This procedure compute incidence numbers between cubes. For a cube \f$A\f$ of + * dimension n and a cube \f$B \subset A\f$ of dimension n-1, an incidence + * between \f$A\f$ and \f$B\f$ is the integer with which \f$B\f$ appears in the boundary of \f$A\f$. + * Note that first parameter is a cube of dimension n, + * and the second parameter is an adjusted cube in dimension n-1. + * Given \f$A = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [b_{j},e_{j}] \times [b_{j+1},e_{j+1}] \times \ldots + *\times [b_{n},e_{n}] \f$ + * such that \f$ b_{j} \neq e_{j} \f$ + * and \f$B = [b_1,e_1] \times \ldots \ [b_{j-1},e_{j-1}] \times [a,a] \times [b_{j+1},e_{j+1}] \times \ldots \times + *[b_{n},e_{n}]s \f$ + * where \f$ a = b_{j}\f$ or \f$ a = e_{j}\f$, the incidence between \f$A\f$ and \f$B\f$ + * computed by this procedure is given by formula: + * \f$ c\ (-1)^{\sum_{i=1}^{j-1} dim [b_{i},e_{i}]} \f$ + * Where \f$ dim [b_{i},e_{i}] = 0 \f$ if \f$ b_{i}=e_{i} \f$ and 1 in other case. + * c is -1 if \f$ a = b_{j}\f$ and 1 if \f$ a = e_{j}\f$. + * @exception std::logic_error In case when the cube \f$B\f$ is not n-1 + * dimensional face of a cube \f$A\f$. + **/ + virtual int compute_incidence_between_cells(std::size_t coface, std::size_t face) { + // first get the counters for coface and face: + std::vector<unsigned> coface_counter = this->compute_counter_for_given_cell(coface); + std::vector<unsigned> face_counter = this->compute_counter_for_given_cell(face); + + // coface_counter and face_counter should agree at all positions except from one: + int number_of_position_in_which_counters_do_not_agree = -1; + std::size_t number_of_full_faces_that_comes_before = 0; + for (std::size_t i = 0; i != coface_counter.size(); ++i) { + if ((coface_counter[i] % 2 == 1) && (number_of_position_in_which_counters_do_not_agree == -1)) { + ++number_of_full_faces_that_comes_before; + } + if (coface_counter[i] != face_counter[i]) { + if (number_of_position_in_which_counters_do_not_agree != -1) { + std::cout << "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face.\n"; + throw std::logic_error( + "Cells given to compute_incidence_between_cells procedure do not form a pair of coface-face."); + } + number_of_position_in_which_counters_do_not_agree = i; + } + } + + int incidence = 1; + if (number_of_full_faces_that_comes_before % 2) incidence = -1; + // if the face cell is on the right from coface cell: + if ((coface_counter[number_of_position_in_which_counters_do_not_agree] + 1 == + face_counter[number_of_position_in_which_counters_do_not_agree]) || + ((coface_counter[number_of_position_in_which_counters_do_not_agree] != 1) && + (face_counter[number_of_position_in_which_counters_do_not_agree] == 0))) { + incidence *= -1; + } + + return incidence; + } protected: - std::vector< bool > directions_in_which_periodic_b_cond_are_to_be_imposed; + std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed; void set_up_containers(const std::vector<unsigned>& sizes) { unsigned multiplier = 1; - for (size_t i = 0; i != sizes.size(); ++i) { + for (std::size_t i = 0; i != sizes.size(); ++i) { this->sizes.push_back(sizes[i]); this->multipliers.push_back(multiplier); @@ -119,19 +183,23 @@ class Bitmap_cubical_complex_periodic_boundary_conditions_base : public Bitmap_c Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes); Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells); - void construct_complex_based_on_top_dimensional_cells(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); + + /** + * A procedure used to construct the data structures in the class. + **/ + void construct_complex_based_on_top_dimensional_cells( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed); }; template <typename T> -void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_complex_based_on_top_dimensional_cells(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_complex_based_on_top_dimensional_cells( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->directions_in_which_periodic_b_cond_are_to_be_imposed = directions_in_which_periodic_b_cond_are_to_be_imposed; this->set_up_containers(dimensions); - size_t i = 0; + std::size_t i = 0; for (auto it = this->top_dimensional_cells_iterator_begin(); it != this->top_dimensional_cells_iterator_end(); ++it) { this->get_cell_data(*it) = topDimensionalCells[i]; ++i; @@ -140,14 +208,16 @@ void Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::construct_comp } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->directions_in_which_periodic_b_cond_are_to_be_imposed(directions_in_which_periodic_b_cond_are_to_be_imposed); this->set_up_containers(sizes); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const char* perseus_style_file) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const char* perseus_style_file) { // for Perseus style files: bool dbg = false; @@ -160,7 +230,7 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp std::vector<unsigned> sizes; sizes.reserve(dimensionOfData); - for (size_t i = 0; i != dimensionOfData; ++i) { + for (std::size_t i = 0; i != dimensionOfData; ++i) { int size_in_this_dimension; inFiltration >> size_in_this_dimension; if (size_in_this_dimension < 0) { @@ -176,14 +246,12 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp while (!inFiltration.eof()) { double filtrationLevel; inFiltration >> filtrationLevel; - if (inFiltration.eof())break; + if (inFiltration.eof()) break; if (dbg) { - std::cerr << "Cell of an index : " - << it.compute_index_in_bitmap() - << " and dimension: " - << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) - << " get the value : " << filtrationLevel << std::endl; + std::cerr << "Cell of an index : " << it.compute_index_in_bitmap() + << " and dimension: " << this->get_dimension_of_a_cell(it.compute_index_in_bitmap()) + << " get the value : " << filtrationLevel << std::endl; } this->get_cell_data(*it) = filtrationLevel; ++it; @@ -193,24 +261,24 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_comp } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& sizes) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& sizes) { this->directions_in_which_periodic_b_cond_are_to_be_imposed = std::vector<bool>(sizes.size(), false); this->set_up_containers(sizes); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells) { std::vector<bool> directions_in_which_periodic_b_cond_are_to_be_imposed = std::vector<bool>(dimensions.size(), false); this->construct_complex_based_on_top_dimensional_cells(dimensions, topDimensionalCells, directions_in_which_periodic_b_cond_are_to_be_imposed); } template <typename T> -Bitmap_cubical_complex_periodic_boundary_conditions_base<T>:: -Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsigned>& dimensions, - const std::vector<T>& topDimensionalCells, - const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { +Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::Bitmap_cubical_complex_periodic_boundary_conditions_base( + const std::vector<unsigned>& dimensions, const std::vector<T>& topDimensionalCells, + const std::vector<bool>& directions_in_which_periodic_b_cond_are_to_be_imposed) { this->construct_complex_based_on_top_dimensional_cells(dimensions, topDimensionalCells, directions_in_which_periodic_b_cond_are_to_be_imposed); } @@ -218,46 +286,65 @@ Bitmap_cubical_complex_periodic_boundary_conditions_base(const std::vector<unsig // ***********************Methods************************ // template <typename T> -std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_boundary_of_a_cell(size_t cell) const { +std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_boundary_of_a_cell( + std::size_t cell) const { bool dbg = false; if (dbg) { std::cerr << "Computations of boundary of a cell : " << cell << std::endl; } - std::vector< size_t > boundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> boundary_elements; + boundary_elements.reserve(this->dimension() * 2); + std::size_t cell1 = cell; + std::size_t sum_of_dimensions = 0; + + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; // this cell have a nonzero length in this direction, therefore we can compute its boundary in this direction. - if (position % 2 == 1) { // if there are no periodic boundary conditions in this direction, we do not have to do anything. if (!directions_in_which_periodic_b_cond_are_to_be_imposed[i - 1]) { // std::cerr << "A\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << cell + this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { // in this direction we have to do boundary conditions. Therefore, we need to check if we are not at the end. - if (position != 2 * this->sizes[ i - 1 ] - 1) { + if (position != 2 * this->sizes[i - 1] - 1) { // std::cerr << "B\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell + this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell + this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell + this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << cell + this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " << cell + this->multipliers[i - 1] << " "; } } else { // std::cerr << "C\n"; - boundary_elements.push_back(cell - this->multipliers[ i - 1 ]); - boundary_elements.push_back(cell - (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[ i - 1 ]); + if (sum_of_dimensions % 2) { + boundary_elements.push_back(cell - this->multipliers[i - 1]); + boundary_elements.push_back(cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); + } else { + boundary_elements.push_back(cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); + boundary_elements.push_back(cell - this->multipliers[i - 1]); + } if (dbg) { - std::cerr << cell - this->multipliers[ i - 1 ] << " " << - cell - (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[ i - 1 ] << " "; + std::cerr << cell - this->multipliers[i - 1] << " " + << cell - (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1] << " "; } } } + ++sum_of_dimensions; } cell1 = cell1 % this->multipliers[i - 1]; } @@ -265,11 +352,12 @@ std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T } template <typename T> -std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_coboundary_of_a_cell(size_t cell) const { +std::vector<std::size_t> Bitmap_cubical_complex_periodic_boundary_conditions_base<T>::get_coboundary_of_a_cell( + std::size_t cell) const { std::vector<unsigned> counter = this->compute_counter_for_given_cell(cell); - std::vector< size_t > coboundary_elements; - size_t cell1 = cell; - for (size_t i = this->multipliers.size(); i != 0; --i) { + std::vector<std::size_t> coboundary_elements; + std::size_t cell1 = cell; + for (std::size_t i = this->multipliers.size(); i != 0; --i) { unsigned position = cell1 / this->multipliers[i - 1]; // if the cell has zero length in this direction, then it will have cbd in this direction. if (position % 2 == 0) { @@ -289,7 +377,7 @@ std::vector< size_t > Bitmap_cubical_complex_periodic_boundary_conditions_base<T } else { // in this case counter[i-1] == 0. coboundary_elements.push_back(cell + this->multipliers[i - 1]); - coboundary_elements.push_back(cell + (2 * this->sizes[ i - 1 ] - 1) * this->multipliers[i - 1]); + coboundary_elements.push_back(cell + (2 * this->sizes[i - 1] - 1) * this->multipliers[i - 1]); } } } diff --git a/include/gudhi/Bottleneck.h b/include/gudhi/Bottleneck.h index 8c97dce9..7aee07bb 100644 --- a/include/gudhi/Bottleneck.h +++ b/include/gudhi/Bottleneck.h @@ -46,7 +46,7 @@ double bottleneck_distance_approx(Persistence_graph& g, double e) { if (step <= b_lower_bound || step >= b_upper_bound) // Avoid precision problem break; m.set_r(step); - while (m.multi_augment()) {}; // compute a maximum matching (in the graph corresponding to the current r) + while (m.multi_augment()) {} // compute a maximum matching (in the graph corresponding to the current r) if (m.perfect()) { m = biggest_unperfect; b_upper_bound = step; @@ -68,7 +68,7 @@ double bottleneck_distance_exact(Persistence_graph& g) { while (lower_bound_i != upper_bound_i) { long step = lower_bound_i + static_cast<long> ((upper_bound_i - lower_bound_i - 1) / alpha); m.set_r(sd.at(step)); - while (m.multi_augment()) {}; // compute a maximum matching (in the graph corresponding to the current r) + while (m.multi_augment()) {} // compute a maximum matching (in the graph corresponding to the current r) if (m.perfect()) { m = biggest_unperfect; upper_bound_i = step; diff --git a/include/gudhi/Debug_utils.h b/include/gudhi/Debug_utils.h index 8ed3b7b3..90d3cf47 100644 --- a/include/gudhi/Debug_utils.h +++ b/include/gudhi/Debug_utils.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 INRIA * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by @@ -32,7 +32,7 @@ // GUDHI_CHECK throw an exception if expression is false in debug mode, but does nothing in release mode // Could assert in release mode, but cmake sets NDEBUG (for "NO DEBUG") in this mode, means assert does nothing. #ifdef GUDHI_DEBUG - #define GUDHI_CHECK(expression, excpt) if ((expression) == 0) throw excpt + #define GUDHI_CHECK(expression, excpt) ((expression) ? (void) 0 : (throw excpt)) #define GUDHI_CHECK_code(CODE) CODE #else #define GUDHI_CHECK(expression, excpt) (void) 0 diff --git a/include/gudhi/Edge_contraction.h b/include/gudhi/Edge_contraction.h index 61f2d945..cf9a2c27 100644 --- a/include/gudhi/Edge_contraction.h +++ b/include/gudhi/Edge_contraction.h @@ -210,7 +210,6 @@ int main (int argc, char *argv[]) } \endcode - \verbatim ./example/Contraction/RipsContraction ../../data/SO3_10000.off 0.3 [ 50%] [100%] Built target SkeletonBlockerIteration @@ -223,9 +222,6 @@ Time to simplify and enumerate simplices: 3.166621s wall, 3.150000s user + 0.010000s system = 3.160000s CPU (99.8%) \endverbatim - - -\copyright GNU General Public License v3. */ /** @} */ // end defgroup } // namespace contraction diff --git a/include/gudhi/GIC.h b/include/gudhi/GIC.h new file mode 100644 index 00000000..40ff7a4a --- /dev/null +++ b/include/gudhi/GIC.h @@ -0,0 +1,1298 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author: Mathieu Carriere + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef GIC_H_ +#define GIC_H_ + +#include <gudhi/Debug_utils.h> +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/reader_utils.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Rips_complex.h> +#include <gudhi/Points_off_io.h> +#include <gudhi/distance_functions.h> +#include <gudhi/Persistent_cohomology.h> +#include <gudhi/Bottleneck.h> + +#include <boost/config.hpp> +#include <boost/graph/graph_traits.hpp> +#include <boost/graph/adjacency_list.hpp> +#include <boost/graph/connected_components.hpp> +#include <boost/graph/dijkstra_shortest_paths.hpp> +#include <boost/graph/subgraph.hpp> +#include <boost/graph/graph_utility.hpp> + +#include <iostream> +#include <vector> +#include <map> +#include <string> +#include <limits> // for numeric_limits +#include <utility> // for std::pair<> +#include <algorithm> // for std::max +#include <random> +#include <cassert> +#include <cmath> + +namespace Gudhi { + +namespace cover_complex { + +using Simplex_tree = Gudhi::Simplex_tree<>; +using Filtration_value = Simplex_tree::Filtration_value; +using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; +using Persistence_diagram = std::vector<std::pair<double, double> >; +using Graph = boost::subgraph< + boost::adjacency_list<boost::setS, boost::vecS, boost::undirectedS, boost::no_property, + boost::property<boost::edge_index_t, int, boost::property<boost::edge_weight_t, double> > > >; +using Vertex_t = boost::graph_traits<Graph>::vertex_descriptor; +using Index_map = boost::property_map<Graph, boost::vertex_index_t>::type; +using Weight_map = boost::property_map<Graph, boost::edge_weight_t>::type; + +/** + * \class Cover_complex + * \brief Cover complex data structure. + * + * \ingroup cover_complex + * + * \details + * The data structure is a simplicial complex, representing a + * Graph Induced simplicial Complex (GIC) or a Nerve, + * and whose simplices are computed with a cover C of a point + * cloud P, which often comes from the preimages of intervals + * covering the image of a function f defined on P. + * These intervals are parameterized by their resolution + * (either their length or their number) + * and their gain (percentage of overlap). + * To compute a GIC, one also needs a graph G built on top of P, + * whose cliques with vertices belonging to different elements of C + * correspond to the simplices of the GIC. + * + */ +template <typename Point> +class Cover_complex { + private: + bool verbose = false; // whether to display information. + std::string type; // Nerve or GIC + + std::vector<Point> point_cloud; // input point cloud. + std::vector<std::vector<double> > distances; // all pairwise distances. + int maximal_dim; // maximal dimension of output simplicial complex. + int data_dimension; // dimension of input data. + int n; // number of points. + + std::map<int, double> func; // function used to compute the output simplicial complex. + std::map<int, double> + func_color; // function used to compute the colors of the nodes of the output simplicial complex. + bool functional_cover = false; // whether we use a cover with preimages of a function or not. + + Graph one_skeleton_OFF; // one-skeleton given by the input OFF file (if it exists). + Graph one_skeleton; // one-skeleton used to compute the connected components. + std::vector<Vertex_t> vertices; // vertices of one_skeleton. + + std::vector<std::vector<int> > simplices; // simplices of output simplicial complex. + std::vector<int> voronoi_subsamples; // Voronoi germs (in case of Voronoi cover). + + Persistence_diagram PD; + std::vector<double> distribution; + + std::map<int, std::vector<int> > + cover; // function associating to each data point its vectors of cover elements to which it belongs. + std::map<int, std::vector<int> > + cover_back; // inverse of cover, in order to get the data points associated to a specific cover element. + std::map<int, double> cover_std; // standard function (induced by func) used to compute the extended persistence + // diagram of the output simplicial complex. + std::map<int, int> + cover_fct; // integer-valued function that allows to state if two elements of the cover are consecutive or not. + std::map<int, std::pair<int, double> > + cover_color; // size and coloring (induced by func_color) of the vertices of the output simplicial complex. + + int resolution_int = -1; + double resolution_double = -1; + double gain = -1; + double rate_constant = 10; // Constant in the subsampling. + double rate_power = 0.001; // Power in the subsampling. + int mask = 0; // Ignore nodes containing less than mask points. + + std::map<int, int> name2id, name2idinv; + + std::string cover_name; + std::string point_cloud_name; + std::string color_name; + + // Point comparator + struct Less { + Less(std::map<int, double> func) { Fct = func; } + std::map<int, double> Fct; + bool operator()(int a, int b) { + if (Fct[a] == Fct[b]) + return a < b; + else + return Fct[a] < Fct[b]; + } + }; + + // Remove all edges of a graph. + void remove_edges(Graph& G) { + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(G); ei != ei_end; ++ei) boost::remove_edge(*ei, G); + } + + // Find random number in [0,1]. + double GetUniform() { + thread_local std::default_random_engine re; + thread_local std::uniform_real_distribution<double> Dist(0, 1); + return Dist(re); + } + + // Subsample points. + void SampleWithoutReplacement(int populationSize, int sampleSize, std::vector<int>& samples) { + int t = 0; + int m = 0; + double u; + while (m < sampleSize) { + u = GetUniform(); + if ((populationSize - t) * u >= sampleSize - m) { + t++; + } else { + samples[m] = t; + t++; + m++; + } + } + } + + // ******************************************************************************************************************* + // Utils. + // ******************************************************************************************************************* + + public: + /** \brief Specifies whether the type of the output simplicial complex. + * + * @param[in] t std::string (either "GIC" or "Nerve"). + * + */ + void set_type(const std::string& t) { type = t; } + + public: + /** \brief Specifies whether the program should display information or not. + * + * @param[in] verb boolean (true = display info, false = do not display info). + * + */ + void set_verbose(bool verb = false) { verbose = verb; } + + public: + /** \brief Sets the constants used to subsample the data set. These constants are + * explained in \cite Carriere17c. + * + * @param[in] constant double. + * @param[in] power double. + * + */ + void set_subsampling(double constant, double power) { + rate_constant = constant; + rate_power = power; + } + + public: + /** \brief Sets the mask, which is a threshold integer such that nodes in the complex that contain a number of data + * points which is less than or equal to + * this threshold are not displayed. + * + * @param[in] nodemask integer. + * + */ + void set_mask(int nodemask) { mask = nodemask; } + + public: + /** \brief Reads and stores the input point cloud. + * + * @param[in] off_file_name name of the input .OFF or .nOFF file. + * + */ + bool read_point_cloud(const std::string& off_file_name) { + point_cloud_name = off_file_name; + std::ifstream input(off_file_name); + std::string line; + + char comment = '#'; + while (comment == '#') { + std::getline(input, line); + if (!line.empty() && !all_of(line.begin(), line.end(), (int (*)(int))isspace)) + comment = line[line.find_first_not_of(' ')]; + } + if (strcmp((char*)line.c_str(), "nOFF") == 0) { + comment = '#'; + while (comment == '#') { + std::getline(input, line); + if (!line.empty() && !all_of(line.begin(), line.end(), (int (*)(int))isspace)) + comment = line[line.find_first_not_of(' ')]; + } + std::stringstream stream(line); + stream >> data_dimension; + } else { + data_dimension = 3; + } + + comment = '#'; + int numedges, numfaces, i, dim; + while (comment == '#') { + std::getline(input, line); + if (!line.empty() && !all_of(line.begin(), line.end(), (int (*)(int))isspace)) + comment = line[line.find_first_not_of(' ')]; + } + std::stringstream stream(line); + stream >> n; + stream >> numfaces; + stream >> numedges; + + i = 0; + while (i < n) { + std::getline(input, line); + if (!line.empty() && line[line.find_first_not_of(' ')] != '#' && + !all_of(line.begin(), line.end(), (int (*)(int))isspace)) { + std::stringstream iss(line); + std::vector<double> point; + point.assign(std::istream_iterator<double>(iss), std::istream_iterator<double>()); + point_cloud.emplace_back(point.begin(), point.begin() + data_dimension); + boost::add_vertex(one_skeleton_OFF); + vertices.push_back(boost::add_vertex(one_skeleton)); + i++; + } + } + + i = 0; + while (i < numfaces) { + std::getline(input, line); + if (!line.empty() && line[line.find_first_not_of(' ')] != '#' && + !all_of(line.begin(), line.end(), (int (*)(int))isspace)) { + std::vector<int> simplex; + std::stringstream iss(line); + simplex.assign(std::istream_iterator<int>(iss), std::istream_iterator<int>()); + dim = simplex[0]; + for (int j = 1; j <= dim; j++) + for (int k = j + 1; k <= dim; k++) + boost::add_edge(vertices[simplex[j]], vertices[simplex[k]], one_skeleton_OFF); + i++; + } + } + + return input.is_open(); + } + + // ******************************************************************************************************************* + // Graphs. + // ******************************************************************************************************************* + + public: // Set graph from file. + /** \brief Creates a graph G from a file containing the edges. + * + * @param[in] graph_file_name name of the input graph file. + * The graph file contains one edge per line, + * each edge being represented by the IDs of its two nodes. + * + */ + void set_graph_from_file(const std::string& graph_file_name) { + remove_edges(one_skeleton); + int neighb; + std::ifstream input(graph_file_name); + std::string line; + int source; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> source; + while (stream >> neighb) boost::add_edge(vertices[source], vertices[neighb], one_skeleton); + } + } + + public: // Set graph from OFF file. + /** \brief Creates a graph G from the triangulation given by the input .OFF file. + * + */ + void set_graph_from_OFF() { + remove_edges(one_skeleton); + if (num_edges(one_skeleton_OFF)) + one_skeleton = one_skeleton_OFF; + else + std::cout << "No triangulation read in OFF file!" << std::endl; + } + + public: // Set graph from Rips complex. + /** \brief Creates a graph G from a Rips complex. + * + * @param[in] threshold threshold value for the Rips complex. + * @param[in] distance distance used to compute the Rips complex. + * + */ + template <typename Distance> + void set_graph_from_rips(double threshold, Distance distance) { + remove_edges(one_skeleton); + if (distances.size() == 0) compute_pairwise_distances(distance); + for (int i = 0; i < n; i++) { + for (int j = i + 1; j < n; j++) { + if (distances[i][j] <= threshold) { + boost::add_edge(vertices[i], vertices[j], one_skeleton); + boost::put(boost::edge_weight, one_skeleton, boost::edge(vertices[i], vertices[j], one_skeleton).first, + distances[i][j]); + } + } + } + } + + public: + void set_graph_weights() { + Index_map index = boost::get(boost::vertex_index, one_skeleton); + Weight_map weight = boost::get(boost::edge_weight, one_skeleton); + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) + boost::put(weight, *ei, + distances[index[boost::source(*ei, one_skeleton)]][index[boost::target(*ei, one_skeleton)]]); + } + + public: // Pairwise distances. + /** \private \brief Computes all pairwise distances. + */ + template <typename Distance> + void compute_pairwise_distances(Distance ref_distance) { + double d; + std::vector<double> zeros(n); + for (int i = 0; i < n; i++) distances.push_back(zeros); + std::string distance = point_cloud_name + "_dist"; + std::ifstream input(distance, std::ios::out | std::ios::binary); + + if (input.good()) { + if (verbose) std::cout << "Reading distances..." << std::endl; + for (int i = 0; i < n; i++) { + for (int j = i; j < n; j++) { + input.read((char*)&d, 8); + distances[i][j] = d; + distances[j][i] = d; + } + } + input.close(); + } else { + if (verbose) std::cout << "Computing distances..." << std::endl; + input.close(); + std::ofstream output(distance, std::ios::out | std::ios::binary); + for (int i = 0; i < n; i++) { + int state = (int)floor(100 * (i * 1.0 + 1) / n) % 10; + if (state == 0 && verbose) std::cout << "\r" << state << "%" << std::flush; + for (int j = i; j < n; j++) { + double dis = ref_distance(point_cloud[i], point_cloud[j]); + distances[i][j] = dis; + distances[j][i] = dis; + output.write((char*)&dis, 8); + } + } + output.close(); + if (verbose) std::cout << std::endl; + } + } + + public: // Automatic tuning of Rips complex. + /** \brief Creates a graph G from a Rips complex whose threshold value is automatically tuned with subsampling---see + * \cite Carriere17c. + * + * @param[in] distance distance between data points. + * @param[in] N number of subsampling iteration (the default reasonable value is 100, but there is no guarantee on + * how to choose it). + * @result delta threshold used for computing the Rips complex. + * + */ + template <typename Distance> + double set_graph_from_automatic_rips(Distance distance, int N = 100) { + int m = floor(n / std::exp((1 + rate_power) * std::log(std::log(n) / std::log(rate_constant)))); + m = std::min(m, n - 1); + std::vector<int> samples(m); + double delta = 0; + + if (verbose) std::cout << n << " points in R^" << data_dimension << std::endl; + if (verbose) std::cout << "Subsampling " << m << " points" << std::endl; + + if (distances.size() == 0) compute_pairwise_distances(distance); + + // #pragma omp parallel for + for (int i = 0; i < N; i++) { + SampleWithoutReplacement(n, m, samples); + double hausdorff_dist = 0; + for (int j = 0; j < n; j++) { + double mj = distances[j][samples[0]]; + for (int k = 1; k < m; k++) mj = std::min(mj, distances[j][samples[k]]); + hausdorff_dist = std::max(hausdorff_dist, mj); + } + delta += hausdorff_dist / N; + } + + if (verbose) std::cout << "delta = " << delta << std::endl; + set_graph_from_rips(delta, distance); + return delta; + } + + // ******************************************************************************************************************* + // Functions. + // ******************************************************************************************************************* + + public: // Set function from file. + /** \brief Creates the function f from a file containing the function values. + * + * @param[in] func_file_name name of the input function file. + * + */ + void set_function_from_file(const std::string& func_file_name) { + int i = 0; + std::ifstream input(func_file_name); + std::string line; + double f; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> f; + func.emplace(i, f); + i++; + } + functional_cover = true; + cover_name = func_file_name; + } + + public: // Set function from kth coordinate + /** \brief Creates the function f from the k-th coordinate of the point cloud P. + * + * @param[in] k coordinate to use (start at 0). + * + */ + void set_function_from_coordinate(int k) { + for (int i = 0; i < n; i++) func.emplace(i, point_cloud[i][k]); + functional_cover = true; + cover_name = "coordinate " + std::to_string(k); + } + + public: // Set function from vector. + /** \brief Creates the function f from a vector stored in memory. + * + * @param[in] function input vector of values. + * + */ + template <class InputRange> + void set_function_from_range(InputRange const& function) { + for (int i = 0; i < n; i++) func.emplace(i, function[i]); + functional_cover = true; + } + + // ******************************************************************************************************************* + // Covers. + // ******************************************************************************************************************* + + public: // Automatic tuning of resolution. + /** \brief Computes the optimal length of intervals + * (i.e. the smallest interval length avoiding discretization artifacts---see \cite Carriere17c) for a functional + * cover. + * + * @result reso interval length used to compute the cover. + * + */ + double set_automatic_resolution() { + if (!functional_cover) { + std::cout << "Cover needs to come from the preimages of a function." << std::endl; + return 0; + } + if (type != "Nerve" && type != "GIC") { + std::cout << "Type of complex needs to be specified." << std::endl; + return 0; + } + + double reso = 0; + Index_map index = boost::get(boost::vertex_index, one_skeleton); + + if (type == "GIC") { + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) + reso = std::max(reso, std::abs(func[index[boost::source(*ei, one_skeleton)]] - + func[index[boost::target(*ei, one_skeleton)]])); + if (verbose) std::cout << "resolution = " << reso << std::endl; + resolution_double = reso; + } + + if (type == "Nerve") { + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) + reso = std::max(reso, std::abs(func[index[boost::source(*ei, one_skeleton)]] - + func[index[boost::target(*ei, one_skeleton)]]) / + gain); + if (verbose) std::cout << "resolution = " << reso << std::endl; + resolution_double = reso; + } + + return reso; + } + + public: + /** \brief Sets a length of intervals from a value stored in memory. + * + * @param[in] reso length of intervals. + * + */ + void set_resolution_with_interval_length(double reso) { resolution_double = reso; } + /** \brief Sets a number of intervals from a value stored in memory. + * + * @param[in] reso number of intervals. + * + */ + void set_resolution_with_interval_number(int reso) { resolution_int = reso; } + /** \brief Sets a gain from a value stored in memory (default value 0.3). + * + * @param[in] g gain. + * + */ + void set_gain(double g = 0.3) { gain = g; } + + public: // Set cover with preimages of function. + /** \brief Creates a cover C from the preimages of the function f. + * + */ + void set_cover_from_function() { + if (resolution_double == -1 && resolution_int == -1) { + std::cout << "Number and/or length of intervals not specified" << std::endl; + return; + } + if (gain == -1) { + std::cout << "Gain not specified" << std::endl; + return; + } + + // Read function values and compute min and max + double minf = std::numeric_limits<float>::max(); + double maxf = std::numeric_limits<float>::lowest(); + for (int i = 0; i < n; i++) { + minf = std::min(minf, func[i]); + maxf = std::max(maxf, func[i]); + } + if (verbose) std::cout << "Min function value = " << minf << " and Max function value = " << maxf << std::endl; + + // Compute cover of im(f) + std::vector<std::pair<double, double> > intervals; + int res; + + if (resolution_double == -1) { // Case we use an integer for the number of intervals. + double incr = (maxf - minf) / resolution_int; + double x = minf; + double alpha = (incr * gain) / (2 - 2 * gain); + double y = minf + incr + alpha; + std::pair<double, double> interm(x, y); + intervals.push_back(interm); + for (int i = 1; i < resolution_int - 1; i++) { + x = minf + i * incr - alpha; + y = minf + (i + 1) * incr + alpha; + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + } + x = minf + (resolution_int - 1) * incr - alpha; + y = maxf; + std::pair<double, double> interM(x, y); + intervals.push_back(interM); + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } else { + if (resolution_int == -1) { // Case we use a double for the length of the intervals. + double x = minf; + double y = x + resolution_double; + while (y <= maxf && maxf - (y - gain * resolution_double) >= resolution_double) { + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + x = y - gain * resolution_double; + y = x + resolution_double; + } + std::pair<double, double> interM(x, maxf); + intervals.push_back(interM); + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } else { // Case we use an integer and a double for the length of the intervals. + double x = minf; + double y = x + resolution_double; + int count = 0; + while (count < resolution_int && y <= maxf && maxf - (y - gain * resolution_double) >= resolution_double) { + std::pair<double, double> inter(x, y); + intervals.push_back(inter); + count++; + x = y - gain * resolution_double; + y = x + resolution_double; + } + res = intervals.size(); + if (verbose) { + for (int i = 0; i < res; i++) + std::cout << "Interval " << i << " = [" << intervals[i].first << ", " << intervals[i].second << "]" + << std::endl; + } + } + } + + // Sort points according to function values + std::vector<int> points(n); + for (int i = 0; i < n; i++) points[i] = i; + std::sort(points.begin(), points.end(), Less(this->func)); + + int id = 0; + int pos = 0; + Index_map index = boost::get(boost::vertex_index, one_skeleton); // int maxc = -1; + std::map<int, std::vector<int> > preimages; + std::map<int, double> funcstd; + + if (verbose) std::cout << "Computing preimages..." << std::endl; + for (int i = 0; i < res; i++) { + // Find points in the preimage + std::pair<double, double> inter1 = intervals[i]; + int tmp = pos; + double u, v; + + if (i != res - 1) { + if (i != 0) { + std::pair<double, double> inter3 = intervals[i - 1]; + while (func[points[tmp]] < inter3.second && tmp != n) { + preimages[i].push_back(points[tmp]); + tmp++; + } + u = inter3.second; + } else { + u = inter1.first; + } + + std::pair<double, double> inter2 = intervals[i + 1]; + while (func[points[tmp]] < inter2.first && tmp != n) { + preimages[i].push_back(points[tmp]); + tmp++; + } + v = inter2.first; + pos = tmp; + while (func[points[tmp]] < inter1.second && tmp != n) { + preimages[i].push_back(points[tmp]); + tmp++; + } + + } else { + std::pair<double, double> inter3 = intervals[i - 1]; + while (func[points[tmp]] < inter3.second && tmp != n) { + preimages[i].push_back(points[tmp]); + tmp++; + } + while (tmp != n) { + preimages[i].push_back(points[tmp]); + tmp++; + } + u = inter3.second; + v = inter1.second; + } + + funcstd[i] = 0.5 * (u + v); + } + + if (verbose) std::cout << "Computing connected components..." << std::endl; + // #pragma omp parallel for + for (int i = 0; i < res; i++) { + // Compute connected components + Graph G = one_skeleton.create_subgraph(); + int num = preimages[i].size(); + std::vector<int> component(num); + for (int j = 0; j < num; j++) boost::add_vertex(index[vertices[preimages[i][j]]], G); + boost::connected_components(G, &component[0]); + int max = 0; + + // For each point in preimage + for (int j = 0; j < num; j++) { + // Update number of components in preimage + if (component[j] > max) max = component[j]; + + // Identify component with Cantor polynomial N^2 -> N + int identifier = (std::pow(i + component[j], 2) + 3 * i + component[j]) / 2; + + // Update covers + cover[preimages[i][j]].push_back(identifier); + cover_back[identifier].push_back(preimages[i][j]); + cover_fct[identifier] = i; + cover_std[identifier] = funcstd[i]; + cover_color[identifier].second += func_color[preimages[i][j]]; + cover_color[identifier].first += 1; + } + + // Maximal dimension is total number of connected components + id += max + 1; + } + + maximal_dim = id - 1; + for (std::map<int, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); iit++) + iit->second.second /= iit->second.first; + } + + public: // Set cover from file. + /** \brief Creates the cover C from a file containing the cover elements of each point (the order has to be the same + * as in the input file!). + * + * @param[in] cover_file_name name of the input cover file. + * + */ + void set_cover_from_file(const std::string& cover_file_name) { + int i = 0; + int cov; + std::vector<int> cov_elts, cov_number; + std::ifstream input(cover_file_name); + std::string line; + while (std::getline(input, line)) { + cov_elts.clear(); + std::stringstream stream(line); + while (stream >> cov) { + cov_elts.push_back(cov); + cov_number.push_back(cov); + cover_fct[cov] = cov; + cover_color[cov].second += func_color[i]; + cover_color[cov].first++; + cover_back[cov].push_back(i); + } + cover[i] = cov_elts; + i++; + } + + std::sort(cov_number.begin(), cov_number.end()); + std::vector<int>::iterator it = std::unique(cov_number.begin(), cov_number.end()); + cov_number.resize(std::distance(cov_number.begin(), it)); + + maximal_dim = cov_number.size() - 1; + for (int i = 0; i <= maximal_dim; i++) cover_color[i].second /= cover_color[i].first; + cover_name = cover_file_name; + } + + public: // Set cover from Voronoi + /** \brief Creates the cover C from the Voronoï cells of a subsampling of the point cloud. + * + * @param[in] distance distance between the points. + * @param[in] m number of points in the subsample. + * + */ + template <typename Distance> + void set_cover_from_Voronoi(Distance distance, int m = 100) { + voronoi_subsamples.resize(m); + SampleWithoutReplacement(n, m, voronoi_subsamples); + if (distances.size() == 0) compute_pairwise_distances(distance); + set_graph_weights(); + Weight_map weight = boost::get(boost::edge_weight, one_skeleton); + Index_map index = boost::get(boost::vertex_index, one_skeleton); + std::vector<double> mindist(n); + for (int j = 0; j < n; j++) mindist[j] = std::numeric_limits<double>::max(); + + // Compute the geodesic distances to subsamples with Dijkstra + // #pragma omp parallel for + for (int i = 0; i < m; i++) { + if (verbose) std::cout << "Computing geodesic distances to seed " << i << "..." << std::endl; + int seed = voronoi_subsamples[i]; + std::vector<double> dmap(n); + boost::dijkstra_shortest_paths( + one_skeleton, vertices[seed], + boost::weight_map(weight).distance_map(boost::make_iterator_property_map(dmap.begin(), index))); + + for (int j = 0; j < n; j++) + if (mindist[j] > dmap[j]) { + mindist[j] = dmap[j]; + if (cover[j].size() == 0) + cover[j].push_back(i); + else + cover[j][0] = i; + } + } + + for (int i = 0; i < n; i++) { + cover_back[cover[i][0]].push_back(i); + cover_color[cover[i][0]].second += func_color[i]; + cover_color[cover[i][0]].first++; + } + for (int i = 0; i < m; i++) cover_color[i].second /= cover_color[i].first; + maximal_dim = m - 1; + cover_name = "Voronoi"; + } + + public: // return subset of data corresponding to a node + /** \brief Returns the data subset corresponding to a specific node of the created complex. + * + * @param[in] c ID of the node. + * @result cover_back(c) vector of IDs of data points. + * + */ + const std::vector<int>& subpopulation(int c) { return cover_back[name2idinv[c]]; } + + // ******************************************************************************************************************* + // Visualization. + // ******************************************************************************************************************* + + public: // Set color from file. + /** \brief Computes the function used to color the nodes of the simplicial complex from a file containing the function + * values. + * + * @param[in] color_file_name name of the input color file. + * + */ + void set_color_from_file(const std::string& color_file_name) { + int i = 0; + std::ifstream input(color_file_name); + std::string line; + double f; + while (std::getline(input, line)) { + std::stringstream stream(line); + stream >> f; + func_color.emplace(i, f); + i++; + } + color_name = color_file_name; + } + + public: // Set color from kth coordinate + /** \brief Computes the function used to color the nodes of the simplicial complex from the k-th coordinate. + * + * @param[in] k coordinate to use (start at 0). + * + */ + void set_color_from_coordinate(int k = 0) { + for (int i = 0; i < n; i++) func_color[i] = point_cloud[i][k]; + color_name = "coordinate "; + color_name.append(std::to_string(k)); + } + + public: // Set color from vector. + /** \brief Computes the function used to color the nodes of the simplicial complex from a vector stored in memory. + * + * @param[in] color input vector of values. + * + */ + void set_color_from_vector(std::vector<double> color) { + for (unsigned int i = 0; i < color.size(); i++) func_color[i] = color[i]; + } + + public: // Create a .dot file that can be compiled with neato to produce a .pdf file. + /** \brief Creates a .dot file called SC.dot for neato (part of the graphviz package) once the simplicial complex is + * computed to get a visualization + * of its 1-skeleton in a .pdf file. + */ + void plot_DOT() { + std::string mapp = point_cloud_name + "_sc.dot"; + std::ofstream graphic(mapp); + + double maxv = std::numeric_limits<double>::lowest(); + double minv = std::numeric_limits<double>::max(); + for (std::map<int, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); iit++) { + maxv = std::max(maxv, iit->second.second); + minv = std::min(minv, iit->second.second); + } + + int k = 0; + std::vector<int> nodes; + nodes.clear(); + + graphic << "graph GIC {" << std::endl; + int id = 0; + for (std::map<int, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); iit++) { + if (iit->second.first > mask) { + nodes.push_back(iit->first); + name2id[iit->first] = id; + name2idinv[id] = iit->first; + id++; + graphic << name2id[iit->first] << "[shape=circle fontcolor=black color=black label=\"" << name2id[iit->first] + << ":" << iit->second.first << "\" style=filled fillcolor=\"" + << (1 - (maxv - iit->second.second) / (maxv - minv)) * 0.6 << ", 1, 1\"]" << std::endl; + k++; + } + } + int ke = 0; + int num_simplices = simplices.size(); + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) { + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) { + graphic << " " << name2id[simplices[i][0]] << " -- " << name2id[simplices[i][1]] << " [weight=15];" + << std::endl; + ke++; + } + } + graphic << "}"; + graphic.close(); + std::cout << mapp << " file generated. It can be visualized with e.g. neato." << std::endl; + } + + public: // Create a .txt file that can be compiled with KeplerMapper. + /** \brief Creates a .txt file called SC.txt describing the 1-skeleton, which can then be plotted with e.g. + * KeplerMapper. + */ + void write_info() { + int num_simplices = simplices.size(); + int num_edges = 0; + std::string mapp = point_cloud_name + "_sc.txt"; + std::ofstream graphic(mapp); + + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) num_edges++; + + graphic << point_cloud_name << std::endl; + graphic << cover_name << std::endl; + graphic << color_name << std::endl; + graphic << resolution_double << " " << gain << std::endl; + graphic << cover_color.size() << " " << num_edges << std::endl; + + int id = 0; + for (std::map<int, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); iit++) { + graphic << id << " " << iit->second.second << " " << iit->second.first << std::endl; + name2id[iit->first] = id; + name2idinv[id] = iit->first; + id++; + } + + for (int i = 0; i < num_simplices; i++) + if (simplices[i].size() == 2) + if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask) + graphic << name2id[simplices[i][0]] << " " << name2id[simplices[i][1]] << std::endl; + graphic.close(); + std::cout << mapp + << " generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox." + << std::endl; + } + + public: // Create a .off file that can be visualized (e.g. with Geomview). + /** \brief Creates a .off file called SC.off for 3D visualization, which contains the 2-skeleton of the GIC. + * This function assumes that the cover has been computed with Voronoi. If data points are in 1D or 2D, + * the remaining coordinates of the points embedded in 3D are set to 0. + */ + void plot_OFF() { + assert(cover_name == "Voronoi"); + + int m = voronoi_subsamples.size(); + int numedges = 0; + int numfaces = 0; + std::vector<std::vector<int> > edges, faces; + int numsimplices = simplices.size(); + + std::string mapp = point_cloud_name + "_sc.off"; + std::ofstream graphic(mapp); + + graphic << "OFF" << std::endl; + for (int i = 0; i < numsimplices; i++) { + if (simplices[i].size() == 2) { + numedges++; + edges.push_back(simplices[i]); + } + if (simplices[i].size() == 3) { + numfaces++; + faces.push_back(simplices[i]); + } + } + graphic << m << " " << numedges + numfaces << std::endl; + for (int i = 0; i < m; i++) { + if (data_dimension <= 3) { + for (int j = 0; j < data_dimension; j++) graphic << point_cloud[voronoi_subsamples[i]][j] << " "; + for (int j = data_dimension; j < 3; j++) graphic << 0 << " "; + graphic << std::endl; + } else { + for (int j = 0; j < 3; j++) graphic << point_cloud[voronoi_subsamples[i]][j] << " "; + } + } + for (int i = 0; i < numedges; i++) graphic << 2 << " " << edges[i][0] << " " << edges[i][1] << std::endl; + for (int i = 0; i < numfaces; i++) + graphic << 3 << " " << faces[i][0] << " " << faces[i][1] << " " << faces[i][2] << std::endl; + graphic.close(); + std::cout << mapp << " generated. It can be visualized with e.g. geomview." << std::endl; + } + + // ******************************************************************************************************************* + // Extended Persistence Diagrams. + // ******************************************************************************************************************* + + public: + /** \brief Computes the extended persistence diagram of the complex. + * + */ + void compute_PD() { + Simplex_tree st; + + // Compute max and min + double maxf = std::numeric_limits<double>::lowest(); + double minf = std::numeric_limits<double>::max(); + for (std::map<int, double>::iterator it = cover_std.begin(); it != cover_std.end(); it++) { + maxf = std::max(maxf, it->second); + minf = std::min(minf, it->second); + } + + for (auto const& simplex : simplices) { + // Add a simplex and a cone on it + std::vector<int> splx = simplex; + splx.push_back(-2); + st.insert_simplex_and_subfaces(splx); + } + + // Build filtration + for (auto simplex : st.complex_simplex_range()) { + double filta = std::numeric_limits<double>::lowest(); + double filts = filta; + bool ascending = true; + for (auto vertex : st.simplex_vertex_range(simplex)) { + if (vertex == -2) { + ascending = false; + continue; + } + filta = std::max(-2 + (cover_std[vertex] - minf) / (maxf - minf), filta); + filts = std::max(2 - (cover_std[vertex] - minf) / (maxf - minf), filts); + } + if (ascending) + st.assign_filtration(simplex, filta); + else + st.assign_filtration(simplex, filts); + } + int magic[] = {-2}; + st.assign_filtration(st.find(magic), -3); + + // Compute PD + st.initialize_filtration(); + Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh(st); + pcoh.init_coefficients(2); + pcoh.compute_persistent_cohomology(); + + // Output PD + int max_dim = st.dimension(); + for (int i = 0; i < max_dim; i++) { + std::vector<std::pair<double, double> > bars = pcoh.intervals_in_dimension(i); + int num_bars = bars.size(); + if(verbose) std::cout << num_bars << " interval(s) in dimension " << i << ":" << std::endl; + for (int j = 0; j < num_bars; j++) { + double birth = bars[j].first; + double death = bars[j].second; + if (i == 0 && std::isinf(death)) continue; + if (birth < 0) + birth = minf + (birth + 2) * (maxf - minf); + else + birth = minf + (2 - birth) * (maxf - minf); + if (death < 0) + death = minf + (death + 2) * (maxf - minf); + else + death = minf + (2 - death) * (maxf - minf); + PD.push_back(std::pair<double, double>(birth, death)); + if (verbose) std::cout << " [" << birth << ", " << death << "]" << std::endl; + } + } + } + + public: + /** \brief Computes bootstrapped distances distribution. + * + * @param[in] N number of bootstrap iterations. + * + */ + template <typename SimplicialComplex> + void compute_distribution(int N = 100) { + if (distribution.size() >= N) { + std::cout << "Already done!" << std::endl; + } else { + for (int i = 0; i < N - distribution.size(); i++) { + Cover_complex Cboot; + Cboot.n = this->n; + std::vector<int> boot(this->n); + for (int j = 0; j < this->n; j++) { + double u = GetUniform(); + int id = std::floor(u * (this->n)); + boot[j] = id; + Cboot.point_cloud[j] = this->point_cloud[id]; + Cboot.func.emplace(j, this->func[id]); + } + for (int j = 0; j < n; j++) { + std::vector<double> dist(n); + for (int k = 0; k < n; k++) dist[k] = distances[boot[j]][boot[k]]; + Cboot.distances.push_back(dist); + } + + Cboot.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); + Cboot.set_automatic_resolution(); + Cboot.set_gain(); + Cboot.set_cover_from_function(); + Cboot.find_simplices(); + Cboot.compute_PD(); + + distribution.push_back(Gudhi::persistence_diagram::bottleneck_distance(this->PD, Cboot.PD)); + } + + std::sort(distribution.begin(), distribution.end()); + } + } + + public: + /** \brief Computes the bottleneck distance threshold corresponding to a specific confidence level. + * + * @param[in] alpha Confidence level. + * + */ + double compute_distance_from_confidence_level(double alpha) { + int N = distribution.size(); + return distribution[std::floor(alpha * N)]; + } + + public: + /** \brief Computes the confidence level of a specific bottleneck distance threshold. + * + * @param[in] d Bottleneck distance. + * + */ + double compute_confidence_level_from_distance(double d) { + int N = distribution.size(); + for (int i = 0; i < N; i++) + if (distribution[i] > d) return i * 1.0 / N; + } + + public: + /** \brief Computes the p-value, i.e. the opposite of the confidence level of the largest bottleneck + * distance preserving the points in the persistence diagram of the output simplicial complex. + * + */ + double compute_p_value() { + double distancemin = -std::numeric_limits<double>::lowest(); + int N = PD.size(); + for (int i = 0; i < N; i++) distancemin = std::min(distancemin, 0.5 * (PD[i].second - PD[i].first)); + return 1 - compute_confidence_level_from_distance(distancemin); + } + + // ******************************************************************************************************************* + // Computation of simplices. + // ******************************************************************************************************************* + + public: + /** \brief Creates the simplicial complex. + * + * @param[in] complex SimplicialComplex to be created. + * + */ + template <typename SimplicialComplex> + void create_complex(SimplicialComplex& complex) { + unsigned int dimension = 0; + for (auto const& simplex : simplices) { + int numvert = simplex.size(); + double filt = std::numeric_limits<double>::lowest(); + for (int i = 0; i < numvert; i++) filt = std::max(cover_color[simplex[i]].second, filt); + complex.insert_simplex_and_subfaces(simplex, filt); + if (dimension < simplex.size() - 1) dimension = simplex.size() - 1; + } + } + + public: + /** \brief Computes the simplices of the simplicial complex. + */ + void find_simplices() { + if (type != "Nerve" && type != "GIC") { + std::cout << "Type of complex needs to be specified." << std::endl; + return; + } + + if (type == "Nerve") { + for(auto& simplex : cover) + simplices.push_back(simplex.second); + std::sort(simplices.begin(), simplices.end()); + std::vector<std::vector<int> >::iterator it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + } + + if (type == "GIC") { + Index_map index = boost::get(boost::vertex_index, one_skeleton); + + if (functional_cover) { + // Computes the simplices in the GIC by looking at all the edges of the graph and adding the + // corresponding edges in the GIC if the images of the endpoints belong to consecutive intervals. + + if (gain >= 0.5) + throw std::invalid_argument( + "the output of this function is correct ONLY if the cover is minimal, i.e. the gain is less than 0.5."); + + // Loop on all edges. + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) { + int nums = cover[index[boost::source(*ei, one_skeleton)]].size(); + for (int i = 0; i < nums; i++) { + int vs = cover[index[boost::source(*ei, one_skeleton)]][i]; + int numt = cover[index[boost::target(*ei, one_skeleton)]].size(); + for (int j = 0; j < numt; j++) { + int vt = cover[index[boost::target(*ei, one_skeleton)]][j]; + if (cover_fct[vs] == cover_fct[vt] + 1 || cover_fct[vt] == cover_fct[vs] + 1) { + std::vector<int> edge(2); + edge[0] = std::min(vs, vt); + edge[1] = std::max(vs, vt); + simplices.push_back(edge); + goto afterLoop; + } + } + } + afterLoop:; + } + std::sort(simplices.begin(), simplices.end()); + std::vector<std::vector<int> >::iterator it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + + } else { + // Find edges to keep + Simplex_tree st; + boost::graph_traits<Graph>::edge_iterator ei, ei_end; + for (boost::tie(ei, ei_end) = boost::edges(one_skeleton); ei != ei_end; ++ei) + if (!(cover[index[boost::target(*ei, one_skeleton)]].size() == 1 && + cover[index[boost::target(*ei, one_skeleton)]] == cover[index[boost::source(*ei, one_skeleton)]])) { + std::vector<int> edge(2); + edge[0] = index[boost::source(*ei, one_skeleton)]; + edge[1] = index[boost::target(*ei, one_skeleton)]; + st.insert_simplex_and_subfaces(edge); + } + + // st.insert_graph(one_skeleton); + + // Build the Simplex Tree corresponding to the graph + st.expansion(maximal_dim); + + // Find simplices of GIC + simplices.clear(); + for (auto simplex : st.complex_simplex_range()) { + if (!st.has_children(simplex)) { + std::vector<int> simplx; + for (auto vertex : st.simplex_vertex_range(simplex)) { + unsigned int sz = cover[vertex].size(); + for (unsigned int i = 0; i < sz; i++) { + simplx.push_back(cover[vertex][i]); + } + } + std::sort(simplx.begin(), simplx.end()); + std::vector<int>::iterator it = std::unique(simplx.begin(), simplx.end()); + simplx.resize(std::distance(simplx.begin(), it)); + simplices.push_back(simplx); + } + } + std::sort(simplices.begin(), simplices.end()); + std::vector<std::vector<int> >::iterator it = std::unique(simplices.begin(), simplices.end()); + simplices.resize(std::distance(simplices.begin(), it)); + } + } + } +}; + +} // namespace cover_complex + +} // namespace Gudhi + +#endif // GIC_H_ diff --git a/include/gudhi/Kd_tree_search.h b/include/gudhi/Kd_tree_search.h index ef428002..96bbeb36 100644 --- a/include/gudhi/Kd_tree_search.h +++ b/include/gudhi/Kd_tree_search.h @@ -271,8 +271,7 @@ class Kd_tree_search { m_tree.search(it, Fuzzy_sphere(p, radius, eps, m_tree.traits())); } - int tree_depth() const - { + int tree_depth() const { return m_tree.root()->depth(); } diff --git a/include/gudhi/Neighbors_finder.h b/include/gudhi/Neighbors_finder.h index a6b9b021..87c7cee5 100644 --- a/include/gudhi/Neighbors_finder.h +++ b/include/gudhi/Neighbors_finder.h @@ -32,6 +32,7 @@ #include <unordered_set> #include <vector> +#include <algorithm> // for std::max namespace Gudhi { @@ -44,7 +45,7 @@ struct Square_query { typedef Internal_point Point_d; typedef double FT; bool contains(Point_d p) const { - return std::abs(p.x()-c.x()) <= size && std::abs(p.y()-c.y()) <= size; + return std::max(std::abs(p.x()-c.x()), std::abs(p.y()-c.y())) <= size; } bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT, D> const&r) const { return diff --git a/include/gudhi/PSSK.h b/include/gudhi/PSSK.h new file mode 100644 index 00000000..630f5623 --- /dev/null +++ b/include/gudhi/PSSK.h @@ -0,0 +1,168 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PSSK_H_ +#define PSSK_H_ + +// gudhi include +#include <gudhi/Persistence_heat_maps.h> + +#include <limits> +#include <utility> +#include <vector> + +namespace Gudhi { +namespace Persistence_representations { + +/** +* This is a version of a representation presented in https://arxiv.org/abs/1412.6821 +* In that paper the authors are using the representation just to compute kernel. Over here, we extend the usability by +*far. +* Note that the version presented here is not exact, since we are discretizing the kernel. +* The only difference with respect to the original class is the method of creation. We have full (square) image, and for +*every point (p,q), we add a kernel at (p,q) and the negative kernel +* at (q,p) +**/ + +class PSSK : public Persistence_heat_maps<constant_scaling_function> { + public: + PSSK() : Persistence_heat_maps() {} + + PSSK(const std::vector<std::pair<double, double> >& interval, + std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), size_t number_of_pixels = 1000, + double min_ = -1, double max_ = -1) + : Persistence_heat_maps() { + this->construct(interval, filter, number_of_pixels, min_, max_); + } + + PSSK(const char* filename, std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), + size_t number_of_pixels = 1000, double min_ = -1, double max_ = -1, + unsigned dimension = std::numeric_limits<unsigned>::max()) + : Persistence_heat_maps() { + std::vector<std::pair<double, double> > intervals_; + if (dimension == std::numeric_limits<unsigned>::max()) { + intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->construct(intervals_, filter, number_of_pixels, min_, max_); + } + + protected: + void construct(const std::vector<std::pair<double, double> >& intervals_, + std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), + size_t number_of_pixels = 1000, double min_ = -1, double max_ = -1); +}; + +// if min_ == max_, then the program is requested to set up the values itself based on persistence intervals +void PSSK::construct(const std::vector<std::pair<double, double> >& intervals_, + std::vector<std::vector<double> > filter, size_t number_of_pixels, double min_, double max_) { + bool dbg = false; + if (dbg) { + std::cerr << "Entering construct procedure \n"; + getchar(); + } + + if (min_ == max_) { + // in this case, we want the program to set up the min_ and max_ values by itself. + min_ = std::numeric_limits<int>::max(); + max_ = -std::numeric_limits<int>::max(); + + for (size_t i = 0; i != intervals_.size(); ++i) { + if (intervals_[i].first < min_) min_ = intervals_[i].first; + if (intervals_[i].second > max_) max_ = intervals_[i].second; + } + // now we have the structure filled in, and moreover we know min_ and max_ values of the interval, so we know the + // range. + + // add some more space: + min_ -= fabs(max_ - min_) / 100; + max_ += fabs(max_ - min_) / 100; + } + + if (dbg) { + std::cerr << "min_ : " << min_ << std::endl; + std::cerr << "max_ : " << max_ << std::endl; + std::cerr << "number_of_pixels : " << number_of_pixels << std::endl; + getchar(); + } + + this->min_ = min_; + this->max_ = max_; + + // initialization of the structure heat_map + std::vector<std::vector<double> > heat_map_; + for (size_t i = 0; i != number_of_pixels; ++i) { + std::vector<double> v(number_of_pixels, 0); + heat_map_.push_back(v); + } + this->heat_map = heat_map_; + + if (dbg) std::cerr << "Done creating of the heat map, now we will fill in the structure \n"; + + for (size_t pt_nr = 0; pt_nr != intervals_.size(); ++pt_nr) { + // compute the value of intervals_[pt_nr] in the grid: + int x_grid = + static_cast<int>((intervals_[pt_nr].first - this->min_) / (this->max_ - this->min_) * number_of_pixels); + int y_grid = + static_cast<int>((intervals_[pt_nr].second - this->min_) / (this->max_ - this->min_) * number_of_pixels); + + if (dbg) { + std::cerr << "point : " << intervals_[pt_nr].first << " , " << intervals_[pt_nr].second << std::endl; + std::cerr << "x_grid : " << x_grid << std::endl; + std::cerr << "y_grid : " << y_grid << std::endl; + } + + // x_grid and y_grid gives a center of the kernel. We want to have its lower left corner. To get this, we need to + // shift x_grid and y_grid by a grid diameter. + x_grid -= filter.size() / 2; + y_grid -= filter.size() / 2; + // note that the numbers x_grid and y_grid may be negative. + + if (dbg) { + std::cerr << "After shift : \n"; + std::cerr << "x_grid : " << x_grid << std::endl; + std::cerr << "y_grid : " << y_grid << std::endl; + std::cerr << "filter.size() : " << filter.size() << std::endl; + getchar(); + } + + for (size_t i = 0; i != filter.size(); ++i) { + for (size_t j = 0; j != filter.size(); ++j) { + // if the point (x_grid+i,y_grid+j) is the correct point in the grid. + if (((x_grid + i) >= 0) && (x_grid + i < this->heat_map.size()) && ((y_grid + j) >= 0) && + (y_grid + j < this->heat_map.size())) { + if (dbg) { + std::cerr << y_grid + j << " " << x_grid + i << std::endl; + } + this->heat_map[y_grid + j][x_grid + i] += filter[i][j]; + this->heat_map[x_grid + i][y_grid + j] += -filter[i][j]; + } + } + } + } +} // construct + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PSSK_H_ diff --git a/include/gudhi/Persistence_heat_maps.h b/include/gudhi/Persistence_heat_maps.h new file mode 100644 index 00000000..a80c3c40 --- /dev/null +++ b/include/gudhi/Persistence_heat_maps.h @@ -0,0 +1,919 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PERSISTENCE_HEAT_MAPS_H_ +#define PERSISTENCE_HEAT_MAPS_H_ + +// gudhi include +#include <gudhi/read_persistence_from_file.h> +#include <gudhi/common_persistence_representations.h> + +// standard include +#include <vector> +#include <sstream> +#include <iostream> +#include <cmath> +#include <limits> +#include <algorithm> +#include <utility> +#include <string> +#include <functional> + +namespace Gudhi { +namespace Persistence_representations { + +/** + * This is a simple procedure to create n by n (or 2*pixel_radius times 2*pixel_radius cubical approximation of a + *Gaussian kernel. +**/ +std::vector<std::vector<double> > create_Gaussian_filter(size_t pixel_radius, double sigma) { + bool dbg = false; + // we are computing the kernel mask to 2 standard deviations away from the center. We discretize it in a grid of a + // size 2*pixel_radius times 2*pixel_radius. + + double r = 0; + double sigma_sqr = sigma * sigma; + + // sum is for normalization + double sum = 0; + + // initialization of a kernel: + std::vector<std::vector<double> > kernel(2 * pixel_radius + 1); + for (size_t i = 0; i != kernel.size(); ++i) { + std::vector<double> v(2 * pixel_radius + 1, 0); + kernel[i] = v; + } + + if (dbg) { + std::cerr << "Kernel initialize \n"; + std::cerr << "pixel_radius : " << pixel_radius << std::endl; + std::cerr << "kernel.size() : " << kernel.size() << std::endl; + getchar(); + } + + for (int x = -pixel_radius; x <= static_cast<int>(pixel_radius); x++) { + for (int y = -pixel_radius; y <= static_cast<int>(pixel_radius); y++) { + double real_x = 2 * sigma * x / pixel_radius; + double real_y = 2 * sigma * y / pixel_radius; + r = sqrt(real_x * real_x + real_y * real_y); + kernel[x + pixel_radius][y + pixel_radius] = (exp(-(r * r) / sigma_sqr)) / (3.141592 * sigma_sqr); + sum += kernel[x + pixel_radius][y + pixel_radius]; + } + } + + // normalize the kernel + for (size_t i = 0; i != kernel.size(); ++i) { + for (size_t j = 0; j != kernel[i].size(); ++j) { + kernel[i][j] /= sum; + } + } + + if (dbg) { + std::cerr << "Here is the kernel : \n"; + for (size_t i = 0; i != kernel.size(); ++i) { + for (size_t j = 0; j != kernel[i].size(); ++j) { + std::cerr << kernel[i][j] << " "; + } + std::cerr << std::endl; + } + } + return kernel; +} + +/* +* There are various options to scale the points depending on their location. One can for instance: +* (1) do nothing (scale all of them with the weight 1), as in the function constant_function +* (2) Scale them by the distance to the diagonal. This is implemented in function +* (3) Scale them with the square of their distance to diagonal. This is implemented in function +* (4) Scale them with +*/ + +/** + * This is one of a scaling functions used to weight points depending on their persistence and/or location in the + *diagram. + * This particular functionality is a function which always assign value 1 to a point in the diagram. +**/ +class constant_scaling_function { + public: + double operator()(const std::pair<double, double>& point_in_diagram) { return 1; } +}; + +/** + * This is one of a scaling functions used to weight points depending on their persistence and/or location in the + *diagram. + * The scaling given by this function to a point (b,d) is Euclidean distance of (b,d) from diagonal. +**/ +class distance_from_diagonal_scaling { + public: + double operator()(const std::pair<double, double>& point_in_diagram) { + // (point_in_diagram.first+point_in_diagram.second)/2.0 + return sqrt(pow((point_in_diagram.first - (point_in_diagram.first + point_in_diagram.second) / 2.0), 2) + + pow((point_in_diagram.second - (point_in_diagram.first + point_in_diagram.second) / 2.0), 2)); + } +}; + +/** + * This is one of a scaling functions used to weight points depending on their persistence and/or location in the + *diagram. + * The scaling given by this function to a point (b,d) is a square of Euclidean distance of (b,d) from diagonal. +**/ +class squared_distance_from_diagonal_scaling { + public: + double operator()(const std::pair<double, double>& point_in_diagram) { + return pow((point_in_diagram.first - (point_in_diagram.first + point_in_diagram.second) / 2.0), 2) + + pow((point_in_diagram.second - (point_in_diagram.first + point_in_diagram.second) / 2.0), 2); + } +}; + +/** + * This is one of a scaling functions used to weight points depending on their persistence and/or location in the + *diagram. + * The scaling given by this function to a point (b,d) is an arctan of a persistence of a point (i.e. arctan( b-d ). +**/ +class arc_tan_of_persistence_of_point { + public: + double operator()(const std::pair<double, double>& point_in_diagram) { + return atan(point_in_diagram.second - point_in_diagram.first); + } +}; + +/** + * This is one of a scaling functions used to weight points depending on their persistence and/or location in the + *diagram. + * This scaling function do not only depend on a point (p,d) in the diagram, but it depends on the whole diagram. + * The longest persistence pair get a scaling 1. Any other pair get a scaling belong to [0,1], which is proportional + * to the persistence of that pair. +**/ +class weight_by_setting_maximal_interval_to_have_length_one { + public: + weight_by_setting_maximal_interval_to_have_length_one(double len) : letngth_of_maximal_interval(len) {} + double operator()(const std::pair<double, double>& point_in_diagram) { + return (point_in_diagram.second - point_in_diagram.first) / this->letngth_of_maximal_interval; + } + + private: + double letngth_of_maximal_interval; +}; + +/** + * \class Persistence_heat_maps Persistence_heat_maps.h gudhi/Persistence_heat_maps.h + * \brief A class implementing persistence heat maps. + * + * \ingroup Persistence_representations +**/ + +// This class implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, +// Real_valued_topological_data, Topological_data_with_averages, Topological_data_with_scalar_product +template <typename Scalling_of_kernels = constant_scaling_function> +class Persistence_heat_maps { + public: + /** + * The default constructor. A scaling function from the diagonal is set up to a constant function. The image is not + *erased below the diagonal. The Gaussian have diameter 5. + **/ + Persistence_heat_maps() { + Scalling_of_kernels f; + this->f = f; + this->erase_below_diagonal = false; + this->min_ = this->max_ = 0; + this->set_up_parameters_for_basic_classes(); + } + + /** + * Construction that takes at the input the following parameters: + * (1) A vector of pairs of doubles (representing persistence intervals). All other parameters are optional. They are: + * (2) a Gaussian filter generated by create_Gaussian_filter filter (the default value of this variable is a Gaussian + *filter of a radius 5), + * (3) a boolean value which determines if the area of image below diagonal should, or should not be erased (it will + *be erased by default). + * (4) a number of pixels in each direction (set to 1000 by default). + * (5) a min x and y value of points that are to be taken into account. By default it is set to + *std::numeric_limits<double>::max(), in which case the program compute the values based on the data, + * (6) a max x and y value of points that are to be taken into account. By default it is set to + *std::numeric_limits<double>::max(), in which case the program compute the values based on the data. + **/ + Persistence_heat_maps(const std::vector<std::pair<double, double> >& interval, + std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), + bool erase_below_diagonal = false, size_t number_of_pixels = 1000, + double min_ = std::numeric_limits<double>::max(), + double max_ = std::numeric_limits<double>::max()); + + /** + * Construction that takes at the input a name of a file with persistence intervals, a filter (radius 5 by + *default), a scaling function (constant by default), a boolean value which determines if the area of image below + *diagonal should, or should not be erased (should by default). The next parameter is the number of pixels in each + *direction (set to 1000 by default) and min and max values of images (both set to std::numeric_limits<double>::max() + *by default. If this is the case, the program will pick the right values based on the data). + **/ + /** + * Construction that takes at the input the following parameters: + * (1) A name of a file with persistence intervals. The file should be readable by the function + *read_persistence_intervals_in_one_dimension_from_file. All other parameters are optional. They are: + * (2) a Gaussian filter generated by create_Gaussian_filter filter (the default value of this variable is a Gaussian + *filter of a radius 5), + * (3) a boolean value which determines if the area of image below diagonal should, or should not be erased (it will + *be erased by default). + * (4) a number of pixels in each direction (set to 1000 by default). + * (5) a min x and y value of points that are to be taken into account. By default it is set to + *std::numeric_limits<double>::max(), in which case the program compute the values based on the data, + * (6) a max x and y value of points that are to be taken into account. By default it is set to + *std::numeric_limits<double>::max(), in which case the program compute the values based on the data. + **/ + Persistence_heat_maps(const char* filename, std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), + bool erase_below_diagonal = false, size_t number_of_pixels = 1000, + double min_ = std::numeric_limits<double>::max(), + double max_ = std::numeric_limits<double>::max(), + unsigned dimension = std::numeric_limits<unsigned>::max()); + + /** + * Compute a mean value of a collection of heat maps and store it in the current object. Note that all the persistence + *maps send in a vector to this procedure need to have the same parameters. + * If this is not the case, the program will throw an exception. + **/ + void compute_mean(const std::vector<Persistence_heat_maps*>& maps); + + /** + * Compute a median value of a collection of heat maps and store it in the current object. Note that all the + *persistence maps send in a vector to this procedure need to have the same parameters. + * If this is not the case, the program will throw an exception. + **/ + void compute_median(const std::vector<Persistence_heat_maps*>& maps); + + /** + * Compute a percentage of active (i.e) values above the cutoff of a collection of heat maps. + **/ + void compute_percentage_of_active(const std::vector<Persistence_heat_maps*>& maps, size_t cutoff = 1); + + // put to file subroutine + /** + * The function outputs the persistence image to a text file. The format as follow: + * In the first line, the values min and max of the image are stored + * In the next lines, we have the persistence images in a form of a bitmap image. + **/ + void print_to_file(const char* filename) const; + + /** + * A function that load a heat map from file to the current object (and erase whatever was stored in the current + *object before). + **/ + void load_from_file(const char* filename); + + /** + * The procedure checks if min_, max_ and this->heat_maps sizes are the same. + **/ + inline bool check_if_the_same(const Persistence_heat_maps& second) const { + bool dbg = false; + if (this->heat_map.size() != second.heat_map.size()) { + if (dbg) + std::cerr << "this->heat_map.size() : " << this->heat_map.size() + << " \n second.heat_map.size() : " << second.heat_map.size() << std::endl; + return false; + } + if (this->min_ != second.min_) { + if (dbg) std::cerr << "this->min_ : " << this->min_ << ", second.min_ : " << second.min_ << std::endl; + return false; + } + if (this->max_ != second.max_) { + if (dbg) std::cerr << "this->max_ : " << this->max_ << ", second.max_ : " << second.max_ << std::endl; + return false; + } + // in the other case we may assume that the persistence images are defined on the same domain. + return true; + } + + /** + * Return minimal range value of persistent image. + **/ + inline double get_min() const { return this->min_; } + + /** + * Return maximal range value of persistent image. + **/ + inline double get_max() const { return this->max_; } + + /** + * Operator == to check if to persistence heat maps are the same. + **/ + bool operator==(const Persistence_heat_maps& rhs) const { + bool dbg = false; + if (!this->check_if_the_same(rhs)) { + if (dbg) std::cerr << "The domains are not the same \n"; + return false; // in this case, the domains are not the same, so the maps cannot be the same. + } + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + if (!almost_equal(this->heat_map[i][j], rhs.heat_map[i][j])) { + if (dbg) { + std::cerr << "this->heat_map[" << i << "][" << j << "] = " << this->heat_map[i][j] << std::endl; + std::cerr << "rhs.heat_map[" << i << "][" << j << "] = " << rhs.heat_map[i][j] << std::endl; + } + return false; + } + } + } + return true; + } + + /** + * Operator != to check if to persistence heat maps are different. + **/ + bool operator!=(const Persistence_heat_maps& rhs) const { return !((*this) == rhs); } + + /** + * A function to generate a gnuplot script to visualize the persistent image. + **/ + void plot(const char* filename) const; + + template <typename Operation_type> + friend Persistence_heat_maps operation_on_pair_of_heat_maps(const Persistence_heat_maps& first, + const Persistence_heat_maps& second, + Operation_type operation) { + // first check if the heat maps are compatible + if (!first.check_if_the_same(second)) { + std::cerr << "Sizes of the heat maps are not compatible. The program will now terminate \n"; + throw "Sizes of the heat maps are not compatible. The program will now terminate \n"; + } + Persistence_heat_maps result; + result.min_ = first.min_; + result.max_ = first.max_; + result.heat_map.reserve(first.heat_map.size()); + for (size_t i = 0; i != first.heat_map.size(); ++i) { + std::vector<double> v; + v.reserve(first.heat_map[i].size()); + for (size_t j = 0; j != first.heat_map[i].size(); ++j) { + v.push_back(operation(first.heat_map[i][j], second.heat_map[i][j])); + } + result.heat_map.push_back(v); + } + return result; + } // operation_on_pair_of_heat_maps + + /** + * Multiplication of Persistence_heat_maps by scalar (so that all values of the heat map gets multiplied by that + *scalar). + **/ + Persistence_heat_maps multiply_by_scalar(double scalar) const { + Persistence_heat_maps result; + result.min_ = this->min_; + result.max_ = this->max_; + result.heat_map.reserve(this->heat_map.size()); + for (size_t i = 0; i != this->heat_map.size(); ++i) { + std::vector<double> v; + v.reserve(this->heat_map[i].size()); + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + v.push_back(this->heat_map[i][j] * scalar); + } + result.heat_map.push_back(v); + } + return result; + } + + /** + * This function computes a sum of two objects of a type Persistence_heat_maps. + **/ + friend Persistence_heat_maps operator+(const Persistence_heat_maps& first, const Persistence_heat_maps& second) { + return operation_on_pair_of_heat_maps(first, second, std::plus<double>()); + } + /** +* This function computes a difference of two objects of a type Persistence_heat_maps. +**/ + friend Persistence_heat_maps operator-(const Persistence_heat_maps& first, const Persistence_heat_maps& second) { + return operation_on_pair_of_heat_maps(first, second, std::minus<double>()); + } + /** +* This function computes a product of an object of a type Persistence_heat_maps with real number. +**/ + friend Persistence_heat_maps operator*(double scalar, const Persistence_heat_maps& A) { + return A.multiply_by_scalar(scalar); + } + /** +* This function computes a product of an object of a type Persistence_heat_maps with real number. +**/ + friend Persistence_heat_maps operator*(const Persistence_heat_maps& A, double scalar) { + return A.multiply_by_scalar(scalar); + } + /** +* This function computes a product of an object of a type Persistence_heat_maps with real number. +**/ + Persistence_heat_maps operator*(double scalar) { return this->multiply_by_scalar(scalar); } + /** + * += operator for Persistence_heat_maps. + **/ + Persistence_heat_maps operator+=(const Persistence_heat_maps& rhs) { + *this = *this + rhs; + return *this; + } + /** + * -= operator for Persistence_heat_maps. + **/ + Persistence_heat_maps operator-=(const Persistence_heat_maps& rhs) { + *this = *this - rhs; + return *this; + } + /** + * *= operator for Persistence_heat_maps. + **/ + Persistence_heat_maps operator*=(double x) { + *this = *this * x; + return *this; + } + /** + * /= operator for Persistence_heat_maps. + **/ + Persistence_heat_maps operator/=(double x) { + if (x == 0) throw("In operator /=, division by 0. Program terminated."); + *this = *this * (1 / x); + return *this; + } + + // Implementations of functions for various concepts. + + /** + * This function produce a vector of doubles based on a persistence heat map. It is required in a concept + * Vectorized_topological_data + */ + std::vector<double> vectorize(int number_of_function) const; + /** + * This function return the number of functions that allows vectorization of persistence heat map. It is required + *in a concept Vectorized_topological_data. + **/ + size_t number_of_vectorize_functions() const { return this->number_of_functions_for_vectorization; } + + /** + * This function is required by the Real_valued_topological_data concept. It returns various projections on the + *persistence heat map to a real line. + * At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when + *using it, keep in mind that it + * will be most likely changed in the next versions. + **/ + double project_to_R(int number_of_function) const; + /** + * The function gives the number of possible projections to R. This function is required by the + *Real_valued_topological_data concept. + **/ + size_t number_of_projections_to_R() const { return this->number_of_functions_for_projections_to_reals; } + + /** + * A function to compute distance between persistence heat maps. + * The parameter of this function is a const reference to an object of a class Persistence_heat_maps. + * This function is required in Topological_data_with_distances concept. +* For max norm distance, set power to std::numeric_limits<double>::max() + **/ + double distance(const Persistence_heat_maps& second_, double power = 1) const; + + /** + * A function to compute averaged persistence heat map, based on vector of persistence heat maps. + * This function is required by Topological_data_with_averages concept. + **/ + void compute_average(const std::vector<Persistence_heat_maps*>& to_average); + + /** + * A function to compute scalar product of persistence heat maps. + * The parameter of this function is a const reference to an object of a class Persistence_heat_maps. + * This function is required in Topological_data_with_scalar_product concept. + **/ + double compute_scalar_product(const Persistence_heat_maps& second_) const; + + // end of implementation of functions needed for concepts. + + /** + * The x-range of the persistence heat map. + **/ + std::pair<double, double> get_x_range() const { return std::make_pair(this->min_, this->max_); } + + /** + * The y-range of the persistence heat map. + **/ + std::pair<double, double> get_y_range() const { return this->get_x_range(); } + + protected: + // private methods + std::vector<std::vector<double> > check_and_initialize_maps(const std::vector<Persistence_heat_maps*>& maps); + size_t number_of_functions_for_vectorization; + size_t number_of_functions_for_projections_to_reals; + void construct(const std::vector<std::pair<double, double> >& intervals_, + std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), + + bool erase_below_diagonal = false, size_t number_of_pixels = 1000, + double min_ = std::numeric_limits<double>::max(), double max_ = std::numeric_limits<double>::max()); + + void set_up_parameters_for_basic_classes() { + this->number_of_functions_for_vectorization = 1; + this->number_of_functions_for_projections_to_reals = 1; + } + + // data + Scalling_of_kernels f; + bool erase_below_diagonal; + double min_; + double max_; + std::vector<std::vector<double> > heat_map; +}; + +// if min_ == max_, then the program is requested to set up the values itself based on persistence intervals +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::construct(const std::vector<std::pair<double, double> >& intervals_, + std::vector<std::vector<double> > filter, + bool erase_below_diagonal, size_t number_of_pixels, + double min_, double max_) { + bool dbg = false; + if (dbg) std::cerr << "Entering construct procedure \n"; + Scalling_of_kernels f; + this->f = f; + + if (dbg) std::cerr << "min and max passed to construct() procedure: " << min_ << " " << max_ << std::endl; + + if (min_ == max_) { + if (dbg) std::cerr << "min and max parameters will be determined based on intervals \n"; + // in this case, we want the program to set up the min_ and max_ values by itself. + min_ = std::numeric_limits<int>::max(); + max_ = -std::numeric_limits<int>::max(); + + for (size_t i = 0; i != intervals_.size(); ++i) { + if (intervals_[i].first < min_) min_ = intervals_[i].first; + if (intervals_[i].second > max_) max_ = intervals_[i].second; + } + // now we have the structure filled in, and moreover we know min_ and max_ values of the interval, so we know the + // range. + + // add some more space: + min_ -= fabs(max_ - min_) / 100; + max_ += fabs(max_ - min_) / 100; + } + + if (dbg) { + std::cerr << "min_ : " << min_ << std::endl; + std::cerr << "max_ : " << max_ << std::endl; + std::cerr << "number_of_pixels : " << number_of_pixels << std::endl; + getchar(); + } + + this->min_ = min_; + this->max_ = max_; + + // initialization of the structure heat_map + std::vector<std::vector<double> > heat_map_; + for (size_t i = 0; i != number_of_pixels; ++i) { + std::vector<double> v(number_of_pixels, 0); + heat_map_.push_back(v); + } + this->heat_map = heat_map_; + + if (dbg) std::cerr << "Done creating of the heat map, now we will fill in the structure \n"; + + for (size_t pt_nr = 0; pt_nr != intervals_.size(); ++pt_nr) { + // compute the value of intervals_[pt_nr] in the grid: + int x_grid = + static_cast<int>((intervals_[pt_nr].first - this->min_) / (this->max_ - this->min_) * number_of_pixels); + int y_grid = + static_cast<int>((intervals_[pt_nr].second - this->min_) / (this->max_ - this->min_) * number_of_pixels); + + if (dbg) { + std::cerr << "point : " << intervals_[pt_nr].first << " , " << intervals_[pt_nr].second << std::endl; + std::cerr << "x_grid : " << x_grid << std::endl; + std::cerr << "y_grid : " << y_grid << std::endl; + } + + // x_grid and y_grid gives a center of the kernel. We want to have its lower left corner. To get this, we need to + // shift x_grid and y_grid by a grid diameter. + x_grid -= filter.size() / 2; + y_grid -= filter.size() / 2; + // note that the numbers x_grid and y_grid may be negative. + + if (dbg) { + std::cerr << "After shift : \n"; + std::cerr << "x_grid : " << x_grid << std::endl; + std::cerr << "y_grid : " << y_grid << std::endl; + } + + double scaling_value = this->f(intervals_[pt_nr]); + + for (size_t i = 0; i != filter.size(); ++i) { + for (size_t j = 0; j != filter.size(); ++j) { + // if the point (x_grid+i,y_grid+j) is the correct point in the grid. + if (((x_grid + i) >= 0) && (x_grid + i < this->heat_map.size()) && ((y_grid + j) >= 0) && + (y_grid + j < this->heat_map.size())) { + if (dbg) { + std::cerr << y_grid + j << " " << x_grid + i << std::endl; + } + this->heat_map[y_grid + j][x_grid + i] += scaling_value * filter[i][j]; + if (dbg) { + std::cerr << "Position : (" << x_grid + i << "," << y_grid + j + << ") got increased by the value : " << filter[i][j] << std::endl; + } + } + } + } + } + + // now it remains to cut everything below diagonal if the user wants us to. + if (erase_below_diagonal) { + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = i; j != this->heat_map.size(); ++j) { + this->heat_map[i][j] = 0; + } + } + } +} // construct + +template <typename Scalling_of_kernels> +Persistence_heat_maps<Scalling_of_kernels>::Persistence_heat_maps( + const std::vector<std::pair<double, double> >& interval, std::vector<std::vector<double> > filter, + bool erase_below_diagonal, size_t number_of_pixels, double min_, double max_) { + this->construct(interval, filter, erase_below_diagonal, number_of_pixels, min_, max_); + this->set_up_parameters_for_basic_classes(); +} + +template <typename Scalling_of_kernels> +Persistence_heat_maps<Scalling_of_kernels>::Persistence_heat_maps(const char* filename, + std::vector<std::vector<double> > filter, + bool erase_below_diagonal, size_t number_of_pixels, + double min_, double max_, unsigned dimension) { + std::vector<std::pair<double, double> > intervals_; + if (dimension == std::numeric_limits<unsigned>::max()) { + intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->construct(intervals_, filter, erase_below_diagonal, number_of_pixels, min_, max_); + this->set_up_parameters_for_basic_classes(); +} + +template <typename Scalling_of_kernels> +std::vector<std::vector<double> > Persistence_heat_maps<Scalling_of_kernels>::check_and_initialize_maps( + const std::vector<Persistence_heat_maps*>& maps) { + // checking if all the heat maps are of the same size: + for (size_t i = 0; i != maps.size(); ++i) { + if (maps[i]->heat_map.size() != maps[0]->heat_map.size()) { + std::cerr << "Sizes of Persistence_heat_maps are not compatible. The program will terminate now \n"; + throw "Sizes of Persistence_heat_maps are not compatible. The program will terminate now \n"; + } + if (maps[i]->heat_map[0].size() != maps[0]->heat_map[0].size()) { + std::cerr << "Sizes of Persistence_heat_maps are not compatible. The program will terminate now \n"; + throw "Sizes of Persistence_heat_maps are not compatible. The program will terminate now \n"; + } + } + std::vector<std::vector<double> > heat_maps(maps[0]->heat_map.size()); + for (size_t i = 0; i != maps[0]->heat_map.size(); ++i) { + std::vector<double> v(maps[0]->heat_map[0].size(), 0); + heat_maps[i] = v; + } + return heat_maps; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::compute_median(const std::vector<Persistence_heat_maps*>& maps) { + std::vector<std::vector<double> > heat_maps = this->check_and_initialize_maps(maps); + + std::vector<double> to_compute_median(maps.size()); + for (size_t i = 0; i != heat_maps.size(); ++i) { + for (size_t j = 0; j != heat_maps[i].size(); ++j) { + for (size_t map_no = 0; map_no != maps.size(); ++map_no) { + to_compute_median[map_no] = maps[map_no]->heat_map[i][j]; + } + std::nth_element(to_compute_median.begin(), to_compute_median.begin() + to_compute_median.size() / 2, + to_compute_median.end()); + heat_maps[i][j] = to_compute_median[to_compute_median.size() / 2]; + } + } + this->heat_map = heat_maps; + this->min_ = maps[0]->min_; + this->max_ = maps[0]->max_; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::compute_mean(const std::vector<Persistence_heat_maps*>& maps) { + std::vector<std::vector<double> > heat_maps = this->check_and_initialize_maps(maps); + for (size_t i = 0; i != heat_maps.size(); ++i) { + for (size_t j = 0; j != heat_maps[i].size(); ++j) { + double mean = 0; + for (size_t map_no = 0; map_no != maps.size(); ++map_no) { + mean += maps[map_no]->heat_map[i][j]; + } + heat_maps[i][j] = mean / static_cast<double>(maps.size()); + } + } + this->heat_map = heat_maps; + this->min_ = maps[0]->min_; + this->max_ = maps[0]->max_; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::compute_percentage_of_active( + const std::vector<Persistence_heat_maps*>& maps, size_t cutoff) { + std::vector<std::vector<double> > heat_maps = this->check_and_initialize_maps(maps); + + for (size_t i = 0; i != heat_maps.size(); ++i) { + for (size_t j = 0; j != heat_maps[i].size(); ++j) { + size_t number_of_active_levels = 0; + for (size_t map_no = 0; map_no != maps.size(); ++map_no) { + if (maps[map_no]->heat_map[i][j]) number_of_active_levels++; + } + if (number_of_active_levels > cutoff) { + heat_maps[i][j] = number_of_active_levels; + } else { + heat_maps[i][j] = 0; + } + } + } + this->heat_map = heat_maps; + this->min_ = maps[0]->min_; + this->max_ = maps[0]->max_; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::plot(const char* filename) const { + std::ofstream out; + std::stringstream gnuplot_script; + gnuplot_script << filename << "_GnuplotScript"; + + out.open(gnuplot_script.str().c_str()); + out << "plot '-' matrix with image" << std::endl; + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + out << this->heat_map[i][j] << " "; + } + out << std::endl; + } + out.close(); + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::print_to_file(const char* filename) const { + std::ofstream out; + out.open(filename); + + // First we store this->min_ and this->max_ values: + out << this->min_ << " " << this->max_ << std::endl; + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + out << this->heat_map[i][j] << " "; + } + out << std::endl; + } + out.close(); +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::load_from_file(const char* filename) { + bool dbg = false; + + std::ifstream in; + in.open(filename); + + // checking if the file exist / if it was open. + if (!in.good()) { + std::cerr << "The file : " << filename << " do not exist. The program will now terminate \n"; + throw "The persistence landscape file do not exist. The program will now terminate \n"; + } + + // now we read the file one by one. + + in >> this->min_ >> this->max_; + if (dbg) { + std::cerr << "Reading the following values of min and max : " << this->min_ << " , " << this->max_ << std::endl; + } + + std::string temp; + std::getline(in, temp); + while (in.good()) { + std::getline(in, temp); + std::stringstream lineSS; + lineSS << temp; + + std::vector<double> line_of_heat_map; + while (lineSS.good()) { + double point; + + lineSS >> point; + line_of_heat_map.push_back(point); + if (dbg) { + std::cout << point << " "; + } + } + if (dbg) { + std::cout << std::endl; + getchar(); + } + + if (in.good()) this->heat_map.push_back(line_of_heat_map); + } + in.close(); + if (dbg) std::cout << "Done \n"; +} + +// Concretizations of virtual methods: +template <typename Scalling_of_kernels> +std::vector<double> Persistence_heat_maps<Scalling_of_kernels>::vectorize(int number_of_function) const { + // convert this->heat_map into one large vector: + size_t size_of_result = 0; + for (size_t i = 0; i != this->heat_map.size(); ++i) { + size_of_result += this->heat_map[i].size(); + } + + std::vector<double> result; + result.reserve(size_of_result); + + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + result.push_back(this->heat_map[i][j]); + } + } + + return result; +} + +template <typename Scalling_of_kernels> +double Persistence_heat_maps<Scalling_of_kernels>::distance(const Persistence_heat_maps& second, double power) const { + // first we need to check if (*this) and second are defined on the same domain and have the same dimensions: + if (!this->check_if_the_same(second)) { + std::cerr << "The persistence images are of non compatible sizes. We cannot therefore compute distance between " + "them. The program will now terminate"; + throw "The persistence images are of non compatible sizes. The program will now terminate"; + } + + // if we are here, we know that the two persistence images are defined on the same domain, so we can start computing + // their distances: + + double distance = 0; + if (power < std::numeric_limits<double>::max()) { + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + distance += pow(fabs(this->heat_map[i][j] - second.heat_map[i][j]), power); + } + } + } else { + // in this case, we compute max norm distance + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + if (distance < fabs(this->heat_map[i][j] - second.heat_map[i][j])) { + distance = fabs(this->heat_map[i][j] - second.heat_map[i][j]); + } + } + } + } + return distance; +} + +template <typename Scalling_of_kernels> +double Persistence_heat_maps<Scalling_of_kernels>::project_to_R(int number_of_function) const { + double result = 0; + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + result += this->heat_map[i][j]; + } + } + return result; +} + +template <typename Scalling_of_kernels> +void Persistence_heat_maps<Scalling_of_kernels>::compute_average( + const std::vector<Persistence_heat_maps*>& to_average) { + this->compute_mean(to_average); +} + +template <typename Scalling_of_kernels> +double Persistence_heat_maps<Scalling_of_kernels>::compute_scalar_product(const Persistence_heat_maps& second) const { + // first we need to check if (*this) and second are defined on the same domain and have the same dimensions: + if (!this->check_if_the_same(second)) { + std::cerr << "The persistence images are of non compatible sizes. We cannot therefore compute distance between " + "them. The program will now terminate"; + throw "The persistence images are of non compatible sizes. The program will now terminate"; + } + + // if we are here, we know that the two persistence images are defined on the same domain, so we can start computing + // their scalar product: + double scalar_prod = 0; + for (size_t i = 0; i != this->heat_map.size(); ++i) { + for (size_t j = 0; j != this->heat_map[i].size(); ++j) { + scalar_prod += this->heat_map[i][j] * second.heat_map[i][j]; + } + } + return scalar_prod; +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_HEAT_MAPS_H_ diff --git a/include/gudhi/Persistence_intervals.h b/include/gudhi/Persistence_intervals.h new file mode 100644 index 00000000..3d04d8b7 --- /dev/null +++ b/include/gudhi/Persistence_intervals.h @@ -0,0 +1,570 @@ +/* This file is part of the Gudhi hiLibrary. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PERSISTENCE_INTERVALS_H_ +#define PERSISTENCE_INTERVALS_H_ + +// gudhi include +#include <gudhi/read_persistence_from_file.h> + +// standard include +#include <limits> +#include <iostream> +#include <fstream> +#include <vector> +#include <algorithm> +#include <cmath> +#include <functional> +#include <utility> +#include <string> + +namespace Gudhi { +namespace Persistence_representations { + +/** + * This class implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, + *Real_valued_topological_data +**/ +class Persistence_intervals { + public: + /** + * This is a constructor of a class Persistence_intervals from a text file. Each line of the input file is supposed to + *contain two numbers of a type double (or convertible to double) + * representing the birth and the death of the persistence interval. If the pairs are not sorted so that birth <= + *death, then the constructor will sort then that way. + * * The second parameter of a constructor is a dimension of intervals to be read from a file. If your file contains + *only birth-death pairs, use the default value. + **/ + Persistence_intervals(const char* filename, unsigned dimension = std::numeric_limits<unsigned>::max()); + + /** + * This is a constructor of a class Persistence_intervals from a vector of pairs. Each pair is assumed to represent a + *persistence interval. We assume that the first elements of pairs + * are smaller or equal the second elements of pairs. + **/ + Persistence_intervals(const std::vector<std::pair<double, double> >& intervals); + + /** + * This procedure returns x-range of a given persistence diagram. + **/ + std::pair<double, double> get_x_range() const { + double min_ = std::numeric_limits<int>::max(); + double max_ = -std::numeric_limits<int>::max(); + for (size_t i = 0; i != this->intervals.size(); ++i) { + if (this->intervals[i].first < min_) min_ = this->intervals[i].first; + if (this->intervals[i].second > max_) max_ = this->intervals[i].second; + } + return std::make_pair(min_, max_); + } + + /** + * This procedure returns y-range of a given persistence diagram. + **/ + std::pair<double, double> get_y_range() const { + double min_ = std::numeric_limits<int>::max(); + double max_ = -std::numeric_limits<int>::max(); + for (size_t i = 0; i != this->intervals.size(); ++i) { + if (this->intervals[i].second < min_) min_ = this->intervals[i].second; + if (this->intervals[i].second > max_) max_ = this->intervals[i].second; + } + return std::make_pair(min_, max_); + } + + /** + * Procedure that compute the vector of lengths of the dominant (i.e. the longest) persistence intervals. The list is + *truncated at the parameter of the call where_to_cut (set by default to 100). + **/ + std::vector<double> length_of_dominant_intervals(size_t where_to_cut = 100) const; + + /** + * Procedure that compute the vector of the dominant (i.e. the longest) persistence intervals. The parameter of + *the procedure (set by default to 100) is the number of dominant intervals returned by the procedure. + **/ + std::vector<std::pair<double, double> > dominant_intervals(size_t where_to_cut = 100) const; + + /** + * Procedure to compute a histogram of interval's length. A histogram is a block plot. The number of blocks is + *determined by the first parameter of the function (set by default to 10). + * For the sake of argument let us assume that the length of the longest interval is 1 and the number of bins is + *10. In this case the i-th block correspond to a range between i-1/10 and i10. + * The vale of a block supported at the interval is the number of persistence intervals of a length between x_0 + *and x_1. + **/ + std::vector<size_t> histogram_of_lengths(size_t number_of_bins = 10) const; + + /** + * Based on a histogram of intervals lengths computed by the function histogram_of_lengths H the procedure below + *computes the cumulative histogram. The i-th position of the resulting histogram + * is the sum of values of H for the positions from 0 to i. + **/ + std::vector<size_t> cumulative_histogram_of_lengths(size_t number_of_bins = 10) const; + + /** + * In this procedure we assume that each barcode is a characteristic function of a hight equal to its length. The + *persistence diagram is a sum of such a functions. The procedure below construct a function being a + * sum of the characteristic functions of persistence intervals. The first two parameters are the range in which the + *function is to be computed and the last parameter is the number of bins in + * the discretization of the interval [_min,_max]. + **/ + std::vector<double> characteristic_function_of_diagram(double x_min, double x_max, size_t number_of_bins = 10) const; + + /** + * Cumulative version of the function characteristic_function_of_diagram + **/ + std::vector<double> cumulative_characteristic_function_of_diagram(double x_min, double x_max, + size_t number_of_bins = 10) const; + + /** + * Compute the function of persistence Betti numbers. The returned value is a vector of pair. First element of each + *pair is a place where persistence Betti numbers change. + * Second element of each pair is the value of Persistence Betti numbers at that point. + **/ + std::vector<std::pair<double, size_t> > compute_persistent_betti_numbers() const; + + /** + *This is a non optimal procedure that compute vector of distances from each point of diagram to its k-th nearest + *neighbor (k is a parameter of the program). The resulting vector is by default truncated to 10 + *elements (this value can be changed by using the second parameter of the program). The points are returned in order + *from the ones which are farthest away from their k-th nearest neighbors. + **/ + std::vector<double> k_n_n(size_t k, size_t where_to_cut = 10) const; + + /** +* Operator that send the diagram to a stream. +**/ + friend std::ostream& operator<<(std::ostream& out, const Persistence_intervals& intervals) { + for (size_t i = 0; i != intervals.intervals.size(); ++i) { + out << intervals.intervals[i].first << " " << intervals.intervals[i].second << std::endl; + } + return out; + } + + /** + * Generating gnuplot script to plot the interval. + **/ + void plot(const char* filename, double min_x = std::numeric_limits<double>::max(), + double max_x = std::numeric_limits<double>::max(), double min_y = std::numeric_limits<double>::max(), + double max_y = std::numeric_limits<double>::max()) const { + // this program create a gnuplot script file that allows to plot persistence diagram. + std::ofstream out; + + std::stringstream gnuplot_script; + gnuplot_script << filename << "_GnuplotScript"; + + out.open(gnuplot_script.str().c_str()); + + std::pair<double, double> min_max_values = this->get_x_range(); + if (min_x == max_x) { + out << "set xrange [" << min_max_values.first - 0.1 * (min_max_values.second - min_max_values.first) << " : " + << min_max_values.second + 0.1 * (min_max_values.second - min_max_values.first) << " ]" << std::endl; + out << "set yrange [" << min_max_values.first - 0.1 * (min_max_values.second - min_max_values.first) << " : " + << min_max_values.second + 0.1 * (min_max_values.second - min_max_values.first) << " ]" << std::endl; + } else { + out << "set xrange [" << min_x << " : " << max_x << " ]" << std::endl; + out << "set yrange [" << min_y << " : " << max_y << " ]" << std::endl; + } + out << "plot '-' using 1:2 notitle \"" << filename << "\", \\" << std::endl; + out << " '-' using 1:2 notitle with lp" << std::endl; + for (size_t i = 0; i != this->intervals.size(); ++i) { + out << this->intervals[i].first << " " << this->intervals[i].second << std::endl; + } + out << "EOF" << std::endl; + out << min_max_values.first - 0.1 * (min_max_values.second - min_max_values.first) << " " + << min_max_values.first - 0.1 * (min_max_values.second - min_max_values.first) << std::endl; + out << min_max_values.second + 0.1 * (min_max_values.second - min_max_values.first) << " " + << min_max_values.second + 0.1 * (min_max_values.second - min_max_values.first) << std::endl; + + out.close(); + + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; + } + + /** +* Return number of points in the diagram. +**/ + size_t size() const { return this->intervals.size(); } + + /** + * Return the persistence interval at the given position. Note that intervals are not sorted with respect to their + *lengths. + **/ + inline std::pair<double, double> operator[](size_t i) const { + if (i >= this->intervals.size()) throw("Index out of range! Operator [], one_d_gaussians class\n"); + return this->intervals[i]; + } + + // Implementations of functions for various concepts. + /** + * This is a simple function projecting the persistence intervals to a real number. The function we use here is a sum + *of squared lengths of intervals. It can be naturally interpreted as + * sum of step function, where the step hight it equal to the length of the interval. + * At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when + *using it, keep in mind that it + * will be most likely changed in the next versions. + **/ + double project_to_R(int number_of_function) const; + /** + * The function gives the number of possible projections to R. This function is required by the + *Real_valued_topological_data concept. + **/ + size_t number_of_projections_to_R() const { return this->number_of_functions_for_projections_to_reals; } + + /** + * Return a family of vectors obtained from the persistence diagram. The i-th vector consist of the length of i + *dominant persistence intervals. + **/ + std::vector<double> vectorize(int number_of_function) const { + return this->length_of_dominant_intervals(number_of_function); + } + /** + * This function return the number of functions that allows vectorization of a persistence diagram. It is required + *in a concept Vectorized_topological_data. + **/ + size_t number_of_vectorize_functions() const { return this->number_of_functions_for_vectorization; } + + // end of implementation of functions needed for concepts. + + // For visualization use output from vectorize and build histograms. + std::vector<std::pair<double, double> > output_for_visualization() { return this->intervals; } + + protected: + void set_up_numbers_of_functions_for_vectorization_and_projections_to_reals() { + // warning, this function can be only called after filling in the intervals vector. + this->number_of_functions_for_vectorization = this->intervals.size(); + this->number_of_functions_for_projections_to_reals = 1; + } + + std::vector<std::pair<double, double> > intervals; + size_t number_of_functions_for_vectorization; + size_t number_of_functions_for_projections_to_reals; +}; + +Persistence_intervals::Persistence_intervals(const char* filename, unsigned dimension) { + if (dimension == std::numeric_limits<unsigned>::max()) { + this->intervals = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + this->intervals = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} // Persistence_intervals + +Persistence_intervals::Persistence_intervals(const std::vector<std::pair<double, double> >& intervals_) + : intervals(intervals_) { + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} + +std::vector<double> Persistence_intervals::length_of_dominant_intervals(size_t where_to_cut) const { + std::vector<double> result(this->intervals.size()); + for (size_t i = 0; i != this->intervals.size(); ++i) { + result[i] = this->intervals[i].second - this->intervals[i].first; + } + std::sort(result.begin(), result.end(), std::greater<double>()); + + result.resize(std::min(where_to_cut, result.size())); + return result; +} // length_of_dominant_intervals + +bool compare(const std::pair<size_t, double>& first, const std::pair<size_t, double>& second) { + return first.second > second.second; +} + +std::vector<std::pair<double, double> > Persistence_intervals::dominant_intervals(size_t where_to_cut) const { + bool dbg = false; + std::vector<std::pair<size_t, double> > position_length_vector(this->intervals.size()); + for (size_t i = 0; i != this->intervals.size(); ++i) { + position_length_vector[i] = std::make_pair(i, this->intervals[i].second - this->intervals[i].first); + } + + std::sort(position_length_vector.begin(), position_length_vector.end(), compare); + + std::vector<std::pair<double, double> > result; + result.reserve(std::min(where_to_cut, position_length_vector.size())); + + for (size_t i = 0; i != std::min(where_to_cut, position_length_vector.size()); ++i) { + result.push_back(this->intervals[position_length_vector[i].first]); + if (dbg) + std::cerr << "Position : " << position_length_vector[i].first << " length : " << position_length_vector[i].second + << std::endl; + } + + return result; +} // dominant_intervals + +std::vector<size_t> Persistence_intervals::histogram_of_lengths(size_t number_of_bins) const { + bool dbg = false; + + if (dbg) std::cerr << "this->intervals.size() : " << this->intervals.size() << std::endl; + // first find the length of the longest interval: + double lengthOfLongest = 0; + for (size_t i = 0; i != this->intervals.size(); ++i) { + if ((this->intervals[i].second - this->intervals[i].first) > lengthOfLongest) { + lengthOfLongest = this->intervals[i].second - this->intervals[i].first; + } + } + + if (dbg) { + std::cerr << "lengthOfLongest : " << lengthOfLongest << std::endl; + } + + // this is a container we will use to store the resulting histogram + std::vector<size_t> result(number_of_bins + 1, 0); + + // for every persistence interval in our collection. + for (size_t i = 0; i != this->intervals.size(); ++i) { + // compute its length relative to the length of the dominant interval: + double relative_length_of_this_interval = (this->intervals[i].second - this->intervals[i].first) / lengthOfLongest; + + // given the relative length (between 0 and 1) compute to which bin should it contribute. + size_t position = (size_t)(relative_length_of_this_interval * number_of_bins); + + ++result[position]; + + if (dbg) { + std::cerr << "i : " << i << std::endl; + std::cerr << "Interval : [" << this->intervals[i].first << " , " << this->intervals[i].second << " ] \n"; + std::cerr << "relative_length_of_this_interval : " << relative_length_of_this_interval << std::endl; + std::cerr << "position : " << position << std::endl; + getchar(); + } + } + + if (dbg) { + for (size_t i = 0; i != result.size(); ++i) std::cerr << result[i] << std::endl; + } + return result; +} + +std::vector<size_t> Persistence_intervals::cumulative_histogram_of_lengths(size_t number_of_bins) const { + std::vector<size_t> histogram = this->histogram_of_lengths(number_of_bins); + std::vector<size_t> result(histogram.size()); + + size_t sum = 0; + for (size_t i = 0; i != histogram.size(); ++i) { + sum += histogram[i]; + result[i] = sum; + } + return result; +} + +std::vector<double> Persistence_intervals::characteristic_function_of_diagram(double x_min, double x_max, + size_t number_of_bins) const { + bool dbg = false; + + std::vector<double> result(number_of_bins); + std::fill(result.begin(), result.end(), 0); + + for (size_t i = 0; i != this->intervals.size(); ++i) { + if (dbg) { + std::cerr << "Interval : " << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; + } + + size_t beginIt = 0; + if (this->intervals[i].first < x_min) beginIt = 0; + if (this->intervals[i].first >= x_max) beginIt = result.size(); + if ((this->intervals[i].first > x_min) && (this->intervals[i].first < x_max)) { + beginIt = number_of_bins * (this->intervals[i].first - x_min) / (x_max - x_min); + } + + size_t endIt = 0; + if (this->intervals[i].second < x_min) endIt = 0; + if (this->intervals[i].second >= x_max) endIt = result.size(); + if ((this->intervals[i].second > x_min) && (this->intervals[i].second < x_max)) { + endIt = number_of_bins * (this->intervals[i].second - x_min) / (x_max - x_min); + } + + if (beginIt > endIt) { + beginIt = endIt; + } + + if (dbg) { + std::cerr << "beginIt : " << beginIt << std::endl; + std::cerr << "endIt : " << endIt << std::endl; + } + + for (size_t pos = beginIt; pos != endIt; ++pos) { + result[pos] += ((x_max - x_min) / static_cast<double>(number_of_bins)) * + (this->intervals[i].second - this->intervals[i].first); + } + if (dbg) { + std::cerr << "Result at this stage \n"; + for (size_t aa = 0; aa != result.size(); ++aa) { + std::cerr << result[aa] << " "; + } + std::cerr << std::endl; + } + } + return result; +} // characteristic_function_of_diagram + +std::vector<double> Persistence_intervals::cumulative_characteristic_function_of_diagram(double x_min, double x_max, + size_t number_of_bins) const { + std::vector<double> intsOfBars = this->characteristic_function_of_diagram(x_min, x_max, number_of_bins); + std::vector<double> result(intsOfBars.size()); + double sum = 0; + for (size_t i = 0; i != intsOfBars.size(); ++i) { + sum += intsOfBars[i]; + result[i] = sum; + } + return result; +} // cumulative_characteristic_function_of_diagram + +template <typename T> +bool compare_first_element_of_pair(const std::pair<T, bool>& f, const std::pair<T, bool>& s) { + return (f.first < s.first); +} + +std::vector<std::pair<double, size_t> > Persistence_intervals::compute_persistent_betti_numbers() const { + std::vector<std::pair<double, bool> > places_where_pbs_change(2 * this->intervals.size()); + + for (size_t i = 0; i != this->intervals.size(); ++i) { + places_where_pbs_change[2 * i] = std::make_pair(this->intervals[i].first, true); + places_where_pbs_change[2 * i + 1] = std::make_pair(this->intervals[i].second, false); + } + + std::sort(places_where_pbs_change.begin(), places_where_pbs_change.end(), compare_first_element_of_pair<double>); + size_t pbn = 0; + std::vector<std::pair<double, size_t> > pbns(places_where_pbs_change.size()); + for (size_t i = 0; i != places_where_pbs_change.size(); ++i) { + if (places_where_pbs_change[i].second == true) { + ++pbn; + } else { + --pbn; + } + pbns[i] = std::make_pair(places_where_pbs_change[i].first, pbn); + } + return pbns; +} + +inline double compute_euclidean_distance(const std::pair<double, double>& f, const std::pair<double, double>& s) { + return sqrt((f.first - s.first) * (f.first - s.first) + (f.second - s.second) * (f.second - s.second)); +} + +std::vector<double> Persistence_intervals::k_n_n(size_t k, size_t where_to_cut) const { + bool dbg = false; + if (dbg) { + std::cerr << "Here are the intervals : \n"; + for (size_t i = 0; i != this->intervals.size(); ++i) { + std::cerr << "[ " << this->intervals[i].first << " , " << this->intervals[i].second << "] \n"; + } + getchar(); + } + + std::vector<double> result; + // compute all to all distance between point in the diagram. Also, consider points in the diagonal with the infinite + // multiplicity. + std::vector<std::vector<double> > distances(this->intervals.size()); + for (size_t i = 0; i != this->intervals.size(); ++i) { + std::vector<double> aa(this->intervals.size()); + std::fill(aa.begin(), aa.end(), 0); + distances[i] = aa; + } + std::vector<double> distances_from_diagonal(this->intervals.size()); + std::fill(distances_from_diagonal.begin(), distances_from_diagonal.end(), 0); + + for (size_t i = 0; i != this->intervals.size(); ++i) { + std::vector<double> distancesFromI; + for (size_t j = i + 1; j != this->intervals.size(); ++j) { + distancesFromI.push_back(compute_euclidean_distance(this->intervals[i], this->intervals[j])); + } + // also add a distance from this guy to diagonal: + double distanceToDiagonal = compute_euclidean_distance( + this->intervals[i], std::make_pair(0.5 * (this->intervals[i].first + this->intervals[i].second), + 0.5 * (this->intervals[i].first + this->intervals[i].second))); + distances_from_diagonal[i] = distanceToDiagonal; + + if (dbg) { + std::cerr << "Here are the distances form the point : [" << this->intervals[i].first << " , " + << this->intervals[i].second << "] in the diagram \n"; + for (size_t aa = 0; aa != distancesFromI.size(); ++aa) { + std::cerr << "To : " << i + aa << " : " << distancesFromI[aa] << " "; + } + std::cerr << std::endl; + getchar(); + } + + // filling in the distances matrix: + for (size_t j = i + 1; j != this->intervals.size(); ++j) { + distances[i][j] = distancesFromI[j - i - 1]; + distances[j][i] = distancesFromI[j - i - 1]; + } + } + if (dbg) { + std::cerr << "Here is the distance matrix : \n"; + for (size_t i = 0; i != distances.size(); ++i) { + for (size_t j = 0; j != distances.size(); ++j) { + std::cerr << distances[i][j] << " "; + } + std::cerr << std::endl; + } + std::cerr << std::endl << std::endl << "And here are the distances to the diagonal : " << std::endl; + for (size_t i = 0; i != distances_from_diagonal.size(); ++i) { + std::cerr << distances_from_diagonal[i] << " "; + } + std::cerr << std::endl << std::endl; + getchar(); + } + + for (size_t i = 0; i != this->intervals.size(); ++i) { + std::vector<double> distancesFromI = distances[i]; + distancesFromI.push_back(distances_from_diagonal[i]); + + // sort it: + std::sort(distancesFromI.begin(), distancesFromI.end(), std::greater<double>()); + + if (k > distancesFromI.size()) { + if (dbg) { + std::cerr << "There are not enough neighbors in your set. We set the result to plus infty \n"; + } + result.push_back(std::numeric_limits<double>::max()); + } else { + if (distances_from_diagonal[i] > distancesFromI[k]) { + if (dbg) { + std::cerr << "The k-th n.n. is on a diagonal. Therefore we set up a distance to diagonal \n"; + } + result.push_back(distances_from_diagonal[i]); + } else { + result.push_back(distancesFromI[k]); + } + } + } + std::sort(result.begin(), result.end(), std::greater<double>()); + result.resize(std::min(result.size(), where_to_cut)); + + return result; +} + +double Persistence_intervals::project_to_R(int number_of_function) const { + double result = 0; + + for (size_t i = 0; i != this->intervals.size(); ++i) { + result += + (this->intervals[i].second - this->intervals[i].first) * (this->intervals[i].second - this->intervals[i].first); + } + + return result; +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_INTERVALS_H_ diff --git a/include/gudhi/Persistence_intervals_with_distances.h b/include/gudhi/Persistence_intervals_with_distances.h new file mode 100644 index 00000000..79908883 --- /dev/null +++ b/include/gudhi/Persistence_intervals_with_distances.h @@ -0,0 +1,63 @@ +/* This file is part of the Gudhi hiLibrary. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PERSISTENCE_INTERVALS_WITH_DISTANCES_H_ +#define PERSISTENCE_INTERVALS_WITH_DISTANCES_H_ + +#include <gudhi/Persistence_intervals.h> +#include <gudhi/Bottleneck.h> + +#include <limits> + +namespace Gudhi { +namespace Persistence_representations { + +class Persistence_intervals_with_distances : public Persistence_intervals { + public: + using Persistence_intervals::Persistence_intervals; + + /** + *Computations of distance from the current persistnce diagram to the persistence diagram given as a parameter of this + *function. + *The last but one parameter, power, is here in case we would like to compute p=th Wasserstein distance. At the + *moment, this method only implement Bottleneck distance, + * which is infinity Wasserstein distance. Therefore any power which is not the default std::numeric_limits< double + *>::max() will be ignored and an + * exception will be thrown. + * The last parameter, tolerance, it is an additiv error of the approimation, set by default to zero. + **/ + double distance(const Persistence_intervals_with_distances& second, double power = std::numeric_limits<double>::max(), + double tolerance = (std::numeric_limits<double>::min)()) const { + if (power >= std::numeric_limits<double>::max()) { + return Gudhi::persistence_diagram::bottleneck_distance(this->intervals, second.intervals, tolerance); + } else { + std::cerr << "At the moment Gudhi do not support Wasserstein distances. We only support Bottleneck distance." + << std::endl; + throw "At the moment Gudhi do not support Wasserstein distances. We only support Bottleneck distance."; + } + } +}; + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_INTERVALS_WITH_DISTANCES_H_ diff --git a/include/gudhi/Persistence_landscape.h b/include/gudhi/Persistence_landscape.h new file mode 100644 index 00000000..c5aa7867 --- /dev/null +++ b/include/gudhi/Persistence_landscape.h @@ -0,0 +1,1376 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PERSISTENCE_LANDSCAPE_H_ +#define PERSISTENCE_LANDSCAPE_H_ + +// gudhi include +#include <gudhi/read_persistence_from_file.h> +#include <gudhi/common_persistence_representations.h> + +// standard include +#include <cmath> +#include <iostream> +#include <vector> +#include <limits> +#include <fstream> +#include <sstream> +#include <algorithm> +#include <string> +#include <utility> +#include <functional> + +namespace Gudhi { +namespace Persistence_representations { + +// pre declaration +class Persistence_landscape; +template <typename operation> +Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscape& land1, + const Persistence_landscape& land2); + +/** + * \class Persistence_landscape Persistence_landscape.h gudhi/Persistence_landscape.h + * \brief A class implementing persistence landscapes data structures. + * + * \ingroup Persistence_representations + * + * \details + * For theoretical description, please consult <i>Statistical topological data analysis using persistence + * landscapes</i>\cite bubenik_landscapes_2015 , and for details of algorithms, + * <i>A persistence landscapes toolbox for topological statistics</i>\cite bubenik_dlotko_landscapes_2016. + * + * Persistence landscapes allow vectorization, computations of distances, computations of projections to Real, + * computations of averages and scalar products. Therefore they implement suitable interfaces. + * It implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, + * Real_valued_topological_data, Topological_data_with_averages, Topological_data_with_scalar_product + * + * Note that at the moment, due to rounding errors during the construction of persistence landscapes, elements which + * are different by 0.000005 are considered the same. If the scale in your persistence diagrams is comparable to this + * value, please rescale them before use this code. + * +**/ +class Persistence_landscape { + public: + /** + * Default constructor. + **/ + Persistence_landscape() { this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); } + + /** + * Constructor that takes as an input a vector of birth-death pairs. + **/ + Persistence_landscape(const std::vector<std::pair<double, double> >& p, + size_t number_of_levels = std::numeric_limits<size_t>::max()); + + /** + * Constructor that reads persistence intervals from file and creates persistence landscape. The format of the + *input file is the following: in each line we put birth-death pair. Last line is assumed + * to be empty. Even if the points within a line are not ordered, they will be ordered while the input is read. + **/ + Persistence_landscape(const char* filename, size_t dimension = std::numeric_limits<unsigned>::max(), + size_t number_of_levels = std::numeric_limits<size_t>::max()); + + /** + * This procedure loads a landscape from file. It erase all the data that was previously stored in this landscape. + **/ + void load_landscape_from_file(const char* filename); + + /** + * The procedure stores a landscape to a file. The file can be later used by a procedure load_landscape_from_file. + **/ + void print_to_file(const char* filename) const; + + /** + * This function compute integral of the landscape (defined formally as sum of integrals on R of all landscape + *functions) + **/ + double compute_integral_of_landscape() const; + + /** + * This function compute integral of the 'level'-level of a landscape. + **/ + double compute_integral_of_a_level_of_a_landscape(size_t level) const; + + /** + * This function compute integral of the landscape p-th power of a landscape (defined formally as sum of integrals + *on R of p-th powers of all landscape functions) + **/ + double compute_integral_of_landscape(double p) const; // this function compute integral of p-th power of landscape. + + /** + * A function that computes the value of a landscape at a given point. The parameters of the function are: unsigned + *level and double x. + * The procedure will compute the value of the level-landscape at the point x. + **/ + double compute_value_at_a_given_point(unsigned level, double x) const; + + /** + * Writing landscape into a stream. A i-th level landscape starts with a string "lambda_i". Then the discontinuity + *points of the landscapes follows. + * Shall those points be joined with lines, we will obtain the i-th landscape function. + **/ + friend std::ostream& operator<<(std::ostream& out, Persistence_landscape& land); + + template <typename operation> + friend Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscape& land1, + const Persistence_landscape& land2); + + /** + *\private A function that compute sum of two landscapes. + **/ + friend Persistence_landscape add_two_landscapes(const Persistence_landscape& land1, + const Persistence_landscape& land2) { + return operation_on_pair_of_landscapes<std::plus<double> >(land1, land2); + } + + /** + *\private A function that compute difference of two landscapes. + **/ + friend Persistence_landscape subtract_two_landscapes(const Persistence_landscape& land1, + const Persistence_landscape& land2) { + return operation_on_pair_of_landscapes<std::minus<double> >(land1, land2); + } + + /** + * An operator +, that compute sum of two landscapes. + **/ + friend Persistence_landscape operator+(const Persistence_landscape& first, const Persistence_landscape& second) { + return add_two_landscapes(first, second); + } + + /** + * An operator -, that compute difference of two landscapes. + **/ + friend Persistence_landscape operator-(const Persistence_landscape& first, const Persistence_landscape& second) { + return subtract_two_landscapes(first, second); + } + + /** + * An operator * that allows multiplication of a landscape by a real number. + **/ + friend Persistence_landscape operator*(const Persistence_landscape& first, double con) { + return first.multiply_lanscape_by_real_number_not_overwrite(con); + } + + /** + * An operator * that allows multiplication of a landscape by a real number (order of parameters swapped). + **/ + friend Persistence_landscape operator*(double con, const Persistence_landscape& first) { + return first.multiply_lanscape_by_real_number_not_overwrite(con); + } + + /** + * Operator +=. The second parameter is persistence landscape. + **/ + Persistence_landscape operator+=(const Persistence_landscape& rhs) { + *this = *this + rhs; + return *this; + } + + /** + * Operator -=. The second parameter is a persistence landscape. + **/ + Persistence_landscape operator-=(const Persistence_landscape& rhs) { + *this = *this - rhs; + return *this; + } + + /** + * Operator *=. The second parameter is a real number by which the y values of all landscape functions are multiplied. + *The x-values remain unchanged. + **/ + Persistence_landscape operator*=(double x) { + *this = *this * x; + return *this; + } + + /** + * Operator /=. The second parameter is a real number. + **/ + Persistence_landscape operator/=(double x) { + if (x == 0) throw("In operator /=, division by 0. Program terminated."); + *this = *this * (1 / x); + return *this; + } + + /** + * An operator to compare two persistence landscapes. + **/ + bool operator==(const Persistence_landscape& rhs) const; + + /** + * An operator to compare two persistence landscapes. + **/ + bool operator!=(const Persistence_landscape& rhs) const { return !((*this) == rhs); } + + /** + * Computations of maximum (y) value of landscape. + **/ + double compute_maximum() const { + double maxValue = 0; + if (this->land.size()) { + maxValue = -std::numeric_limits<int>::max(); + for (size_t i = 0; i != this->land[0].size(); ++i) { + if (this->land[0][i].second > maxValue) maxValue = this->land[0][i].second; + } + } + return maxValue; + } + + /** + *\private Computations of minimum (y) value of landscape. + **/ + double compute_minimum() const { + double minValue = 0; + if (this->land.size()) { + minValue = std::numeric_limits<int>::max(); + for (size_t i = 0; i != this->land[0].size(); ++i) { + if (this->land[0][i].second < minValue) minValue = this->land[0][i].second; + } + } + return minValue; + } + + /** + *\private Computations of a \f$L^i\f$ norm of landscape, where i is the input parameter. + **/ + double compute_norm_of_landscape(double i) { + Persistence_landscape l; + if (i < std::numeric_limits<double>::max()) { + return compute_distance_of_landscapes(*this, l, i); + } else { + return compute_max_norm_distance_of_landscapes(*this, l); + } + } + + /** + * An operator to compute the value of a landscape in the level 'level' at the argument 'x'. + **/ + double operator()(unsigned level, double x) const { return this->compute_value_at_a_given_point(level, x); } + + /** + *\private Computations of \f$L^{\infty}\f$ distance between two landscapes. + **/ + friend double compute_max_norm_distance_of_landscapes(const Persistence_landscape& first, + const Persistence_landscape& second); + + /** + *\private Computations of \f$L^{p}\f$ distance between two landscapes. p is the parameter of the procedure. + **/ + friend double compute_distance_of_landscapes(const Persistence_landscape& first, const Persistence_landscape& second, + double p); + + /** + * Function to compute absolute value of a PL function. The representation of persistence landscapes allow to store + *general PL-function. When computing distance between two landscapes, we compute difference between + * them. In this case, a general PL-function with negative value can appear as a result. Then in order to compute + *distance, we need to take its absolute value. This is the purpose of this procedure. + **/ + Persistence_landscape abs(); + + Persistence_landscape* new_abs(); + + /** + * Computes the number of landscape functions. + **/ + size_t size() const { return this->land.size(); } + + /** + * Compute maximal value of lambda-level landscape. + **/ + double find_max(unsigned lambda) const; + + /** + *\private Function to compute inner (scalar) product of two landscapes. + **/ + friend double compute_inner_product(const Persistence_landscape& l1, const Persistence_landscape& l2); + + // Implementations of functions for various concepts. + + /** + * The number of projections to R is defined to the number of nonzero landscape functions. I-th projection is an + *integral of i-th landscape function over whole R. + * This function is required by the Real_valued_topological_data concept. + * At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when + *using it, keep in mind that it + * will be most likely changed in the next versions. + **/ + double project_to_R(int number_of_function) const { + return this->compute_integral_of_a_level_of_a_landscape((size_t)number_of_function); + } + + /** + * The function gives the number of possible projections to R. This function is required by the + *Real_valued_topological_data concept. + **/ + size_t number_of_projections_to_R() const { return this->number_of_functions_for_projections_to_reals; } + + /** + * This function produce a vector of doubles based on a landscape. It is required in a concept + * Vectorized_topological_data + */ + std::vector<double> vectorize(int number_of_function) const { + // TODO(PD) think of something smarter over here + std::vector<double> v; + if ((size_t)number_of_function > this->land.size()) { + return v; + } + v.reserve(this->land[number_of_function].size()); + for (size_t i = 0; i != this->land[number_of_function].size(); ++i) { + v.push_back(this->land[number_of_function][i].second); + } + return v; + } + /** + * This function return the number of functions that allows vectorization of persistence landscape. It is required in + *a concept Vectorized_topological_data. + **/ + size_t number_of_vectorize_functions() const { return this->number_of_functions_for_vectorization; } + + /** + * A function to compute averaged persistence landscape, based on vector of persistence landscapes. + * This function is required by Topological_data_with_averages concept. + **/ + void compute_average(const std::vector<Persistence_landscape*>& to_average) { + bool dbg = false; + + if (dbg) { + std::cerr << "to_average.size() : " << to_average.size() << std::endl; + } + + std::vector<Persistence_landscape*> nextLevelMerge(to_average.size()); + for (size_t i = 0; i != to_average.size(); ++i) { + nextLevelMerge[i] = to_average[i]; + } + bool is_this_first_level = true; // in the loop, we will create dynamically a number of intermediate complexes. We + // have to clean that up, but we cannot erase the initial landscapes we have + // to average. In this case, we simply check if the nextLevelMerge are the input landscapes or the ones created in + // that loop by using this extra variable. + + while (nextLevelMerge.size() != 1) { + if (dbg) { + std::cerr << "nextLevelMerge.size() : " << nextLevelMerge.size() << std::endl; + } + std::vector<Persistence_landscape*> nextNextLevelMerge; + nextNextLevelMerge.reserve(to_average.size()); + for (size_t i = 0; i < nextLevelMerge.size(); i = i + 2) { + if (dbg) { + std::cerr << "i : " << i << std::endl; + } + Persistence_landscape* l = new Persistence_landscape; + if (i + 1 != nextLevelMerge.size()) { + (*l) = (*nextLevelMerge[i]) + (*nextLevelMerge[i + 1]); + } else { + (*l) = *nextLevelMerge[i]; + } + nextNextLevelMerge.push_back(l); + } + if (dbg) { + std::cerr << "After this iteration \n"; + getchar(); + } + + if (!is_this_first_level) { + // deallocate the memory if the vector nextLevelMerge do not consist of the initial landscapes + for (size_t i = 0; i != nextLevelMerge.size(); ++i) { + delete nextLevelMerge[i]; + } + } + is_this_first_level = false; + nextLevelMerge.swap(nextNextLevelMerge); + } + (*this) = (*nextLevelMerge[0]); + (*this) *= 1 / static_cast<double>(to_average.size()); + } + + /** + * A function to compute distance between persistence landscape. + * The parameter of this function is a Persistence_landscape. + * This function is required in Topological_data_with_distances concept. + * For max norm distance, set power to std::numeric_limits<double>::max() + **/ + double distance(const Persistence_landscape& second, double power = 1) const { + if (power < std::numeric_limits<double>::max()) { + return compute_distance_of_landscapes(*this, second, power); + } else { + return compute_max_norm_distance_of_landscapes(*this, second); + } + } + + /** + * A function to compute scalar product of persistence landscapes. + * The parameter of this function is a Persistence_landscape. + * This function is required in Topological_data_with_scalar_product concept. + **/ + double compute_scalar_product(const Persistence_landscape& second) const { + return compute_inner_product((*this), second); + } + // end of implementation of functions needed for concepts. + + /** + * This procedure returns y-range of a given level persistence landscape. If a default value is used, the y-range + * of 0th level landscape is given (and this range contains the ranges of all other landscapes). + **/ + std::pair<double, double> get_y_range(size_t level = 0) const { + std::pair<double, double> result; + if (level < this->land.size()) { + double maxx = this->compute_maximum(); + double minn = this->compute_minimum(); + result = std::make_pair(minn, maxx); + } else { + result = std::make_pair(0, 0); + } + return result; + } + + // a function used to create a gnuplot script for visualization of landscapes + void plot(const char* filename, double xRangeBegin = std::numeric_limits<double>::max(), + double xRangeEnd = std::numeric_limits<double>::max(), + double yRangeBegin = std::numeric_limits<double>::max(), + double yRangeEnd = std::numeric_limits<double>::max(), int from = std::numeric_limits<int>::max(), + int to = std::numeric_limits<int>::max()); + + protected: + std::vector<std::vector<std::pair<double, double> > > land; + size_t number_of_functions_for_vectorization; + size_t number_of_functions_for_projections_to_reals; + + void construct_persistence_landscape_from_barcode(const std::vector<std::pair<double, double> >& p, + size_t number_of_levels = std::numeric_limits<size_t>::max()); + Persistence_landscape multiply_lanscape_by_real_number_not_overwrite(double x) const; + void multiply_lanscape_by_real_number_overwrite(double x); + friend double compute_maximal_distance_non_symmetric(const Persistence_landscape& pl1, + const Persistence_landscape& pl2); + + void set_up_numbers_of_functions_for_vectorization_and_projections_to_reals() { + // warning, this function can be only called after filling in the intervals vector. + this->number_of_functions_for_vectorization = this->land.size(); + this->number_of_functions_for_projections_to_reals = this->land.size(); + } +}; + +Persistence_landscape::Persistence_landscape(const char* filename, size_t dimension, size_t number_of_levels) { + std::vector<std::pair<double, double> > barcode; + if (dimension < std::numeric_limits<double>::max()) { + barcode = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } else { + barcode = read_persistence_intervals_in_one_dimension_from_file(filename); + } + this->construct_persistence_landscape_from_barcode(barcode, number_of_levels); + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} + +bool operatorEqualDbg = false; +bool Persistence_landscape::operator==(const Persistence_landscape& rhs) const { + if (this->land.size() != rhs.land.size()) { + if (operatorEqualDbg) std::cerr << "1\n"; + return false; + } + for (size_t level = 0; level != this->land.size(); ++level) { + if (this->land[level].size() != rhs.land[level].size()) { + if (operatorEqualDbg) std::cerr << "this->land[level].size() : " << this->land[level].size() << "\n"; + if (operatorEqualDbg) std::cerr << "rhs.land[level].size() : " << rhs.land[level].size() << "\n"; + if (operatorEqualDbg) std::cerr << "2\n"; + return false; + } + for (size_t i = 0; i != this->land[level].size(); ++i) { + if (!(almost_equal(this->land[level][i].first, rhs.land[level][i].first) && + almost_equal(this->land[level][i].second, rhs.land[level][i].second))) { + if (operatorEqualDbg) + std::cerr << "this->land[level][i] : " << this->land[level][i].first << " " << this->land[level][i].second + << "\n"; + if (operatorEqualDbg) + std::cerr << "rhs.land[level][i] : " << rhs.land[level][i].first << " " << rhs.land[level][i].second << "\n"; + if (operatorEqualDbg) std::cerr << "3\n"; + return false; + } + } + } + return true; +} + +Persistence_landscape::Persistence_landscape(const std::vector<std::pair<double, double> >& p, + size_t number_of_levels) { + this->construct_persistence_landscape_from_barcode(p, number_of_levels); + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} + +void Persistence_landscape::construct_persistence_landscape_from_barcode( + const std::vector<std::pair<double, double> >& p, size_t number_of_levels) { + bool dbg = false; + if (dbg) { + std::cerr << "Persistence_landscape::Persistence_landscape( const std::vector< std::pair< double , double > >& p )" + << std::endl; + } + + // this is a general algorithm to construct persistence landscapes. + std::vector<std::pair<double, double> > bars; + bars.insert(bars.begin(), p.begin(), p.end()); + std::sort(bars.begin(), bars.end(), compare_points_sorting); + + if (dbg) { + std::cerr << "Bars : \n"; + for (size_t i = 0; i != bars.size(); ++i) { + std::cerr << bars[i].first << " " << bars[i].second << "\n"; + } + getchar(); + } + + std::vector<std::pair<double, double> > characteristicPoints(p.size()); + for (size_t i = 0; i != bars.size(); ++i) { + characteristicPoints[i] = + std::make_pair((bars[i].first + bars[i].second) / 2.0, (bars[i].second - bars[i].first) / 2.0); + } + std::vector<std::vector<std::pair<double, double> > > Persistence_landscape; + size_t number_of_levels_in_the_landscape = 0; + while (!characteristicPoints.empty()) { + if (dbg) { + for (size_t i = 0; i != characteristicPoints.size(); ++i) { + std::cout << "(" << characteristicPoints[i].first << " " << characteristicPoints[i].second << ")\n"; + } + std::cin.ignore(); + } + + std::vector<std::pair<double, double> > lambda_n; + lambda_n.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); + lambda_n.push_back(std::make_pair(minus_length(characteristicPoints[0]), 0)); + lambda_n.push_back(characteristicPoints[0]); + + if (dbg) { + std::cerr << "1 Adding to lambda_n : (" << -std::numeric_limits<int>::max() << " " << 0 << ") , (" + << minus_length(characteristicPoints[0]) << " " << 0 << ") , (" << characteristicPoints[0].first << " " + << characteristicPoints[0].second << ") \n"; + } + + size_t i = 1; + std::vector<std::pair<double, double> > newCharacteristicPoints; + while (i < characteristicPoints.size()) { + size_t p = 1; + if ((minus_length(characteristicPoints[i]) >= minus_length(lambda_n[lambda_n.size() - 1])) && + (birth_plus_deaths(characteristicPoints[i]) > birth_plus_deaths(lambda_n[lambda_n.size() - 1]))) { + if (minus_length(characteristicPoints[i]) < birth_plus_deaths(lambda_n[lambda_n.size() - 1])) { + std::pair<double, double> point = std::make_pair( + (minus_length(characteristicPoints[i]) + birth_plus_deaths(lambda_n[lambda_n.size() - 1])) / 2, + (birth_plus_deaths(lambda_n[lambda_n.size() - 1]) - minus_length(characteristicPoints[i])) / 2); + lambda_n.push_back(point); + if (dbg) { + std::cerr << "2 Adding to lambda_n : (" << point.first << " " << point.second << ")\n"; + } + + if (dbg) { + std::cerr << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " + << characteristicPoints[i + p].second << "\n"; + std::cerr << "point : " << point.first << " " << point.second << "\n"; + getchar(); + } + + while ((i + p < characteristicPoints.size()) && + (almost_equal(minus_length(point), minus_length(characteristicPoints[i + p]))) && + (birth_plus_deaths(point) <= birth_plus_deaths(characteristicPoints[i + p]))) { + newCharacteristicPoints.push_back(characteristicPoints[i + p]); + if (dbg) { + std::cerr << "3.5 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " + << characteristicPoints[i + p].second << ")\n"; + getchar(); + } + ++p; + } + + newCharacteristicPoints.push_back(point); + if (dbg) { + std::cerr << "4 Adding to newCharacteristicPoints : (" << point.first << " " << point.second << ")\n"; + } + + while ((i + p < characteristicPoints.size()) && + (minus_length(point) <= minus_length(characteristicPoints[i + p])) && + (birth_plus_deaths(point) >= birth_plus_deaths(characteristicPoints[i + p]))) { + newCharacteristicPoints.push_back(characteristicPoints[i + p]); + if (dbg) { + std::cerr << "characteristicPoints[i+p] : " << characteristicPoints[i + p].first << " " + << characteristicPoints[i + p].second << "\n"; + std::cerr << "point : " << point.first << " " << point.second << "\n"; + std::cerr << "characteristicPoints[i+p] birth and death : " << minus_length(characteristicPoints[i + p]) + << " , " << birth_plus_deaths(characteristicPoints[i + p]) << "\n"; + std::cerr << "point birth and death : " << minus_length(point) << " , " << birth_plus_deaths(point) + << "\n"; + + std::cerr << "3 Adding to newCharacteristicPoints : (" << characteristicPoints[i + p].first << " " + << characteristicPoints[i + p].second << ")\n"; + getchar(); + } + ++p; + } + + } else { + lambda_n.push_back(std::make_pair(birth_plus_deaths(lambda_n[lambda_n.size() - 1]), 0)); + lambda_n.push_back(std::make_pair(minus_length(characteristicPoints[i]), 0)); + if (dbg) { + std::cerr << "5 Adding to lambda_n : (" << birth_plus_deaths(lambda_n[lambda_n.size() - 1]) << " " << 0 + << ")\n"; + std::cerr << "5 Adding to lambda_n : (" << minus_length(characteristicPoints[i]) << " " << 0 << ")\n"; + } + } + lambda_n.push_back(characteristicPoints[i]); + if (dbg) { + std::cerr << "6 Adding to lambda_n : (" << characteristicPoints[i].first << " " + << characteristicPoints[i].second << ")\n"; + } + } else { + newCharacteristicPoints.push_back(characteristicPoints[i]); + if (dbg) { + std::cerr << "7 Adding to newCharacteristicPoints : (" << characteristicPoints[i].first << " " + << characteristicPoints[i].second << ")\n"; + } + } + i = i + p; + } + lambda_n.push_back(std::make_pair(birth_plus_deaths(lambda_n[lambda_n.size() - 1]), 0)); + lambda_n.push_back(std::make_pair(std::numeric_limits<int>::max(), 0)); + + characteristicPoints = newCharacteristicPoints; + + lambda_n.erase(std::unique(lambda_n.begin(), lambda_n.end()), lambda_n.end()); + this->land.push_back(lambda_n); + + ++number_of_levels_in_the_landscape; + if (number_of_levels == number_of_levels_in_the_landscape) { + break; + } + } +} + +// this function find maximum of lambda_n +double Persistence_landscape::find_max(unsigned lambda) const { + if (this->land.size() < lambda) return 0; + double maximum = -std::numeric_limits<int>::max(); + for (size_t i = 0; i != this->land[lambda].size(); ++i) { + if (this->land[lambda][i].second > maximum) maximum = this->land[lambda][i].second; + } + return maximum; +} + +double Persistence_landscape::compute_integral_of_landscape() const { + double result = 0; + for (size_t i = 0; i != this->land.size(); ++i) { + for (size_t nr = 2; nr != this->land[i].size() - 1; ++nr) { + // it suffices to compute every planar integral and then sum them up for each lambda_n + result += 0.5 * (this->land[i][nr].first - this->land[i][nr - 1].first) * + (this->land[i][nr].second + this->land[i][nr - 1].second); + } + } + return result; +} + +double Persistence_landscape::compute_integral_of_a_level_of_a_landscape(size_t level) const { + double result = 0; + if (level >= this->land.size()) { + // this landscape function is constantly equal 0, so is the integral. + return result; + } + // also negative landscapes are assumed to be zero. + if (level < 0) return 0; + + for (size_t nr = 2; nr != this->land[level].size() - 1; ++nr) { + // it suffices to compute every planar integral and then sum them up for each lambda_n + result += 0.5 * (this->land[level][nr].first - this->land[level][nr - 1].first) * + (this->land[level][nr].second + this->land[level][nr - 1].second); + } + + return result; +} + +double Persistence_landscape::compute_integral_of_landscape(double p) const { + bool dbg = false; + double result = 0; + for (size_t i = 0; i != this->land.size(); ++i) { + for (size_t nr = 2; nr != this->land[i].size() - 1; ++nr) { + if (dbg) std::cout << "nr : " << nr << "\n"; + // In this interval, the landscape has a form f(x) = ax+b. We want to compute integral of (ax+b)^p = 1/a * + // (ax+b)^{p+1}/(p+1) + std::pair<double, double> coef = compute_parameters_of_a_line(this->land[i][nr], this->land[i][nr - 1]); + double a = coef.first; + double b = coef.second; + + if (dbg) + std::cout << "(" << this->land[i][nr].first << "," << this->land[i][nr].second << ") , " + << this->land[i][nr - 1].first << "," << this->land[i][nr].second << ")" << std::endl; + if (this->land[i][nr].first == this->land[i][nr - 1].first) continue; + if (a != 0) { + result += 1 / (a * (p + 1)) * + (pow((a * this->land[i][nr].first + b), p + 1) - pow((a * this->land[i][nr - 1].first + b), p + 1)); + } else { + result += (this->land[i][nr].first - this->land[i][nr - 1].first) * (pow(this->land[i][nr].second, p)); + } + if (dbg) { + std::cout << "a : " << a << " , b : " << b << std::endl; + std::cout << "result : " << result << std::endl; + } + } + } + return result; +} + +// this is O(log(n)) algorithm, where n is number of points in this->land. +double Persistence_landscape::compute_value_at_a_given_point(unsigned level, double x) const { + bool compute_value_at_a_given_pointDbg = false; + // in such a case lambda_level = 0. + if (level > this->land.size()) return 0; + + // we know that the points in this->land[level] are ordered according to x coordinate. Therefore, we can find the + // point by using bisection: + unsigned coordBegin = 1; + unsigned coordEnd = this->land[level].size() - 2; + + if (compute_value_at_a_given_pointDbg) { + std::cerr << "Here \n"; + std::cerr << "x : " << x << "\n"; + std::cerr << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; + std::cerr << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; + } + + // in this case x is outside the support of the landscape, therefore the value of the landscape is 0. + if (x <= this->land[level][coordBegin].first) return 0; + if (x >= this->land[level][coordEnd].first) return 0; + + if (compute_value_at_a_given_pointDbg) std::cerr << "Entering to the while loop \n"; + + while (coordBegin + 1 != coordEnd) { + if (compute_value_at_a_given_pointDbg) { + std::cerr << "coordBegin : " << coordBegin << "\n"; + std::cerr << "coordEnd : " << coordEnd << "\n"; + std::cerr << "this->land[level][coordBegin].first : " << this->land[level][coordBegin].first << "\n"; + std::cerr << "this->land[level][coordEnd].first : " << this->land[level][coordEnd].first << "\n"; + } + + unsigned newCord = (unsigned)floor((coordEnd + coordBegin) / 2.0); + + if (compute_value_at_a_given_pointDbg) { + std::cerr << "newCord : " << newCord << "\n"; + std::cerr << "this->land[level][newCord].first : " << this->land[level][newCord].first << "\n"; + std::cin.ignore(); + } + + if (this->land[level][newCord].first <= x) { + coordBegin = newCord; + if (this->land[level][newCord].first == x) return this->land[level][newCord].second; + } else { + coordEnd = newCord; + } + } + + if (compute_value_at_a_given_pointDbg) { + std::cout << "x : " << x << " is between : " << this->land[level][coordBegin].first << " a " + << this->land[level][coordEnd].first << "\n"; + std::cout << "the y coords are : " << this->land[level][coordBegin].second << " a " + << this->land[level][coordEnd].second << "\n"; + std::cerr << "coordBegin : " << coordBegin << "\n"; + std::cerr << "coordEnd : " << coordEnd << "\n"; + std::cin.ignore(); + } + return function_value(this->land[level][coordBegin], this->land[level][coordEnd], x); +} + +std::ostream& operator<<(std::ostream& out, Persistence_landscape& land) { + for (size_t level = 0; level != land.land.size(); ++level) { + out << "Lambda_" << level << ":" << std::endl; + for (size_t i = 0; i != land.land[level].size(); ++i) { + if (land.land[level][i].first == -std::numeric_limits<int>::max()) { + out << "-inf"; + } else { + if (land.land[level][i].first == std::numeric_limits<int>::max()) { + out << "+inf"; + } else { + out << land.land[level][i].first; + } + } + out << " , " << land.land[level][i].second << std::endl; + } + } + return out; +} + +void Persistence_landscape::multiply_lanscape_by_real_number_overwrite(double x) { + for (size_t dim = 0; dim != this->land.size(); ++dim) { + for (size_t i = 0; i != this->land[dim].size(); ++i) { + this->land[dim][i].second *= x; + } + } +} + +bool AbsDbg = false; +Persistence_landscape Persistence_landscape::abs() { + Persistence_landscape result; + for (size_t level = 0; level != this->land.size(); ++level) { + if (AbsDbg) { + std::cout << "level: " << level << std::endl; + } + std::vector<std::pair<double, double> > lambda_n; + lambda_n.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); + for (size_t i = 1; i != this->land[level].size(); ++i) { + if (AbsDbg) { + std::cout << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " + << this->land[level][i].second << std::endl; + } + // if a line segment between this->land[level][i-1] and this->land[level][i] crosses the x-axis, then we have to + // add one landscape point t o result + if ((this->land[level][i - 1].second) * (this->land[level][i].second) < 0) { + double zero = + find_zero_of_a_line_segment_between_those_two_points(this->land[level][i - 1], this->land[level][i]); + + lambda_n.push_back(std::make_pair(zero, 0)); + lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); + if (AbsDbg) { + std::cout << "Adding pair : (" << zero << ",0)" << std::endl; + std::cout << "In the same step adding pair : (" << this->land[level][i].first << "," + << fabs(this->land[level][i].second) << ") " << std::endl; + std::cin.ignore(); + } + } else { + lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); + if (AbsDbg) { + std::cout << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) + << ") " << std::endl; + std::cin.ignore(); + } + } + } + result.land.push_back(lambda_n); + } + return result; +} + +Persistence_landscape* Persistence_landscape::new_abs() { + Persistence_landscape* result = new Persistence_landscape(*this); + for (size_t level = 0; level != this->land.size(); ++level) { + if (AbsDbg) { + std::cout << "level: " << level << std::endl; + } + std::vector<std::pair<double, double> > lambda_n; + lambda_n.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); + for (size_t i = 1; i != this->land[level].size(); ++i) { + if (AbsDbg) { + std::cout << "this->land[" << level << "][" << i << "] : " << this->land[level][i].first << " " + << this->land[level][i].second << std::endl; + } + // if a line segment between this->land[level][i-1] and this->land[level][i] crosses the x-axis, then we have to + // add one landscape point t o result + if ((this->land[level][i - 1].second) * (this->land[level][i].second) < 0) { + double zero = + find_zero_of_a_line_segment_between_those_two_points(this->land[level][i - 1], this->land[level][i]); + + lambda_n.push_back(std::make_pair(zero, 0)); + lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); + if (AbsDbg) { + std::cout << "Adding pair : (" << zero << ",0)" << std::endl; + std::cout << "In the same step adding pair : (" << this->land[level][i].first << "," + << fabs(this->land[level][i].second) << ") " << std::endl; + std::cin.ignore(); + } + } else { + lambda_n.push_back(std::make_pair(this->land[level][i].first, fabs(this->land[level][i].second))); + if (AbsDbg) { + std::cout << "Adding pair : (" << this->land[level][i].first << "," << fabs(this->land[level][i].second) + << ") " << std::endl; + std::cin.ignore(); + } + } + } + result->land.push_back(lambda_n); + } + return result; +} + +Persistence_landscape Persistence_landscape::multiply_lanscape_by_real_number_not_overwrite(double x) const { + std::vector<std::vector<std::pair<double, double> > > result(this->land.size()); + for (size_t dim = 0; dim != this->land.size(); ++dim) { + std::vector<std::pair<double, double> > lambda_dim(this->land[dim].size()); + for (size_t i = 0; i != this->land[dim].size(); ++i) { + lambda_dim[i] = std::make_pair(this->land[dim][i].first, x * this->land[dim][i].second); + } + result[dim] = lambda_dim; + } + Persistence_landscape res; + // CHANGE + // res.land = result; + res.land.swap(result); + return res; +} // multiply_lanscape_by_real_number_overwrite + +void Persistence_landscape::print_to_file(const char* filename) const { + std::ofstream write; + write.open(filename); + for (size_t dim = 0; dim != this->land.size(); ++dim) { + write << "#lambda_" << dim << std::endl; + for (size_t i = 1; i != this->land[dim].size() - 1; ++i) { + write << this->land[dim][i].first << " " << this->land[dim][i].second << std::endl; + } + } + write.close(); +} + +void Persistence_landscape::load_landscape_from_file(const char* filename) { + bool dbg = false; + // removing the current content of the persistence landscape. + this->land.clear(); + + // this constructor reads persistence landscape form a file. This file have to be created by this software before head + std::ifstream in; + in.open(filename); + if (!in.good()) { + std::cerr << "The file : " << filename << " do not exist. The program will now terminate \n"; + throw "The persistence landscape file do not exist. The program will now terminate \n"; + } + + std::string line; + std::vector<std::pair<double, double> > landscapeAtThisLevel; + + bool isThisAFirsLine = true; + while (in.good()) { + getline(in, line); + if (!(line.length() == 0 || line[0] == '#')) { + std::stringstream lineSS; + lineSS << line; + double beginn, endd; + lineSS >> beginn; + lineSS >> endd; + landscapeAtThisLevel.push_back(std::make_pair(beginn, endd)); + if (dbg) { + std::cerr << "Reading a point : " << beginn << " , " << endd << std::endl; + } + } else { + if (dbg) { + std::cout << "IGNORE LINE\n"; + getchar(); + } + if (!isThisAFirsLine) { + landscapeAtThisLevel.push_back(std::make_pair(std::numeric_limits<int>::max(), 0)); + this->land.push_back(landscapeAtThisLevel); + std::vector<std::pair<double, double> > newLevelOdLandscape; + landscapeAtThisLevel.swap(newLevelOdLandscape); + } + landscapeAtThisLevel.push_back(std::make_pair(-std::numeric_limits<int>::max(), 0)); + isThisAFirsLine = false; + } + } + if (landscapeAtThisLevel.size() > 1) { + // seems that the last line of the file is not finished with the newline sign. We need to put what we have in + // landscapeAtThisLevel to the constructed landscape. + landscapeAtThisLevel.push_back(std::make_pair(std::numeric_limits<int>::max(), 0)); + this->land.push_back(landscapeAtThisLevel); + } + + in.close(); +} + +template <typename T> +Persistence_landscape operation_on_pair_of_landscapes(const Persistence_landscape& land1, + const Persistence_landscape& land2) { + bool operation_on_pair_of_landscapesDBG = false; + if (operation_on_pair_of_landscapesDBG) { + std::cout << "operation_on_pair_of_landscapes\n"; + std::cin.ignore(); + } + Persistence_landscape result; + std::vector<std::vector<std::pair<double, double> > > land(std::max(land1.land.size(), land2.land.size())); + result.land = land; + T oper; + + if (operation_on_pair_of_landscapesDBG) { + for (size_t i = 0; i != std::min(land1.land.size(), land2.land.size()); ++i) { + std::cerr << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; + std::cerr << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; + } + getchar(); + } + + for (size_t i = 0; i != std::min(land1.land.size(), land2.land.size()); ++i) { + std::vector<std::pair<double, double> > lambda_n; + size_t p = 0; + size_t q = 0; + while ((p + 1 < land1.land[i].size()) && (q + 1 < land2.land[i].size())) { + if (operation_on_pair_of_landscapesDBG) { + std::cerr << "p : " << p << "\n"; + std::cerr << "q : " << q << "\n"; + std::cerr << "land1.land.size() : " << land1.land.size() << std::endl; + std::cerr << "land2.land.size() : " << land2.land.size() << std::endl; + std::cerr << "land1.land[" << i << "].size() : " << land1.land[i].size() << std::endl; + std::cerr << "land2.land[" << i << "].size() : " << land2.land[i].size() << std::endl; + std::cout << "land1.land[i][p].first : " << land1.land[i][p].first << "\n"; + std::cout << "land2.land[i][q].first : " << land2.land[i][q].first << "\n"; + } + + if (land1.land[i][p].first < land2.land[i][q].first) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "first \n"; + std::cout << " function_value(land2.land[i][q-1],land2.land[i][q],land1.land[i][p].first) : " + << function_value(land2.land[i][q - 1], land2.land[i][q], land1.land[i][p].first) << "\n"; + } + lambda_n.push_back( + std::make_pair(land1.land[i][p].first, + oper(static_cast<double>(land1.land[i][p].second), + function_value(land2.land[i][q - 1], land2.land[i][q], land1.land[i][p].first)))); + ++p; + continue; + } + if (land1.land[i][p].first > land2.land[i][q].first) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "Second \n"; + std::cout << "function_value(" << land1.land[i][p - 1].first << " " << land1.land[i][p - 1].second << " ," + << land1.land[i][p].first << " " << land1.land[i][p].second << ", " << land2.land[i][q].first + << " ) : " << function_value(land1.land[i][p - 1], land1.land[i][p - 1], land2.land[i][q].first) + << "\n"; + std::cout << "oper( " << function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first) << "," + << land2.land[i][q].second << " : " + << oper(land2.land[i][q].second, + function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first)) + << "\n"; + } + lambda_n.push_back(std::make_pair( + land2.land[i][q].first, oper(function_value(land1.land[i][p], land1.land[i][p - 1], land2.land[i][q].first), + land2.land[i][q].second))); + ++q; + continue; + } + if (land1.land[i][p].first == land2.land[i][q].first) { + if (operation_on_pair_of_landscapesDBG) std::cout << "Third \n"; + lambda_n.push_back( + std::make_pair(land2.land[i][q].first, oper(land1.land[i][p].second, land2.land[i][q].second))); + ++p; + ++q; + } + if (operation_on_pair_of_landscapesDBG) { + std::cout << "Next iteration \n"; + } + } + while ((p + 1 < land1.land[i].size()) && (q + 1 >= land2.land[i].size())) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "New point : " << land1.land[i][p].first + << " oper(land1.land[i][p].second,0) : " << oper(land1.land[i][p].second, 0) << std::endl; + } + lambda_n.push_back(std::make_pair(land1.land[i][p].first, oper(land1.land[i][p].second, 0))); + ++p; + } + while ((p + 1 >= land1.land[i].size()) && (q + 1 < land2.land[i].size())) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "New point : " << land2.land[i][q].first + << " oper(0,land2.land[i][q].second) : " << oper(0, land2.land[i][q].second) << std::endl; + } + lambda_n.push_back(std::make_pair(land2.land[i][q].first, oper(0, land2.land[i][q].second))); + ++q; + } + lambda_n.push_back(std::make_pair(std::numeric_limits<int>::max(), 0)); + // CHANGE + // result.land[i] = lambda_n; + result.land[i].swap(lambda_n); + } + if (land1.land.size() > std::min(land1.land.size(), land2.land.size())) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "land1.land.size() > std::min( land1.land.size() , land2.land.size() )" << std::endl; + } + for (size_t i = std::min(land1.land.size(), land2.land.size()); i != std::max(land1.land.size(), land2.land.size()); + ++i) { + std::vector<std::pair<double, double> > lambda_n(land1.land[i]); + for (size_t nr = 0; nr != land1.land[i].size(); ++nr) { + lambda_n[nr] = std::make_pair(land1.land[i][nr].first, oper(land1.land[i][nr].second, 0)); + } + // CHANGE + // result.land[i] = lambda_n; + result.land[i].swap(lambda_n); + } + } + if (land2.land.size() > std::min(land1.land.size(), land2.land.size())) { + if (operation_on_pair_of_landscapesDBG) { + std::cout << "( land2.land.size() > std::min( land1.land.size() , land2.land.size() ) ) " << std::endl; + } + for (size_t i = std::min(land1.land.size(), land2.land.size()); i != std::max(land1.land.size(), land2.land.size()); + ++i) { + std::vector<std::pair<double, double> > lambda_n(land2.land[i]); + for (size_t nr = 0; nr != land2.land[i].size(); ++nr) { + lambda_n[nr] = std::make_pair(land2.land[i][nr].first, oper(0, land2.land[i][nr].second)); + } + // CHANGE + // result.land[i] = lambda_n; + result.land[i].swap(lambda_n); + } + } + if (operation_on_pair_of_landscapesDBG) { + std::cout << "operation_on_pair_of_landscapes END\n"; + std::cin.ignore(); + } + return result; +} // operation_on_pair_of_landscapes + +double compute_maximal_distance_non_symmetric(const Persistence_landscape& pl1, const Persistence_landscape& pl2) { + bool dbg = false; + if (dbg) std::cerr << " compute_maximal_distance_non_symmetric \n"; + // this distance is not symmetric. It compute ONLY distance between inflection points of pl1 and pl2. + double maxDist = 0; + size_t minimalNumberOfLevels = std::min(pl1.land.size(), pl2.land.size()); + for (size_t level = 0; level != minimalNumberOfLevels; ++level) { + if (dbg) { + std::cerr << "Level : " << level << std::endl; + std::cerr << "PL1 : \n"; + for (size_t i = 0; i != pl1.land[level].size(); ++i) { + std::cerr << "(" << pl1.land[level][i].first << "," << pl1.land[level][i].second << ") \n"; + } + std::cerr << "PL2 : \n"; + for (size_t i = 0; i != pl2.land[level].size(); ++i) { + std::cerr << "(" << pl2.land[level][i].first << "," << pl2.land[level][i].second << ") \n"; + } + std::cin.ignore(); + } + + int p2Count = 0; + // In this case, I consider points at the infinity + for (size_t i = 1; i != pl1.land[level].size() - 1; ++i) { + while (true) { + if ((pl1.land[level][i].first >= pl2.land[level][p2Count].first) && + (pl1.land[level][i].first <= pl2.land[level][p2Count + 1].first)) + break; + p2Count++; + } + double val = + fabs(function_value(pl2.land[level][p2Count], pl2.land[level][p2Count + 1], pl1.land[level][i].first) - + pl1.land[level][i].second); + if (maxDist <= val) maxDist = val; + + if (dbg) { + std::cerr << pl1.land[level][i].first << "in [" << pl2.land[level][p2Count].first << "," + << pl2.land[level][p2Count + 1].first << "] \n"; + std::cerr << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; + std::cerr << "function_value( pl2[level][p2Count] , pl2[level][p2Count+1] , pl1[level][i].first ) : " + << function_value(pl2.land[level][p2Count], pl2.land[level][p2Count + 1], pl1.land[level][i].first) + << std::endl; + std::cerr << "val : " << val << std::endl; + std::cin.ignore(); + } + } + } + + if (dbg) std::cerr << "minimalNumberOfLevels : " << minimalNumberOfLevels << std::endl; + + if (minimalNumberOfLevels < pl1.land.size()) { + for (size_t level = minimalNumberOfLevels; level != pl1.land.size(); ++level) { + for (size_t i = 0; i != pl1.land[level].size(); ++i) { + if (dbg) std::cerr << "pl1[level][i].second : " << pl1.land[level][i].second << std::endl; + if (maxDist < pl1.land[level][i].second) maxDist = pl1.land[level][i].second; + } + } + } + return maxDist; +} + +double compute_distance_of_landscapes(const Persistence_landscape& first, const Persistence_landscape& second, + double p) { + bool dbg = false; + // This is what we want to compute: (\int_{- \infty}^{+\infty}| first-second |^p)^(1/p). We will do it one step at a + // time: + + // first-second : + Persistence_landscape lan = first - second; + + //| first-second |: + lan = lan.abs(); + + if (dbg) { + std::cerr << "Abs of difference ; " << lan << std::endl; + getchar(); + } + + if (p < std::numeric_limits<double>::max()) { + // \int_{- \infty}^{+\infty}| first-second |^p + double result; + if (p != 1) { + if (dbg) std::cerr << "Power != 1, compute integral to the power p\n"; + result = lan.compute_integral_of_landscape(p); + } else { + if (dbg) std::cerr << "Power = 1, compute integral \n"; + result = lan.compute_integral_of_landscape(); + } + // (\int_{- \infty}^{+\infty}| first-second |^p)^(1/p) + return pow(result, 1.0 / p); + } else { + // p == infty + if (dbg) std::cerr << "Power = infty, compute maximum \n"; + return lan.compute_maximum(); + } +} + +double compute_max_norm_distance_of_landscapes(const Persistence_landscape& first, + const Persistence_landscape& second) { + return std::max(compute_maximal_distance_non_symmetric(first, second), + compute_maximal_distance_non_symmetric(second, first)); +} + +bool comparePairsForMerging(std::pair<double, unsigned> first, std::pair<double, unsigned> second) { + return (first.first < second.first); +} + +double compute_inner_product(const Persistence_landscape& l1, const Persistence_landscape& l2) { + bool dbg = false; + double result = 0; + + for (size_t level = 0; level != std::min(l1.size(), l2.size()); ++level) { + if (dbg) { + std::cerr << "Computing inner product for a level : " << level << std::endl; + getchar(); + } + if (l1.land[level].size() * l2.land[level].size() == 0) continue; + + // endpoints of the interval on which we will compute the inner product of two locally linear functions: + double x1 = -std::numeric_limits<int>::max(); + double x2; + if (l1.land[level][1].first < l2.land[level][1].first) { + x2 = l1.land[level][1].first; + } else { + x2 = l2.land[level][1].first; + } + + // iterators for the landscapes l1 and l2 + size_t l1It = 0; + size_t l2It = 0; + + while ((l1It < l1.land[level].size() - 1) && (l2It < l2.land[level].size() - 1)) { + // compute the value of a inner product on a interval [x1,x2] + + double a, b, c, d; + + if (l1.land[level][l1It + 1].first != l1.land[level][l1It].first) { + a = (l1.land[level][l1It + 1].second - l1.land[level][l1It].second) / + (l1.land[level][l1It + 1].first - l1.land[level][l1It].first); + } else { + a = 0; + } + b = l1.land[level][l1It].second - a * l1.land[level][l1It].first; + if (l2.land[level][l2It + 1].first != l2.land[level][l2It].first) { + c = (l2.land[level][l2It + 1].second - l2.land[level][l2It].second) / + (l2.land[level][l2It + 1].first - l2.land[level][l2It].first); + } else { + c = 0; + } + d = l2.land[level][l2It].second - c * l2.land[level][l2It].first; + + double contributionFromThisPart = (a * c * x2 * x2 * x2 / 3 + (a * d + b * c) * x2 * x2 / 2 + b * d * x2) - + (a * c * x1 * x1 * x1 / 3 + (a * d + b * c) * x1 * x1 / 2 + b * d * x1); + + result += contributionFromThisPart; + + if (dbg) { + std::cerr << "[l1.land[level][l1It].first,l1.land[level][l1It+1].first] : " << l1.land[level][l1It].first + << " , " << l1.land[level][l1It + 1].first << std::endl; + std::cerr << "[l2.land[level][l2It].first,l2.land[level][l2It+1].first] : " << l2.land[level][l2It].first + << " , " << l2.land[level][l2It + 1].first << std::endl; + std::cerr << "a : " << a << ", b : " << b << " , c: " << c << ", d : " << d << std::endl; + std::cerr << "x1 : " << x1 << " , x2 : " << x2 << std::endl; + std::cerr << "contributionFromThisPart : " << contributionFromThisPart << std::endl; + std::cerr << "result : " << result << std::endl; + getchar(); + } + + // we have two intervals in which functions are constant: + // [l1.land[level][l1It].first , l1.land[level][l1It+1].first] + // and + // [l2.land[level][l2It].first , l2.land[level][l2It+1].first] + // We also have an interval [x1,x2]. Since the intervals in the landscapes cover the whole R, then it is clear + // that x2 + // is either l1.land[level][l1It+1].first of l2.land[level][l2It+1].first or both. Lets test it. + if (x2 == l1.land[level][l1It + 1].first) { + if (x2 == l2.land[level][l2It + 1].first) { + // in this case, we increment both: + ++l2It; + if (dbg) { + std::cerr << "Incrementing both \n"; + } + } else { + if (dbg) { + std::cerr << "Incrementing first \n"; + } + } + ++l1It; + } else { + // in this case we increment l2It + ++l2It; + if (dbg) { + std::cerr << "Incrementing second \n"; + } + } + // Now, we shift x1 and x2: + x1 = x2; + if (l1.land[level][l1It + 1].first < l2.land[level][l2It + 1].first) { + x2 = l1.land[level][l1It + 1].first; + } else { + x2 = l2.land[level][l2It + 1].first; + } + } + } + return result; +} + +void Persistence_landscape::plot(const char* filename, double xRangeBegin, double xRangeEnd, double yRangeBegin, + double yRangeEnd, int from, int to) { + // this program create a gnuplot script file that allows to plot persistence diagram. + std::ofstream out; + + std::ostringstream gnuplot_script; + gnuplot_script << filename << "_GnuplotScript"; + out.open(gnuplot_script.str().c_str()); + + if ((xRangeBegin != std::numeric_limits<double>::max()) || (xRangeEnd != std::numeric_limits<double>::max()) || + (yRangeBegin != std::numeric_limits<double>::max()) || (yRangeEnd != std::numeric_limits<double>::max())) { + out << "set xrange [" << xRangeBegin << " : " << xRangeEnd << "]" << std::endl; + out << "set yrange [" << yRangeBegin << " : " << yRangeEnd << "]" << std::endl; + } + + if (from == std::numeric_limits<int>::max()) { + from = 0; + } + if (to == std::numeric_limits<int>::max()) { + to = this->land.size(); + } + + out << "plot "; + for (size_t lambda = std::min((size_t)from, this->land.size()); lambda != std::min((size_t)to, this->land.size()); + ++lambda) { + // out << " '-' using 1:2 title 'l" << lambda << "' with lp"; + out << " '-' using 1:2 notitle with lp"; + if (lambda + 1 != std::min((size_t)to, this->land.size())) { + out << ", \\"; + } + out << std::endl; + } + + for (size_t lambda = std::min((size_t)from, this->land.size()); lambda != std::min((size_t)to, this->land.size()); + ++lambda) { + for (size_t i = 1; i != this->land[lambda].size() - 1; ++i) { + out << this->land[lambda][i].first << " " << this->land[lambda][i].second << std::endl; + } + out << "EOF" << std::endl; + } + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_LANDSCAPE_H_ diff --git a/include/gudhi/Persistence_landscape_on_grid.h b/include/gudhi/Persistence_landscape_on_grid.h new file mode 100644 index 00000000..84fd22ed --- /dev/null +++ b/include/gudhi/Persistence_landscape_on_grid.h @@ -0,0 +1,1348 @@ +/** This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + **/ + +#ifndef PERSISTENCE_LANDSCAPE_ON_GRID_H_ +#define PERSISTENCE_LANDSCAPE_ON_GRID_H_ + +// gudhi include +#include <gudhi/read_persistence_from_file.h> +#include <gudhi/common_persistence_representations.h> + +// standard include +#include <iostream> +#include <vector> +#include <limits> +#include <fstream> +#include <sstream> +#include <algorithm> +#include <cmath> +#include <functional> +#include <utility> +#include <string> +#include <cstdint> + +namespace Gudhi { +namespace Persistence_representations { + +// pre declaration +class Persistence_landscape_on_grid; +template <typename operation> +Persistence_landscape_on_grid operation_on_pair_of_landscapes_on_grid(const Persistence_landscape_on_grid& land1, + const Persistence_landscape_on_grid& land2); + +/** + * \class Persistence_landscape_on_grid Persistence_landscape_on_grid.h gudhi/Persistence_landscape_on_grid.h + * \brief A class implementing persistence landscapes by approximating them on a collection of grid points. + * + * \ingroup Persistence_representations + * + * \details + * Persistence landscapes on grid allows vectorization, computations of distances, computations of projections to Real, + * computations of averages and scalar products. Therefore they implement suitable interfaces. + * It implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, + * Real_valued_topological_data, Topological_data_with_averages, Topological_data_with_scalar_product + * + * Note that at the moment, due to rounding errors during the construction of persistence landscapes on a grid, + * elements which are different by 0.000005 are considered the same. If the scale in your persistence diagrams + * is comparable to this value, please rescale them before use this code. +**/ + +// this class implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, +// Real_valued_topological_data, Topological_data_with_averages, Topological_data_with_scalar_product +class Persistence_landscape_on_grid { + public: + /** + * Default constructor. + **/ + Persistence_landscape_on_grid() { + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); + this->grid_min = this->grid_max = 0; + } + + /** + * Constructor that takes as an input a vector of birth-death pairs. + **/ + Persistence_landscape_on_grid(const std::vector<std::pair<double, double> >& p, double grid_min_, double grid_max_, + size_t number_of_points_); + + /** + * Constructor that takes as an input a vector of birth-death pairs, parameters of the grid and number of + *landscape function to be created. + **/ + Persistence_landscape_on_grid(const std::vector<std::pair<double, double> >& p, double grid_min_, double grid_max_, + size_t number_of_points_, unsigned number_of_levels_of_landscape); + + /** + * Constructor that reads persistence intervals from file and creates persistence landscape. The format of the + *input file is the following: in each line we put birth-death pair. Last line is assumed + * to be empty. Even if the points within a line are not ordered, they will be ordered while the input is read. + *The additional parameters of this procedure are: ranges of grid, resolution of a grid + * number of landscape functions to be created and the dimension of intervals that are need to be read from a file + *(in case of Gudhi format files). + **/ + Persistence_landscape_on_grid(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, + unsigned number_of_levels_of_landscape, + uint16_t dimension_ = std::numeric_limits<uint16_t>::max()); + + /** + * Constructor that reads persistence intervals from file and creates persistence landscape. The format of the + *input file is the following: in each line we put birth-death pair. Last line is assumed + * to be empty. Even if the points within a line are not ordered, they will be ordered while the input is read. The + *additional parameters of this procedure are: ranges of grid, resolution of a grid + * and the dimension of intervals that are need to be read from a file (in case of Gudhi format files). + **/ + Persistence_landscape_on_grid(const char* filename, double grid_min_, double grid_max_, size_t number_of_points_, + uint16_t dimension_ = std::numeric_limits<uint16_t>::max()); + + /** + * Constructor that reads persistence intervals from file and creates persistence landscape. The format of the + *input file is the following: in each line we put birth-death pair. Last line is assumed + * to be empty. Even if the points within a line are not ordered, they will be ordered while the input is read. + *The additional parameter is the resolution of a grid and the number of landscape + * functions to be created. The remaining parameters are calculated based on data. + **/ + Persistence_landscape_on_grid(const char* filename, size_t number_of_points, unsigned number_of_levels_of_landscape, + uint16_t dimension = std::numeric_limits<uint16_t>::max()); + + /** + * Constructor that reads persistence intervals from file and creates persistence landscape. The format of the input + *file is the following: in each line we put birth-death pair. Last line is assumed + * to be empty. Even if the points within a line are not ordered, they will be ordered while the input is read. The + *additional parameter is the resolution of a grid. The last parameter is the dimension + * of a persistence to read from the file. If your file contains only persistence pair in a single dimension, please + *set it up to std::numeric_limits<unsigned>::max(). + * The remaining parameters are calculated based on data. + **/ + Persistence_landscape_on_grid(const char* filename, size_t number_of_points, + uint16_t dimension = std::numeric_limits<uint16_t>::max()); + + /** + * This procedure loads a landscape from file. It erase all the data that was previously stored in this landscape. + **/ + void load_landscape_from_file(const char* filename); + + /** + * The procedure stores a landscape to a file. The file can be later used by a procedure load_landscape_from_file. + **/ + void print_to_file(const char* filename) const; + + /** + * This function compute integral of the landscape (defined formally as sum of integrals on R of all landscape + *functions) + **/ + double compute_integral_of_landscape() const { + size_t maximal_level = this->number_of_nonzero_levels(); + double result = 0; + for (size_t i = 0; i != maximal_level; ++i) { + result += this->compute_integral_of_landscape(i); + } + return result; + } + + /** + * This function compute integral of the 'level'-level of a landscape. + **/ + double compute_integral_of_landscape(size_t level) const { + bool dbg = false; + double result = 0; + double dx = (this->grid_max - this->grid_min) / static_cast<double>(this->values_of_landscapes.size() - 1); + + if (dbg) { + std::cerr << "this->grid_max : " << this->grid_max << std::endl; + std::cerr << "this->grid_min : " << this->grid_min << std::endl; + std::cerr << "this->values_of_landscapes.size() : " << this->values_of_landscapes.size() << std::endl; + getchar(); + } + + double previous_x = this->grid_min - dx; + double previous_y = 0; + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + double current_x = previous_x + dx; + double current_y = 0; + if (this->values_of_landscapes[i].size() > level) current_y = this->values_of_landscapes[i][level]; + + if (dbg) { + std::cerr << "this->values_of_landscapes[i].size() : " << this->values_of_landscapes[i].size() + << " , level : " << level << std::endl; + if (this->values_of_landscapes[i].size() > level) + std::cerr << "this->values_of_landscapes[i][level] : " << this->values_of_landscapes[i][level] << std::endl; + std::cerr << "previous_y : " << previous_y << std::endl; + std::cerr << "current_y : " << current_y << std::endl; + std::cerr << "dx : " << dx << std::endl; + std::cerr << "0.5*dx*( previous_y + current_y ); " << 0.5 * dx * (previous_y + current_y) << std::endl; + } + + result += 0.5 * dx * (previous_y + current_y); + previous_x = current_x; + previous_y = current_y; + } + return result; + } + + /** + * This function compute integral of the landscape p-th power of a landscape (defined formally as sum of integrals on + *R of p-th powers of all landscape functions) + **/ + double compute_integral_of_landscape(double p) const { + size_t maximal_level = this->number_of_nonzero_levels(); + double result = 0; + for (size_t i = 0; i != maximal_level; ++i) { + result += this->compute_integral_of_landscape(p, i); + } + return result; + } + + /** + * This function compute integral of the landscape p-th power of a level of a landscape (defined formally as sum + *of integrals on R of p-th powers of all landscape functions) + **/ + double compute_integral_of_landscape(double p, size_t level) const { + bool dbg = false; + + double result = 0; + double dx = (this->grid_max - this->grid_min) / static_cast<double>(this->values_of_landscapes.size() - 1); + double previous_x = this->grid_min; + double previous_y = 0; + if (this->values_of_landscapes[0].size() > level) previous_y = this->values_of_landscapes[0][level]; + + if (dbg) { + std::cerr << "dx : " << dx << std::endl; + std::cerr << "previous_x : " << previous_x << std::endl; + std::cerr << "previous_y : " << previous_y << std::endl; + std::cerr << "power : " << p << std::endl; + getchar(); + } + + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + double current_x = previous_x + dx; + double current_y = 0; + if (this->values_of_landscapes[i].size() > level) current_y = this->values_of_landscapes[i][level]; + + if (dbg) std::cerr << "current_y : " << current_y << std::endl; + + if (current_y == previous_y) continue; + + std::pair<double, double> coef = + compute_parameters_of_a_line(std::make_pair(previous_x, previous_y), std::make_pair(current_x, current_y)); + double a = coef.first; + double b = coef.second; + + if (dbg) { + std::cerr << "A line passing through points : (" << previous_x << "," << previous_y << ") and (" << current_x + << "," << current_y << ") is : " << a << "x+" << b << std::endl; + } + + // In this interval, the landscape has a form f(x) = ax+b. We want to compute integral of (ax+b)^p = 1/a * + // (ax+b)^{p+1}/(p+1) + double value_to_add = 0; + if (a != 0) { + value_to_add = 1 / (a * (p + 1)) * (pow((a * current_x + b), p + 1) - pow((a * previous_x + b), p + 1)); + } else { + value_to_add = (current_x - previous_x) * (pow(b, p)); + } + result += value_to_add; + if (dbg) { + std::cerr << "Increasing result by : " << value_to_add << std::endl; + std::cerr << "result : " << result << std::endl; + getchar(); + } + previous_x = current_x; + previous_y = current_y; + } + if (dbg) std::cerr << "The total result is : " << result << std::endl; + return result; + } + + /** +* Writing landscape into a stream. A i-th level landscape starts with a string "lambda_i". Then the discontinuity points +*of the landscapes follows. +* Shall those points be joined with lines, we will obtain the i-th landscape function. +**/ + friend std::ostream& operator<<(std::ostream& out, const Persistence_landscape_on_grid& land) { + double dx = (land.grid_max - land.grid_min) / static_cast<double>(land.values_of_landscapes.size() - 1); + double x = land.grid_min; + for (size_t i = 0; i != land.values_of_landscapes.size(); ++i) { + out << x << " : "; + for (size_t j = 0; j != land.values_of_landscapes[i].size(); ++j) { + out << land.values_of_landscapes[i][j] << " "; + } + out << std::endl; + x += dx; + } + return out; + } + + template <typename oper> + friend Persistence_landscape_on_grid operation_on_pair_of_landscapes_on_grid( + const Persistence_landscape_on_grid& land1, const Persistence_landscape_on_grid& land2); + + /** + * A function that computes the value of a landscape at a given point. The parameters of the function are: unsigned + *level and double x. + * The procedure will compute the value of the level-landscape at the point x. + **/ + double compute_value_at_a_given_point(unsigned level, double x) const { + bool dbg = false; + if ((x < this->grid_min) || (x > this->grid_max)) return 0; + + // find a position of a vector closest to x: + double dx = (this->grid_max - this->grid_min) / static_cast<double>(this->values_of_landscapes.size() - 1); + size_t position = size_t((x - this->grid_min) / dx); + + if (dbg) { + std::cerr << "This is a procedure compute_value_at_a_given_point \n"; + std::cerr << "level : " << level << std::endl; + std::cerr << "x : " << x << std::endl; + std::cerr << "position : " << position << std::endl; + } + // check if we are not exactly in the grid point: + if (almost_equal(position * dx + this->grid_min, x)) { + if (this->values_of_landscapes[position].size() < level) { + return this->values_of_landscapes[position][level]; + } else { + return 0; + } + } + // in the other case, approximate with a line: + std::pair<double, double> line; + if ((this->values_of_landscapes[position].size() > level) && + (this->values_of_landscapes[position + 1].size() > level)) { + line = compute_parameters_of_a_line( + std::make_pair(position * dx + this->grid_min, this->values_of_landscapes[position][level]), + std::make_pair((position + 1) * dx + this->grid_min, this->values_of_landscapes[position + 1][level])); + } else { + if ((this->values_of_landscapes[position].size() > level) || + (this->values_of_landscapes[position + 1].size() > level)) { + if ((this->values_of_landscapes[position].size() > level)) { + line = compute_parameters_of_a_line( + std::make_pair(position * dx + this->grid_min, this->values_of_landscapes[position][level]), + std::make_pair((position + 1) * dx + this->grid_min, 0)); + } else { + line = compute_parameters_of_a_line( + std::make_pair(position * dx + this->grid_min, 0), + std::make_pair((position + 1) * dx + this->grid_min, this->values_of_landscapes[position + 1][level])); + } + } else { + return 0; + } + } + // compute the value of the linear function parametrized by line on a point x: + return line.first * x + line.second; + } + + public: + /** + *\private A function that compute sum of two landscapes. + **/ + friend Persistence_landscape_on_grid add_two_landscapes(const Persistence_landscape_on_grid& land1, + const Persistence_landscape_on_grid& land2) { + return operation_on_pair_of_landscapes_on_grid<std::plus<double> >(land1, land2); + } + + /** + *\private A function that compute difference of two landscapes. + **/ + friend Persistence_landscape_on_grid subtract_two_landscapes(const Persistence_landscape_on_grid& land1, + const Persistence_landscape_on_grid& land2) { + return operation_on_pair_of_landscapes_on_grid<std::minus<double> >(land1, land2); + } + + /** + * An operator +, that compute sum of two landscapes. + **/ + friend Persistence_landscape_on_grid operator+(const Persistence_landscape_on_grid& first, + const Persistence_landscape_on_grid& second) { + return add_two_landscapes(first, second); + } + + /** + * An operator -, that compute difference of two landscapes. + **/ + friend Persistence_landscape_on_grid operator-(const Persistence_landscape_on_grid& first, + const Persistence_landscape_on_grid& second) { + return subtract_two_landscapes(first, second); + } + + /** + * An operator * that allows multiplication of a landscape by a real number. + **/ + friend Persistence_landscape_on_grid operator*(const Persistence_landscape_on_grid& first, double con) { + return first.multiply_lanscape_by_real_number_not_overwrite(con); + } + + /** + * An operator * that allows multiplication of a landscape by a real number (order of parameters swapped). + **/ + friend Persistence_landscape_on_grid operator*(double con, const Persistence_landscape_on_grid& first) { + return first.multiply_lanscape_by_real_number_not_overwrite(con); + } + + friend bool check_if_defined_on_the_same_domain(const Persistence_landscape_on_grid& land1, + const Persistence_landscape_on_grid& land2) { + if (land1.values_of_landscapes.size() != land2.values_of_landscapes.size()) return false; + if (land1.grid_min != land2.grid_min) return false; + if (land1.grid_max != land2.grid_max) return false; + return true; + } + + /** + * Operator +=. The second parameter is persistence landscape. + **/ + Persistence_landscape_on_grid operator+=(const Persistence_landscape_on_grid& rhs) { + *this = *this + rhs; + return *this; + } + + /** + * Operator -=. The second parameter is persistence landscape. + **/ + Persistence_landscape_on_grid operator-=(const Persistence_landscape_on_grid& rhs) { + *this = *this - rhs; + return *this; + } + + /** + * Operator *=. The second parameter is a real number by which the y values of all landscape functions are multiplied. + *The x-values remain unchanged. + **/ + Persistence_landscape_on_grid operator*=(double x) { + *this = *this * x; + return *this; + } + + /** + * Operator /=. The second parameter is a real number. + **/ + Persistence_landscape_on_grid operator/=(double x) { + if (x == 0) throw("In operator /=, division by 0. Program terminated."); + *this = *this * (1 / x); + return *this; + } + + /** + * An operator to compare two persistence landscapes. + **/ + bool operator==(const Persistence_landscape_on_grid& rhs) const { + bool dbg = true; + if (this->values_of_landscapes.size() != rhs.values_of_landscapes.size()) { + if (dbg) std::cerr << "values_of_landscapes of incompatible sizes\n"; + return false; + } + if (!almost_equal(this->grid_min, rhs.grid_min)) { + if (dbg) std::cerr << "grid_min not equal\n"; + return false; + } + if (!almost_equal(this->grid_max, rhs.grid_max)) { + if (dbg) std::cerr << "grid_max not equal\n"; + return false; + } + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + for (size_t aa = 0; aa != this->values_of_landscapes[i].size(); ++aa) { + if (!almost_equal(this->values_of_landscapes[i][aa], rhs.values_of_landscapes[i][aa])) { + if (dbg) { + std::cerr << "Problem in the position : " << i << " of values_of_landscapes. \n"; + std::cerr << this->values_of_landscapes[i][aa] << " " << rhs.values_of_landscapes[i][aa] << std::endl; + } + return false; + } + } + } + return true; + } + + /** + * An operator to compare two persistence landscapes. + **/ + bool operator!=(const Persistence_landscape_on_grid& rhs) const { return !((*this) == rhs); } + + /** + * Computations of maximum (y) value of landscape. + **/ + double compute_maximum() const { + // since the function can only be entirely positive or negative, the maximal value will be an extremal value in the + // arrays: + double max_value = -std::numeric_limits<double>::max(); + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + if (this->values_of_landscapes[i].size()) { + if (this->values_of_landscapes[i][0] > max_value) max_value = this->values_of_landscapes[i][0]; + if (this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1] > max_value) + max_value = this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1]; + } + } + return max_value; + } + + /** + * Computations of minimum and maximum value of landscape. + **/ + std::pair<double, double> compute_minimum_maximum() const { + // since the function can only be entirely positive or negative, the maximal value will be an extremal value in the + // arrays: + double max_value = -std::numeric_limits<double>::max(); + double min_value = 0; + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + if (this->values_of_landscapes[i].size()) { + if (this->values_of_landscapes[i][0] > max_value) max_value = this->values_of_landscapes[i][0]; + if (this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1] > max_value) + max_value = this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1]; + + if (this->values_of_landscapes[i][0] < min_value) min_value = this->values_of_landscapes[i][0]; + if (this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1] < min_value) + min_value = this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1]; + } + } + return std::make_pair(min_value, max_value); + } + + /** + * This procedure returns x-range of a given level persistence landscape. If a default value is used, the x-range + * of 0th level landscape is given (and this range contains the ranges of all other landscapes). + **/ + std::pair<double, double> get_x_range(size_t level = 0) const { + return std::make_pair(this->grid_min, this->grid_max); + } + + /** + * This procedure returns y-range of a persistence landscape. If a default value is used, the y-range + * of 0th level landscape is given (and this range contains the ranges of all other landscapes). + **/ + std::pair<double, double> get_y_range(size_t level = 0) const { return this->compute_minimum_maximum(); } + + /** + * This function computes maximal lambda for which lambda-level landscape is nonzero. + **/ + size_t number_of_nonzero_levels() const { + size_t result = 0; + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + if (this->values_of_landscapes[i].size() > result) result = this->values_of_landscapes[i].size(); + } + return result; + } + + /** + * Computations of a \f$L^i\f$ norm of landscape, where i is the input parameter. + **/ + double compute_norm_of_landscape(double i) const { + std::vector<std::pair<double, double> > p; + Persistence_landscape_on_grid l(p, this->grid_min, this->grid_max, this->values_of_landscapes.size() - 1); + + if (i < std::numeric_limits<double>::max()) { + return compute_distance_of_landscapes_on_grid(*this, l, i); + } else { + return compute_max_norm_distance_of_landscapes(*this, l); + } + } + + /** + * An operator to compute the value of a landscape in the level 'level' at the argument 'x'. + **/ + double operator()(unsigned level, double x) const { return this->compute_value_at_a_given_point(level, x); } + + /** + * Computations of \f$L^{\infty}\f$ distance between two landscapes. + **/ + friend double compute_max_norm_distance_of_landscapes(const Persistence_landscape_on_grid& first, + const Persistence_landscape_on_grid& second); + + /** + * Function to compute absolute value of a PL function. The representation of persistence landscapes allow to store + *general PL-function. When computing distance between two landscapes, we compute difference between + * them. In this case, a general PL-function with negative value can appear as a result. Then in order to compute + *distance, we need to take its absolute value. This is the purpose of this procedure. + **/ + void abs() { + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + for (size_t j = 0; j != this->values_of_landscapes[i].size(); ++j) { + this->values_of_landscapes[i][j] = std::abs(this->values_of_landscapes[i][j]); + } + } + } + + /** + * Computes the number of landscape functions. + **/ + size_t size() const { return this->number_of_nonzero_levels(); } + + /** + * Compute maximal value of lambda-level landscape. + **/ + double find_max(unsigned lambda) const { + double max_value = -std::numeric_limits<double>::max(); + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + if (this->values_of_landscapes[i].size() > lambda) { + if (this->values_of_landscapes[i][lambda] > max_value) max_value = this->values_of_landscapes[i][lambda]; + } + } + return max_value; + } + + /** + * Function to compute inner (scalar) product of two landscapes. + **/ + friend double compute_inner_product(const Persistence_landscape_on_grid& l1, + const Persistence_landscape_on_grid& l2) { + if (!check_if_defined_on_the_same_domain(l1, l2)) + throw "Landscapes are not defined on the same grid, the program will now terminate"; + size_t maximal_level = l1.number_of_nonzero_levels(); + double result = 0; + for (size_t i = 0; i != maximal_level; ++i) { + result += compute_inner_product(l1, l2, i); + } + return result; + } + + /** + * Function to compute inner (scalar) product of given levels of two landscapes. + **/ + friend double compute_inner_product(const Persistence_landscape_on_grid& l1, const Persistence_landscape_on_grid& l2, + size_t level) { + bool dbg = false; + + if (!check_if_defined_on_the_same_domain(l1, l2)) + throw "Landscapes are not defined on the same grid, the program will now terminate"; + double result = 0; + + double dx = (l1.grid_max - l1.grid_min) / static_cast<double>(l1.values_of_landscapes.size() - 1); + + double previous_x = l1.grid_min - dx; + double previous_y_l1 = 0; + double previous_y_l2 = 0; + for (size_t i = 0; i != l1.values_of_landscapes.size(); ++i) { + if (dbg) std::cerr << "i : " << i << std::endl; + + double current_x = previous_x + dx; + double current_y_l1 = 0; + if (l1.values_of_landscapes[i].size() > level) current_y_l1 = l1.values_of_landscapes[i][level]; + + double current_y_l2 = 0; + if (l2.values_of_landscapes[i].size() > level) current_y_l2 = l2.values_of_landscapes[i][level]; + + if (dbg) { + std::cerr << "previous_x : " << previous_x << std::endl; + std::cerr << "previous_y_l1 : " << previous_y_l1 << std::endl; + std::cerr << "current_y_l1 : " << current_y_l1 << std::endl; + std::cerr << "previous_y_l2 : " << previous_y_l2 << std::endl; + std::cerr << "current_y_l2 : " << current_y_l2 << std::endl; + } + + std::pair<double, double> l1_coords = compute_parameters_of_a_line(std::make_pair(previous_x, previous_y_l1), + std::make_pair(current_x, current_y_l1)); + std::pair<double, double> l2_coords = compute_parameters_of_a_line(std::make_pair(previous_x, previous_y_l2), + std::make_pair(current_x, current_y_l2)); + + // let us assume that the first line is of a form y = ax+b, and the second one is of a form y = cx + d. Then here + // are a,b,c,d: + double a = l1_coords.first; + double b = l1_coords.second; + + double c = l2_coords.first; + double d = l2_coords.second; + + if (dbg) { + std::cerr << "Here are the formulas for a line: \n"; + std::cerr << "a : " << a << std::endl; + std::cerr << "b : " << b << std::endl; + std::cerr << "c : " << c << std::endl; + std::cerr << "d : " << d << std::endl; + } + + // now, to compute the inner product in this interval we need to compute the integral of (ax+b)(cx+d) = acx^2 + + // (ad+bc)x + bd in the interval from previous_x to current_x: + // The integral is ac/3*x^3 + (ac+bd)/2*x^2 + bd*x + + double added_value = (a * c / 3 * current_x * current_x * current_x + + (a * d + b * c) / 2 * current_x * current_x + b * d * current_x) - + (a * c / 3 * previous_x * previous_x * previous_x + + (a * d + b * c) / 2 * previous_x * previous_x + b * d * previous_x); + + if (dbg) { + std::cerr << "Value of the integral on the left end i.e. : " << previous_x << " is : " + << a * c / 3 * previous_x * previous_x * previous_x + (a * d + b * c) / 2 * previous_x * previous_x + + b * d * previous_x + << std::endl; + std::cerr << "Value of the integral on the right end i.e. : " << current_x << " is " + << a * c / 3 * current_x * current_x * current_x + (a * d + b * c) / 2 * current_x * current_x + + b * d * current_x + << std::endl; + } + + result += added_value; + + if (dbg) { + std::cerr << "added_value : " << added_value << std::endl; + std::cerr << "result : " << result << std::endl; + getchar(); + } + + previous_x = current_x; + previous_y_l1 = current_y_l1; + previous_y_l2 = current_y_l2; + } + return result; + } + + /** + * Computations of \f$L^{p}\f$ distance between two landscapes on a grid. p is the parameter of the procedure. + * FIXME: Note that, due to the grid representation, the method below may give non--accurate results in case when the + *landscape P and Q the difference of which we want to compute + * are intersecting. This is a consequence of a general way they are computed. In the future, an integral of absolute + *value of a difference of P and Q will be given as a separated + * function to fix that inaccuracy. + **/ + friend double compute_distance_of_landscapes_on_grid(const Persistence_landscape_on_grid& first, + const Persistence_landscape_on_grid& second, double p) { + bool dbg = false; + // This is what we want to compute: (\int_{- \infty}^{+\infty}| first-second |^p)^(1/p). We will do it one step at a + // time: + + if (dbg) { + std::cerr << "first : " << first << std::endl; + std::cerr << "second : " << second << std::endl; + getchar(); + } + + // first-second : + Persistence_landscape_on_grid lan = first - second; + + if (dbg) { + std::cerr << "Difference : " << lan << std::endl; + } + + //| first-second |: + lan.abs(); + + if (dbg) { + std::cerr << "Abs : " << lan << std::endl; + } + + if (p < std::numeric_limits<double>::max()) { + // \int_{- \infty}^{+\infty}| first-second |^p + double result; + if (p != 1) { + if (dbg) { + std::cerr << "p : " << p << std::endl; + getchar(); + } + result = lan.compute_integral_of_landscape(p); + if (dbg) { + std::cerr << "integral : " << result << std::endl; + getchar(); + } + } else { + result = lan.compute_integral_of_landscape(); + if (dbg) { + std::cerr << "integral, without power : " << result << std::endl; + getchar(); + } + } + // (\int_{- \infty}^{+\infty}| first-second |^p)^(1/p) + return pow(result, 1.0 / p); + } else { + // p == infty + return lan.compute_maximum(); + } + } + + // Functions that are needed for that class to implement the concept. + + /** + * The number of projections to R is defined to the number of nonzero landscape functions. I-th projection is an + *integral of i-th landscape function over whole R. + * This function is required by the Real_valued_topological_data concept. + * At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when + *using it, keep in mind that it + * will be most likely changed in the next versions. + **/ + double project_to_R(int number_of_function) const { + return this->compute_integral_of_landscape((size_t)number_of_function); + } + + /** + * The function gives the number of possible projections to R. This function is required by the + *Real_valued_topological_data concept. + **/ + size_t number_of_projections_to_R() const { return number_of_functions_for_projections_to_reals; } + + /** + * This function produce a vector of doubles based on a landscape. It is required in a concept + * Vectorized_topological_data + */ + std::vector<double> vectorize(int number_of_function) const { + // TODO(PD) think of something smarter over here + if ((number_of_function < 0) || ((size_t)number_of_function >= this->values_of_landscapes.size())) { + throw "Wrong number of function\n"; + } + std::vector<double> v(this->values_of_landscapes.size()); + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + v[i] = 0; + if (this->values_of_landscapes[i].size() > (size_t)number_of_function) { + v[i] = this->values_of_landscapes[i][number_of_function]; + } + } + return v; + } + + /** + * This function return the number of functions that allows vectorization of persistence landscape. It is required in + *a concept Vectorized_topological_data. + **/ + size_t number_of_vectorize_functions() const { return number_of_functions_for_vectorization; } + + /** + * A function to compute averaged persistence landscape on a grid, based on vector of persistence landscapes on grid. + * This function is required by Topological_data_with_averages concept. + **/ + void compute_average(const std::vector<Persistence_landscape_on_grid*>& to_average) { + bool dbg = false; + // After execution of this procedure, the average is supposed to be in the current object. To make sure that this is + // the case, we need to do some cleaning first. + this->values_of_landscapes.clear(); + this->grid_min = this->grid_max = 0; + + // if there is nothing to average, then the average is a zero landscape. + if (to_average.size() == 0) return; + + // now we need to check if the grids in all objects of to_average are the same: + for (size_t i = 0; i != to_average.size(); ++i) { + if (!check_if_defined_on_the_same_domain(*(to_average[0]), *(to_average[i]))) + throw "Two grids are not compatible"; + } + + this->values_of_landscapes = std::vector<std::vector<double> >((to_average[0])->values_of_landscapes.size()); + this->grid_min = (to_average[0])->grid_min; + this->grid_max = (to_average[0])->grid_max; + + if (dbg) { + std::cerr << "Computations of average. The data from the current landscape have been cleared. We are ready to do " + "the computations. \n"; + } + + // for every point in the grid: + for (size_t grid_point = 0; grid_point != (to_average[0])->values_of_landscapes.size(); ++grid_point) { + // set up a vector of the correct size: + size_t maximal_size_of_vector = 0; + for (size_t land_no = 0; land_no != to_average.size(); ++land_no) { + if ((to_average[land_no])->values_of_landscapes[grid_point].size() > maximal_size_of_vector) + maximal_size_of_vector = (to_average[land_no])->values_of_landscapes[grid_point].size(); + } + this->values_of_landscapes[grid_point] = std::vector<double>(maximal_size_of_vector); + + if (dbg) { + std::cerr << "We are considering the point : " << grid_point + << " of the grid. In this point, there are at most : " << maximal_size_of_vector + << " nonzero landscape functions \n"; + } + + // and compute an arithmetic average: + for (size_t land_no = 0; land_no != to_average.size(); ++land_no) { + // summing: + for (size_t i = 0; i != (to_average[land_no])->values_of_landscapes[grid_point].size(); ++i) { + // compute the average in a smarter way. + this->values_of_landscapes[grid_point][i] += (to_average[land_no])->values_of_landscapes[grid_point][i]; + } + } + // normalizing: + for (size_t i = 0; i != this->values_of_landscapes[grid_point].size(); ++i) { + this->values_of_landscapes[grid_point][i] /= static_cast<double>(to_average.size()); + } + } + } // compute_average + + /** + * A function to compute distance between persistence landscape on a grid. + * The parameter of this function is a Persistence_landscape_on_grid. + * This function is required in Topological_data_with_distances concept. + * For max norm distance, set power to std::numeric_limits<double>::max() + **/ + double distance(const Persistence_landscape_on_grid& second, double power = 1) const { + if (power < std::numeric_limits<double>::max()) { + return compute_distance_of_landscapes_on_grid(*this, second, power); + } else { + return compute_max_norm_distance_of_landscapes(*this, second); + } + } + + /** + * A function to compute scalar product of persistence landscape on a grid. + * The parameter of this function is a Persistence_landscape_on_grid. + * This function is required in Topological_data_with_scalar_product concept. + **/ + double compute_scalar_product(const Persistence_landscape_on_grid& second) { + return compute_inner_product((*this), second); + } + + // end of implementation of functions needed for concepts. + + /** + * A function that returns values of landscapes. It can be used for visualization + **/ + std::vector<std::vector<double> > output_for_visualization() const { return this->values_of_landscapes; } + + /** + * function used to create a gnuplot script for visualization of landscapes. Over here we need to specify which + *landscapes do we want to plot. + * In addition, the user may specify the range (min and max) where landscape is plot. The default values for min and + *max are std::numeric_limits<double>::max(). If the procedure detect those + * values, it will determine the range so that the whole landscape is supported there. If at least one min or max value + *is different from std::numeric_limits<double>::max(), then the values + * provided by the user will be used. + **/ + void plot(const char* filename, size_t from_, size_t to_) const { + this->plot(filename, std::numeric_limits<double>::max(), std::numeric_limits<double>::max(), + std::numeric_limits<double>::max(), std::numeric_limits<double>::max(), from_, to_); + } + + /** + * function used to create a gnuplot script for visualization of landscapes. Over here we can restrict also x and y + *range of the landscape. + **/ + void plot(const char* filename, double min_x = std::numeric_limits<double>::max(), + double max_x = std::numeric_limits<double>::max(), double min_y = std::numeric_limits<double>::max(), + double max_y = std::numeric_limits<double>::max(), size_t from_ = std::numeric_limits<size_t>::max(), + size_t to_ = std::numeric_limits<size_t>::max()) const; + + protected: + double grid_min; + double grid_max; + std::vector<std::vector<double> > values_of_landscapes; + size_t number_of_functions_for_vectorization; + size_t number_of_functions_for_projections_to_reals; + + void set_up_numbers_of_functions_for_vectorization_and_projections_to_reals() { + // warning, this function can be only called after filling in the values_of_landscapes vector. + this->number_of_functions_for_vectorization = this->values_of_landscapes.size(); + this->number_of_functions_for_projections_to_reals = this->values_of_landscapes.size(); + } + void set_up_values_of_landscapes(const std::vector<std::pair<double, double> >& p, double grid_min_, double grid_max_, + size_t number_of_points_, + unsigned number_of_levels = std::numeric_limits<unsigned>::max()); + Persistence_landscape_on_grid multiply_lanscape_by_real_number_not_overwrite(double x) const; +}; + +void Persistence_landscape_on_grid::set_up_values_of_landscapes(const std::vector<std::pair<double, double> >& p, + double grid_min_, double grid_max_, + size_t number_of_points_, unsigned number_of_levels) { + bool dbg = false; + if (dbg) { + std::cerr << "Here is the procedure : set_up_values_of_landscapes. The parameters are : grid_min_ : " << grid_min_ + << ", grid_max_ : " << grid_max_ << ", number_of_points_ : " << number_of_points_ + << ", number_of_levels: " << number_of_levels << std::endl; + std::cerr << "Here are the intervals at our disposal : \n"; + for (size_t i = 0; i != p.size(); ++i) { + std::cerr << p[i].first << " , " << p[i].second << std::endl; + } + } + + if ((grid_min_ == std::numeric_limits<double>::max()) || (grid_max_ == std::numeric_limits<double>::max())) { + // in this case, we need to find grid_min_ and grid_min_ based on the data. + double min = std::numeric_limits<double>::max(); + double max = std::numeric_limits<double>::min(); + for (size_t i = 0; i != p.size(); ++i) { + if (p[i].first < min) min = p[i].first; + if (p[i].second > max) max = p[i].second; + } + if (grid_min_ == std::numeric_limits<double>::max()) { + grid_min_ = min; + } else { + // in this case grid_max_ == std::numeric_limits<double>::max() + grid_max_ = max; + } + } + + // if number_of_levels == std::numeric_limits<size_t>::max(), then we will have all the nonzero values of landscapes, + // and will store them in a vector + // if number_of_levels != std::numeric_limits<size_t>::max(), then we will use those vectors as heaps. + this->values_of_landscapes = std::vector<std::vector<double> >(number_of_points_ + 1); + + this->grid_min = grid_min_; + this->grid_max = grid_max_; + + if (grid_max_ <= grid_min_) { + throw "Wrong parameters of grid_min and grid_max given to the procedure. The program will now terminate.\n"; + } + + double dx = (grid_max_ - grid_min_) / static_cast<double>(number_of_points_); + // for every interval in the diagram: + for (size_t int_no = 0; int_no != p.size(); ++int_no) { + size_t grid_interval_begin = (p[int_no].first - grid_min_) / dx; + size_t grid_interval_end = (p[int_no].second - grid_min_) / dx; + size_t grid_interval_midpoint = (size_t)(0.5 * (grid_interval_begin + grid_interval_end)); + + if (dbg) { + std::cerr << "Considering an interval : " << p[int_no].first << "," << p[int_no].second << std::endl; + + std::cerr << "grid_interval_begin : " << grid_interval_begin << std::endl; + std::cerr << "grid_interval_end : " << grid_interval_end << std::endl; + std::cerr << "grid_interval_midpoint : " << grid_interval_midpoint << std::endl; + } + + double landscape_value = dx; + for (size_t i = grid_interval_begin + 1; i < grid_interval_midpoint; ++i) { + if (dbg) { + std::cerr << "Adding landscape value (going up) for a point : " << i << " equal : " << landscape_value + << std::endl; + } + if (number_of_levels != std::numeric_limits<unsigned>::max()) { + // we have a heap of no more that number_of_levels values. + // Note that if we are using heaps, we want to know the shortest distance in the heap. + // This is achieved by putting -distance to the heap. + if (this->values_of_landscapes[i].size() >= number_of_levels) { + // in this case, the full heap is build, and we need to check if the landscape_value is not larger than the + // smallest element in the heap. + if (-landscape_value < this->values_of_landscapes[i].front()) { + // if it is, we remove the largest value in the heap, and move on. + std::pop_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1] = -landscape_value; + std::push_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + } + } else { + // in this case we are still filling in the array. + this->values_of_landscapes[i].push_back(-landscape_value); + if (this->values_of_landscapes[i].size() == number_of_levels - 1) { + // this->values_of_landscapes[i].size() == number_of_levels + // in this case we need to create the heap. + std::make_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + } + } + } else { + // we have vector of all values + this->values_of_landscapes[i].push_back(landscape_value); + } + landscape_value += dx; + } + for (size_t i = grid_interval_midpoint; i <= grid_interval_end; ++i) { + if (landscape_value > 0) { + if (number_of_levels != std::numeric_limits<unsigned>::max()) { + // we have a heap of no more that number_of_levels values + if (this->values_of_landscapes[i].size() >= number_of_levels) { + // in this case, the full heap is build, and we need to check if the landscape_value is not larger than the + // smallest element in the heap. + if (-landscape_value < this->values_of_landscapes[i].front()) { + // if it is, we remove the largest value in the heap, and move on. + std::pop_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + this->values_of_landscapes[i][this->values_of_landscapes[i].size() - 1] = -landscape_value; + std::push_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + } + } else { + // in this case we are still filling in the array. + this->values_of_landscapes[i].push_back(-landscape_value); + if (this->values_of_landscapes[i].size() == number_of_levels - 1) { + // this->values_of_landscapes[i].size() == number_of_levels + // in this case we need to create the heap. + std::make_heap(this->values_of_landscapes[i].begin(), this->values_of_landscapes[i].end()); + } + } + } else { + this->values_of_landscapes[i].push_back(landscape_value); + } + + if (dbg) { + std::cerr << "Adding landscape value (going down) for a point : " << i << " equal : " << landscape_value + << std::endl; + } + } + landscape_value -= dx; + } + } + + if (number_of_levels != std::numeric_limits<unsigned>::max()) { + // in this case, vectors are used as heaps. And, since we want to have the smallest element at the top of + // each heap, we store minus distances. To get if right at the end, we need to multiply each value + // in the heap by -1 to get real vector of distances. + for (size_t pt = 0; pt != this->values_of_landscapes.size(); ++pt) { + for (size_t j = 0; j != this->values_of_landscapes[pt].size(); ++j) { + this->values_of_landscapes[pt][j] *= -1; + } + } + } + + // and now we need to sort the values: + for (size_t pt = 0; pt != this->values_of_landscapes.size(); ++pt) { + std::sort(this->values_of_landscapes[pt].begin(), this->values_of_landscapes[pt].end(), std::greater<double>()); + } +} // set_up_values_of_landscapes + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const std::vector<std::pair<double, double> >& p, + double grid_min_, double grid_max_, + size_t number_of_points_) { + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_); +} // Persistence_landscape_on_grid + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const std::vector<std::pair<double, double> >& p, + double grid_min_, double grid_max_, + size_t number_of_points_, + unsigned number_of_levels_of_landscape) { + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_, number_of_levels_of_landscape); +} + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const char* filename, double grid_min_, double grid_max_, + size_t number_of_points_, uint16_t dimension) { + std::vector<std::pair<double, double> > p; + if (dimension == std::numeric_limits<uint16_t>::max()) { + p = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + p = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_); +} + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const char* filename, double grid_min_, double grid_max_, + size_t number_of_points_, + unsigned number_of_levels_of_landscape, + uint16_t dimension) { + std::vector<std::pair<double, double> > p; + if (dimension == std::numeric_limits<uint16_t>::max()) { + p = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + p = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_, number_of_levels_of_landscape); +} + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const char* filename, size_t number_of_points_, + uint16_t dimension) { + std::vector<std::pair<double, double> > p; + if (dimension == std::numeric_limits<uint16_t>::max()) { + p = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + p = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + double grid_min_ = std::numeric_limits<double>::max(); + double grid_max_ = -std::numeric_limits<double>::max(); + for (size_t i = 0; i != p.size(); ++i) { + if (p[i].first < grid_min_) grid_min_ = p[i].first; + if (p[i].second > grid_max_) grid_max_ = p[i].second; + } + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_); +} + +Persistence_landscape_on_grid::Persistence_landscape_on_grid(const char* filename, size_t number_of_points_, + unsigned number_of_levels_of_landscape, + uint16_t dimension) { + std::vector<std::pair<double, double> > p; + if (dimension == std::numeric_limits<uint16_t>::max()) { + p = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + p = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + double grid_min_ = std::numeric_limits<double>::max(); + double grid_max_ = -std::numeric_limits<double>::max(); + for (size_t i = 0; i != p.size(); ++i) { + if (p[i].first < grid_min_) grid_min_ = p[i].first; + if (p[i].second > grid_max_) grid_max_ = p[i].second; + } + this->set_up_values_of_landscapes(p, grid_min_, grid_max_, number_of_points_, number_of_levels_of_landscape); +} + +void Persistence_landscape_on_grid::load_landscape_from_file(const char* filename) { + std::ifstream in; + in.open(filename); + // check if the file exist. + if (!in.good()) { + std::cerr << "The file : " << filename << " do not exist. The program will now terminate \n"; + throw "The persistence landscape file do not exist. The program will now terminate \n"; + } + + size_t number_of_points_in_the_grid = 0; + in >> this->grid_min >> this->grid_max >> number_of_points_in_the_grid; + + std::vector<std::vector<double> > v(number_of_points_in_the_grid); + std::string line; + std::getline(in, line); + double number; + for (size_t i = 0; i != number_of_points_in_the_grid; ++i) { + // read a line of a file and convert it to a vector. + std::vector<double> vv; + std::getline(in, line); + std::istringstream stream(line); + while (stream >> number) { + vv.push_back(number); + } + v[i] = vv; + } + this->values_of_landscapes = v; + in.close(); +} + +void Persistence_landscape_on_grid::print_to_file(const char* filename) const { + std::ofstream out; + out.open(filename); + + // first we store the parameters of the grid: + out << grid_min << std::endl << grid_max << std::endl << this->values_of_landscapes.size() << std::endl; + + // and now in the following lines, the values of this->values_of_landscapes for the following arguments: + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + for (size_t j = 0; j != this->values_of_landscapes[i].size(); ++j) { + out << this->values_of_landscapes[i][j] << " "; + } + out << std::endl; + } + + out.close(); +} + +void Persistence_landscape_on_grid::plot(const char* filename, double min_x, double max_x, double min_y, double max_y, + size_t from_, size_t to_) const { + // this program create a gnuplot script file that allows to plot persistence diagram. + std::ofstream out; + + std::ostringstream gnuplot_script; + gnuplot_script << filename << "_GnuplotScript"; + out.open(gnuplot_script.str().c_str()); + + if (min_x == max_x) { + std::pair<double, double> min_max = compute_minimum_maximum(); + out << "set xrange [" << this->grid_min << " : " << this->grid_max << "]" << std::endl; + out << "set yrange [" << min_max.first << " : " << min_max.second << "]" << std::endl; + } else { + out << "set xrange [" << min_x << " : " << max_x << "]" << std::endl; + out << "set yrange [" << min_y << " : " << max_y << "]" << std::endl; + } + + size_t number_of_nonzero_levels = this->number_of_nonzero_levels(); + double dx = (this->grid_max - this->grid_min) / static_cast<double>(this->values_of_landscapes.size() - 1); + + size_t from = 0; + if (from_ != std::numeric_limits<size_t>::max()) { + if (from_ < number_of_nonzero_levels) { + from = from_; + } else { + return; + } + } + size_t to = number_of_nonzero_levels; + if (to_ != std::numeric_limits<size_t>::max()) { + if (to_ < number_of_nonzero_levels) { + to = to_; + } + } + + out << "plot "; + for (size_t lambda = from; lambda != to; ++lambda) { + out << " '-' using 1:2 notitle with lp"; + if (lambda + 1 != to) { + out << ", \\"; + } + out << std::endl; + } + + for (size_t lambda = from; lambda != to; ++lambda) { + double point = this->grid_min; + for (size_t i = 0; i != this->values_of_landscapes.size(); ++i) { + double value = 0; + if (this->values_of_landscapes[i].size() > lambda) { + value = this->values_of_landscapes[i][lambda]; + } + out << point << " " << value << std::endl; + point += dx; + } + out << "EOF" << std::endl; + } + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; +} + +template <typename T> +Persistence_landscape_on_grid operation_on_pair_of_landscapes_on_grid(const Persistence_landscape_on_grid& land1, + const Persistence_landscape_on_grid& land2) { + // first we need to check if the domains are the same: + if (!check_if_defined_on_the_same_domain(land1, land2)) throw "Two grids are not compatible"; + + T oper; + Persistence_landscape_on_grid result; + result.values_of_landscapes = std::vector<std::vector<double> >(land1.values_of_landscapes.size()); + result.grid_min = land1.grid_min; + result.grid_max = land1.grid_max; + + // now we perform the operations: + for (size_t grid_point = 0; grid_point != land1.values_of_landscapes.size(); ++grid_point) { + result.values_of_landscapes[grid_point] = std::vector<double>( + std::max(land1.values_of_landscapes[grid_point].size(), land2.values_of_landscapes[grid_point].size())); + for (size_t lambda = 0; lambda != std::max(land1.values_of_landscapes[grid_point].size(), + land2.values_of_landscapes[grid_point].size()); + ++lambda) { + double value1 = 0; + double value2 = 0; + if (lambda < land1.values_of_landscapes[grid_point].size()) + value1 = land1.values_of_landscapes[grid_point][lambda]; + if (lambda < land2.values_of_landscapes[grid_point].size()) + value2 = land2.values_of_landscapes[grid_point][lambda]; + result.values_of_landscapes[grid_point][lambda] = oper(value1, value2); + } + } + + return result; +} + +Persistence_landscape_on_grid Persistence_landscape_on_grid::multiply_lanscape_by_real_number_not_overwrite( + double x) const { + Persistence_landscape_on_grid result; + result.values_of_landscapes = std::vector<std::vector<double> >(this->values_of_landscapes.size()); + result.grid_min = this->grid_min; + result.grid_max = this->grid_max; + + for (size_t grid_point = 0; grid_point != this->values_of_landscapes.size(); ++grid_point) { + result.values_of_landscapes[grid_point] = std::vector<double>(this->values_of_landscapes[grid_point].size()); + for (size_t i = 0; i != this->values_of_landscapes[grid_point].size(); ++i) { + result.values_of_landscapes[grid_point][i] = x * this->values_of_landscapes[grid_point][i]; + } + } + + return result; +} + +double compute_max_norm_distance_of_landscapes(const Persistence_landscape_on_grid& first, + const Persistence_landscape_on_grid& second) { + double result = 0; + + // first we need to check if first and second is defined on the same domain" + if (!check_if_defined_on_the_same_domain(first, second)) throw "Two grids are not compatible"; + + for (size_t i = 0; i != first.values_of_landscapes.size(); ++i) { + for (size_t j = 0; j != std::min(first.values_of_landscapes[i].size(), second.values_of_landscapes[i].size()); + ++j) { + if (result < abs(first.values_of_landscapes[i][j] - second.values_of_landscapes[i][j])) { + result = abs(first.values_of_landscapes[i][j] - second.values_of_landscapes[i][j]); + } + } + if (first.values_of_landscapes[i].size() == + std::min(first.values_of_landscapes[i].size(), second.values_of_landscapes[i].size())) { + for (size_t j = first.values_of_landscapes[i].size(); j != second.values_of_landscapes[i].size(); ++j) { + if (result < second.values_of_landscapes[i][j]) result = second.values_of_landscapes[i][j]; + } + } + if (second.values_of_landscapes[i].size() == + std::min(first.values_of_landscapes[i].size(), second.values_of_landscapes[i].size())) { + for (size_t j = second.values_of_landscapes[i].size(); j != first.values_of_landscapes[i].size(); ++j) { + if (result < first.values_of_landscapes[i][j]) result = first.values_of_landscapes[i][j]; + } + } + } + return result; +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_LANDSCAPE_ON_GRID_H_ diff --git a/include/gudhi/Persistence_vectors.h b/include/gudhi/Persistence_vectors.h new file mode 100644 index 00000000..63577e46 --- /dev/null +++ b/include/gudhi/Persistence_vectors.h @@ -0,0 +1,640 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef PERSISTENCE_VECTORS_H_ +#define PERSISTENCE_VECTORS_H_ + +// gudhi include +#include <gudhi/read_persistence_from_file.h> +#include <gudhi/common_persistence_representations.h> +#include <gudhi/distance_functions.h> + +#include <fstream> +#include <cmath> +#include <algorithm> +#include <iostream> +#include <limits> +#include <functional> +#include <utility> +#include <vector> + +namespace Gudhi { +namespace Persistence_representations { + +template <typename T> +struct Maximum_distance { + double operator()(const std::pair<T, T>& f, const std::pair<T, T>& s) { + return std::max(fabs(f.first - s.first), fabs(f.second - s.second)); + } +}; + +/** + * \class Vector_distances_in_diagram Persistence_vectors.h gudhi/Persistence_vectors.h + * \brief A class implementing persistence vectors. + * + * \ingroup Persistence_representations + * + * \details + * This is an implementation of idea presented in the paper <i>Stable Topological Signatures for Points on 3D + * Shapes</i> \cite Carriere_Oudot_Ovsjanikov_top_signatures_3d .<br> + * The parameter of the class is the class that computes distance used to construct the vectors. The typical function + * is either Euclidean of maximum (Manhattan) distance. + * + * This class implements the following concepts: Vectorized_topological_data, Topological_data_with_distances, + * Real_valued_topological_data, Topological_data_with_averages, Topological_data_with_scalar_product + **/ +template <typename F> +class Vector_distances_in_diagram { + public: + /** + * The default constructor. + **/ + Vector_distances_in_diagram() {} + + /** + * The constructor that takes as an input a multiset of persistence intervals (given as vector of birth-death + *pairs). The second parameter is the desired length of the output vectors. + **/ + Vector_distances_in_diagram(const std::vector<std::pair<double, double> >& intervals, size_t where_to_cut); + + /** + * The constructor taking as an input a file with birth-death pairs. The second parameter is the desired length of + *the output vectors. + **/ + Vector_distances_in_diagram(const char* filename, size_t where_to_cut, + unsigned dimension = std::numeric_limits<unsigned>::max()); + + /** + * Writing to a stream. + **/ + template <typename K> + friend std::ostream& operator<<(std::ostream& out, const Vector_distances_in_diagram<K>& d) { + for (size_t i = 0; i != std::min(d.sorted_vector_of_distances.size(), d.where_to_cut); ++i) { + out << d.sorted_vector_of_distances[i] << " "; + } + return out; + } + + /** + * This procedure gives the value of a vector on a given position. + **/ + inline double vector_in_position(size_t position) const { + if (position >= this->sorted_vector_of_distances.size()) + throw("Wrong position in accessing Vector_distances_in_diagram::sorted_vector_of_distances\n"); + return this->sorted_vector_of_distances[position]; + } + + /** + * Return a size of a vector. + **/ + inline size_t size() const { return this->sorted_vector_of_distances.size(); } + + /** + * Write a vector to a file. + **/ + void write_to_file(const char* filename) const; + + /** + * Write a vector to a file. + **/ + void print_to_file(const char* filename) const { this->write_to_file(filename); } + + /** + * Loading a vector to a file. + **/ + void load_from_file(const char* filename); + + /** + * Comparison operators: + **/ + bool operator==(const Vector_distances_in_diagram& second) const { + if (this->sorted_vector_of_distances.size() != second.sorted_vector_of_distances.size()) return false; + for (size_t i = 0; i != this->sorted_vector_of_distances.size(); ++i) { + if (!almost_equal(this->sorted_vector_of_distances[i], second.sorted_vector_of_distances[i])) return false; + } + return true; + } + + bool operator!=(const Vector_distances_in_diagram& second) const { return !(*this == second); } + + // Implementations of functions for various concepts. + /** + * Compute projection to real numbers of persistence vector. This function is required by the + *Real_valued_topological_data concept + * At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when + *using it, keep in mind that it + * will be most likely changed in the next versions. + **/ + double project_to_R(int number_of_function) const; + /** + * The function gives the number of possible projections to R. This function is required by the + *Real_valued_topological_data concept. + **/ + size_t number_of_projections_to_R() const { return this->number_of_functions_for_projections_to_reals; } + + /** + * Compute a vectorization of a persistent vectors. It is required in a concept Vectorized_topological_data. + **/ + std::vector<double> vectorize(int number_of_function) const; + /** + * This function return the number of functions that allows vectorization of a persistence vector. It is required + *in a concept Vectorized_topological_data. + **/ + size_t number_of_vectorize_functions() const { return this->number_of_functions_for_vectorization; } + + /** + * Compute a average of two persistent vectors. This function is required by Topological_data_with_averages concept. + **/ + void compute_average(const std::vector<Vector_distances_in_diagram*>& to_average); + + /** + * Compute a distance of two persistent vectors. This function is required in Topological_data_with_distances concept. + * For max norm distance, set power to std::numeric_limits<double>::max() + **/ + double distance(const Vector_distances_in_diagram& second, double power = 1) const; + + /** + * Compute a scalar product of two persistent vectors. This function is required in + *Topological_data_with_scalar_product concept. + **/ + double compute_scalar_product(const Vector_distances_in_diagram& second) const; + // end of implementation of functions needed for concepts. + + /** + * For visualization use output from vectorize and build histograms. + **/ + std::vector<double> output_for_visualization() const { return this->sorted_vector_of_distances; } + + /** + * Create a gnuplot script to visualize the data structure. + **/ + void plot(const char* filename) const { + std::stringstream gnuplot_script; + gnuplot_script << filename << "_GnuplotScript"; + std::ofstream out; + out.open(gnuplot_script.str().c_str()); + out << "set style data histogram" << std::endl; + out << "set style histogram cluster gap 1" << std::endl; + out << "set style fill solid border -1" << std::endl; + out << "plot '-' notitle" << std::endl; + for (size_t i = 0; i != this->sorted_vector_of_distances.size(); ++i) { + out << this->sorted_vector_of_distances[i] << std::endl; + } + out << std::endl; + out.close(); + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; + } + + /** + * The x-range of the persistence vector. + **/ + std::pair<double, double> get_x_range() const { return std::make_pair(0, this->sorted_vector_of_distances.size()); } + + /** + * The y-range of the persistence vector. + **/ + std::pair<double, double> get_y_range() const { + if (this->sorted_vector_of_distances.size() == 0) return std::make_pair(0, 0); + return std::make_pair(this->sorted_vector_of_distances[0], 0); + } + + // arithmetic operations: + template <typename Operation_type> + friend Vector_distances_in_diagram operation_on_pair_of_vectors(const Vector_distances_in_diagram& first, + const Vector_distances_in_diagram& second, + Operation_type opertion) { + Vector_distances_in_diagram result; + // Operation_type operation; + result.sorted_vector_of_distances.reserve( + std::max(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size())); + for (size_t i = 0; i != std::min(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size()); + ++i) { + result.sorted_vector_of_distances.push_back( + opertion(first.sorted_vector_of_distances[i], second.sorted_vector_of_distances[i])); + } + if (first.sorted_vector_of_distances.size() == + std::min(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size())) { + for (size_t i = std::min(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size()); + i != std::max(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size()); ++i) { + result.sorted_vector_of_distances.push_back(opertion(0, second.sorted_vector_of_distances[i])); + } + } else { + for (size_t i = std::min(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size()); + i != std::max(first.sorted_vector_of_distances.size(), second.sorted_vector_of_distances.size()); ++i) { + result.sorted_vector_of_distances.push_back(opertion(first.sorted_vector_of_distances[i], 0)); + } + } + return result; + } // operation_on_pair_of_vectors + + /** + * This function implements an operation of multiplying Vector_distances_in_diagram by a scalar. + **/ + Vector_distances_in_diagram multiply_by_scalar(double scalar) const { + Vector_distances_in_diagram result; + result.sorted_vector_of_distances.reserve(this->sorted_vector_of_distances.size()); + for (size_t i = 0; i != this->sorted_vector_of_distances.size(); ++i) { + result.sorted_vector_of_distances.push_back(scalar * this->sorted_vector_of_distances[i]); + } + return result; + } // multiply_by_scalar + + /** + * This function computes a sum of two objects of a type Vector_distances_in_diagram. + **/ + friend Vector_distances_in_diagram operator+(const Vector_distances_in_diagram& first, + const Vector_distances_in_diagram& second) { + return operation_on_pair_of_vectors(first, second, std::plus<double>()); + } + /** +* This function computes a difference of two objects of a type Vector_distances_in_diagram. +**/ + friend Vector_distances_in_diagram operator-(const Vector_distances_in_diagram& first, + const Vector_distances_in_diagram& second) { + return operation_on_pair_of_vectors(first, second, std::minus<double>()); + } + /** +* This function computes a product of an object of a type Vector_distances_in_diagram with real number. +**/ + friend Vector_distances_in_diagram operator*(double scalar, const Vector_distances_in_diagram& A) { + return A.multiply_by_scalar(scalar); + } + /** +* This function computes a product of an object of a type Vector_distances_in_diagram with real number. +**/ + friend Vector_distances_in_diagram operator*(const Vector_distances_in_diagram& A, double scalar) { + return A.multiply_by_scalar(scalar); + } + /** +* This function computes a product of an object of a type Vector_distances_in_diagram with real number. +**/ + Vector_distances_in_diagram operator*(double scalar) { return this->multiply_by_scalar(scalar); } + /** + * += operator for Vector_distances_in_diagram. + **/ + Vector_distances_in_diagram operator+=(const Vector_distances_in_diagram& rhs) { + *this = *this + rhs; + return *this; + } + /** + * -= operator for Vector_distances_in_diagram. + **/ + Vector_distances_in_diagram operator-=(const Vector_distances_in_diagram& rhs) { + *this = *this - rhs; + return *this; + } + /** + * *= operator for Vector_distances_in_diagram. + **/ + Vector_distances_in_diagram operator*=(double x) { + *this = *this * x; + return *this; + } + /** + * /= operator for Vector_distances_in_diagram. + **/ + Vector_distances_in_diagram operator/=(double x) { + if (x == 0) throw("In operator /=, division by 0. Program terminated."); + *this = *this * (1 / x); + return *this; + } + + private: + std::vector<std::pair<double, double> > intervals; + std::vector<double> sorted_vector_of_distances; + size_t number_of_functions_for_vectorization; + size_t number_of_functions_for_projections_to_reals; + size_t where_to_cut; + + void compute_sorted_vector_of_distances_via_heap(size_t where_to_cut); + void compute_sorted_vector_of_distances_via_vector_sorting(size_t where_to_cut); + + Vector_distances_in_diagram(const std::vector<double>& sorted_vector_of_distances_) + : sorted_vector_of_distances(sorted_vector_of_distances_) { + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); + } + + void set_up_numbers_of_functions_for_vectorization_and_projections_to_reals() { + // warning, this function can be only called after filling in the intervals vector. + this->number_of_functions_for_vectorization = this->sorted_vector_of_distances.size(); + this->number_of_functions_for_projections_to_reals = this->sorted_vector_of_distances.size(); + } +}; + +template <typename F> +Vector_distances_in_diagram<F>::Vector_distances_in_diagram(const std::vector<std::pair<double, double> >& intervals_, + size_t where_to_cut_) + : where_to_cut(where_to_cut_) { + std::vector<std::pair<double, double> > i(intervals_); + this->intervals = i; + // this->compute_sorted_vector_of_distances_via_heap( where_to_cut ); + this->compute_sorted_vector_of_distances_via_vector_sorting(where_to_cut); + this->set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} + +template <typename F> +Vector_distances_in_diagram<F>::Vector_distances_in_diagram(const char* filename, size_t where_to_cut, + unsigned dimension) + : where_to_cut(where_to_cut) { + std::vector<std::pair<double, double> > intervals; + if (dimension == std::numeric_limits<unsigned>::max()) { + intervals = read_persistence_intervals_in_one_dimension_from_file(filename); + } else { + intervals = read_persistence_intervals_in_one_dimension_from_file(filename, dimension); + } + this->intervals = intervals; + this->compute_sorted_vector_of_distances_via_heap(where_to_cut); + // this->compute_sorted_vector_of_distances_via_vector_sorting( where_to_cut ); + set_up_numbers_of_functions_for_vectorization_and_projections_to_reals(); +} + +template <typename F> +void Vector_distances_in_diagram<F>::compute_sorted_vector_of_distances_via_heap(size_t where_to_cut) { + bool dbg = false; + if (dbg) { + std::cerr << "Here are the intervals : \n"; + for (size_t i = 0; i != this->intervals.size(); ++i) { + std::cerr << this->intervals[i].first << " , " << this->intervals[i].second << std::endl; + } + } + where_to_cut = std::min( + where_to_cut, (size_t)(0.5 * this->intervals.size() * (this->intervals.size() - 1) + this->intervals.size())); + + std::vector<double> heap(where_to_cut, std::numeric_limits<int>::max()); + std::make_heap(heap.begin(), heap.end()); + F f; + + // for every pair of points in the diagram, compute the minimum of their distance, and distance of those points from + // diagonal + for (size_t i = 0; i < this->intervals.size(); ++i) { + for (size_t j = i + 1; j < this->intervals.size(); ++j) { + double value = std::min( + f(this->intervals[i], this->intervals[j]), + std::min( + f(this->intervals[i], std::make_pair(0.5 * (this->intervals[i].first + this->intervals[i].second), + 0.5 * (this->intervals[i].first + this->intervals[i].second))), + f(this->intervals[j], std::make_pair(0.5 * (this->intervals[j].first + this->intervals[j].second), + 0.5 * (this->intervals[j].first + this->intervals[j].second))))); + + if (dbg) { + std::cerr << "Value : " << value << std::endl; + std::cerr << "heap.front() : " << heap.front() << std::endl; + getchar(); + } + + if (-value < heap.front()) { + if (dbg) { + std::cerr << "Replacing : " << heap.front() << " with : " << -value << std::endl; + getchar(); + } + // remove the first element from the heap + std::pop_heap(heap.begin(), heap.end()); + // heap.pop_back(); + // and put value there instead: + // heap.push_back(-value); + heap[where_to_cut - 1] = -value; + std::push_heap(heap.begin(), heap.end()); + } + } + } + + // now add distances of all points from diagonal + for (size_t i = 0; i < this->intervals.size(); ++i) { + double value = f(this->intervals[i], std::make_pair(0.5 * (this->intervals[i].first + this->intervals[i].second), + 0.5 * (this->intervals[i].first + this->intervals[i].second))); + if (-value < heap.front()) { + // remove the first element from the heap + std::pop_heap(heap.begin(), heap.end()); + // heap.pop_back(); + // and put value there instead: + // heap.push_back(-value); + heap[where_to_cut - 1] = -value; + std::push_heap(heap.begin(), heap.end()); + } + } + + std::sort_heap(heap.begin(), heap.end()); + for (size_t i = 0; i != heap.size(); ++i) { + if (heap[i] == std::numeric_limits<int>::max()) { + heap[i] = 0; + } else { + heap[i] *= -1; + } + } + + if (dbg) { + std::cerr << "This is the heap after all the operations :\n"; + for (size_t i = 0; i != heap.size(); ++i) { + std::cout << heap[i] << " "; + } + std::cout << std::endl; + } + + this->sorted_vector_of_distances = heap; +} + +template <typename F> +void Vector_distances_in_diagram<F>::compute_sorted_vector_of_distances_via_vector_sorting(size_t where_to_cut) { + std::vector<double> distances; + distances.reserve((size_t)(0.5 * this->intervals.size() * (this->intervals.size() - 1) + this->intervals.size())); + F f; + + // for every pair of points in the diagram, compute the minimum of their distance, and distance of those points from + // diagonal + for (size_t i = 0; i < this->intervals.size(); ++i) { + // add distance of i-th point in the diagram from the diagonal to the distances vector + distances.push_back( + f(this->intervals[i], std::make_pair(0.5 * (this->intervals[i].first + this->intervals[i].second), + 0.5 * (this->intervals[i].first + this->intervals[i].second)))); + for (size_t j = i + 1; j < this->intervals.size(); ++j) { + double value = std::min( + f(this->intervals[i], this->intervals[j]), + std::min( + f(this->intervals[i], std::make_pair(0.5 * (this->intervals[i].first + this->intervals[i].second), + 0.5 * (this->intervals[i].first + this->intervals[i].second))), + f(this->intervals[j], std::make_pair(0.5 * (this->intervals[j].first + this->intervals[j].second), + 0.5 * (this->intervals[j].first + this->intervals[j].second))))); + distances.push_back(value); + } + } + std::sort(distances.begin(), distances.end(), std::greater<double>()); + if (distances.size() > where_to_cut) distances.resize(where_to_cut); + + this->sorted_vector_of_distances = distances; +} + +// Implementations of functions for various concepts. +template <typename F> +double Vector_distances_in_diagram<F>::project_to_R(int number_of_function) const { + if ((size_t)number_of_function > this->number_of_functions_for_projections_to_reals) + throw "Wrong index of a function in a method Vector_distances_in_diagram<F>::project_to_R"; + if (number_of_function < 0) + throw "Wrong index of a function in a method Vector_distances_in_diagram<F>::project_to_R"; + + double result = 0; + for (size_t i = 0; i != (size_t)number_of_function; ++i) { + result += sorted_vector_of_distances[i]; + } + return result; +} + +template <typename F> +void Vector_distances_in_diagram<F>::compute_average(const std::vector<Vector_distances_in_diagram*>& to_average) { + if (to_average.size() == 0) { + (*this) = Vector_distances_in_diagram<F>(); + return; + } + + size_t maximal_length_of_vector = 0; + for (size_t i = 0; i != to_average.size(); ++i) { + if (to_average[i]->sorted_vector_of_distances.size() > maximal_length_of_vector) { + maximal_length_of_vector = to_average[i]->sorted_vector_of_distances.size(); + } + } + + std::vector<double> av(maximal_length_of_vector, 0); + for (size_t i = 0; i != to_average.size(); ++i) { + for (size_t j = 0; j != to_average[i]->sorted_vector_of_distances.size(); ++j) { + av[j] += to_average[i]->sorted_vector_of_distances[j]; + } + } + + for (size_t i = 0; i != maximal_length_of_vector; ++i) { + av[i] /= static_cast<double>(to_average.size()); + } + this->sorted_vector_of_distances = av; + this->where_to_cut = av.size(); +} + +template <typename F> +double Vector_distances_in_diagram<F>::distance(const Vector_distances_in_diagram& second_, double power) const { + bool dbg = false; + + if (dbg) { + std::cerr << "Entering double Vector_distances_in_diagram<F>::distance( const Abs_Topological_data_with_distances* " + "second , double power ) procedure \n"; + std::cerr << "Power : " << power << std::endl; + std::cerr << "This : " << *this << std::endl; + std::cerr << "second : " << second_ << std::endl; + } + + double result = 0; + for (size_t i = 0; i != std::min(this->sorted_vector_of_distances.size(), second_.sorted_vector_of_distances.size()); + ++i) { + if (power == 1) { + if (dbg) { + std::cerr << "|" << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] + << " | : " << fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]) + << std::endl; + } + result += fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]); + } else { + if (power < std::numeric_limits<double>::max()) { + result += std::pow(fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]), power); + } else { + // max norm + if (result < fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i])) + result = fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]); + } + if (dbg) { + std::cerr << "| " << this->sorted_vector_of_distances[i] << " - " << second_.sorted_vector_of_distances[i] + << " : " << fabs(this->sorted_vector_of_distances[i] - second_.sorted_vector_of_distances[i]) + << std::endl; + } + } + } + if (this->sorted_vector_of_distances.size() != second_.sorted_vector_of_distances.size()) { + if (this->sorted_vector_of_distances.size() > second_.sorted_vector_of_distances.size()) { + for (size_t i = second_.sorted_vector_of_distances.size(); i != this->sorted_vector_of_distances.size(); ++i) { + result += fabs(this->sorted_vector_of_distances[i]); + } + } else { + // this->sorted_vector_of_distances.size() < second_.sorted_vector_of_distances.size() + for (size_t i = this->sorted_vector_of_distances.size(); i != second_.sorted_vector_of_distances.size(); ++i) { + result += fabs(second_.sorted_vector_of_distances[i]); + } + } + } + + if (power != 1) { + result = std::pow(result, (1.0 / power)); + } + return result; +} + +template <typename F> +std::vector<double> Vector_distances_in_diagram<F>::vectorize(int number_of_function) const { + if ((size_t)number_of_function > this->number_of_functions_for_vectorization) + throw "Wrong index of a function in a method Vector_distances_in_diagram<F>::vectorize"; + if (number_of_function < 0) throw "Wrong index of a function in a method Vector_distances_in_diagram<F>::vectorize"; + + std::vector<double> result(std::min((size_t)number_of_function, this->sorted_vector_of_distances.size())); + for (size_t i = 0; i != std::min((size_t)number_of_function, this->sorted_vector_of_distances.size()); ++i) { + result[i] = this->sorted_vector_of_distances[i]; + } + return result; +} + +template <typename F> +void Vector_distances_in_diagram<F>::write_to_file(const char* filename) const { + std::ofstream out; + out.open(filename); + + for (size_t i = 0; i != this->sorted_vector_of_distances.size(); ++i) { + out << this->sorted_vector_of_distances[i] << " "; + } + + out.close(); +} + +template <typename F> +void Vector_distances_in_diagram<F>::load_from_file(const char* filename) { + std::ifstream in; + in.open(filename); + // check if the file exist. + if (!in.good()) { + std::cerr << "The file : " << filename << " do not exist. The program will now terminate \n"; + throw "The persistence landscape file do not exist. The program will now terminate \n"; + } + + double number; + while (in >> number) { + this->sorted_vector_of_distances.push_back(number); + } + in.close(); +} + +template <typename F> +double Vector_distances_in_diagram<F>::compute_scalar_product(const Vector_distances_in_diagram& second_vector) const { + double result = 0; + for (size_t i = 0; + i != std::min(this->sorted_vector_of_distances.size(), second_vector.sorted_vector_of_distances.size()); ++i) { + result += this->sorted_vector_of_distances[i] * second_vector.sorted_vector_of_distances[i]; + } + return result; +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // PERSISTENCE_VECTORS_H_ diff --git a/include/gudhi/Simplex_tree.h b/include/gudhi/Simplex_tree.h index 37b3ea97..7456cb1f 100644 --- a/include/gudhi/Simplex_tree.h +++ b/include/gudhi/Simplex_tree.h @@ -49,6 +49,7 @@ #include <initializer_list> #include <algorithm> // for std::max #include <cstdint> // for std::uint32_t +#include <iterator> // for std::distance namespace Gudhi { @@ -106,8 +107,9 @@ class Simplex_tree { }; struct Key_simplex_base_dummy { Key_simplex_base_dummy() {} - void assign_key(Simplex_key) { } - Simplex_key key() const { assert(false); return -1; } + // Undefined so it will not link + void assign_key(Simplex_key); + Simplex_key key() const; }; typedef typename std::conditional<Options::store_key, Key_simplex_base_real, Key_simplex_base_dummy>::type Key_simplex_base; @@ -121,7 +123,7 @@ class Simplex_tree { }; struct Filtration_simplex_base_dummy { Filtration_simplex_base_dummy() {} - void assign_filtration(Filtration_value f) { assert(f == 0); } + void assign_filtration(Filtration_value GUDHI_CHECK_code(f)) { GUDHI_CHECK(f == 0, "filtration value specified for a complex that does not store them"); } Filtration_value filtration() const { return 0; } }; typedef typename std::conditional<Options::store_filtration, Filtration_simplex_base_real, @@ -391,13 +393,13 @@ class Simplex_tree { return sh->second.key(); } - /** \brief Returns the simplex associated to a key. + /** \brief Returns the simplex that has index idx in the filtration. * * The filtration must be initialized. * \pre SimplexTreeOptions::store_key */ - Simplex_handle simplex(Simplex_key key) const { - return filtration_vect_[key]; + Simplex_handle simplex(Simplex_key idx) const { + return filtration_vect_[idx]; } /** \brief Returns the filtration value of a simplex. @@ -482,7 +484,17 @@ class Simplex_tree { } /** \brief Returns an upper bound on the dimension of the simplicial complex. */ - int dimension() const { + int upper_bound_dimension() const { + return dimension_; + } + + /** \brief Returns the dimension of the simplicial complex. + \details This function is not constant time because it can recompute dimension if required (can be triggered by + `remove_maximal_simplex()` or `prune_above_filtration()`). + */ + int dimension() { + if (dimension_to_be_lowered_) + lower_upper_bound_dimension(); return dimension_; } @@ -490,6 +502,7 @@ class Simplex_tree { * sh has children.*/ template<class SimplexHandle> bool has_children(SimplexHandle sh) const { + // Here we rely on the root using null_vertex(), which cannot match any real vertex. return (sh->second.children()->parent() == sh->first); } @@ -519,18 +532,30 @@ class Simplex_tree { Simplex_handle find_simplex(const std::vector<Vertex_handle> & simplex) { Siblings * tmp_sib = &root_; Dictionary_it tmp_dit; - Vertex_handle last = simplex.back(); - for (auto v : simplex) { - tmp_dit = tmp_sib->members_.find(v); - if (tmp_dit == tmp_sib->members_.end()) { + auto vi = simplex.begin(); + if (Options::contiguous_vertices) { + // Equivalent to the first iteration of the normal loop + GUDHI_CHECK(contiguous_vertices(), "non-contiguous vertices"); + Vertex_handle v = *vi++; + if(v < 0 || v >= static_cast<Vertex_handle>(root_.members_.size())) return null_simplex(); - } - if (!has_children(tmp_dit) && v != last) { + tmp_dit = root_.members_.begin() + v; + if (vi == simplex.end()) + return tmp_dit; + if (!has_children(tmp_dit)) + return null_simplex(); + tmp_sib = tmp_dit->second.children(); + } + for (;;) { + tmp_dit = tmp_sib->members_.find(*vi++); + if (tmp_dit == tmp_sib->members_.end()) + return null_simplex(); + if (vi == simplex.end()) + return tmp_dit; + if (!has_children(tmp_dit)) return null_simplex(); - } tmp_sib = tmp_dit->second.children(); } - return tmp_dit; } /** \brief Returns the Simplex_handle corresponding to the 0-simplex @@ -574,12 +599,14 @@ class Simplex_tree { std::pair<Simplex_handle, bool> res_insert; auto vi = simplex.begin(); for (; vi != simplex.end() - 1; ++vi) { + GUDHI_CHECK(*vi != null_vertex(), "cannot use the dummy null_vertex() as a real vertex"); res_insert = curr_sib->members_.emplace(*vi, Node(curr_sib, filtration)); if (!(has_children(res_insert.first))) { res_insert.first->second.assign_children(new Siblings(curr_sib, *vi)); } curr_sib = res_insert.first->second.children(); } + GUDHI_CHECK(*vi != null_vertex(), "cannot use the dummy null_vertex() as a real vertex"); res_insert = curr_sib->members_.emplace(*vi, Node(curr_sib, filtration)); if (!res_insert.second) { // if already in the complex @@ -591,7 +618,11 @@ class Simplex_tree { // if filtration value unchanged return std::pair<Simplex_handle, bool>(null_simplex(), false); } - // otherwise the insertion has succeeded + // otherwise the insertion has succeeded - size is a size_type + if (static_cast<int>(simplex.size()) - 1 > dimension_) { + // Update dimension if needed + dimension_ = static_cast<int>(simplex.size()) - 1; + } return res_insert; } @@ -650,71 +681,67 @@ class Simplex_tree { */ template<class InputVertexRange = std::initializer_list<Vertex_handle>> std::pair<Simplex_handle, bool> insert_simplex_and_subfaces(const InputVertexRange& Nsimplex, - Filtration_value filtration = 0) { + Filtration_value filtration = 0) { auto first = std::begin(Nsimplex); auto last = std::end(Nsimplex); if (first == last) - return std::pair<Simplex_handle, bool>(null_simplex(), true); // ----->> + return { null_simplex(), true }; // ----->> // Copy before sorting - std::vector<Vertex_handle> copy(first, last); + thread_local std::vector<Vertex_handle> copy; + copy.clear(); + copy.insert(copy.end(), first, last); std::sort(std::begin(copy), std::end(copy)); + GUDHI_CHECK_code( + for (Vertex_handle v : copy) + GUDHI_CHECK(v != null_vertex(), "cannot use the dummy null_vertex() as a real vertex"); + ) - std::vector<std::vector<Vertex_handle>> to_be_inserted; - std::vector<std::vector<Vertex_handle>> to_be_propagated; - return rec_insert_simplex_and_subfaces(copy, to_be_inserted, to_be_propagated, filtration); + return insert_simplex_and_subfaces_sorted(copy, filtration); } private: - std::pair<Simplex_handle, bool> rec_insert_simplex_and_subfaces(std::vector<Vertex_handle>& the_simplex, - std::vector<std::vector<Vertex_handle>>& to_be_inserted, - std::vector<std::vector<Vertex_handle>>& to_be_propagated, - Filtration_value filtration = 0.0) { - std::pair<Simplex_handle, bool> insert_result; - if (the_simplex.size() > 1) { - // Get and remove last vertex - Vertex_handle last_vertex = the_simplex.back(); - the_simplex.pop_back(); - // Recursive call after last vertex removal - insert_result = rec_insert_simplex_and_subfaces(the_simplex, to_be_inserted, to_be_propagated, filtration); - - // Concatenation of to_be_inserted and to_be_propagated - to_be_inserted.insert(to_be_inserted.begin(), to_be_propagated.begin(), to_be_propagated.end()); - to_be_propagated = to_be_inserted; - - // to_be_inserted treatment - for (auto& simplex_tbi : to_be_inserted) { - simplex_tbi.push_back(last_vertex); - } - std::vector<Vertex_handle> last_simplex(1, last_vertex); - to_be_inserted.insert(to_be_inserted.begin(), last_simplex); - // i.e. (0,1,2) => - // [to_be_inserted | to_be_propagated] = [(1) (0,1) | (0)] - // [to_be_inserted | to_be_propagated] = [(2) (0,2) (1,2) (0,1,2) | (0) (1) (0,1)] - // N.B. : it is important the last inserted to be the highest in dimension - // in order to return the "last" insert_simplex result - - // insert all to_be_inserted - for (auto& simplex_tbi : to_be_inserted) { - insert_result = insert_vertex_vector(simplex_tbi, filtration); - } - } else if (the_simplex.size() == 1) { - // When reaching the end of recursivity, vector of simplices shall be empty and filled on back recursive - if ((to_be_inserted.size() != 0) || (to_be_propagated.size() != 0)) { - std::cerr << "Simplex_tree::rec_insert_simplex_and_subfaces - Error vector not empty\n"; - exit(-1); + /// Same as insert_simplex_and_subfaces but assumes that the range of vertices is sorted + template<class ForwardVertexRange = std::initializer_list<Vertex_handle>> + std::pair<Simplex_handle, bool> insert_simplex_and_subfaces_sorted(const ForwardVertexRange& Nsimplex, Filtration_value filt = 0) { + auto first = std::begin(Nsimplex); + auto last = std::end(Nsimplex); + if (first == last) + return { null_simplex(), true }; // FIXME: false would make more sense to me. + GUDHI_CHECK(std::is_sorted(first, last), "simplex vertices listed in unsorted order"); + // Update dimension if needed. We could wait to see if the insertion succeeds, but I doubt there is much to gain. + dimension_ = (std::max)(dimension_, static_cast<int>(std::distance(first, last)) - 1); + return rec_insert_simplex_and_subfaces_sorted(root(), first, last, filt); + } + // To insert {1,2,3,4}, we insert {2,3,4} twice, once at the root, and once below 1. + template<class ForwardVertexIterator> + std::pair<Simplex_handle, bool> rec_insert_simplex_and_subfaces_sorted(Siblings* sib, ForwardVertexIterator first, ForwardVertexIterator last, Filtration_value filt) { + // An alternative strategy would be: + // - try to find the complete simplex, if found (and low filtration) exit + // - insert all the vertices at once in sib + // - loop over those (new or not) simplices, with a recursive call(++first, last) + Vertex_handle vertex_one = *first; + auto&& dict = sib->members(); + auto insertion_result = dict.emplace(vertex_one, Node(sib, filt)); + Simplex_handle simplex_one = insertion_result.first; + bool one_is_new = insertion_result.second; + if (!one_is_new) { + if (filtration(simplex_one) > filt) { + assign_filtration(simplex_one, filt); + } else { + // FIXME: this interface makes no sense, and it doesn't seem to be tested. + insertion_result.first = null_simplex(); } - std::vector<Vertex_handle> first_simplex(1, the_simplex.back()); - // i.e. (0,1,2) => [to_be_inserted | to_be_propagated] = [(0) | ] - to_be_inserted.push_back(first_simplex); - - insert_result = insert_vertex_vector(first_simplex, filtration); - } else { - std::cerr << "Simplex_tree::rec_insert_simplex_and_subfaces - Recursivity error\n"; - exit(-1); } - return insert_result; + if (++first == last) return insertion_result; + if (!has_children(simplex_one)) + // TODO: have special code here, we know we are building the whole subtree from scratch. + simplex_one->second.assign_children(new Siblings(sib, vertex_one)); + auto res = rec_insert_simplex_and_subfaces_sorted(simplex_one->second.children(), first, last, filt); + // No need to continue if the full simplex was already there with a low enough filtration value. + if (res.first != null_simplex()) rec_insert_simplex_and_subfaces_sorted(sib, first, last, filt); + return res; } public: @@ -747,8 +774,12 @@ class Simplex_tree { return &root_; } - /** Set a dimension for the simplicial complex. */ + /** \brief Set a dimension for the simplicial complex. + * \details This function must be used with caution because it disables dimension recomputation when required + * (this recomputation can be triggered by `remove_maximal_simplex()` or `prune_above_filtration()`). + */ void set_dimension(int dimension) { + dimension_to_be_lowered_ = false; dimension_ = dimension; } @@ -923,8 +954,9 @@ class Simplex_tree { * called. * * Inserts all vertices and edges given by a OneSkeletonGraph. - * OneSkeletonGraph must be a model of boost::AdjacencyGraph, - * boost::EdgeListGraph and boost::PropertyGraph. + * OneSkeletonGraph must be a model of + * <a href="http://www.boost.org/doc/libs/1_65_1/libs/graph/doc/EdgeListGraph.html">boost::EdgeListGraph</a> + * and <a href="http://www.boost.org/doc/libs/1_65_1/libs/graph/doc/PropertyGraph.html">boost::PropertyGraph</a>. * * The vertex filtration value is accessible through the property tag * vertex_filtration_t. @@ -934,7 +966,10 @@ class Simplex_tree { * boost::graph_traits<OneSkeletonGraph>::vertex_descriptor * must be Vertex_handle. * boost::graph_traits<OneSkeletonGraph>::directed_category - * must be undirected_tag. */ + * must be undirected_tag. + * + * If an edge appears with multiplicity, the function will arbitrarily pick + * one representative to read the filtration value. */ template<class OneSkeletonGraph> void insert_graph(const OneSkeletonGraph& skel_graph) { // the simplex tree must be empty @@ -965,18 +1000,22 @@ class Simplex_tree { ++e_it) { auto u = source(*e_it, skel_graph); auto v = target(*e_it, skel_graph); - if (u < v) { - // count edges only once { std::swap(u,v); } // u < v - auto sh = find_vertex(u); - if (!has_children(sh)) { - sh->second.assign_children(new Siblings(&root_, sh->first)); - } - - sh->second.children()->members().emplace( - v, - Node(sh->second.children(), - boost::get(edge_filtration_t(), skel_graph, *e_it))); + if (u == v) throw "Self-loops are not simplicial"; + // We cannot skip edges with the wrong orientation and expect them to + // come a second time with the right orientation, that does not always + // happen in practice. emplace() should be a NOP when an element with the + // same key is already there, so seeing the same edge multiple times is + // ok. + // Should we actually forbid multiple edges? That would be consistent + // with rejecting self-loops. + if (v < u) std::swap(u, v); + auto sh = find_vertex(u); + if (!has_children(sh)) { + sh->second.assign_children(new Siblings(&root_, sh->first)); } + + sh->second.children()->members().emplace(v, + Node(sh->second.children(), boost::get(edge_filtration_t(), skel_graph, *e_it))); } } @@ -1067,6 +1106,120 @@ class Simplex_tree { } public: + /** \brief Expands a simplex tree containing only a graph. Simplices corresponding to cliques in the graph are added + * incrementally, faces before cofaces, unless the simplex has dimension larger than `max_dim` or `block_simplex` + * returns true for this simplex. + * + * @param[in] max_dim Expansion maximal dimension value. + * @param[in] block_simplex Blocker oracle. Its concept is <CODE>bool block_simplex(Simplex_handle sh)</CODE> + * + * The function identifies a candidate simplex whose faces are all already in the complex, inserts + * it with a filtration value corresponding to the maximum of the filtration values of the faces, then calls + * `block_simplex` on a `Simplex_handle` for this new simplex. If `block_simplex` returns true, the simplex is + * removed, otherwise it is kept. Note that the evaluation of `block_simplex` is a good time to update the + * filtration value of the simplex if you want a customized value. The algorithm then proceeds with the next + * candidate. + * + * @warning several candidates of the same dimension may be inserted simultaneously before calling `block_simplex`, + * so if you examine the complex in `block_simplex`, you may hit a few simplices of the same dimension that have not + * been vetted by `block_simplex` yet, or have already been rejected but not yet removed. + */ + template< typename Blocker > + void expansion_with_blockers(int max_dim, Blocker block_simplex) { + // Loop must be from the end to the beginning, as higher dimension simplex are always on the left part of the tree + for (auto& simplex : boost::adaptors::reverse(root_.members())) { + if (has_children(&simplex)) { + siblings_expansion_with_blockers(simplex.second.children(), max_dim, max_dim - 1, block_simplex); + } + } + } + + private: + /** \brief Recursive expansion with blockers of the simplex tree.*/ + template< typename Blocker > + void siblings_expansion_with_blockers(Siblings* siblings, int max_dim, int k, Blocker block_simplex) { + if (dimension_ < max_dim - k) { + dimension_ = max_dim - k; + } + if (k == 0) + return; + // No need to go deeper + if (siblings->members().size() < 2) + return; + // Reverse loop starting before the last one for 'next' to be the last one + for (auto simplex = siblings->members().rbegin() + 1; simplex != siblings->members().rend(); simplex++) { + std::vector<std::pair<Vertex_handle, Node> > intersection; + for(auto next = siblings->members().rbegin(); next != simplex; next++) { + bool to_be_inserted = true; + Filtration_value filt = simplex->second.filtration(); + // If all the boundaries are present, 'next' needs to be inserted + for (Simplex_handle border : boundary_simplex_range(simplex)) { + Simplex_handle border_child = find_child(border, next->first); + if (border_child == null_simplex()) { + to_be_inserted=false; + break; + } + filt = (std::max)(filt, filtration(border_child)); + } + if (to_be_inserted) { + intersection.emplace_back(next->first, Node(nullptr, filt)); + } + } + if (intersection.size() != 0) { + // Reverse the order to insert + Siblings * new_sib = new Siblings(siblings, // oncles + simplex->first, // parent + boost::adaptors::reverse(intersection)); // boost::container::ordered_unique_range_t + std::vector<Vertex_handle> blocked_new_sib_vertex_list; + // As all intersections are inserted, we can call the blocker function on all new_sib members + for (auto new_sib_member = new_sib->members().begin(); + new_sib_member != new_sib->members().end(); + new_sib_member++) { + bool blocker_result = block_simplex(new_sib_member); + // new_sib member has been blocked by the blocker function + // add it to the list to be removed - do not perform it while looping on it + if (blocker_result) { + blocked_new_sib_vertex_list.push_back(new_sib_member->first); + } + } + if (blocked_new_sib_vertex_list.size() == new_sib->members().size()) { + // Specific case where all have to be deleted + delete new_sib; + // ensure the children property + simplex->second.assign_children(siblings); + } else { + for (auto& blocked_new_sib_member : blocked_new_sib_vertex_list) { + new_sib->members().erase(blocked_new_sib_member); + } + // ensure recursive call + simplex->second.assign_children(new_sib); + siblings_expansion_with_blockers(new_sib, max_dim, k - 1, block_simplex); + } + } else { + // ensure the children property + simplex->second.assign_children(siblings); + } + } + } + + /* \private Returns the Simplex_handle composed of the vertex list (from the Simplex_handle), plus the given + * Vertex_handle if the Vertex_handle is found in the Simplex_handle children list. + * Returns null_simplex() if it does not exist + */ + Simplex_handle find_child(Simplex_handle sh, Vertex_handle vh) const { + if (!has_children(sh)) + return null_simplex(); + + Simplex_handle child = sh->second.children()->find(vh); + // Specific case of boost::flat_map does not find, returns boost::flat_map::end() + // in simplex tree we want a null_simplex() + if (child == sh->second.children()->members().end()) + return null_simplex(); + + return child; + } + + public: /** \brief Write the hasse diagram of the simplicial complex in os. * * Each row in the file correspond to a simplex. A line is written: @@ -1142,6 +1295,9 @@ class Simplex_tree { * \post Some simplex tree functions require the filtration to be valid. `prune_above_filtration()` * function is not launching `initialize_filtration()` but returns the filtration modification information. If the * complex has changed , please call `initialize_filtration()` to recompute it. + * \post Note that the dimension of the simplicial complex may be lower after calling `prune_above_filtration()` + * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper + * bound. If you care, you can call `dimension()` to recompute the exact dimension. */ bool prune_above_filtration(Filtration_value filtration) { return rec_prune_above_filtration(root(), filtration); @@ -1153,6 +1309,8 @@ class Simplex_tree { auto last = std::remove_if(list.begin(), list.end(), [=](Dit_value_t& simplex) { if (simplex.second.filtration() <= filt) return false; if (has_children(&simplex)) rec_delete(simplex.second.children()); + // dimension may need to be lowered + dimension_to_be_lowered_ = true; return true; }); @@ -1161,6 +1319,8 @@ class Simplex_tree { // Removing the whole siblings, parent becomes a leaf. sib->oncles()->members()[sib->parent()].assign_children(sib->oncles()); delete sib; + // dimension may need to be lowered + dimension_to_be_lowered_ = true; return true; } else { // Keeping some elements of siblings. Remove the others, and recurse in the remaining ones. @@ -1172,12 +1332,45 @@ class Simplex_tree { return modified; } + private: + /** \brief Deep search simplex tree dimension recompute. + * @return True if the dimension was modified, false otherwise. + * \pre Be sure the simplex tree has not a too low dimension value as the deep search stops when the former dimension + * has been reached (cf. `upper_bound_dimension()` and `set_dimension()` methods). + */ + bool lower_upper_bound_dimension() { + // reset automatic detection to recompute + dimension_to_be_lowered_ = false; + int new_dimension = -1; + // Browse the tree from the left to the right as higher dimension cells are more likely on the left part of the tree + for (Simplex_handle sh : complex_simplex_range()) { +#ifdef DEBUG_TRACES + for (auto vertex : simplex_vertex_range(sh)) { + std::cout << " " << vertex; + } + std::cout << std::endl; +#endif // DEBUG_TRACES + + int sh_dimension = dimension(sh); + if (sh_dimension >= dimension_) + // Stop browsing as soon as the dimension is reached, no need to go furter + return false; + new_dimension = (std::max)(new_dimension, sh_dimension); + } + dimension_ = new_dimension; + return true; + } + + public: /** \brief Remove a maximal simplex. * @param[in] sh Simplex handle on the maximal simplex to remove. * \pre Please check the simplex has no coface before removing it. * \exception std::invalid_argument In debug mode, if sh has children. * \post Be aware that removing is shifting data in a flat_map (initialize_filtration to be done). + * \post Note that the dimension of the simplicial complex may be lower after calling `remove_maximal_simplex()` + * than it was before. However, `upper_bound_dimension()` will return the old value, which remains a valid upper + * bound. If you care, you can call `dimension()` to recompute the exact dimension. */ void remove_maximal_simplex(Simplex_handle sh) { // Guarantee the simplex has no children @@ -1195,6 +1388,8 @@ class Simplex_tree { // Sibling is emptied : must be deleted, and its parent must point on his own Sibling child->oncles()->members().at(child->parent()).assign_children(child->oncles()); delete child; + // dimension may need to be lowered + dimension_to_be_lowered_ = true; } } @@ -1207,6 +1402,7 @@ class Simplex_tree { std::vector<Simplex_handle> filtration_vect_; /** \brief Upper bound on the dimension of the simplicial complex.*/ int dimension_; + bool dimension_to_be_lowered_ = false; }; // Print a Simplex_tree in os. diff --git a/include/gudhi/Simplex_tree/Simplex_tree_iterators.h b/include/gudhi/Simplex_tree/Simplex_tree_iterators.h index 7e0a454d..ab7346d4 100644 --- a/include/gudhi/Simplex_tree/Simplex_tree_iterators.h +++ b/include/gudhi/Simplex_tree/Simplex_tree_iterators.h @@ -23,6 +23,8 @@ #ifndef SIMPLEX_TREE_SIMPLEX_TREE_ITERATORS_H_ #define SIMPLEX_TREE_SIMPLEX_TREE_ITERATORS_H_ +#include <gudhi/Debug_utils.h> + #include <boost/iterator/iterator_facade.hpp> #include <boost/version.hpp> #if BOOST_VERSION >= 105600 @@ -109,11 +111,18 @@ class Simplex_tree_boundary_simplex_iterator : public boost::iterator_facade< : last_(sh->first), sib_(nullptr), st_(st) { + // Only check once at the beginning instead of for every increment, as this is expensive. + if (SimplexTree::Options::contiguous_vertices) + GUDHI_CHECK(st_->contiguous_vertices(), "The set of vertices is not { 0, ..., n } without holes"); Siblings * sib = st->self_siblings(sh); next_ = sib->parent(); sib_ = sib->oncles(); if (sib_ != nullptr) { - sh_ = sib_->find(next_); + if (SimplexTree::Options::contiguous_vertices && sib_->oncles() == nullptr) + // Only relevant for edges + sh_ = sib_->members_.begin()+next_; + else + sh_ = sib_->find(next_); } else { sh_ = st->null_simplex(); } // vertex: == end() @@ -140,14 +149,19 @@ class Simplex_tree_boundary_simplex_iterator : public boost::iterator_facade< Siblings * for_sib = sib_; Siblings * new_sib = sib_->oncles(); auto rit = suffix_.rbegin(); - if (SimplexTree::Options::contiguous_vertices && new_sib == nullptr && rit != suffix_.rend()) { - // We reached the root, use a short-cut to find a vertex. We could also - // optimize finding the second vertex of a segment, but people are - // expected to call endpoints(). - assert(st_->contiguous_vertices()); - sh_ = for_sib->members_.begin()+*rit; - for_sib = sh_->second.children(); - ++rit; + if (SimplexTree::Options::contiguous_vertices && new_sib == nullptr) { + // We reached the root, use a short-cut to find a vertex. + if (rit == suffix_.rend()) { + // Segment, this vertex is the last boundary simplex + sh_ = for_sib->members_.begin()+last_; + sib_ = nullptr; + return; + } else { + // Dim >= 2, initial step of the descent + sh_ = for_sib->members_.begin()+*rit; + for_sib = sh_->second.children(); + ++rit; + } } for (; rit != suffix_.rend(); ++rit) { sh_ = for_sib->find(*rit); diff --git a/include/gudhi/Skeleton_blocker.h b/include/gudhi/Skeleton_blocker.h index 32fe411c..aca2aa57 100644 --- a/include/gudhi/Skeleton_blocker.h +++ b/include/gudhi/Skeleton_blocker.h @@ -239,9 +239,6 @@ their collaboration to write the two initial papers about this data-structure and also Dominique for leaving him use a prototype. - -\copyright GNU General Public License v3. - @} */ } // namespace skeleton_blocker diff --git a/include/gudhi/Strong_witness_complex.h b/include/gudhi/Strong_witness_complex.h index 6f4bcf60..b3d00b11 100644 --- a/include/gudhi/Strong_witness_complex.h +++ b/include/gudhi/Strong_witness_complex.h @@ -34,7 +34,8 @@ namespace Gudhi { namespace witness_complex { -/* \private + /** + * \private * \class Strong_witness_complex * \brief Constructs strong witness complex for a given table of nearest landmarks with respect to witnesses. * \ingroup witness_complex @@ -127,10 +128,11 @@ class Strong_witness_complex { if ((Landmark_id)simplex.size() - 1 > complex_dim) complex_dim = simplex.size() - 1; } - complex.set_dimension(complex_dim); return true; } + //@} + private: /* \brief Adds recursively all the faces of a certain dimension dim-1 witnessed by the same witness. * Iterator is needed to know until how far we can take landmarks to form simplexes. @@ -171,7 +173,6 @@ class Strong_witness_complex { simplex.pop_back(); } } - //@} }; } // namespace witness_complex diff --git a/include/gudhi/Tangential_complex.h b/include/gudhi/Tangential_complex.h index a5cefd6a..6f061922 100644 --- a/include/gudhi/Tangential_complex.h +++ b/include/gudhi/Tangential_complex.h @@ -155,7 +155,7 @@ class Tangential_complex { >::type Triangulation; typedef typename Triangulation::Geom_traits Tr_traits; typedef typename Triangulation::Weighted_point Tr_point; - typedef typename Triangulation::Bare_point Tr_bare_point; + typedef typename Tr_traits::Base::Point_d Tr_bare_point; typedef typename Triangulation::Vertex_handle Tr_vertex_handle; typedef typename Triangulation::Full_cell_handle Tr_full_cell_handle; typedef typename Tr_traits::Vector_d Tr_vector; diff --git a/include/gudhi/Unitary_tests_utils.h b/include/gudhi/Unitary_tests_utils.h new file mode 100644 index 00000000..8394a062 --- /dev/null +++ b/include/gudhi/Unitary_tests_utils.h @@ -0,0 +1,40 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ +#ifndef UNITARY_TESTS_UTILS_H_ +#define UNITARY_TESTS_UTILS_H_ + +#include <boost/test/unit_test.hpp> + +#include <iostream> +#include <limits> // for std::numeric_limits<> + +template<typename FloatingType > +void GUDHI_TEST_FLOAT_EQUALITY_CHECK(FloatingType a, FloatingType b, + FloatingType epsilon = std::numeric_limits<FloatingType>::epsilon()) { +#ifdef DEBUG_TRACES + std::cout << "GUDHI_TEST_FLOAT_EQUALITY_CHECK - " << a << " versus " << b + << " | diff = " << std::fabs(a - b) << " - epsilon = " << epsilon << std::endl; +#endif + BOOST_CHECK(std::fabs(a - b) < epsilon); +} + +#endif // UNITARY_TESTS_UTILS_H_ diff --git a/include/gudhi/Witness_complex.h b/include/gudhi/Witness_complex.h index bcfe8484..53c38520 100644 --- a/include/gudhi/Witness_complex.h +++ b/include/gudhi/Witness_complex.h @@ -130,7 +130,6 @@ class Witness_complex { } k++; } - complex.set_dimension(k-1); return true; } diff --git a/include/gudhi/choose_n_farthest_points.h b/include/gudhi/choose_n_farthest_points.h index 86500b28..8390b4c9 100644 --- a/include/gudhi/choose_n_farthest_points.h +++ b/include/gudhi/choose_n_farthest_points.h @@ -93,7 +93,7 @@ void choose_n_farthest_points(Kernel const &k, // Choose randomly the first landmark std::random_device rd; std::mt19937 gen(rd()); - std::uniform_int_distribution<std::size_t> dis(0, (input_pts.size() - 1)); + std::uniform_int_distribution<std::size_t> dis(0, nb_points - 1); starting_point = dis(gen); } @@ -110,7 +110,7 @@ void choose_n_farthest_points(Kernel const &k, *output_it++ = input_pts[curr_max_w]; *dist_it++ = dist_to_L[curr_max_w]; std::size_t i = 0; - for (auto& p : input_pts) { + for (auto&& p : input_pts) { double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w)); if (curr_dist < dist_to_L[i]) dist_to_L[i] = curr_dist; diff --git a/include/gudhi/common_persistence_representations.h b/include/gudhi/common_persistence_representations.h new file mode 100644 index 00000000..44e125a7 --- /dev/null +++ b/include/gudhi/common_persistence_representations.h @@ -0,0 +1,127 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef COMMON_PERSISTENCE_REPRESENTATIONS_H_ +#define COMMON_PERSISTENCE_REPRESENTATIONS_H_ + +#include <utility> +#include <string> +#include <cmath> + +namespace Gudhi { +namespace Persistence_representations { +// this file contain an implementation of some common procedures used in Persistence_representations. + +// double epsi = std::numeric_limits<double>::epsilon(); +double epsi = 0.000005; + +/** + * A procedure used to compare doubles. Typically given two doubles A and B, comparing A == B is not good idea. In this + *case, we use the procedure almostEqual with the epsi defined at + * the top of the file. Setting up the epsi gives the user a tolerance on what should be consider equal. +**/ +inline bool almost_equal(double a, double b) { + if (std::fabs(a - b) < epsi) return true; + return false; +} + +// landscapes +/** + * Extra functions needed in construction of barcodes. +**/ +double minus_length(std::pair<double, double> a) { return a.first - a.second; } +double birth_plus_deaths(std::pair<double, double> a) { return a.first + a.second; } + +// landscapes +/** + * Given two points in R^2, the procedure compute the parameters A and B of the line y = Ax + B that crosses those two + *points. +**/ +std::pair<double, double> compute_parameters_of_a_line(std::pair<double, double> p1, std::pair<double, double> p2) { + double a = (p2.second - p1.second) / (p2.first - p1.first); + double b = p1.second - a * p1.first; + return std::make_pair(a, b); +} + +// landscapes +/** + * This procedure given two points which lies on the opposite sides of x axis, compute x for which the line connecting + *those two points crosses x axis. +**/ +double find_zero_of_a_line_segment_between_those_two_points(std::pair<double, double> p1, + std::pair<double, double> p2) { + if (p1.first == p2.first) return p1.first; + if (p1.second * p2.second > 0) { + std::ostringstream errMessage; + errMessage << "In function find_zero_of_a_line_segment_between_those_two_points the arguments are: (" << p1.first + << "," << p1.second << ") and (" << p2.first << "," << p2.second + << "). There is no zero in line between those two points. Program terminated."; + std::string errMessageStr = errMessage.str(); + const char* err = errMessageStr.c_str(); + throw(err); + } + // we assume here, that x \in [ p1.first, p2.first ] and p1 and p2 are points between which we will put the line + // segment + double a = (p2.second - p1.second) / (p2.first - p1.first); + double b = p1.second - a * p1.first; + return -b / a; +} + +// landscapes +/** + * This method provides a comparison of points that is used in construction of persistence landscapes. The ordering is + *lexicographical for the first coordinate, and reverse-lexicographical for the + * second coordinate. +**/ +bool compare_points_sorting(std::pair<double, double> f, std::pair<double, double> s) { + if (f.first < s.first) { + return true; + } else { // f.first >= s.first + if (f.first > s.first) { + return false; + } else { // f.first == s.first + if (f.second > s.second) { + return true; + } else { + return false; + } + } + } +} + +// landscapes +/** + * This procedure takes two points in R^2 and a double value x. It computes the line parsing through those two points + *and return the value of that linear function at x. +**/ +double function_value(std::pair<double, double> p1, std::pair<double, double> p2, double x) { + // we assume here, that x \in [ p1.first, p2.first ] and p1 and p2 are points between which we will put the line + // segment + double a = (p2.second - p1.second) / (p2.first - p1.first); + double b = p1.second - a * p1.first; + return (a * x + b); +} + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // COMMON_PERSISTENCE_REPRESENTATIONS_H_ diff --git a/include/gudhi/distance_functions.h b/include/gudhi/distance_functions.h index f6e2ab5a..3a5d1fd5 100644 --- a/include/gudhi/distance_functions.h +++ b/include/gudhi/distance_functions.h @@ -23,9 +23,14 @@ #ifndef DISTANCE_FUNCTIONS_H_ #define DISTANCE_FUNCTIONS_H_ +#include <gudhi/Debug_utils.h> + +#include <boost/range/metafunctions.hpp> + #include <cmath> // for std::sqrt #include <type_traits> // for std::decay #include <iterator> // for std::begin, std::end +#include <utility> namespace Gudhi { @@ -37,16 +42,29 @@ namespace Gudhi { * have the same dimension. */ class Euclidean_distance { public: + // boost::range_value is not SFINAE-friendly so we cannot use it in the return type template< typename Point > - auto operator()(const Point& p1, const Point& p2) const -> typename std::decay<decltype(*std::begin(p1))>::type { - auto it1 = p1.begin(); - auto it2 = p2.begin(); - typename Point::value_type dist = 0.; - for (; it1 != p1.end(); ++it1, ++it2) { - typename Point::value_type tmp = (*it1) - (*it2); + typename std::iterator_traits<typename boost::range_iterator<Point>::type>::value_type + operator()(const Point& p1, const Point& p2) const { + auto it1 = std::begin(p1); + auto it2 = std::begin(p2); + typedef typename boost::range_value<Point>::type NT; + NT dist = 0; + for (; it1 != std::end(p1); ++it1, ++it2) { + GUDHI_CHECK(it2 != std::end(p2), "inconsistent point dimensions"); + NT tmp = *it1 - *it2; dist += tmp*tmp; } - return std::sqrt(dist); + GUDHI_CHECK(it2 == std::end(p2), "inconsistent point dimensions"); + using std::sqrt; + return sqrt(dist); + } + template< typename T > + T operator() (const std::pair< T, T >& f, const std::pair< T, T >& s) const { + T dx = f.first - s.first; + T dy = f.second - s.second; + using std::sqrt; + return sqrt(dx*dx + dy*dy); } }; diff --git a/include/gudhi/graph_simplicial_complex.h b/include/gudhi/graph_simplicial_complex.h index 5fe7c826..d84421b2 100644 --- a/include/gudhi/graph_simplicial_complex.h +++ b/include/gudhi/graph_simplicial_complex.h @@ -28,6 +28,9 @@ #include <utility> // for pair<> #include <vector> #include <map> +#include <tuple> // for std::tie + +namespace Gudhi { /* Edge tag for Boost PropertyGraph. */ struct edge_filtration_t { @@ -39,4 +42,64 @@ struct vertex_filtration_t { typedef boost::vertex_property_tag kind; }; +template <typename SimplicialComplexForProximityGraph> +using Proximity_graph = typename boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS +, boost::property < vertex_filtration_t, typename SimplicialComplexForProximityGraph::Filtration_value > +, boost::property < edge_filtration_t, typename SimplicialComplexForProximityGraph::Filtration_value >>; + +/** \brief Computes the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph with n vertices, and an edge [u,v] iff the + * distance function between points u and v is smaller than threshold. + * + * \tparam ForwardPointRange furnishes `.begin()` and `.end()` methods. + * + * \tparam Distance furnishes `operator()(const Point& p1, const Point& p2)`, where + * `Point` is a point from the `ForwardPointRange`, and that returns a `Filtration_value`. + */ +template< typename SimplicialComplexForProximityGraph + , typename ForwardPointRange + , typename Distance > +Proximity_graph<SimplicialComplexForProximityGraph> compute_proximity_graph( + const ForwardPointRange& points, + typename SimplicialComplexForProximityGraph::Filtration_value threshold, + Distance distance) { + using Vertex_handle = typename SimplicialComplexForProximityGraph::Vertex_handle; + using Filtration_value = typename SimplicialComplexForProximityGraph::Filtration_value; + + std::vector<std::pair< Vertex_handle, Vertex_handle >> edges; + std::vector< Filtration_value > edges_fil; + std::map< Vertex_handle, Filtration_value > vertices; + + Vertex_handle idx_u, idx_v; + Filtration_value fil; + idx_u = 0; + for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { + idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { + fil = distance(*it_u, *it_v); + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + ++idx_u; + } + + // Points are labeled from 0 to idx_u-1 + Proximity_graph<SimplicialComplexForProximityGraph> skel_graph(edges.begin(), edges.end(), edges_fil.begin(), idx_u); + + auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph); + + typename boost::graph_traits<Proximity_graph<SimplicialComplexForProximityGraph>>::vertex_iterator vi, vi_end; + for (std::tie(vi, vi_end) = boost::vertices(skel_graph); + vi != vi_end; ++vi) { + boost::put(vertex_prop, *vi, 0.); + } + + return skel_graph; +} + +} // namespace Gudhi + #endif // GRAPH_SIMPLICIAL_COMPLEX_H_ diff --git a/include/gudhi/read_persistence_from_file.h b/include/gudhi/read_persistence_from_file.h new file mode 100644 index 00000000..83b89d0e --- /dev/null +++ b/include/gudhi/read_persistence_from_file.h @@ -0,0 +1,120 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef READ_PERSISTENCE_FROM_FILE_H_ +#define READ_PERSISTENCE_FROM_FILE_H_ + +#include <gudhi/reader_utils.h> + +#include <iostream> +#include <fstream> +#include <sstream> +#include <vector> +#include <algorithm> +#include <string> +#include <utility> +#include <limits> // for std::numeric_limits<> + +namespace Gudhi { +namespace Persistence_representations { + +/** + * Universal procedure to read files with persistence. It ignores the lines starting from # (treat them as comments). + * It reads the fist line which is not a comment and assume that there are some numerical entries over there. The + *program assume + * that each other line in the file, which is not a comment, have the same number of numerical entries (2, 3 or 4). + * If there are two numerical entries per line, then the function assume that they are birth/death coordinates. + * If there are three numerical entries per line, then the function assume that they are: dimension and birth/death + *coordinates. + * If there are four numerical entries per line, then the function assume that they are: the characteristic of a filed + *over which + * persistence was computed, dimension and birth/death coordinates. + * The 'inf' string can appear only as a last element of a line. + * The procedure returns vector of persistence pairs. +**/ +std::vector<std::pair<double, double> > read_persistence_intervals_in_one_dimension_from_file( + std::string const& filename, int dimension = -1, double what_to_substitute_for_infinite_bar = -1) { + bool dbg = false; + + std::string line; + std::vector<std::pair<double, double> > barcode_initial = + read_persistence_intervals_in_dimension(filename, (int)dimension); + std::vector<std::pair<double, double> > final_barcode; + final_barcode.reserve(barcode_initial.size()); + + if (dbg) { + std::cerr << "Here are the intervals that we read from the file : \n"; + for (size_t i = 0; i != barcode_initial.size(); ++i) { + std::cout << barcode_initial[i].first << " " << barcode_initial[i].second << std::endl; + } + getchar(); + } + + for (size_t i = 0; i != barcode_initial.size(); ++i) { + if (dbg) { + std::cout << "COnsidering interval : " << barcode_initial[i].first << " " << barcode_initial[i].second + << std::endl; + } + + if (barcode_initial[i].first > barcode_initial[i].second) { + // note that in this case barcode_initial[i].second != std::numeric_limits<double>::infinity() + if (dbg) std::cout << "Swap and enter \n"; + // swap them to make sure that birth < death + final_barcode.push_back(std::pair<double, double>(barcode_initial[i].second, barcode_initial[i].first)); + continue; + } else { + if (barcode_initial[i].second != std::numeric_limits<double>::infinity()) { + if (dbg) std::cout << "Simply enters\n"; + // in this case, due to the previous conditions we know that barcode_initial[i].first < + // barcode_initial[i].second, so we put them as they are + final_barcode.push_back(std::pair<double, double>(barcode_initial[i].first, barcode_initial[i].second)); + } + } + + if ((barcode_initial[i].second == std::numeric_limits<double>::infinity()) && + (what_to_substitute_for_infinite_bar != -1)) { + if (barcode_initial[i].first < what_to_substitute_for_infinite_bar) // if only birth < death. + { + final_barcode.push_back( + std::pair<double, double>(barcode_initial[i].first, what_to_substitute_for_infinite_bar)); + } + } else { + // if the variable what_to_substitute_for_infinite_bar is not set, then we ignore all the infinite bars. + } + } + + if (dbg) { + std::cerr << "Here are the final bars that we are sending further : \n"; + for (size_t i = 0; i != final_barcode.size(); ++i) { + std::cout << final_barcode[i].first << " " << final_barcode[i].second << std::endl; + } + std::cerr << "final_barcode.size() : " << final_barcode.size() << std::endl; + getchar(); + } + + return final_barcode; +} // read_persistence_intervals_in_one_dimension_from_file + +} // namespace Persistence_representations +} // namespace Gudhi + +#endif // READ_PERSISTENCE_FROM_FILE_H_ diff --git a/utilities/Alpha_complex/CMakeLists.txt b/utilities/Alpha_complex/CMakeLists.txt new file mode 100644 index 00000000..a2dfac20 --- /dev/null +++ b/utilities/Alpha_complex/CMakeLists.txt @@ -0,0 +1,65 @@ +cmake_minimum_required(VERSION 2.6) +project(Alpha_complex_utilities) + +if(CGAL_FOUND) + add_executable(alpha_complex_3d_persistence alpha_complex_3d_persistence.cpp) + target_link_libraries(alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + add_executable(exact_alpha_complex_3d_persistence exact_alpha_complex_3d_persistence.cpp) + target_link_libraries(exact_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + add_executable(weighted_alpha_complex_3d_persistence weighted_alpha_complex_3d_persistence.cpp) + target_link_libraries(weighted_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(alpha_complex_3d_persistence ${TBB_LIBRARIES}) + target_link_libraries(exact_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + target_link_libraries(weighted_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Alpha_complex_utilities_alpha_complex_3d_persistence COMMAND $<TARGET_FILE:alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_exact_alpha_complex_3d COMMAND $<TARGET_FILE:exact_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_weighted_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.weights" "-p" "2" "-m" "0.45") + + install(TARGETS alpha_complex_3d_persistence DESTINATION bin) + install(TARGETS exact_alpha_complex_3d_persistence DESTINATION bin) + install(TARGETS weighted_alpha_complex_3d_persistence DESTINATION bin) + + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + add_executable (alpha_complex_persistence alpha_complex_persistence.cpp) + target_link_libraries(alpha_complex_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + add_executable(periodic_alpha_complex_3d_persistence periodic_alpha_complex_3d_persistence.cpp) + target_link_libraries(periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(alpha_complex_persistence ${TBB_LIBRARIES}) + target_link_libraries(periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + add_test(NAME Alpha_complex_utilities_alpha_complex_persistence COMMAND $<TARGET_FILE:alpha_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-p" "2" "-m" "0.45") + add_test(NAME Alpha_complex_utilities_periodic_alpha_complex_3d_persistence COMMAND $<TARGET_FILE:periodic_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "-p" "2" "-m" "0") + + install(TARGETS alpha_complex_persistence DESTINATION bin) + install(TARGETS periodic_alpha_complex_3d_persistence DESTINATION bin) + + endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + + if (NOT CGAL_VERSION VERSION_LESS 4.11.0) + add_executable(weighted_periodic_alpha_complex_3d_persistence weighted_periodic_alpha_complex_3d_persistence.cpp) + target_link_libraries(weighted_periodic_alpha_complex_3d_persistence ${CGAL_LIBRARY}) + if (TBB_FOUND) + target_link_libraries(weighted_periodic_alpha_complex_3d_persistence ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Alpha_complex_utilities_weigted_periodic_alpha_complex_3d COMMAND $<TARGET_FILE:weighted_periodic_alpha_complex_3d_persistence> + "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.off" "${CMAKE_SOURCE_DIR}/data/points/grid_10_10_10_in_0_1.weights" + "${CMAKE_SOURCE_DIR}/data/points/iso_cuboid_3_in_0_1.txt" "3" "1.0") + + install(TARGETS weighted_periodic_alpha_complex_3d_persistence DESTINATION bin) + + endif (NOT CGAL_VERSION VERSION_LESS 4.11.0) + +endif(CGAL_FOUND) diff --git a/example/Persistent_cohomology/alpha_complex_3d_helper.h b/utilities/Alpha_complex/alpha_complex_3d_helper.h index 7865e4ec..a59f0654 100644 --- a/example/Persistent_cohomology/alpha_complex_3d_helper.h +++ b/utilities/Alpha_complex/alpha_complex_3d_helper.h @@ -23,7 +23,7 @@ #ifndef ALPHA_COMPLEX_3D_HELPER_H_ #define ALPHA_COMPLEX_3D_HELPER_H_ -template<class Vertex_list, class Cell_handle> +template <class Vertex_list, class Cell_handle> Vertex_list from_cell(const Cell_handle& ch) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { @@ -35,7 +35,7 @@ Vertex_list from_cell(const Cell_handle& ch) { return the_list; } -template<class Vertex_list, class Facet> +template <class Vertex_list, class Facet> Vertex_list from_facet(const Facet& fct) { Vertex_list the_list; for (auto i = 0; i < 4; i++) { @@ -49,21 +49,19 @@ Vertex_list from_facet(const Facet& fct) { return the_list; } -template<class Vertex_list, class Edge_3> +template <class Vertex_list, class Edge_3> Vertex_list from_edge(const Edge_3& edg) { Vertex_list the_list; - for (auto i = 0; i < 4; i++) { - if ((edg.second == i) || (edg.third == i)) { + for (auto i : {edg.second, edg.third}) { #ifdef DEBUG_TRACES - std::cout << "from edge[" << i << "]=" << edg.first->vertex(i)->point() << std::endl; + std::cout << "from edge[" << i << "]=" << edg.first->vertex(i)->point() << std::endl; #endif // DEBUG_TRACES - the_list.push_back(edg.first->vertex(i)); - } + the_list.push_back(edg.first->vertex(i)); } return the_list; } -template<class Vertex_list, class Vertex_handle> +template <class Vertex_list, class Vertex_handle> Vertex_list from_vertex(const Vertex_handle& vh) { Vertex_list the_list; #ifdef DEBUG_TRACES diff --git a/example/Persistent_cohomology/alpha_complex_3d_persistence.cpp b/utilities/Alpha_complex/alpha_complex_3d_persistence.cpp index f63ff0f6..8ef5ffb2 100644 --- a/example/Persistent_cohomology/alpha_complex_3d_persistence.cpp +++ b/utilities/Alpha_complex/alpha_complex_3d_persistence.cpp @@ -20,8 +20,14 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/version.hpp> +#include <boost/program_options.hpp> #include <boost/variant.hpp> +#if BOOST_VERSION >= 105400 +#include <boost/container/static_vector.hpp> +#endif + #include <gudhi/Simplex_tree.h> #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_3D_off_io.h> @@ -37,8 +43,8 @@ #include <tuple> #include <map> #include <utility> -#include <list> #include <vector> +#include <cstdlib> #include "alpha_complex_3d_helper.h" @@ -56,59 +62,50 @@ using Point_3 = Kernel::Point_3; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; using Vertex_handle = Alpha_shape_3::Vertex_handle; -using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; + +#if BOOST_VERSION >= 105400 +using Vertex_list = boost::container::static_vector<Alpha_shape_3::Vertex_handle, 4>; +#else +using Vertex_list = std::vector<Alpha_shape_3::Vertex_handle>; +#endif // gudhi type definition using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; -using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using PCOH = Gudhi::persistent_cohomology::Persistent_cohomology< ST, Gudhi::persistent_cohomology::Field_Zp >; - -void usage(const std::string& progName) { - std::cerr << "Usage:\n" << progName << " path_to_OFF_file coeff_field_characteristic[integer " << - "> 0] min_persistence[float >= -1.0]\n"; - std::cerr << " path_to_OFF_file is the path to your points cloud in OFF format.\n"; - exit(-1); -} +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -int main(int argc, char * const argv[]) { - // program args management - if (argc != 4) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence); - int coeff_field_characteristic = atoi(argv[2]); +int main(int argc, char **argv) { + std::string off_file_points; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; - Filtration_value min_persistence = 0.0; - int returnedScanValue = sscanf(argv[3], "%f", &min_persistence); - if ((returnedScanValue == EOF) || (min_persistence < -1.0)) { - std::cerr << "Error: " << argv[3] << " is not correct\n"; - usage(argv[0]); - } + program_options(argc, argv, off_file_points, output_file_diag, coeff_field_characteristic, min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read file " << off_file_points << std::endl; + exit(-1); } - // Retrieve the triangulation + // Retrieve the points std::vector<Point_3> lp = off_reader.get_point_cloud(); // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. @@ -139,37 +136,23 @@ int main(int argc, char * const argv[]) { ST simplex_tree; Alpha_shape_simplex_tree_map map_cgal_simplex_tree; std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); - int dim_max = 0; - Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle *cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; - if (dim_max < 3) { - // Cell is of dim 3 - dim_max = 3; - } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet *facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; - if (dim_max < 2) { - // Facet is of dim 2 - dim_max = 2; - } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3 *edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; - if (dim_max < 1) { - // Edge_3 is of dim 1 - dim_max = 1; - } - } else if (const Vertex_handle * vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle *vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex - Simplex_tree_vector_vertex the_simplex_tree; + Simplex_tree_vector_vertex the_simplex; for (auto the_alpha_shape_vertex : vertex_list) { Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); if (the_map_iterator == map_cgal_simplex_tree.end()) { @@ -178,32 +161,26 @@ int main(int argc, char * const argv[]) { #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); - map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); + the_simplex.push_back(vertex); + map_cgal_simplex_tree.emplace(the_alpha_shape_vertex, vertex); } else { // alpha shape found Simplex_tree_vertex vertex = the_map_iterator->second; #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); + the_simplex.push_back(vertex); } } // Construction of the simplex_tree - Filtration_value filtr = /*std::sqrt*/(*the_alpha_value_iterator); + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES - if (filtr > filtration_max) { - filtration_max = filtr; - } - simplex_tree.insert_simplex(the_simplex_tree, filtr); - if (the_alpha_value_iterator != the_alpha_values.end()) - ++the_alpha_value_iterator; - else - std::cout << "This shall not happen" << std::endl; + simplex_tree.insert_simplex(the_simplex, filtr); + GUDHI_CHECK(the_alpha_value_iterator != the_alpha_values.end(), "CGAL provided more simplices than values"); + ++the_alpha_value_iterator; } - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -211,7 +188,6 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; @@ -230,13 +206,64 @@ int main(int argc, char * const argv[]) { std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; // Compute the persistence diagram of the complex - PCOH pcoh(simplex_tree); + Persistent_cohomology pcoh(simplex_tree, true); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/example/Persistent_cohomology/alpha_complex_persistence.cpp b/utilities/Alpha_complex/alpha_complex_persistence.cpp index 9e84e91f..2105220a 100644 --- a/example/Persistent_cohomology/alpha_complex_persistence.cpp +++ b/utilities/Alpha_complex/alpha_complex_persistence.cpp @@ -14,12 +14,9 @@ using Simplex_tree = Gudhi::Simplex_tree<>; using Filtration_value = Simplex_tree::Filtration_value; -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & output_file_diag - , Filtration_value & alpha_square_max_value - , int & coeff_field_characteristic - , Filtration_value & min_persistence); +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + Filtration_value &alpha_square_max_value, int &coeff_field_characteristic, + Filtration_value &min_persistence); int main(int argc, char **argv) { std::string off_file_points; @@ -28,13 +25,13 @@ int main(int argc, char **argv) { int coeff_field_characteristic; Filtration_value min_persistence; - program_options(argc, argv, off_file_points, output_file_diag, alpha_square_max_value, - coeff_field_characteristic, min_persistence); + program_options(argc, argv, off_file_points, output_file_diag, alpha_square_max_value, coeff_field_characteristic, + min_persistence); // ---------------------------------------------------------------------------- // Init of an alpha complex from an OFF file // ---------------------------------------------------------------------------- - using Kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; + using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>; Gudhi::alpha_complex::Alpha_complex<Kernel> alpha_complex_from_file(off_file_points); Simplex_tree simplex; @@ -42,17 +39,16 @@ int main(int argc, char **argv) { // ---------------------------------------------------------------------------- // Display information about the alpha complex // ---------------------------------------------------------------------------- - std::cout << "Simplicial complex is of dimension " << simplex.dimension() << - " - " << simplex.num_simplices() << " simplices - " << - simplex.num_vertices() << " vertices." << std::endl; + std::cout << "Simplicial complex is of dimension " << simplex.dimension() << " - " << simplex.num_simplices() + << " simplices - " << simplex.num_vertices() << " vertices." << std::endl; // Sort the simplices in the order of the filtration simplex.initialize_filtration(); std::cout << "Simplex_tree dim: " << simplex.dimension() << std::endl; // Compute the persistence diagram of the complex - Gudhi::persistent_cohomology::Persistent_cohomology< Simplex_tree, - Gudhi::persistent_cohomology::Field_Zp > pcoh(simplex); + Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh( + simplex); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); @@ -72,30 +68,26 @@ int main(int argc, char **argv) { return 0; } -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & output_file_diag - , Filtration_value & alpha_square_max_value - , int & coeff_field_characteristic - , Filtration_value & min_persistence) { +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + Filtration_value &alpha_square_max_value, int &coeff_field_characteristic, + Filtration_value &min_persistence) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&off_file_points), - "Name of file containing a point set. Format is one point per line: X1 ... Xd "); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd "); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-alpha-square-value,r", - po::value<Filtration_value>(&alpha_square_max_value)->default_value(std::numeric_limits<Filtration_value>::infinity()), - "Maximal alpha square value for the Alpha complex construction.") - ("field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals"); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-alpha-square-value,r", po::value<Filtration_value>(&alpha_square_max_value) + ->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal alpha square value for the Alpha complex construction.")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); po::positional_options_description pos; pos.add("input-file", 1); @@ -104,8 +96,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { diff --git a/utilities/Alpha_complex/alphacomplex.md b/utilities/Alpha_complex/alphacomplex.md new file mode 100644 index 00000000..aace85d3 --- /dev/null +++ b/utilities/Alpha_complex/alphacomplex.md @@ -0,0 +1,158 @@ +
+
+# Alpha complex #
+
+
+## alpha_complex_persistence ##
+
+This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
+The output diagram contains one bar per line, written with the convention:
+
+```
+ p dim birth death
+```
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+```
+ alpha_complex_persistence [options] <input OFF file>
+```
+
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+ alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
+```
+
+N.B.:
+
+* Filtration values are alpha square values.
+
+
+## alpha_complex_3d_persistence ##
+This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
+
+```
+p dim birth death
+```
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+```
+ alpha_complex_3d_persistence [options] <input OFF file>
+```
+
+where `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45
+```
+
+N.B.:
+
+* `alpha_complex_3d_persistence` only accepts OFF files in dimension 3.
+* Filtration values are alpha square values.
+
+
+## exact_alpha_complex_3d_persistence ##
+
+Same as `alpha_complex_3d_persistence`, but using exact computation.
+It is slower, but it is necessary when points are on a grid for instance.
+
+
+
+## weighted_alpha_complex_3d_persistence ##
+
+Same as `alpha_complex_3d_persistence`, but using weighted points.
+
+**Usage**
+
+```
+ weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>
+```
+
+where
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<input weights file>` is the path to the file containing the weights of the points (one value per line).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+ weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45
+```
+
+
+N.B.:
+
+* Weights values are explained on CGAL [Alpha shape](https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0)
+and [Regular triangulation](https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation) documentation.
+* Filtration values are alpha square values.
+
+
+## periodic_alpha_complex_3d_persistence ##
+Same as `alpha_complex_3d_persistence`, but using periodic alpha shape 3d.
+Refer to the [CGAL's 3D Periodic Triangulations User Manual](https://doc.cgal.org/latest/Periodic_3_triangulation_3/index.html) for more details.
+
+**Usage**
+
+```
+ periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>
+```
+
+where
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described
+[here](/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals
+
+
+**Example**
+
+```
+periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0
+```
+
+N.B.:
+
+* Cuboid file must be in the format described [here](/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+* Filtration values are alpha square values.
diff --git a/example/Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp b/utilities/Alpha_complex/exact_alpha_complex_3d_persistence.cpp index 09561d03..cceac46e 100644 --- a/example/Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp +++ b/utilities/Alpha_complex/exact_alpha_complex_3d_persistence.cpp @@ -4,7 +4,7 @@ * * Author(s): Vincent Rouvreau * - * Copyright (C) 2014 INRIA Saclay (France) + * Copyright (C) 2014 INRIA * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by @@ -20,6 +20,7 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/program_options.hpp> #include <boost/variant.hpp> #include <gudhi/Simplex_tree.h> @@ -37,8 +38,8 @@ #include <tuple> #include <map> #include <utility> -#include <list> #include <vector> +#include <cstdlib> #include "alpha_complex_3d_helper.h" @@ -57,59 +58,45 @@ using Point_3 = Kernel::Point_3; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; using Vertex_handle = Alpha_shape_3::Vertex_handle; -using Vertex_list = std::list<Vertex_handle>; +using Vertex_list = std::vector<Vertex_handle>; // gudhi type definition using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; -using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using PCOH = Gudhi::persistent_cohomology::Persistent_cohomology< ST, Gudhi::persistent_cohomology::Field_Zp >; - -void usage(char * const progName) { - std::cerr << "Usage:\n" << progName << " path_to_OFF_file coeff_field_characteristic[integer " << - "> 0] min_persistence[float >= -1.0]\n"; - std::cerr << " path_to_OFF_file is the path to your points cloud in OFF format.\n"; - exit(-1); -} +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -int main(int argc, char * const argv[]) { - // program args management - if (argc != 4) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence); - int coeff_field_characteristic = atoi(argv[2]); +int main(int argc, char **argv) { + std::string off_file_points; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; - Filtration_value min_persistence = 0.0; - int returnedScanValue = sscanf(argv[3], "%f", &min_persistence); - if ((returnedScanValue == EOF) || (min_persistence < -1.0)) { - std::cerr << "Error: " << argv[3] << " is not correct\n"; - usage(argv[0]); - } + program_options(argc, argv, off_file_points, output_file_diag, coeff_field_characteristic, min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read file " << off_file_points << std::endl; + exit(-1); } - // Retrieve the triangulation + // Retrieve the points std::vector<Point_3> lp = off_reader.get_point_cloud(); // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. @@ -140,37 +127,23 @@ int main(int argc, char * const argv[]) { ST simplex_tree; Alpha_shape_simplex_tree_map map_cgal_simplex_tree; std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); - int dim_max = 0; - Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle *cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; - if (dim_max < 3) { - // Cell is of dim 3 - dim_max = 3; - } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet *facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; - if (dim_max < 2) { - // Facet is of dim 2 - dim_max = 2; - } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3 *edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; - if (dim_max < 1) { - // Edge_3 is of dim 1 - dim_max = 1; - } - } else if (const Vertex_handle * vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle *vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex - Simplex_tree_vector_vertex the_simplex_tree; + Simplex_tree_vector_vertex the_simplex; for (auto the_alpha_shape_vertex : vertex_list) { Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); if (the_map_iterator == map_cgal_simplex_tree.end()) { @@ -179,33 +152,29 @@ int main(int argc, char * const argv[]) { #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); - map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); + the_simplex.push_back(vertex); + map_cgal_simplex_tree.emplace(the_alpha_shape_vertex, vertex); } else { // alpha shape found Simplex_tree_vertex vertex = the_map_iterator->second; #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); + the_simplex.push_back(vertex); } } // Construction of the simplex_tree // you can also use the_alpha_value_iterator->exact() - Filtration_value filtr = /*std::sqrt*/CGAL::to_double(the_alpha_value_iterator->exact()); + Filtration_value filtr = /*std::sqrt*/ CGAL::to_double(the_alpha_value_iterator->exact()); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES - if (filtr > filtration_max) { - filtration_max = filtr; - } - simplex_tree.insert_simplex(the_simplex_tree, filtr); + simplex_tree.insert_simplex(the_simplex, filtr); if (the_alpha_value_iterator != the_alpha_values.end()) ++the_alpha_value_iterator; else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -213,7 +182,6 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; @@ -232,13 +200,64 @@ int main(int argc, char * const argv[]) { std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; // Compute the persistence diagram of the complex - PCOH pcoh(simplex_tree); + Persistent_cohomology pcoh(simplex_tree, true); // initializes the coefficient field for homology pcoh.init_coefficients(coeff_field_characteristic); pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &output_file_diag, + int &coeff_field_characteristic, Filtration_value &min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/utilities/Alpha_complex/periodic_alpha_complex_3d_persistence.cpp b/utilities/Alpha_complex/periodic_alpha_complex_3d_persistence.cpp new file mode 100644 index 00000000..188cf604 --- /dev/null +++ b/utilities/Alpha_complex/periodic_alpha_complex_3d_persistence.cpp @@ -0,0 +1,300 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Vincent Rouvreau + * Pawel Dlotko - 2017 - Swansea University, UK + * + * Copyright (C) 2014 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <boost/program_options.hpp> +#include <boost/variant.hpp> + +#include <gudhi/Simplex_tree.h> +#include <gudhi/Persistent_cohomology.h> +#include <gudhi/Points_3D_off_io.h> + +#include <CGAL/Exact_predicates_inexact_constructions_kernel.h> +#include <CGAL/Periodic_3_Delaunay_triangulation_traits_3.h> +#include <CGAL/Periodic_3_Delaunay_triangulation_3.h> +#include <CGAL/Alpha_shape_3.h> +#include <CGAL/iterator.h> + +#include <fstream> +#include <cmath> +#include <string> +#include <tuple> +#include <map> +#include <utility> +#include <vector> +#include <cstdlib> + +#include "alpha_complex_3d_helper.h" + +// Traits +using K = CGAL::Exact_predicates_inexact_constructions_kernel; +using PK = CGAL::Periodic_3_Delaunay_triangulation_traits_3<K>; +// Vertex type +using DsVb = CGAL::Periodic_3_triangulation_ds_vertex_base_3<>; +using Vb = CGAL::Triangulation_vertex_base_3<PK, DsVb>; +using AsVb = CGAL::Alpha_shape_vertex_base_3<PK, Vb>; +// Cell type +using DsCb = CGAL::Periodic_3_triangulation_ds_cell_base_3<>; +using Cb = CGAL::Triangulation_cell_base_3<PK, DsCb>; +using AsCb = CGAL::Alpha_shape_cell_base_3<PK, Cb>; +using Tds = CGAL::Triangulation_data_structure_3<AsVb, AsCb>; +using P3DT3 = CGAL::Periodic_3_Delaunay_triangulation_3<PK, Tds>; +using Alpha_shape_3 = CGAL::Alpha_shape_3<P3DT3>; +using Point_3 = PK::Point_3; + +// filtration with alpha values needed type definition +using Alpha_value_type = Alpha_shape_3::FT; +using Object = CGAL::Object; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; +using Cell_handle = Alpha_shape_3::Cell_handle; +using Facet = Alpha_shape_3::Facet; +using Edge_3 = Alpha_shape_3::Edge; +using Vertex_handle = Alpha_shape_3::Vertex_handle; +using Vertex_list = std::vector<Alpha_shape_3::Vertex_handle>; + +// gudhi type definition +using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; +using Filtration_value = ST::Filtration_value; +using Simplex_tree_vertex = ST::Vertex_handle; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &cuboid_file, + std::string &output_file_diag, int &coeff_field_characteristic, Filtration_value &min_persistence); + +int main(int argc, char **argv) { + std::string off_file_points; + std::string cuboid_file; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; + + program_options(argc, argv, off_file_points, cuboid_file, output_file_diag, coeff_field_characteristic, + min_persistence); + + // Read the OFF file (input file name given as parameter) and triangulate points + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); + // Check the read operation was correct + if (!off_reader.is_valid()) { + std::cerr << "Unable to read OFF file " << off_file_points << std::endl; + exit(-1); + } + + // Read iso_cuboid_3 information from file + std::ifstream iso_cuboid_str(cuboid_file); + double x_min, y_min, z_min, x_max, y_max, z_max; + if (iso_cuboid_str.good()) { + iso_cuboid_str >> x_min >> y_min >> z_min >> x_max >> y_max >> z_max; + } else { + std::cerr << "Unable to read file " << cuboid_file << std::endl; + exit(-1); + } + // Checking if the cuboid is the same in x,y and z direction. If not, CGAL will not process it. + if ((x_max - x_min != y_max - y_min) || (x_max - x_min != z_max - z_min) || (z_max - z_min != y_max - y_min)) { + std::cerr << "The size of the cuboid in every directions is not the same." << std::endl; + exit(-1); + } + + // Retrieve the points + std::vector<Point_3> lp = off_reader.get_point_cloud(); + + // Define the periodic cube + P3DT3 pdt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); + // Heuristic for inserting large point sets (if pts is reasonably large) + pdt.insert(lp.begin(), lp.end(), true); + // As pdt won't be modified anymore switch to 1-sheeted cover if possible + if (pdt.is_triangulation_in_1_sheet()) { + pdt.convert_to_1_sheeted_covering(); + } else { + std::cerr << "ERROR: we were not able to construct a triangulation within a single periodic domain." << std::endl; + exit(-1); + } + std::cout << "Periodic Delaunay computed." << std::endl; + + // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. This is the default mode + // Maybe need to set it to GENERAL mode + Alpha_shape_3 as(pdt, 0, Alpha_shape_3::GENERAL); + + // filtration with alpha values from alpha shape + std::vector<Object> the_objects; + std::vector<Alpha_value_type> the_alpha_values; + + Dispatch disp = CGAL::dispatch_output<Object, Alpha_value_type>(std::back_inserter(the_objects), + std::back_inserter(the_alpha_values)); + + as.filtration_with_alpha_values(disp); +#ifdef DEBUG_TRACES + std::cout << "filtration_with_alpha_values returns : " << the_objects.size() << " objects" << std::endl; +#endif // DEBUG_TRACES + + Alpha_shape_3::size_type count_vertices = 0; + Alpha_shape_3::size_type count_edges = 0; + Alpha_shape_3::size_type count_facets = 0; + Alpha_shape_3::size_type count_cells = 0; + + // Loop on objects vector + Vertex_list vertex_list; + ST simplex_tree; + Alpha_shape_simplex_tree_map map_cgal_simplex_tree; + std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); + for (auto object_iterator : the_objects) { + // Retrieve Alpha shape vertex list from object + if (const Cell_handle *cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); + count_cells++; + } else if (const Facet *facet = CGAL::object_cast<Facet>(&object_iterator)) { + vertex_list = from_facet<Vertex_list, Facet>(*facet); + count_facets++; + } else if (const Edge_3 *edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + vertex_list = from_edge<Vertex_list, Edge_3>(*edge); + count_edges++; + } else if (const Vertex_handle *vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { + count_vertices++; + vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); + } + // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex + Simplex_tree_vector_vertex the_simplex; + for (auto the_alpha_shape_vertex : vertex_list) { + Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); + if (the_map_iterator == map_cgal_simplex_tree.end()) { + // alpha shape not found + Simplex_tree_vertex vertex = map_cgal_simplex_tree.size(); +#ifdef DEBUG_TRACES + std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; +#endif // DEBUG_TRACES + the_simplex.push_back(vertex); + map_cgal_simplex_tree.emplace(the_alpha_shape_vertex, vertex); + } else { + // alpha shape found + Simplex_tree_vertex vertex = the_map_iterator->second; +#ifdef DEBUG_TRACES + std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; +#endif // DEBUG_TRACES + the_simplex.push_back(vertex); + } + } + // Construction of the simplex_tree + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); +#ifdef DEBUG_TRACES + std::cout << "filtration = " << filtr << std::endl; +#endif // DEBUG_TRACES + simplex_tree.insert_simplex(the_simplex, filtr); + if (the_alpha_value_iterator != the_alpha_values.end()) + ++the_alpha_value_iterator; + else + std::cout << "This shall not happen" << std::endl; + } + +#ifdef DEBUG_TRACES + std::cout << "vertices \t\t" << count_vertices << std::endl; + std::cout << "edges \t\t" << count_edges << std::endl; + std::cout << "facets \t\t" << count_facets << std::endl; + std::cout << "cells \t\t" << count_cells << std::endl; + + std::cout << "Information of the Simplex Tree: " << std::endl; + std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; + std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; + std::cout << " Dimension = " << simplex_tree.dimension() << " "; +#endif // DEBUG_TRACES + +#ifdef DEBUG_TRACES + std::cout << "Iterator on vertices: " << std::endl; + for (auto vertex : simplex_tree.complex_vertex_range()) { + std::cout << vertex << " "; + } +#endif // DEBUG_TRACES + + // Sort the simplices in the order of the filtration + simplex_tree.initialize_filtration(); + + std::cout << "Simplex_tree dim: " << simplex_tree.dimension() << std::endl; + // Compute the persistence diagram of the complex + Persistent_cohomology pcoh(simplex_tree, true); + // initializes the coefficient field for homology + pcoh.init_coefficients(coeff_field_characteristic); + + pcoh.compute_persistent_cohomology(min_persistence); + + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } + + return 0; +} + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &cuboid_file, + std::string &output_file_diag, int &coeff_field_characteristic, + Filtration_value &min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd ")( + "cuboid-file", po::value<std::string>(&cuboid_file), + "Name of file describing the periodic domain. Format is: min_hx min_hy min_hz\nmax_hx max_hy max_hz"); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + pos.add("cuboid-file", 2); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file") || !vm.count("cuboid-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a periodic 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file cuboid-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/example/Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp b/utilities/Alpha_complex/weighted_alpha_complex_3d_persistence.cpp index 72268511..93be8a05 100644 --- a/example/Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp +++ b/utilities/Alpha_complex/weighted_alpha_complex_3d_persistence.cpp @@ -20,102 +20,114 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ +#include <boost/program_options.hpp> #include <boost/variant.hpp> #include <gudhi/Simplex_tree.h> #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_3D_off_io.h> +#include <CGAL/config.h> #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> -#include <CGAL/Regular_triangulation_euclidean_traits_3.h> #include <CGAL/Regular_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> #include <CGAL/iterator.h> +// For CGAL < 4.11 +#if CGAL_VERSION_NR < 1041100000 +#include <CGAL/Regular_triangulation_euclidean_traits_3.h> +#endif // CGAL_VERSION_NR < 1041100000 + #include <fstream> #include <cmath> #include <string> #include <tuple> #include <map> #include <utility> -#include <list> #include <vector> #include <cstdlib> #include "alpha_complex_3d_helper.h" -// Traits +// Alpha_shape_3 templates type definitions using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel; + +// For CGAL < 4.11 +#if CGAL_VERSION_NR < 1041100000 using Gt = CGAL::Regular_triangulation_euclidean_traits_3<Kernel>; using Vb = CGAL::Alpha_shape_vertex_base_3<Gt>; using Fb = CGAL::Alpha_shape_cell_base_3<Gt>; using Tds = CGAL::Triangulation_data_structure_3<Vb, Fb>; using Triangulation_3 = CGAL::Regular_triangulation_3<Gt, Tds>; -using Alpha_shape_3 = CGAL::Alpha_shape_3<Triangulation_3>; // From file type definition using Point_3 = Gt::Bare_point; using Weighted_point_3 = Gt::Weighted_point; +// For CGAL >= 4.11 +#else // CGAL_VERSION_NR < 1041100000 +using Rvb = CGAL::Regular_triangulation_vertex_base_3<Kernel>; +using Vb = CGAL::Alpha_shape_vertex_base_3<Kernel, Rvb>; +using Rcb = CGAL::Regular_triangulation_cell_base_3<Kernel>; +using Cb = CGAL::Alpha_shape_cell_base_3<Kernel, Rcb>; +using Tds = CGAL::Triangulation_data_structure_3<Vb, Cb>; +using Triangulation_3 = CGAL::Regular_triangulation_3<Kernel, Tds>; + +// From file type definition +using Point_3 = Triangulation_3::Bare_point; +using Weighted_point_3 = Triangulation_3::Weighted_point; +#endif // CGAL_VERSION_NR < 1041100000 + +using Alpha_shape_3 = CGAL::Alpha_shape_3<Triangulation_3>; + // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; using Vertex_handle = Alpha_shape_3::Vertex_handle; -using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; +using Vertex_list = std::vector<Alpha_shape_3::Vertex_handle>; // gudhi type definition using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; -using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology< - ST, Gudhi::persistent_cohomology::Field_Zp >; - -void usage(char * const progName) { - std::cerr << "Usage:\n" << progName << " path_to_OFF_file path_to_weight_file coeff_field_characteristic[integer " << - "> 0] min_persistence[float >= -1.0]\n"; - std::cerr << " path_to_OFF_file is the path to your points cloud in OFF format.\n"; - std::cerr << " path_to_weight_file is the path to the weights of your points cloud (one value per line.)\n"; - std::cerr << " Weights values are explained on CGAL documentation:\n"; - std::cerr << " https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0\n"; - std::cerr << " https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation\n"; - exit(-1); -} +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; -int main(int argc, char * const argv[]) { - // program args management - if (argc != 5) { - std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; - usage(argv[0]); - } +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &weight_file, + std::string &output_file_diag, int &coeff_field_characteristic, Filtration_value &min_persistence); - int coeff_field_characteristic = atoi(argv[3]); - Filtration_value min_persistence = strtof(argv[4], nullptr); +int main(int argc, char **argv) { + std::string off_file_points; + std::string weight_file; + std::string output_file_diag; + int coeff_field_characteristic; + Filtration_value min_persistence; + + program_options(argc, argv, off_file_points, weight_file, output_file_diag, coeff_field_characteristic, + min_persistence); - // Read points from file - std::string offInputFile(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_file_points); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; - usage(argv[0]); + std::cerr << "Unable to read OFF file " << off_file_points << std::endl; + exit(-1); } - // Retrieve the triangulation + // Retrieve the points std::vector<Point_3> lp = off_reader.get_point_cloud(); // Read weights information from file - std::ifstream weights_ifstr(argv[2]); + std::ifstream weights_ifstr(weight_file); std::vector<Weighted_point_3> wp; if (weights_ifstr.good()) { double weight = 0.0; @@ -127,12 +139,12 @@ int main(int argc, char * const argv[]) { index++; } if (index != lp.size()) { - std::cerr << "Bad number of weights in file " << argv[2] << std::endl; - usage(argv[0]); + std::cerr << "Bad number of weights in file " << weight_file << std::endl; + exit(-1); } } else { - std::cerr << "Unable to read file " << argv[2] << std::endl; - usage(argv[0]); + std::cerr << "Unable to read weights file " << weight_file << std::endl; + exit(-1); } // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. @@ -163,38 +175,23 @@ int main(int argc, char * const argv[]) { ST simplex_tree; Alpha_shape_simplex_tree_map map_cgal_simplex_tree; std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); - int dim_max = 0; - Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle *cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; - if (dim_max < 3) { - // Cell is of dim 3 - dim_max = 3; - } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet *facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; - if (dim_max < 2) { - // Facet is of dim 2 - dim_max = 2; - } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3 *edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; - if (dim_max < 1) { - // Edge_3 is of dim 1 - dim_max = 1; - } - } else if (const Alpha_shape_3::Vertex_handle * vertex = - CGAL::object_cast<Alpha_shape_3::Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle *vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex - Simplex_tree_vector_vertex the_simplex_tree; + Simplex_tree_vector_vertex the_simplex; for (auto the_alpha_shape_vertex : vertex_list) { Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); if (the_map_iterator == map_cgal_simplex_tree.end()) { @@ -203,32 +200,28 @@ int main(int argc, char * const argv[]) { #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); - map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); + the_simplex.push_back(vertex); + map_cgal_simplex_tree.emplace(the_alpha_shape_vertex, vertex); } else { // alpha shape found Simplex_tree_vertex vertex = the_map_iterator->second; #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); + the_simplex.push_back(vertex); } } // Construction of the simplex_tree - Filtration_value filtr = /*std::sqrt*/(*the_alpha_value_iterator); + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES - if (filtr > filtration_max) { - filtration_max = filtr; - } - simplex_tree.insert_simplex(the_simplex_tree, filtr); + simplex_tree.insert_simplex(the_simplex, filtr); if (the_alpha_value_iterator != the_alpha_values.end()) ++the_alpha_value_iterator; else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -236,7 +229,6 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; @@ -261,7 +253,62 @@ int main(int argc, char * const argv[]) { pcoh.compute_persistent_cohomology(min_persistence); - pcoh.output_diagram(); + // Output the diagram in filediag + if (output_file_diag.empty()) { + pcoh.output_diagram(); + } else { + std::cout << "Result in file: " << output_file_diag << std::endl; + std::ofstream out(output_file_diag); + pcoh.output_diagram(out); + out.close(); + } return 0; } + +void program_options(int argc, char *argv[], std::string &off_file_points, std::string &weight_file, + std::string &output_file_diag, int &coeff_field_characteristic, + Filtration_value &min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of file containing a point set. Format is one point per line: X1 ... Xd ")( + "weight-file", po::value<std::string>(&weight_file), + "Name of file containing a point weights. Format is one weigt per line: W1\n...\nWn "); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&output_file_diag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "field-charac,p", po::value<int>(&coeff_field_characteristic)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + pos.add("weight-file", 2); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file") || !vm.count("weight-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a weighted 3D Alpha complex defined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file weight-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/example/Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp b/utilities/Alpha_complex/weighted_periodic_alpha_complex_3d_persistence.cpp index 8140a3c5..5321bb0a 100644 --- a/example/Persistent_cohomology/periodic_alpha_complex_3d_persistence.cpp +++ b/utilities/Alpha_complex/weighted_periodic_alpha_complex_3d_persistence.cpp @@ -3,6 +3,7 @@ * library for computational topology. * * Author(s): Vincent Rouvreau + * Pawel Dlotko - 2017 - Swansea University, UK * * Copyright (C) 2014 INRIA * @@ -27,8 +28,8 @@ #include <gudhi/Points_3D_off_io.h> #include <CGAL/Exact_predicates_inexact_constructions_kernel.h> -#include <CGAL/Periodic_3_Delaunay_triangulation_traits_3.h> -#include <CGAL/Periodic_3_Delaunay_triangulation_3.h> +#include <CGAL/Periodic_3_regular_triangulation_traits_3.h> +#include <CGAL/Periodic_3_regular_triangulation_3.h> #include <CGAL/Alpha_shape_3.h> #include <CGAL/iterator.h> @@ -38,74 +39,67 @@ #include <tuple> #include <map> #include <utility> -#include <list> #include <vector> #include <cstdlib> #include "alpha_complex_3d_helper.h" // Traits -using K = CGAL::Exact_predicates_inexact_constructions_kernel; -using PK = CGAL::Periodic_3_Delaunay_triangulation_traits_3<K>; +using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel; +using PK = CGAL::Periodic_3_regular_triangulation_traits_3<Kernel>; + // Vertex type using DsVb = CGAL::Periodic_3_triangulation_ds_vertex_base_3<>; -using Vb = CGAL::Triangulation_vertex_base_3<PK, DsVb>; +using Vb = CGAL::Regular_triangulation_vertex_base_3<PK, DsVb>; using AsVb = CGAL::Alpha_shape_vertex_base_3<PK, Vb>; // Cell type using DsCb = CGAL::Periodic_3_triangulation_ds_cell_base_3<>; -using Cb = CGAL::Triangulation_cell_base_3<PK, DsCb>; +using Cb = CGAL::Regular_triangulation_cell_base_3<PK, DsCb>; using AsCb = CGAL::Alpha_shape_cell_base_3<PK, Cb>; using Tds = CGAL::Triangulation_data_structure_3<AsVb, AsCb>; -using P3DT3 = CGAL::Periodic_3_Delaunay_triangulation_3<PK, Tds>; -using Alpha_shape_3 = CGAL::Alpha_shape_3<P3DT3>; -using Point_3 = PK::Point_3; +using P3RT3 = CGAL::Periodic_3_regular_triangulation_3<PK, Tds>; +using Alpha_shape_3 = CGAL::Alpha_shape_3<P3RT3>; + +using Point_3 = P3RT3::Bare_point; +using Weighted_point_3 = P3RT3::Weighted_point; // filtration with alpha values needed type definition using Alpha_value_type = Alpha_shape_3::FT; using Object = CGAL::Object; -using Dispatch = CGAL::Dispatch_output_iterator< - CGAL::cpp11::tuple<Object, Alpha_value_type>, - CGAL::cpp11::tuple<std::back_insert_iterator< std::vector<Object> >, - std::back_insert_iterator< std::vector<Alpha_value_type> > > >; +using Dispatch = + CGAL::Dispatch_output_iterator<CGAL::cpp11::tuple<Object, Alpha_value_type>, + CGAL::cpp11::tuple<std::back_insert_iterator<std::vector<Object> >, + std::back_insert_iterator<std::vector<Alpha_value_type> > > >; using Cell_handle = Alpha_shape_3::Cell_handle; using Facet = Alpha_shape_3::Facet; using Edge_3 = Alpha_shape_3::Edge; using Vertex_handle = Alpha_shape_3::Vertex_handle; -using Vertex_list = std::list<Alpha_shape_3::Vertex_handle>; +using Vertex_list = std::vector<Alpha_shape_3::Vertex_handle>; // gudhi type definition using ST = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; using Filtration_value = ST::Filtration_value; using Simplex_tree_vertex = ST::Vertex_handle; -using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex >; -using Alpha_shape_simplex_tree_pair = std::pair<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; -using Simplex_tree_vector_vertex = std::vector< Simplex_tree_vertex >; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology< - ST, Gudhi::persistent_cohomology::Field_Zp >; - -void usage(char * const progName) { - std::cerr << "Usage:\n" << progName << " path_to_OFF_file path_to_iso_cuboid_3_file coeff_field_characteristic[" << - "integer > 0] min_persistence[float >= -1.0]\n" << - " path_to_OFF_file is the path to your points cloud in OFF format.\n" << - " path_to_iso_cuboid_3_file is the path to the iso cuboid file with the following format :\n" << - " x_min y_min z_min x_max y_max z_max\n" << - " In this example, the periodic cube will be " << - "{ x = [x_min,x_max]; y = [y_min,y_max]; z = [z_min,z_max] }.\n" << - " For more information, please refer to\n" << - " https://doc.cgal.org/latest/Kernel_23/classCGAL_1_1Iso__cuboid__3.html\n"; +using Alpha_shape_simplex_tree_map = std::map<Alpha_shape_3::Vertex_handle, Simplex_tree_vertex>; +using Simplex_tree_vector_vertex = std::vector<Simplex_tree_vertex>; +using Persistent_cohomology = + Gudhi::persistent_cohomology::Persistent_cohomology<ST, Gudhi::persistent_cohomology::Field_Zp>; +void usage(const std::string& progName) { + std::cerr << "Usage: " << progName << " path_to_the_OFF_file path_to_weight_file path_to_the_cuboid_file " + "coeff_field_characteristic[integer > 0] min_persistence[float >= -1.0]\n"; exit(-1); } -int main(int argc, char * const argv[]) { +int main(int argc, char* const argv[]) { // program args management - if (argc != 5) { + if (argc != 6) { std::cerr << "Error: Number of arguments (" << argc << ") is not correct\n"; usage(argv[0]); } - int coeff_field_characteristic = atoi(argv[3]); - Filtration_value min_persistence = strtof(argv[4], nullptr); + int coeff_field_characteristic = atoi(argv[4]); + Filtration_value min_persistence = strtof(argv[5], nullptr); // Read points from file std::string offInputFile(argv[1]); @@ -117,30 +111,74 @@ int main(int argc, char * const argv[]) { usage(argv[0]); } + // Retrieve the points + std::vector<Point_3> lp = off_reader.get_point_cloud(); + // Read iso_cuboid_3 information from file - std::ifstream iso_cuboid_str(argv[2]); + std::ifstream iso_cuboid_str(argv[3]); double x_min, y_min, z_min, x_max, y_max, z_max; - if (iso_cuboid_str.good()) { - iso_cuboid_str >> x_min >> y_min >> z_min >> x_max >> y_max >> z_max; + if (iso_cuboid_str.is_open()) { + if (!(iso_cuboid_str >> x_min >> y_min >> z_min >> x_max >> y_max >> z_max)) { + std::cerr << argv[3] << " - Bad file format." << std::endl; + usage(argv[0]); + } + } else { - std::cerr << "Unable to read file " << argv[2] << std::endl; + std::cerr << "Unable to read file " << argv[3] << std::endl; usage(argv[0]); } + // Checking if the cuboid is the same in x,y and z direction. If not, CGAL will not process it. + if ((x_max - x_min != y_max - y_min) || (x_max - x_min != z_max - z_min) || (z_max - z_min != y_max - y_min)) { + std::cerr << "The size of the cuboid in every directions is not the same." << std::endl; + exit(-1); + } - // Retrieve the triangulation - std::vector<Point_3> lp = off_reader.get_point_cloud(); + double maximal_possible_weight = 0.015625 * (x_max - x_min) * (x_max - x_min); + + // Read weights information from file + std::ifstream weights_ifstr(argv[2]); + std::vector<Weighted_point_3> wp; + if (weights_ifstr.is_open()) { + double weight = 0.0; + std::size_t index = 0; + wp.reserve(lp.size()); + // Attempt read the weight in a double format, return false if it fails + while ((weights_ifstr >> weight) && (index < lp.size())) { + if ((weight >= maximal_possible_weight) || (weight < 0)) { + std::cerr << "At line " << (index + 1) << ", the weight (" << weight + << ") is negative or more than or equal to maximal possible weight (" << maximal_possible_weight + << ") = 1/64*cuboid length squared, which is not an acceptable input." << std::endl; + exit(-1); + } + + wp.push_back(Weighted_point_3(lp[index], weight)); + index++; + } + if (index != lp.size()) { + std::cerr << "Bad number of weights in file " << argv[2] << std::endl; + usage(argv[0]); + } + } else { + std::cerr << "Unable to read file " << argv[2] << std::endl; + usage(argv[0]); + } // Define the periodic cube - P3DT3 pdt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); + P3RT3 prt(PK::Iso_cuboid_3(x_min, y_min, z_min, x_max, y_max, z_max)); // Heuristic for inserting large point sets (if pts is reasonably large) - pdt.insert(lp.begin(), lp.end(), true); - // As pdt won't be modified anymore switch to 1-sheeted cover if possible - if (pdt.is_triangulation_in_1_sheet()) pdt.convert_to_1_sheeted_covering(); - std::cout << "Periodic Delaunay computed." << std::endl; + prt.insert(wp.begin(), wp.end(), true); + // As prt won't be modified anymore switch to 1-sheeted cover if possible + if (prt.is_triangulation_in_1_sheet()) { + prt.convert_to_1_sheeted_covering(); + } else { + std::cerr << "ERROR: we were not able to construct a triangulation within a single periodic domain." << std::endl; + exit(-1); + } + std::cout << "Weighted Periodic Delaunay computed." << std::endl; // alpha shape construction from points. CGAL has a strange behavior in REGULARIZED mode. This is the default mode // Maybe need to set it to GENERAL mode - Alpha_shape_3 as(pdt, 0, Alpha_shape_3::GENERAL); + Alpha_shape_3 as(prt, 0, Alpha_shape_3::GENERAL); // filtration with alpha values from alpha shape std::vector<Object> the_objects; @@ -164,38 +202,23 @@ int main(int argc, char * const argv[]) { ST simplex_tree; Alpha_shape_simplex_tree_map map_cgal_simplex_tree; std::vector<Alpha_value_type>::iterator the_alpha_value_iterator = the_alpha_values.begin(); - int dim_max = 0; - Filtration_value filtration_max = 0.0; for (auto object_iterator : the_objects) { // Retrieve Alpha shape vertex list from object - if (const Cell_handle * cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { + if (const Cell_handle* cell = CGAL::object_cast<Cell_handle>(&object_iterator)) { vertex_list = from_cell<Vertex_list, Cell_handle>(*cell); count_cells++; - if (dim_max < 3) { - // Cell is of dim 3 - dim_max = 3; - } - } else if (const Facet * facet = CGAL::object_cast<Facet>(&object_iterator)) { + } else if (const Facet* facet = CGAL::object_cast<Facet>(&object_iterator)) { vertex_list = from_facet<Vertex_list, Facet>(*facet); count_facets++; - if (dim_max < 2) { - // Facet is of dim 2 - dim_max = 2; - } - } else if (const Edge_3 * edge = CGAL::object_cast<Edge_3>(&object_iterator)) { + } else if (const Edge_3* edge = CGAL::object_cast<Edge_3>(&object_iterator)) { vertex_list = from_edge<Vertex_list, Edge_3>(*edge); count_edges++; - if (dim_max < 1) { - // Edge_3 is of dim 1 - dim_max = 1; - } - } else if (const Alpha_shape_3::Vertex_handle * vertex = - CGAL::object_cast<Alpha_shape_3::Vertex_handle>(&object_iterator)) { + } else if (const Vertex_handle* vertex = CGAL::object_cast<Vertex_handle>(&object_iterator)) { count_vertices++; vertex_list = from_vertex<Vertex_list, Vertex_handle>(*vertex); } // Construction of the vector of simplex_tree vertex from list of alpha_shapes vertex - Simplex_tree_vector_vertex the_simplex_tree; + Simplex_tree_vector_vertex the_simplex; for (auto the_alpha_shape_vertex : vertex_list) { Alpha_shape_simplex_tree_map::iterator the_map_iterator = map_cgal_simplex_tree.find(the_alpha_shape_vertex); if (the_map_iterator == map_cgal_simplex_tree.end()) { @@ -204,32 +227,28 @@ int main(int argc, char * const argv[]) { #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] not found - insert " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); - map_cgal_simplex_tree.insert(Alpha_shape_simplex_tree_pair(the_alpha_shape_vertex, vertex)); + the_simplex.push_back(vertex); + map_cgal_simplex_tree.emplace(the_alpha_shape_vertex, vertex); } else { // alpha shape found Simplex_tree_vertex vertex = the_map_iterator->second; #ifdef DEBUG_TRACES std::cout << "vertex [" << the_alpha_shape_vertex->point() << "] found in " << vertex << std::endl; #endif // DEBUG_TRACES - the_simplex_tree.push_back(vertex); + the_simplex.push_back(vertex); } } // Construction of the simplex_tree - Filtration_value filtr = /*std::sqrt*/(*the_alpha_value_iterator); + Filtration_value filtr = /*std::sqrt*/ (*the_alpha_value_iterator); #ifdef DEBUG_TRACES std::cout << "filtration = " << filtr << std::endl; #endif // DEBUG_TRACES - if (filtr > filtration_max) { - filtration_max = filtr; - } - simplex_tree.insert_simplex(the_simplex_tree, filtr); + simplex_tree.insert_simplex(the_simplex, filtr); if (the_alpha_value_iterator != the_alpha_values.end()) ++the_alpha_value_iterator; else std::cout << "This shall not happen" << std::endl; } - simplex_tree.set_dimension(dim_max); #ifdef DEBUG_TRACES std::cout << "vertices \t\t" << count_vertices << std::endl; @@ -237,7 +256,6 @@ int main(int argc, char * const argv[]) { std::cout << "facets \t\t" << count_facets << std::endl; std::cout << "cells \t\t" << count_cells << std::endl; - std::cout << "Information of the Simplex Tree: " << std::endl; std::cout << " Number of vertices = " << simplex_tree.num_vertices() << " "; std::cout << " Number of simplices = " << simplex_tree.num_simplices() << std::endl << std::endl; diff --git a/utilities/Bitmap_cubical_complex/CMakeLists.txt b/utilities/Bitmap_cubical_complex/CMakeLists.txt new file mode 100644 index 00000000..676a730a --- /dev/null +++ b/utilities/Bitmap_cubical_complex/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required(VERSION 2.6) +project(Bitmap_cubical_complex_utilities) + +add_executable ( cubical_complex_persistence cubical_complex_persistence.cpp ) +if (TBB_FOUND) + target_link_libraries(cubical_complex_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Bitmap_cubical_complex_utility_persistence_one_sphere COMMAND $<TARGET_FILE:cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalOneSphere.txt") + +add_test(NAME Bitmap_cubical_complex_utility_persistence_two_sphere COMMAND $<TARGET_FILE:cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt") + +add_executable ( periodic_cubical_complex_persistence periodic_cubical_complex_persistence.cpp ) +if (TBB_FOUND) + target_link_libraries(periodic_cubical_complex_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Bitmap_cubical_complex_utility_periodic_boundary_conditions_2d_torus + COMMAND $<TARGET_FILE:periodic_cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/2d_torus.txt") + +add_test(NAME Bitmap_cubical_complex_utility_periodic_boundary_conditions_3d_torus + COMMAND $<TARGET_FILE:periodic_cubical_complex_persistence> + "${CMAKE_SOURCE_DIR}/data/bitmap/3d_torus.txt") + +install(TARGETS cubical_complex_persistence DESTINATION bin) +install(TARGETS periodic_cubical_complex_persistence DESTINATION bin) diff --git a/example/Bitmap_cubical_complex/Bitmap_cubical_complex.cpp b/utilities/Bitmap_cubical_complex/cubical_complex_persistence.cpp index 67735ba1..9d1bc08c 100644 --- a/example/Bitmap_cubical_complex/Bitmap_cubical_complex.cpp +++ b/utilities/Bitmap_cubical_complex/cubical_complex_persistence.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> #include <gudhi/Persistent_cohomology.h> @@ -32,16 +31,18 @@ #include <cstddef> int main(int argc, char** argv) { - std::cout << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " << - "complexes provided in text files in Perseus style (the only numbered in the first line is a dimension D of a" << - "bitmap. In the lines I between 2 and D+1 there are numbers of top dimensional cells in the direction I. Let " << - "N denote product of the numbers in the lines between 2 and D. In the lines D+2 to D+2+N there are " << - "filtrations of top dimensional cells. We assume that the cells are in the lexicographical order. See " << - "CubicalOneSphere.txt or CubicalTwoSphere.txt for example.\n" << std::endl; + std::cout + << "This program computes persistent homology, by using bitmap_cubical_complex class, of cubical " + << "complexes provided in text files in Perseus style (the only numbered in the first line is a dimension D of a" + << "bitmap. In the lines I between 2 and D+1 there are numbers of top dimensional cells in the direction I. Let " + << "N denote product of the numbers in the lines between 2 and D. In the lines D+2 to D+2+N there are " + << "filtrations of top dimensional cells. We assume that the cells are in the lexicographical order. See " + << "CubicalOneSphere.txt or CubicalTwoSphere.txt for example.\n" + << std::endl; if (argc != 2) { - std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " << - "the input. The program will now terminate.\n"; + std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " + << "the input. The program will now terminate.\n"; return 1; } @@ -54,7 +55,7 @@ int main(int argc, char** argv) { // Compute the persistence diagram of the complex Persistent_cohomology pcoh(b); - int p = 2; + int p = 11; double min_persistence = 0; pcoh.init_coefficients(p); // initializes the coefficient field for homology @@ -66,7 +67,7 @@ int main(int argc, char** argv) { std::size_t last_in_path = output_file_name.find_last_of("/\\"); if (last_in_path != std::string::npos) { - output_file_name = output_file_name.substr(last_in_path+1); + output_file_name = output_file_name.substr(last_in_path + 1); } std::ofstream out(output_file_name.c_str()); @@ -77,4 +78,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/utilities/Bitmap_cubical_complex/cubicalcomplex.md b/utilities/Bitmap_cubical_complex/cubicalcomplex.md new file mode 100644 index 00000000..6e1b2578 --- /dev/null +++ b/utilities/Bitmap_cubical_complex/cubicalcomplex.md @@ -0,0 +1,29 @@ +
+
+# Cubical complex#
+
+## cubical_complex_persistence ##
+This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style.
+See [here](/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
+
+**Example**
+
+```
+ cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt
+```
+
+* Creates a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`.
+
+## periodic_cubical_complex_persistence ##
+
+Same as above, but with periodic boundary conditions.
+
+**Example**
+
+```
+ periodic_cubical_complex_persistence data/bitmap/3d_torus.txt
+```
+
+* Creates a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`.
diff --git a/example/Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp b/utilities/Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp index 122160a2..c812cb3a 100644 --- a/example/Bitmap_cubical_complex/Bitmap_cubical_complex_periodic_boundary_conditions.cpp +++ b/utilities/Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp @@ -20,7 +20,6 @@ * along with this program. If not, see <http://www.gnu.org/licenses/>. */ - #include <gudhi/reader_utils.h> #include <gudhi/Bitmap_cubical_complex.h> #include <gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h> @@ -33,22 +32,24 @@ #include <string> int main(int argc, char** argv) { - std::cout << "This program computes persistent homology, by using " << - "Bitmap_cubical_complex_periodic_boundary_conditions class, of cubical complexes provided in text files in " << - "Perseus style (the only numbered in the first line is a dimension D of a bitmap. In the lines I between 2 " << - "and D+1 there are numbers of top dimensional cells in the direction I. Let N denote product of the numbers " << - "in the lines between 2 and D. In the lines D+2 to D+2+N there are filtrations of top dimensional cells. We " << - "assume that the cells are in the lexicographical order. See CubicalOneSphere.txt or CubicalTwoSphere.txt for" << - " example.\n" << std::endl; + std::cout + << "This program computes persistent homology, by using " + << "Bitmap_cubical_complex_periodic_boundary_conditions class, of cubical complexes provided in text files in " + << "Perseus style (the only numbered in the first line is a dimension D of a bitmap. In the lines I between 2 " + << "and D+1 there are numbers of top dimensional cells in the direction I. Let N denote product of the numbers " + << "in the lines between 2 and D. In the lines D+2 to D+2+N there are filtrations of top dimensional cells. We " + << "assume that the cells are in the lexicographical order. See CubicalOneSphere.txt or CubicalTwoSphere.txt for" + << " example.\n" + << std::endl; if (argc != 2) { - std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " << - "the input. The program will now terminate.\n"; + std::cerr << "Wrong number of parameters. Please provide the name of a file with a Perseus style bitmap at " + << "the input. The program will now terminate.\n"; return 1; } typedef Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double> Bitmap_base; - typedef Gudhi::cubical_complex::Bitmap_cubical_complex< Bitmap_base > Bitmap_cubical_complex; + typedef Gudhi::cubical_complex::Bitmap_cubical_complex<Bitmap_base> Bitmap_cubical_complex; Bitmap_cubical_complex b(argv[1]); @@ -57,7 +58,7 @@ int main(int argc, char** argv) { // Compute the persistence diagram of the complex Persistent_cohomology pcoh(b, true); - int p = 2; + int p = 11; double min_persistence = 0; pcoh.init_coefficients(p); // initializes the coefficient field for homology pcoh.compute_persistent_cohomology(min_persistence); @@ -68,7 +69,7 @@ int main(int argc, char** argv) { std::size_t last_in_path = output_file_name.find_last_of("/\\"); if (last_in_path != std::string::npos) { - output_file_name = output_file_name.substr(last_in_path+1); + output_file_name = output_file_name.substr(last_in_path + 1); } std::ofstream out(output_file_name.c_str()); @@ -79,4 +80,3 @@ int main(int argc, char** argv) { return 0; } - diff --git a/utilities/Bottleneck_distance/CMakeLists.txt b/utilities/Bottleneck_distance/CMakeLists.txt new file mode 100644 index 00000000..d19e3b1c --- /dev/null +++ b/utilities/Bottleneck_distance/CMakeLists.txt @@ -0,0 +1,16 @@ +cmake_minimum_required(VERSION 2.6) +project(Bottleneck_distance_utilities) + +if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + add_executable (bottleneck_distance bottleneck_distance.cpp) + if (TBB_FOUND) + target_link_libraries(bottleneck_distance ${TBB_LIBRARIES}) + endif(TBB_FOUND) + + add_test(NAME Bottleneck_distance_utilities_Bottleneck_read_file + COMMAND $<TARGET_FILE:bottleneck_distance> + "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers" "${CMAKE_SOURCE_DIR}/data/persistence_diagram/second.pers") + + install(TARGETS bottleneck_distance DESTINATION bin) + +endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) diff --git a/example/Bottleneck_distance/bottleneck_read_file_example.cpp b/utilities/Bottleneck_distance/bottleneck_distance.cpp index 24d73c57..9dd52b31 100644 --- a/example/Bottleneck_distance/bottleneck_read_file_example.cpp +++ b/utilities/Bottleneck_distance/bottleneck_distance.cpp @@ -31,7 +31,7 @@ int main(int argc, char** argv) { if (argc < 3) { std::cout << "To run this program please provide as an input two files with persistence diagrams. Each file" << - " should contain a birth-death pair per line. Third, optional parameter is an error bound on a bottleneck" << + " should contain a birth-death pair per line. Third, optional parameter is an error bound on the bottleneck" << " distance (set by default to the smallest positive double value). If you set the error bound to 0, be" << " aware this version is exact but expensive. The program will now terminate \n"; return -1; diff --git a/utilities/Bottleneck_distance/bottleneckdistance.md b/utilities/Bottleneck_distance/bottleneckdistance.md new file mode 100644 index 00000000..526f5822 --- /dev/null +++ b/utilities/Bottleneck_distance/bottleneckdistance.md @@ -0,0 +1,18 @@ +
+
+# Bottleneck distance #
+
+## bottleneck_read_file_example ##
+
+This program computes the Bottleneck distance between two persistence diagram files.
+
+**Usage**
+
+```
+ bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]
+```
+
+where
+
+* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](/doc/latest/fileformats.html#FileFormatsPers).
+* `<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/utilities/Nerve_GIC/CMakeLists.txt b/utilities/Nerve_GIC/CMakeLists.txt new file mode 100644 index 00000000..7762c8a0 --- /dev/null +++ b/utilities/Nerve_GIC/CMakeLists.txt @@ -0,0 +1,24 @@ +cmake_minimum_required(VERSION 2.6) +project(Nerve_GIC_examples) + +if (NOT CGAL_VERSION VERSION_LESS 4.8.1) + + add_executable ( Nerve Nerve.cpp ) + add_executable ( VoronoiGIC VoronoiGIC.cpp ) + + if (TBB_FOUND) + target_link_libraries(Nerve ${TBB_LIBRARIES}) + target_link_libraries(VoronoiGIC ${TBB_LIBRARIES}) + endif() + + file(COPY KeplerMapperVisuFromTxtFile.py km.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + # Copy files for not to pollute sources when testing + file(COPY "${CMAKE_SOURCE_DIR}/data/points/human.off" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + + add_test(NAME Nerve_GIC_utilities_nerve COMMAND $<TARGET_FILE:Nerve> + "human.off" "2" "10" "0.3") + + add_test(NAME Nerve_GIC_utilities_VoronoiGIC COMMAND $<TARGET_FILE:VoronoiGIC> + "human.off" "100") + +endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) diff --git a/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py b/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py new file mode 100755 index 00000000..c811f610 --- /dev/null +++ b/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python + +import km +import numpy as np +from collections import defaultdict +import argparse + +"""This file is part of the Gudhi Library. The Gudhi library + (Geometric Understanding in Higher Dimensions) is a generic C++ + library for computational topology. + + Author(s): Mathieu Carriere + + Copyright (C) 2017 INRIA + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see <http://www.gnu.org/licenses/>. +""" + +__author__ = "Mathieu Carriere" +__copyright__ = "Copyright (C) 2017 INRIA" +__license__ = "GPL v3" + +parser = argparse.ArgumentParser(description='Creates an html Keppler Mapper ' + 'file to visualize a SC.txt file.', + epilog='Example: ' + './KeplerMapperVisuFromTxtFile.py ' + '-f ../../data/points/human.off_sc.txt' + '- Constructs an human.off_sc.html file.') +parser.add_argument("-f", "--file", type=str, required=True) + +args = parser.parse_args() + +with open(args.file, 'r') as f: + network = {} + mapper = km.KeplerMapper(verbose=0) + data = np.zeros((3,3)) + projected_data = mapper.fit_transform( data, projection="sum", scaler=None ) + + nodes = defaultdict(list) + links = defaultdict(list) + custom = defaultdict(list) + + dat = f.readline() + lens = f.readline() + color = f.readline(); + param = [float(i) for i in f.readline().split(" ")] + + nums = [int(i) for i in f.readline().split(" ")] + num_nodes = nums[0] + num_edges = nums[1] + + for i in range(0,num_nodes): + point = [float(j) for j in f.readline().split(" ")] + nodes[ str(int(point[0])) ] = [ int(point[0]), point[1], int(point[2]) ] + links[ str(int(point[0])) ] = [] + custom[ int(point[0]) ] = point[1] + + m = min([custom[i] for i in range(0,num_nodes)]) + M = max([custom[i] for i in range(0,num_nodes)]) + + for i in range(0,num_edges): + edge = [int(j) for j in f.readline().split(" ")] + links[ str(edge[0]) ].append( str(edge[1]) ) + links[ str(edge[1]) ].append( str(edge[0]) ) + + network["nodes"] = nodes + network["links"] = links + network["meta"] = lens + + html_output_filename = args.file.rsplit('.', 1)[0] + '.html' + mapper.visualize(network, color_function = color, path_html=html_output_filename, title=dat, + graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0, + height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M) + message = repr(html_output_filename) + " is generated. You can now use your favorite web browser to visualize it." + print(message) + + + f.close() diff --git a/utilities/Nerve_GIC/Nerve.cpp b/utilities/Nerve_GIC/Nerve.cpp new file mode 100644 index 00000000..aefc3874 --- /dev/null +++ b/utilities/Nerve_GIC/Nerve.cpp @@ -0,0 +1,96 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off coordinate resolution gain [-v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 2 10 0.3 -v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 5) && (argc != 6)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int coord = atoi(argv[2]); + int resolution = atoi(argv[3]); + double gain = atof(argv[4]); + bool verb = 0; + if (argc == 6) verb = 1; + + // -------------------------------- + // Init of a Nerve from an OFF file + // -------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> SC; + SC.set_verbose(verb); + + bool check = SC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + SC.set_type("Nerve"); + + SC.set_color_from_coordinate(coord); + SC.set_function_from_coordinate(coord); + + SC.set_graph_from_OFF(); + SC.set_resolution_with_interval_number(resolution); + SC.set_gain(gain); + SC.set_cover_from_function(); + + SC.find_simplices(); + + SC.write_info(); + + Gudhi::Simplex_tree<> stree; + SC.create_complex(stree); + SC.compute_PD(); + + // ---------------------------------------------------------------------------- + // Display information about the graph induced complex + // ---------------------------------------------------------------------------- + + if (verb) { + std::cout << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on Nerve simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/utilities/Nerve_GIC/Nerve.txt b/utilities/Nerve_GIC/Nerve.txt new file mode 100644 index 00000000..839ff45e --- /dev/null +++ b/utilities/Nerve_GIC/Nerve.txt @@ -0,0 +1,63 @@ +Min function value = -0.979672 and Max function value = 0.816414 +Interval 0 = [-0.979672, -0.761576] +Interval 1 = [-0.838551, -0.581967] +Interval 2 = [-0.658942, -0.402359] +Interval 3 = [-0.479334, -0.22275] +Interval 4 = [-0.299725, -0.0431415] +Interval 5 = [-0.120117, 0.136467] +Interval 6 = [0.059492, 0.316076] +Interval 7 = [0.239101, 0.495684] +Interval 8 = [0.418709, 0.675293] +Interval 9 = [0.598318, 0.816414] +Computing preimages... +Computing connected components... +.txt generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox. +5 interval(s) in dimension 0: + [-0.909111, 0.00817529] + [-0.171433, 0.367392] + [-0.171433, 0.367392] + [-0.909111, 0.745853] +0 interval(s) in dimension 1: +Nerve is of dimension 1 - 41 simplices - 21 vertices. +Iterator on Nerve simplices +1 +0 +4 +4 0 +2 +2 1 +8 +8 2 +5 +5 4 +9 +9 8 +13 +13 5 +14 +14 9 +19 +19 13 +25 +32 +20 +32 20 +33 +33 25 +26 +26 14 +26 19 +42 +42 26 +34 +34 33 +27 +27 20 +35 +35 27 +35 34 +42 35 +44 +44 35 +54 +54 44
\ No newline at end of file diff --git a/utilities/Nerve_GIC/VoronoiGIC.cpp b/utilities/Nerve_GIC/VoronoiGIC.cpp new file mode 100644 index 00000000..54bb871e --- /dev/null +++ b/utilities/Nerve_GIC/VoronoiGIC.cpp @@ -0,0 +1,90 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Mathieu Carrière + * + * Copyright (C) 2017 INRIA + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/GIC.h> + +#include <string> +#include <vector> + +void usage(int nbArgs, char *const progName) { + std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n"; + std::cerr << "Usage: " << progName << " filename.off N [-v] \n"; + std::cerr << " i.e.: " << progName << " ../../data/points/human.off 100 -v \n"; + exit(-1); // ----- >> +} + +int main(int argc, char **argv) { + if ((argc != 3) && (argc != 4)) usage(argc, argv[0]); + + using Point = std::vector<float>; + + std::string off_file_name(argv[1]); + int m = atoi(argv[2]); + bool verb = 0; + if (argc == 4) verb = 1; + + // ---------------------------------------------------------------------------- + // Init of a graph induced complex from an OFF file + // ---------------------------------------------------------------------------- + + Gudhi::cover_complex::Cover_complex<Point> GIC; + GIC.set_verbose(verb); + + bool check = GIC.read_point_cloud(off_file_name); + + if (!check) { + std::cout << "Incorrect OFF file." << std::endl; + } else { + GIC.set_type("GIC"); + + GIC.set_color_from_coordinate(); + + GIC.set_graph_from_OFF(); + GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m); + + GIC.find_simplices(); + + GIC.plot_OFF(); + + Gudhi::Simplex_tree<> stree; + GIC.create_complex(stree); + + // ---------------------------------------------------------------------------- + // Display information about the graph induced complex + // ---------------------------------------------------------------------------- + + if (verb) { + std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on graph induced complex simplices" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << std::endl; + } + } + } + + return 0; +} diff --git a/utilities/Nerve_GIC/covercomplex.md b/utilities/Nerve_GIC/covercomplex.md new file mode 100644 index 00000000..f33cb2e0 --- /dev/null +++ b/utilities/Nerve_GIC/covercomplex.md @@ -0,0 +1,62 @@ + + +# Cover complex # + + +## Nerve ## +This program builds the Nerve of a point cloud sampled on an OFF file. +The cover C comes from the preimages of intervals covering a coordinate function, +which are then refined into their connected components using the triangulation of the .OFF file. + +The program also writes a file SC.txt. +The first three lines in this file are the location of the input point cloud and the function used to compute the cover. +The fourth line contains the number of vertices nv and edges ne of the Nerve. The next nv lines represent the vertices. +Each line contains the vertex ID, the number of data points it contains, and their average color function value. +Finally, the next ne lines represent the edges, characterized by the ID of their vertices. + +**Usage** + +`Nerve <OFF input file> coordinate resolution gain [-v]` + +where + +* `coordinate` is the coordinate function to cover +* `resolution` is the number of the intervals +* `gain` is the gain for each interval +* `-v` is optional, it activates verbose mode. + +**Example** + +`Nerve ../../data/points/human.off 2 10 0.3` + +* Builds the Nerve of a point cloud sampled on a 3D human shape (human.off). +The cover C comes from the preimages of intervals (10 intervals with gain 0.3) covering the height function (coordinate 2). + +`python KeplerMapperVisuFromTxtFile.py -f ../../data/points/human.off_sc.txt` + +* Constructs `human.off_sc.html` file. You can now use your favorite web browser to visualize it. + +## VoronoiGIC ## + +This util builds the Graph Induced Complex (GIC) of a point cloud. +It subsamples *N* points in the point cloud, which act as seeds of a geodesic Voronoï diagram. +Each cell of the diagram is then an element of C. + +The program also writes a file `*_sc.off`, that is an OFF file that can be visualized with GeomView. + +**Usage** + +`VoroniGIC <OFF input file> samples_number [-v]` + +where + +* `samples_number` is the number of samples to take from the point cloud +* `-v` is optional, it activates verbose mode. + +**Example** + +`VoroniGIC ../../data/points/human.off 700` + +* Builds the Voronoi Graph Induced Complex with 700 subsamples from `human.off` file. +`../../data/points/human_sc.off` can be visualized with GeomView. + diff --git a/utilities/Nerve_GIC/km.py b/utilities/Nerve_GIC/km.py new file mode 100755 index 00000000..53024aab --- /dev/null +++ b/utilities/Nerve_GIC/km.py @@ -0,0 +1,390 @@ +from __future__ import division +import numpy as np +from collections import defaultdict +import json +import itertools +from sklearn import cluster, preprocessing, manifold +from datetime import datetime +import sys + +class KeplerMapper(object): + # With this class you can build topological networks from (high-dimensional) data. + # + # 1) Fit a projection/lens/function to a dataset and transform it. + # For instance "mean_of_row(x) for x in X" + # 2) Map this projection with overlapping intervals/hypercubes. + # Cluster the points inside the interval + # (Note: we cluster on the inverse image/original data to lessen projection loss). + # If two clusters/nodes have the same members (due to the overlap), then: + # connect these with an edge. + # 3) Visualize the network using HTML and D3.js. + # + # functions + # --------- + # fit_transform: Create a projection (lens) from a dataset + # map: Apply Mapper algorithm on this projection and build a simplicial complex + # visualize: Turns the complex dictionary into a HTML/D3.js visualization + + def __init__(self, verbose=2): + self.verbose = verbose + + self.chunk_dist = [] + self.overlap_dist = [] + self.d = [] + self.nr_cubes = 0 + self.overlap_perc = 0 + self.clusterer = False + + def fit_transform(self, X, projection="sum", scaler=preprocessing.MinMaxScaler()): + # Creates the projection/lens from X. + # + # Input: X. Input features as a numpy array. + # Output: projected_X. original data transformed to a projection (lens). + # + # parameters + # ---------- + # projection: Projection parameter is either a string, + # a scikit class with fit_transform, like manifold.TSNE(), + # or a list of dimension indices. + # scaler: if None, do no scaling, else apply scaling to the projection + # Default: Min-Max scaling + + self.scaler = scaler + self.projection = str(projection) + + # Detect if projection is a class (for scikit-learn) + #if str(type(projection))[1:6] == "class": #TODO: de-ugly-fy + # reducer = projection + # if self.verbose > 0: + # try: + # projection.set_params(**{"verbose":self.verbose}) + # except: + # pass + # print("\n..Projecting data using: \n\t%s\n"%str(projection)) + # X = reducer.fit_transform(X) + + # Detect if projection is a string (for standard functions) + if isinstance(projection, str): + if self.verbose > 0: + print("\n..Projecting data using: %s"%(projection)) + # Stats lenses + if projection == "sum": # sum of row + X = np.sum(X, axis=1).reshape((X.shape[0],1)) + if projection == "mean": # mean of row + X = np.mean(X, axis=1).reshape((X.shape[0],1)) + if projection == "median": # mean of row + X = np.median(X, axis=1).reshape((X.shape[0],1)) + if projection == "max": # max of row + X = np.max(X, axis=1).reshape((X.shape[0],1)) + if projection == "min": # min of row + X = np.min(X, axis=1).reshape((X.shape[0],1)) + if projection == "std": # std of row + X = np.std(X, axis=1).reshape((X.shape[0],1)) + + if projection == "dist_mean": # Distance of x to mean of X + X_mean = np.mean(X, axis=0) + X = np.sum(np.sqrt((X - X_mean)**2), axis=1).reshape((X.shape[0],1)) + + # Detect if projection is a list (with dimension indices) + if isinstance(projection, list): + if self.verbose > 0: + print("\n..Projecting data using: %s"%(str(projection))) + X = X[:,np.array(projection)] + + # Scaling + if scaler is not None: + if self.verbose > 0: + print("\n..Scaling with: %s\n"%str(scaler)) + X = scaler.fit_transform(X) + + return X + + def map(self, projected_X, inverse_X=None, clusterer=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10, overlap_perc=0.1): + # This maps the data to a simplicial complex. Returns a dictionary with nodes and links. + # + # Input: projected_X. A Numpy array with the projection/lens. + # Output: complex. A dictionary with "nodes", "links" and "meta information" + # + # parameters + # ---------- + # projected_X projected_X. A Numpy array with the projection/lens. Required. + # inverse_X Numpy array or None. If None then the projection itself is used for clustering. + # clusterer Scikit-learn API compatible clustering algorithm. Default: DBSCAN + # nr_cubes Int. The number of intervals/hypercubes to create. + # overlap_perc Float. The percentage of overlap "between" the intervals/hypercubes. + + start = datetime.now() + + # Helper function + def cube_coordinates_all(nr_cubes, nr_dimensions): + # Helper function to get origin coordinates for our intervals/hypercubes + # Useful for looping no matter the number of cubes or dimensions + # Example: if there are 4 cubes per dimension and 3 dimensions + # return the bottom left (origin) coordinates of 64 hypercubes, + # as a sorted list of Numpy arrays + # TODO: elegance-ify... + l = [] + for x in range(nr_cubes): + l += [x] * nr_dimensions + return [np.array(list(f)) for f in sorted(set(itertools.permutations(l,nr_dimensions)))] + + nodes = defaultdict(list) + links = defaultdict(list) + complex = {} + self.nr_cubes = nr_cubes + self.clusterer = clusterer + self.overlap_perc = overlap_perc + + if self.verbose > 0: + print("Mapping on data shaped %s using dimensions\n"%(str(projected_X.shape))) + + # If inverse image is not provided, we use the projection as the inverse image (suffer projection loss) + if inverse_X is None: + inverse_X = projected_X + + # We chop up the min-max column ranges into 'nr_cubes' parts + self.chunk_dist = (np.max(projected_X, axis=0) - np.min(projected_X, axis=0))/nr_cubes + + # We calculate the overlapping windows distance + self.overlap_dist = self.overlap_perc * self.chunk_dist + + # We find our starting point + self.d = np.min(projected_X, axis=0) + + # Use a dimension index array on the projected X + # (For now this uses the entire dimensionality, but we keep for experimentation) + di = np.array([x for x in range(projected_X.shape[1])]) + + # Prefix'ing the data with ID's + ids = np.array([x for x in range(projected_X.shape[0])]) + projected_X = np.c_[ids,projected_X] + inverse_X = np.c_[ids,inverse_X] + + # Subdivide the projected data X in intervals/hypercubes with overlap + if self.verbose > 0: + total_cubes = len(cube_coordinates_all(nr_cubes,projected_X.shape[1])) + print("Creating %s hypercubes."%total_cubes) + + for i, coor in enumerate(cube_coordinates_all(nr_cubes,di.shape[0])): + # Slice the hypercube + hypercube = projected_X[ np.invert(np.any((projected_X[:,di+1] >= self.d[di] + (coor * self.chunk_dist[di])) & + (projected_X[:,di+1] < self.d[di] + (coor * self.chunk_dist[di]) + self.chunk_dist[di] + self.overlap_dist[di]) == False, axis=1 )) ] + + if self.verbose > 1: + print("There are %s points in cube_%s / %s with starting range %s"% + (hypercube.shape[0],i,total_cubes,self.d[di] + (coor * self.chunk_dist[di]))) + + # If at least one sample inside the hypercube + if hypercube.shape[0] > 0: + # Cluster the data point(s) in the cube, skipping the id-column + # Note that we apply clustering on the inverse image (original data samples) that fall inside the cube. + inverse_x = inverse_X[[int(nn) for nn in hypercube[:,0]]] + + clusterer.fit(inverse_x[:,1:]) + + if self.verbose > 1: + print("Found %s clusters in cube_%s\n"%(np.unique(clusterer.labels_[clusterer.labels_ > -1]).shape[0],i)) + + #Now for every (sample id in cube, predicted cluster label) + for a in np.c_[hypercube[:,0],clusterer.labels_]: + if a[1] != -1: #if not predicted as noise + cluster_id = str(coor[0])+"_"+str(i)+"_"+str(a[1])+"_"+str(coor)+"_"+str(self.d[di] + (coor * self.chunk_dist[di])) # TODO: de-rudimentary-ify + nodes[cluster_id].append( int(a[0]) ) # Append the member id's as integers + else: + if self.verbose > 1: + print("Cube_%s is empty.\n"%(i)) + + # Create links when clusters from different hypercubes have members with the same sample id. + candidates = itertools.combinations(nodes.keys(),2) + for candidate in candidates: + # if there are non-unique members in the union + if len(nodes[candidate[0]]+nodes[candidate[1]]) != len(set(nodes[candidate[0]]+nodes[candidate[1]])): + links[candidate[0]].append( candidate[1] ) + + # Reporting + if self.verbose > 0: + nr_links = 0 + for k in links: + nr_links += len(links[k]) + print("\ncreated %s edges and %s nodes in %s."%(nr_links,len(nodes),str(datetime.now()-start))) + + complex["nodes"] = nodes + complex["links"] = links + complex["meta"] = self.projection + + return complex + + def visualize(self, complex, color_function="", path_html="mapper_visualization_output.html", title="My Data", + graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=None, width_html=0, + height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=0,gain=0,minimum=0,maximum=0): + # Turns the dictionary 'complex' in a html file with d3.js + # + # Input: complex. Dictionary (output from calling .map()) + # Output: a HTML page saved as a file in 'path_html'. + # + # parameters + # ---------- + # color_function string. Not fully implemented. Default: "" (distance to origin) + # path_html file path as string. Where to save the HTML page. + # title string. HTML page document title and first heading. + # graph_link_distance int. Edge length. + # graph_gravity float. "Gravity" to center of layout. + # graph_charge int. charge between nodes. + # custom_tooltips None or Numpy Array. You could use "y"-label array for this. + # width_html int. Width of canvas. Default: 0 (full width) + # height_html int. Height of canvas. Default: 0 (full height) + # show_tooltips bool. default:True + # show_title bool. default:True + # show_meta bool. default:True + + # Format JSON for D3 graph + json_s = {} + json_s["nodes"] = [] + json_s["links"] = [] + k2e = {} # a key to incremental int dict, used for id's when linking + + for e, k in enumerate(complex["nodes"]): + # Tooltip and node color formatting, TODO: de-mess-ify + if custom_tooltips is not None: + tooltip_s = "<h2>Cluster %s</h2>"%k + " ".join(str(custom_tooltips[complex["nodes"][k][0]]).split(" ")) + if maximum == minimum: + tooltip_i = 0 + else: + tooltip_i = int(30*(custom_tooltips[complex["nodes"][k][0]]-minimum)/(maximum-minimum)) + json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(complex["nodes"][k][2])), "color": tooltip_i}) + else: + tooltip_s = "<h2>Cluster %s</h2>Contains %s members."%(k,len(complex["nodes"][k])) + json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(len(complex["nodes"][k]))), "color": str(k.split("_")[0])}) + k2e[k] = e + for k in complex["links"]: + for link in complex["links"][k]: + json_s["links"].append({"source": k2e[k], "target":k2e[link],"value":1}) + + # Width and height of graph in HTML output + if width_html == 0: + width_css = "100%" + width_js = 'document.getElementById("holder").offsetWidth-20' + else: + width_css = "%spx" % width_html + width_js = "%s" % width_html + if height_html == 0: + height_css = "100%" + height_js = 'document.getElementById("holder").offsetHeight-20' + else: + height_css = "%spx" % height_html + height_js = "%s" % height_html + + # Whether to show certain UI elements or not + if show_tooltips == False: + tooltips_display = "display: none;" + else: + tooltips_display = "" + + if show_meta == False: + meta_display = "display: none;" + else: + meta_display = "" + + if show_title == False: + title_display = "display: none;" + else: + title_display = "" + + with open(path_html,"wb") as outfile: + html = """<!DOCTYPE html> + <meta charset="utf-8"> + <meta name="generator" content="KeplerMapper"> + <title>%s | KeplerMapper</title> + <link href='https://fonts.googleapis.com/css?family=Roboto:700,300' rel='stylesheet' type='text/css'> + <style> + * {margin: 0; padding: 0;} + html { height: 100%%;} + body {background: #111; height: 100%%; font: 100 16px Roboto, Sans-serif;} + .link { stroke: #999; stroke-opacity: .333; } + .divs div { border-radius: 50%%; background: red; position: absolute; } + .divs { position: absolute; top: 0; left: 0; } + #holder { position: relative; width: %s; height: %s; background: #111; display: block;} + h1 { %s padding: 20px; color: #fafafa; text-shadow: 0px 1px #000,0px -1px #000; position: absolute; font: 300 30px Roboto, Sans-serif;} + h2 { text-shadow: 0px 1px #000,0px -1px #000; font: 700 16px Roboto, Sans-serif;} + .meta { position: absolute; opacity: 0.9; width: 220px; top: 80px; left: 20px; display: block; %s background: #000; line-height: 25px; color: #fafafa; border: 20px solid #000; font: 100 16px Roboto, Sans-serif;} + div.tooltip { position: absolute; width: 380px; display: block; %s padding: 20px; background: #000; border: 0px; border-radius: 3px; pointer-events: none; z-index: 999; color: #FAFAFA;} + } + </style> + <body> + <div id="holder"> + <h1>%s</h1> + <p class="meta"> + <b>Lens</b><br>%s<br><br> + <b>Length of intervals</b><br>%s<br><br> + <b>Overlap percentage</b><br>%s%%<br><br> + <b>Color Function</b><br>%s + </p> + </div> + <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script> + <script> + var width = %s, + height = %s; + var color = d3.scale.ordinal() + .domain(["0","1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"]) + .range(["#FF0000","#FF1400","#FF2800","#FF3c00","#FF5000","#FF6400","#FF7800","#FF8c00","#FFa000","#FFb400","#FFc800","#FFdc00","#FFf000","#fdff00","#b0ff00","#65ff00","#17ff00","#00ff36","#00ff83","#00ffd0","#00e4ff","#00c4ff","#00a4ff","#00a4ff","#0084ff","#0064ff","#0044ff","#0022ff","#0002ff","#0100ff","#0300ff","#0500ff"]); + var force = d3.layout.force() + .charge(%s) + .linkDistance(%s) + .gravity(%s) + .size([width, height]); + var svg = d3.select("#holder").append("svg") + .attr("width", width) + .attr("height", height); + + var div = d3.select("#holder").append("div") + .attr("class", "tooltip") + .style("opacity", 0.0); + + var divs = d3.select('#holder').append('div') + .attr('class', 'divs') + .attr('style', function(d) { return 'overflow: hidden; width: ' + width + 'px; height: ' + height + 'px;'; }); + + graph = %s; + force + .nodes(graph.nodes) + .links(graph.links) + .start(); + var link = svg.selectAll(".link") + .data(graph.links) + .enter().append("line") + .attr("class", "link") + .style("stroke-width", function(d) { return Math.sqrt(d.value); }); + var node = divs.selectAll('div') + .data(graph.nodes) + .enter().append('div') + .on("mouseover", function(d) { + div.transition() + .duration(200) + .style("opacity", .9); + div .html(d.tooltip + "<br/>") + .style("left", (d3.event.pageX + 100) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }) + .on("mouseout", function(d) { + div.transition() + .duration(500) + .style("opacity", 0); + }) + .call(force.drag); + + node.append("title") + .text(function(d) { return d.name; }); + force.on("tick", function() { + link.attr("x1", function(d) { return d.source.x; }) + .attr("y1", function(d) { return d.source.y; }) + .attr("x2", function(d) { return d.target.x; }) + .attr("y2", function(d) { return d.target.y; }); + node.attr("cx", function(d) { return d.x; }) + .attr("cy", function(d) { return d.y; }) + .attr('style', function(d) { return 'width: ' + (d.group * 2) + 'px; height: ' + (d.group * 2) + 'px; ' + 'left: '+(d.x-(d.group))+'px; ' + 'top: '+(d.y-(d.group))+'px; background: '+color(d.color)+'; box-shadow: 0px 0px 3px #111; box-shadow: 0px 0px 33px '+color(d.color)+', inset 0px 0px 5px rgba(0, 0, 0, 0.2);'}) + ; + }); + </script>"""%(title,width_css, height_css, title_display, meta_display, tooltips_display, title,complex["meta"],res,gain*100,color_function,width_js,height_js,graph_charge,graph_link_distance,graph_gravity,json.dumps(json_s)) + outfile.write(html.encode("utf-8")) + if self.verbose > 0: + print("\nWrote d3.js graph to '%s'"%path_html) diff --git a/utilities/Nerve_GIC/km.py.COPYRIGHT b/utilities/Nerve_GIC/km.py.COPYRIGHT new file mode 100644 index 00000000..bef7b121 --- /dev/null +++ b/utilities/Nerve_GIC/km.py.COPYRIGHT @@ -0,0 +1,26 @@ +km.py is a fork of https://github.com/MLWave/kepler-mapper. +Only the visualization part has been kept (Mapper part has been removed). + +This file has te following Copyright : + +The MIT License (MIT) + +Copyright (c) 2015 Triskelion - HJ van Veen - info@mlwave.com + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/utilities/Persistence_representations/CMakeLists.txt b/utilities/Persistence_representations/CMakeLists.txt new file mode 100644 index 00000000..137eb0c1 --- /dev/null +++ b/utilities/Persistence_representations/CMakeLists.txt @@ -0,0 +1,53 @@ +# Copy files, otherwise result files are created in sources +file(COPY "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/") +file(COPY "${CMAKE_SOURCE_DIR}/data/persistence_diagram/second.pers" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/") + +function(add_persistence_representation_creation_utility creation_utility) + add_executable ( ${creation_utility} ${creation_utility}.cpp ) + + # as the function is called in a subdirectory level, need to '../' to find persistence files + # ARGN will add all the other arguments (except creation_utility) sent to the CMake functions + add_test(NAME Persistence_representation_utilities_${creation_utility} COMMAND $<TARGET_FILE:${creation_utility}> + ${ARGN} "${CMAKE_CURRENT_BINARY_DIR}/../first.pers" + "${CMAKE_CURRENT_BINARY_DIR}/../second.pers") +endfunction(add_persistence_representation_creation_utility) + +function(add_persistence_representation_plot_utility plot_utility tool_extension) + add_executable ( ${plot_utility} ${plot_utility}.cpp ) + + # as the function is called in a subdirectory level, need to '../' to find persistence heat maps files + add_test(NAME Persistence_representation_utilities_${plot_utility}_first COMMAND $<TARGET_FILE:${plot_utility}> + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}") + #add_test(NAME Persistence_representation_utilities_${plot_utility}_second COMMAND $<TARGET_FILE:${plot_utility}> + # "${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}") + if(GNUPLOT_PATH) + add_test(NAME Persistence_representation_utilities_${plot_utility}_first_gnuplot COMMAND ${GNUPLOT_PATH} + "-e" "load '${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}_GnuplotScript'") + #add_test(NAME Persistence_representation_utilities_${plot_utility}_second_gnuplot COMMAND ${GNUPLOT_PATH} + # "-e" "load '${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}_GnuplotScript'") + endif() +endfunction(add_persistence_representation_plot_utility) + +function(add_persistence_representation_function_utility function_utility tool_extension) + add_executable ( ${function_utility} ${function_utility}.cpp ) + + # ARGV2 is an optional argument + if (${ARGV2}) + # as the function is called in a subdirectory level, need to '../' to find persistence heat maps files + add_test(NAME Persistence_representation_utilities_${function_utility} COMMAND $<TARGET_FILE:${function_utility}> + "${ARGV2}" + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}" + "${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}") + else() + # as the function is called in a subdirectory level, need to '../' to find persistence heat maps files + add_test(NAME Persistence_representation_utilities_${function_utility} COMMAND $<TARGET_FILE:${function_utility}> + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers${tool_extension}" + "${CMAKE_CURRENT_BINARY_DIR}/../second.pers${tool_extension}") + endif() +endfunction(add_persistence_representation_function_utility) + +add_subdirectory(persistence_heat_maps) +add_subdirectory(persistence_intervals) +add_subdirectory(persistence_landscapes) +add_subdirectory(persistence_landscapes_on_grid) +add_subdirectory(persistence_vectors) diff --git a/utilities/Persistence_representations/persistence_heat_maps/CMakeLists.txt b/utilities/Persistence_representations/persistence_heat_maps/CMakeLists.txt new file mode 100644 index 00000000..386e9fa5 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/CMakeLists.txt @@ -0,0 +1,15 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_representations_heat_maps_utilities) + +add_persistence_representation_creation_utility(create_pssk "10" "-1" "-1" "4" "-1") +add_persistence_representation_creation_utility(create_p_h_m_weighted_by_arctan_of_their_persistence "10" "-1" "-1" "4" "-1") +add_persistence_representation_creation_utility(create_p_h_m_weighted_by_distance_from_diagonal "10" "-1" "-1" "4" "-1") +add_persistence_representation_creation_utility(create_p_h_m_weighted_by_squared_diag_distance "10" "-1" "-1" "4" "-1") +# Need to set grid min and max for further average, distance and scalar_product +add_persistence_representation_creation_utility(create_persistence_heat_maps "10" "0" "35" "10" "-1") + +add_persistence_representation_plot_utility(plot_persistence_heat_map ".mps") + +add_persistence_representation_function_utility(average_persistence_heat_maps ".mps") +add_persistence_representation_function_utility(compute_distance_of_persistence_heat_maps ".mps" "1") +add_persistence_representation_function_utility(compute_scalar_product_of_persistence_heat_maps ".mps") diff --git a/utilities/Persistence_representations/persistence_heat_maps/average_persistence_heat_maps.cpp b/utilities/Persistence_representations/persistence_heat_maps/average_persistence_heat_maps.cpp new file mode 100644 index 00000000..6739e0b6 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/average_persistence_heat_maps.cpp @@ -0,0 +1,63 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <vector> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + std::cout << "This program computes average of persistence heat maps stored in files (the files needs to be " + << "created beforehand).\n" + << "The parameters of this programs are names of files with persistence heat maps.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<Persistence_heat_maps*> maps; + for (size_t i = 0; i != filenames.size(); ++i) { + Persistence_heat_maps* l = new Persistence_heat_maps; + l->load_from_file(filenames[i]); + maps.push_back(l); + } + + Persistence_heat_maps av; + av.compute_average(maps); + av.print_to_file("average.mps"); + + for (size_t i = 0; i != filenames.size(); ++i) { + delete maps[i]; + } + + std::cout << "Average can be found in 'average.mps' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp b/utilities/Persistence_representations/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp new file mode 100644 index 00000000..ed8278a2 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp @@ -0,0 +1,94 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + std::cout << "This program computes distance of persistence heat maps stored in files (the files needs to be " + << "created beforehand).\n" + << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " + << "maps. For L^infty distance choose p = -1. \n" + << "The remaining parameters of this program are names of files with persistence heat maps.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + int pp = atoi(argv[1]); + double p = std::numeric_limits<double>::max(); + if (pp != -1) { + p = pp; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_heat_maps> maps; + maps.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_heat_maps l; + l.load_from_file(filenames[file_no]); + maps.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > distance(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + distance[i] = v; + } + + // and now we can compute the distances: + for (size_t i = 0; i != filenames.size(); ++i) { + for (size_t j = i; j != filenames.size(); ++j) { + distance[i][j] = distance[j][i] = maps[i].distance(maps[j], p); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("distance.mps"); + for (size_t i = 0; i != distance.size(); ++i) { + for (size_t j = 0; j != distance.size(); ++j) { + std::cout << distance[i][j] << " "; + out << distance[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'distance.mps' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp b/utilities/Persistence_representations/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp new file mode 100644 index 00000000..63626853 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp @@ -0,0 +1,85 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <vector> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + std::cout << "This program computes scalar product of persistence heat maps stored in a file (the file needs to be " + << "created beforehand). \n" + << "The parameters of this programs are names of files with persistence heat maps.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_heat_maps> maps; + maps.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_heat_maps l; + l.load_from_file(filenames[file_no]); + maps.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > scalar_product(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + scalar_product[i] = v; + } + + // and now we can compute the scalar product: + for (size_t i = 0; i != maps.size(); ++i) { + for (size_t j = i; j != maps.size(); ++j) { + scalar_product[i][j] = scalar_product[j][i] = maps[i].compute_scalar_product(maps[j]); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("scalar_product.mps"); + for (size_t i = 0; i != scalar_product.size(); ++i) { + for (size_t j = 0; j != scalar_product.size(); ++j) { + std::cout << scalar_product[i][j] << " "; + out << scalar_product[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'scalar_product.mps' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp new file mode 100644 index 00000000..b4a1daa5 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp @@ -0,0 +1,81 @@ +/* This file is part of the Gudhi Library. The Gudhi library +* (Geometric Understanding in Higher Dimensions) is a generic C++ +* library for computational topology. +* +* Author(s): Pawel Dlotko +* +* Copyright (C) 2016 INRIA (France) +* +* This program is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* This program is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with this program. If not, see <http://www.gnu.org/licenses/>. +*/ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using arc_tan_of_persistence_of_point = Gudhi::Persistence_representations::arc_tan_of_persistence_of_point; +using Persistence_heat_maps = + Gudhi::Persistence_representations::Persistence_heat_maps<arc_tan_of_persistence_of_point>; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + << "provided as an input.The Gaussian kernels are weighted by the arc tangential of their persistence.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter is an integer, the standard deviation of a Gaussian kernel expressed in a number " + << "of pixels.\n" + << "The fifth parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a fifth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the fifth parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 7) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + size_t stdiv = atof(argv[4]); + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[5]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 6; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); + + std::stringstream ss; + ss << filenames[i] << ".mps"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp new file mode 100644 index 00000000..c50f9ddb --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp @@ -0,0 +1,81 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using distance_from_diagonal_scaling = Gudhi::Persistence_representations::distance_from_diagonal_scaling; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<distance_from_diagonal_scaling>; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + << "provided as an input.The Gaussian kernels are weighted by the distance of a center from the " + << "diagonal.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter is an integer, the standard deviation of a Gaussian kernel expressed in a number " + << "of pixels.\n" + << "The fifth parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a fifth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the fifth parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 7) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + size_t stdiv = atof(argv[4]); + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[5]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 6; i != argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); + + std::stringstream ss; + ss << filenames[i] << ".mps"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp new file mode 100644 index 00000000..59ff3c24 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp @@ -0,0 +1,83 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using squared_distance_from_diagonal_scaling = + Gudhi::Persistence_representations::squared_distance_from_diagonal_scaling; +using Persistence_heat_maps = + Gudhi::Persistence_representations::Persistence_heat_maps<squared_distance_from_diagonal_scaling>; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + << "provided as an input.The Gaussian kernels are weighted by the square of distance of a center from the " + << "diagonal.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter is an integer, the standard deviation of a Gaussian kernel expressed in a number " + << "of pixels.\n" + << "The fifth parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a fifth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the fifth parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 7) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + size_t stdiv = atof(argv[4]); + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[5]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 6; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a heat map based on a file : " << filenames[i] << std::endl; + Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); + + std::stringstream ss; + ss << filenames[i] << ".mps"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/create_persistence_heat_maps.cpp b/utilities/Persistence_representations/persistence_heat_maps/create_persistence_heat_maps.cpp new file mode 100644 index 00000000..25cd1067 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/create_persistence_heat_maps.cpp @@ -0,0 +1,78 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence heat map files (*.mps) of persistence diagrams files (*.pers) " + << "provided as an input.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter is an integer, the standard deviation of a Gaussian kernel expressed in a number " + << "of pixels.\n" + << "The fifth parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a fifth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the fifth parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 7) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + size_t stdiv = atof(argv[4]); + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[5]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + std::vector<const char*> filenames; + for (int i = 6; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a heat map based on file : " << filenames[i] << std::endl; + Persistence_heat_maps l(filenames[i], filter, false, size_of_grid, min_, max_, dimension); + + std::stringstream ss; + ss << filenames[i] << ".mps"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/create_pssk.cpp b/utilities/Persistence_representations/persistence_heat_maps/create_pssk.cpp new file mode 100644 index 00000000..97ddb8f0 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/create_pssk.cpp @@ -0,0 +1,79 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/PSSK.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using PSSK = Gudhi::Persistence_representations::PSSK; + +int main(int argc, char** argv) { + std::cout << "This program creates PSSK files (*.pssk) of persistence diagrams files (*.pers) " + << "provided as an input.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter is an integer, the standard deviation of a Gaussian kernel expressed in a number " + << "of pixels.\n" + << "The fifth parameter of this program is a dimension of persistence that will be used in creation of " + << "the PSSK." + << "If your input files contains persistence pairs of various dimension, as a fifth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the first parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 7) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + size_t stdiv = atof(argv[4]); + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[5]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 6; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<std::vector<double> > filter = Gudhi::Persistence_representations::create_Gaussian_filter(stdiv, 1); + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a PSSK based on a file : " << filenames[i] << std::endl; + PSSK l(filenames[i], filter, size_of_grid, min_, max_, dimension); + + std::stringstream ss; + ss << filenames[i] << ".pssk"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_heat_maps/plot_persistence_heat_map.cpp b/utilities/Persistence_representations/persistence_heat_maps/plot_persistence_heat_map.cpp new file mode 100644 index 00000000..63711d83 --- /dev/null +++ b/utilities/Persistence_representations/persistence_heat_maps/plot_persistence_heat_map.cpp @@ -0,0 +1,42 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_heat_maps.h> + +#include <iostream> +#include <sstream> + +using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function; +using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>; + +int main(int argc, char** argv) { + std::cout << "This program creates a gnuplot script from a persistence heat maps stored in a file (the file needs " + << "to be created beforehand). Please call the code with the name of a single heat maps file \n"; + if (argc != 2) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + Persistence_heat_maps l; + l.load_from_file(argv[1]); + l.plot(argv[1]); + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/CMakeLists.txt b/utilities/Persistence_representations/persistence_intervals/CMakeLists.txt new file mode 100644 index 00000000..897e12a3 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/CMakeLists.txt @@ -0,0 +1,32 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_representations_intervals_utilities) + + +add_executable ( plot_histogram_of_intervals_lengths plot_histogram_of_intervals_lengths.cpp ) + +add_test(NAME plot_histogram_of_intervals_lengths COMMAND $<TARGET_FILE:plot_histogram_of_intervals_lengths> + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers" "-1") + +add_persistence_representation_plot_utility(plot_persistence_intervals "") +add_persistence_representation_plot_utility(plot_persistence_Betti_numbers "") + +add_persistence_representation_creation_utility(compute_birth_death_range_in_persistence_diagram "-1") + + +add_executable ( compute_number_of_dominant_intervals compute_number_of_dominant_intervals.cpp ) +add_test(NAME Persistence_representation_utilities_compute_number_of_dominant_intervals + COMMAND $<TARGET_FILE:compute_number_of_dominant_intervals> + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers" "-1" "2") + + +if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + add_executable ( compute_bottleneck_distance compute_bottleneck_distance.cpp ) + if (TBB_FOUND) + target_link_libraries(compute_bottleneck_distance ${TBB_LIBRARIES}) + endif(TBB_FOUND) + add_test(NAME Persistence_representation_utilities_compute_bottleneck_distance + COMMAND $<TARGET_FILE:compute_bottleneck_distance> + "-1" + "${CMAKE_CURRENT_BINARY_DIR}/../first.pers" + "${CMAKE_CURRENT_BINARY_DIR}/../second.pers") +endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) diff --git a/utilities/Persistence_representations/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp b/utilities/Persistence_representations/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp new file mode 100644 index 00000000..9102da79 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp @@ -0,0 +1,68 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <vector> +#include <limits> +#include <utility> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + std::cout << "This program computes the range of birth and death times of persistence pairs in diagrams provided as " + << "an input.\n" + << "The first parameter is the dimension of persistence to be used to create persistence intervals. " + << "If your file contains the information about dimension of persistence pairs, please provide here the " + << "dimension of persistence pairs you want to use. " + << "If your input files consist only of birth-death pairs, please set this first parameter to -1.\n" + << "The remaining parameters of the program are the names of files with persistence diagrams.\n"; + + if (argc < 3) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[1]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + double min_ = std::numeric_limits<double>::max(); + double max_ = -std::numeric_limits<double>::max(); + + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + std::cout << "Creating diagram based on a file : " << filenames[file_no] << std::endl; + Persistence_intervals p(filenames[file_no], dimension); + std::pair<double, double> min_max_ = p.get_x_range(); + if (min_max_.first < min_) min_ = min_max_.first; + if (min_max_.second > max_) max_ = min_max_.second; + } + std::cout << "Birth-death range : min: " << min_ << ", max: " << max_ << std::endl; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/compute_bottleneck_distance.cpp b/utilities/Persistence_representations/persistence_intervals/compute_bottleneck_distance.cpp new file mode 100644 index 00000000..c8290845 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/compute_bottleneck_distance.cpp @@ -0,0 +1,95 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals_with_distances.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Persistence_intervals_with_distances = Gudhi::Persistence_representations::Persistence_intervals_with_distances; + +int main(int argc, char** argv) { + std::cout << "This program computes the bottleneck distance of persistence pairs in diagrams provided as " + << "an input.\n" + << "The first parameter is the dimension of persistence to be used to create persistence intervals. " + << "If your file contains the information about dimension of persistence pairs, please provide here the " + << "dimension of persistence pairs you want to use. " + << "If your input files consist only of birth-death pairs, please set this first parameter to -1.\n" + << "The remaining parameters of the program are the names of files with persistence diagrams.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = atoi(argv[1]); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + // reading the persistence intervals: + std::vector<Persistence_intervals_with_distances> persistence_intervals; + for (size_t i = 0; i != filenames.size(); ++i) { + Persistence_intervals_with_distances pers(filenames[i], dimension); + persistence_intervals.push_back(pers); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > distance(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + distance[i] = v; + } + + // and now we can compute the distances: + for (size_t i = 0; i != persistence_intervals.size(); ++i) { + for (size_t j = i + 1; j != persistence_intervals.size(); ++j) { + distance[i][j] = distance[j][i] = persistence_intervals[i].distance(persistence_intervals[j]); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("distance.itv"); + for (size_t i = 0; i != distance.size(); ++i) { + for (size_t j = 0; j != distance.size(); ++j) { + std::cout << distance[i][j] << " "; + out << distance[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'distance.itv' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/compute_number_of_dominant_intervals.cpp b/utilities/Persistence_representations/persistence_intervals/compute_number_of_dominant_intervals.cpp new file mode 100644 index 00000000..b3d126f0 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/compute_number_of_dominant_intervals.cpp @@ -0,0 +1,54 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <limits> +#include <vector> +#include <utility> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + std::cout << "This program compute the dominant intervals. A number of intervals to be displayed is a parameter of " + "this program. \n"; + if (argc != 4) { + std::cout << "To run this program, please provide the name of a file with persistence diagram, dimension of " + "intervals that should be taken into account (if your file contains only persistence pairs in a " + "single dimension, set it up to -1) and number of dominant intervals you would like to get \n"; + return 1; + } + int dim = atoi(argv[2]); + unsigned dimension = std::numeric_limits<unsigned>::max(); + if (dim >= 0) { + dimension = (unsigned)dim; + } + Persistence_intervals p(argv[1], dimension); + std::vector<std::pair<double, double> > dominant_intervals = p.dominant_intervals(atoi(argv[3])); + std::cout << "Here are the dominant intervals : " << std::endl; + for (size_t i = 0; i != dominant_intervals.size(); ++i) { + std::cout << " " << dominant_intervals[i].first << "," << dominant_intervals[i].second << " " << std::endl; + } + + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/plot_histogram_of_intervals_lengths.cpp b/utilities/Persistence_representations/persistence_intervals/plot_histogram_of_intervals_lengths.cpp new file mode 100644 index 00000000..ccb5b645 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/plot_histogram_of_intervals_lengths.cpp @@ -0,0 +1,77 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <vector> +#include <limits> +#include <utility> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + std::cout << "This program computes a histogram of barcode's length. A number of bins in the histogram is a " + << "parameter of this program. \n"; + if ((argc != 3) && (argc != 4)) { + std::cout << "To run this program, please provide the name of a file with persistence diagram and number of " + << "dominant intervals you would like to get. Set a negative number dominant intervals value " + << "If your file contains only birth-death pairs.\n" + << "The third parameter is the dimension of the persistence that is to be used. If your " + << "file contains only birth-death pairs, you can skip this parameter\n"; + return 1; + } + + unsigned dominant_interval_number = std::numeric_limits<unsigned>::max(); + int nbr = atoi(argv[2]); + if (nbr >= 0) { + dominant_interval_number = static_cast<unsigned>(nbr); + } + + int persistence_dimension = -1; + if (argc == 4) { + persistence_dimension = atoi(argv[3]); + } + + Persistence_intervals p(argv[1], dominant_interval_number); + std::vector<std::pair<double, double> > dominant_intervals = p.dominant_intervals(persistence_dimension); + std::vector<size_t> histogram = p.histogram_of_lengths(10); + + std::stringstream gnuplot_script; + gnuplot_script << argv[1] << "_GnuplotScript"; + std::ofstream out; + out.open(gnuplot_script.str().c_str()); + + out << "set style data histogram" << std::endl; + out << "set style histogram cluster gap 1" << std::endl; + out << "set style fill solid border -1" << std::endl; + out << "plot '-' notitle" << std::endl; + for (size_t i = 0; i != histogram.size(); ++i) { + out << histogram[i] << std::endl; + } + out << std::endl; + out.close(); + + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/plot_persistence_Betti_numbers.cpp b/utilities/Persistence_representations/persistence_intervals/plot_persistence_Betti_numbers.cpp new file mode 100644 index 00000000..b433c2b3 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/plot_persistence_Betti_numbers.cpp @@ -0,0 +1,87 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <vector> +#include <limits> +#include <utility> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + if ((argc != 3) && (argc != 2)) { + std::cout << "This program creates a gnuplot script of Betti numbers from a single persistence diagram file" + << "(*.pers).\n" + << "To run this program, please provide the name of a file with persistence diagram.\n" + << "The second optional parameter of a program is the dimension of the persistence that is to be used. " + << "If your file contains only birth-death pairs, you can skip this parameter.\n"; + return 1; + } + + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = -1; + if (argc == 3) { + dim = atoi(argv[2]); + } + if (dim >= 0) { + dimension = (unsigned)dim; + } + + Persistence_intervals p(argv[1], dimension); + std::vector<std::pair<double, size_t> > pbns = p.compute_persistent_betti_numbers(); + + // set up the ranges so that we see the image well. + double xRangeBegin = pbns[0].first; + double xRangeEnd = pbns[pbns.size() - 1].first; + double yRangeBegin = 0; + double yRangeEnd = 0; + for (size_t i = 0; i != pbns.size(); ++i) { + if (pbns[i].second > yRangeEnd) yRangeEnd = pbns[i].second; + } + xRangeBegin -= (xRangeEnd - xRangeBegin) / 100.0; + xRangeEnd += (xRangeEnd - xRangeBegin) / 100.0; + yRangeEnd += yRangeEnd / 100; + + std::stringstream gnuplot_script; + gnuplot_script << argv[1] << "_GnuplotScript"; + std::ofstream out; + out.open(gnuplot_script.str().c_str()); + + out << "set xrange [" << xRangeBegin << " : " << xRangeEnd << "]" << std::endl; + out << "set yrange [" << yRangeBegin << " : " << yRangeEnd << "]" << std::endl; + out << "plot '-' using 1:2 notitle with lp " << std::endl; + double previous_y = 0; + for (size_t i = 0; i != pbns.size(); ++i) { + out << pbns[i].first << " " << previous_y << std::endl; + out << pbns[i].first << " " << pbns[i].second << std::endl; + previous_y = pbns[i].second; + } + out << std::endl; + out.close(); + + std::cout << "To visualize, install gnuplot and type the command: gnuplot -persist -e \"load \'" + << gnuplot_script.str().c_str() << "\'\"" << std::endl; + + return 0; +} diff --git a/utilities/Persistence_representations/persistence_intervals/plot_persistence_intervals.cpp b/utilities/Persistence_representations/persistence_intervals/plot_persistence_intervals.cpp new file mode 100644 index 00000000..33387802 --- /dev/null +++ b/utilities/Persistence_representations/persistence_intervals/plot_persistence_intervals.cpp @@ -0,0 +1,53 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_intervals.h> + +#include <iostream> +#include <limits> +#include <vector> +#include <utility> + +using Persistence_intervals = Gudhi::Persistence_representations::Persistence_intervals; + +int main(int argc, char** argv) { + if ((argc != 3) && (argc != 2)) { + std::cout << "This program creates a gnuplot script from a single persistence diagram file (*.pers).\n" + << "To run this program, please provide the name of a file with persistence diagram.\n" + << "The second optional parameter of a program is the dimension of the persistence that is to be used. " + << "If your file contains only birth-death pairs, you can skip this parameter.\n"; + return 1; + } + unsigned dimension = std::numeric_limits<unsigned>::max(); + int dim = -1; + if (argc == 3) { + dim = atoi(argv[2]); + } + if (dim >= 0) { + dimension = (unsigned)dim; + } + std::vector<std::pair<double, double> > intervals = + Gudhi::Persistence_representations::read_persistence_intervals_in_one_dimension_from_file(argv[1], dimension); + Persistence_intervals b(intervals); + b.plot(argv[1]); + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes/CMakeLists.txt b/utilities/Persistence_representations/persistence_landscapes/CMakeLists.txt new file mode 100644 index 00000000..d7087ed8 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/CMakeLists.txt @@ -0,0 +1,10 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_representations_landscapes_utilities) + +add_persistence_representation_creation_utility(create_landscapes "-1") + +add_persistence_representation_plot_utility(plot_landscapes ".land") + +add_persistence_representation_function_utility(average_landscapes ".land") +add_persistence_representation_function_utility(compute_distance_of_landscapes ".land" "1") +add_persistence_representation_function_utility(compute_scalar_product_of_landscapes ".land") diff --git a/utilities/Persistence_representations/persistence_landscapes/average_landscapes.cpp b/utilities/Persistence_representations/persistence_landscapes/average_landscapes.cpp new file mode 100644 index 00000000..1a59be8c --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/average_landscapes.cpp @@ -0,0 +1,63 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <vector> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + std::cout << "This program computes average of persistence landscapes stored in files (the files needs to be " + << "created beforehand).\n" + << "The parameters of this programs are names of files with persistence landscapes.\n"; + std::vector<const char*> filenames; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<Persistence_landscape*> lands; + for (size_t i = 0; i != filenames.size(); ++i) { + Persistence_landscape* l = new Persistence_landscape; + l->load_landscape_from_file(filenames[i]); + lands.push_back(l); + } + + Persistence_landscape av; + av.compute_average(lands); + + av.print_to_file("average.land"); + + for (size_t i = 0; i != filenames.size(); ++i) { + delete lands[i]; + } + + std::cout << "Average can be found in 'average.land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes/compute_distance_of_landscapes.cpp b/utilities/Persistence_representations/persistence_landscapes/compute_distance_of_landscapes.cpp new file mode 100644 index 00000000..5062f521 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/compute_distance_of_landscapes.cpp @@ -0,0 +1,93 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + std::cout << "This program computes distance of persistence landscapes stored in files (the files needs to be " + << "created beforehand).\n" + << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " + << "maps. For L^infty distance choose p = -1. \n" + << "The remaining parameters of this program are names of files with persistence landscapes.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + int pp = atoi(argv[1]); + double p = std::numeric_limits<double>::max(); + if (pp != -1) { + p = pp; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_landscape> landscaspes; + landscaspes.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_landscape l; + l.load_landscape_from_file(filenames[file_no]); + landscaspes.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > distance(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + distance[i] = v; + } + + // and now we can compute the distances: + for (size_t i = 0; i != landscaspes.size(); ++i) { + for (size_t j = i; j != landscaspes.size(); ++j) { + distance[i][j] = distance[j][i] = compute_distance_of_landscapes(landscaspes[i], landscaspes[j], p); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("distance.land"); + for (size_t i = 0; i != distance.size(); ++i) { + for (size_t j = 0; j != distance.size(); ++j) { + std::cout << distance[i][j] << " "; + out << distance[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'distance.land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes/compute_scalar_product_of_landscapes.cpp b/utilities/Persistence_representations/persistence_landscapes/compute_scalar_product_of_landscapes.cpp new file mode 100644 index 00000000..5b5e9fa3 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/compute_scalar_product_of_landscapes.cpp @@ -0,0 +1,84 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <sstream> +#include <vector> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + std::cout << "This program computes scalar product of persistence landscapes stored in a file (the file needs to be " + << "created beforehand). \n" + << "The parameters of this programs are names of files with persistence landscapes.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_landscape> landscaspes; + landscaspes.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_landscape l; + l.load_landscape_from_file(filenames[file_no]); + landscaspes.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > scalar_product(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + scalar_product[i] = v; + } + + // and now we can compute the scalar product: + for (size_t i = 0; i != landscaspes.size(); ++i) { + for (size_t j = i; j != landscaspes.size(); ++j) { + scalar_product[i][j] = scalar_product[j][i] = compute_inner_product(landscaspes[i], landscaspes[j]); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("scalar_product.land"); + for (size_t i = 0; i != scalar_product.size(); ++i) { + for (size_t j = 0; j != scalar_product.size(); ++j) { + std::cout << scalar_product[i][j] << " "; + out << scalar_product[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'scalar_product.land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes/create_landscapes.cpp b/utilities/Persistence_representations/persistence_landscapes/create_landscapes.cpp new file mode 100644 index 00000000..6030e994 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/create_landscapes.cpp @@ -0,0 +1,65 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <sstream> +#include <vector> +#include <limits> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence landscapes files (*.land) of persistence diagrams files (*.pers) " + << "provided as an input.\n" + << "The first parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a first parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the first parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 3) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + std::vector<const char*> filenames; + int dim = atoi(argv[1]); + unsigned dimension = std::numeric_limits<unsigned>::max(); + if (dim >= 0) { + dimension = (unsigned)dim; + } + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating a landscape based on file : " << filenames[i] << std::endl; + Persistence_landscape l(filenames[i], dimension); + std::stringstream ss; + ss << filenames[i] << ".land"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes/plot_landscapes.cpp b/utilities/Persistence_representations/persistence_landscapes/plot_landscapes.cpp new file mode 100644 index 00000000..c797a7a8 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes/plot_landscapes.cpp @@ -0,0 +1,43 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape.h> + +#include <iostream> +#include <sstream> + +using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; + +int main(int argc, char** argv) { + std::cout << "This program creates a gnuplot script from a persistence landscape stored in a file (the file needs " + << "to be created beforehand). Please call the code with the name of a single landscape file.\n"; + if (argc != 2) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + Persistence_landscape l; + l.load_landscape_from_file(argv[1]); + l.plot(argv[1]); + + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/CMakeLists.txt b/utilities/Persistence_representations/persistence_landscapes_on_grid/CMakeLists.txt new file mode 100644 index 00000000..c5ea4bbf --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/CMakeLists.txt @@ -0,0 +1,11 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_representations_lanscapes_on_grid_utilities) + +# Need to set grid min and max for further average, distance and scalar_product +add_persistence_representation_creation_utility(create_landscapes_on_grid "100" "0" "35" "-1") + +add_persistence_representation_plot_utility(plot_landscapes_on_grid ".g_land") + +add_persistence_representation_function_utility(average_landscapes_on_grid ".g_land") +add_persistence_representation_function_utility(compute_distance_of_landscapes_on_grid ".g_land" "1") +add_persistence_representation_function_utility(compute_scalar_product_of_landscapes_on_grid ".g_land") diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp b/utilities/Persistence_representations/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp new file mode 100644 index 00000000..0b098d1a --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp @@ -0,0 +1,63 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <vector> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + std::cout << "This program computes average of persistence landscapes on grid stored in files (the files needs to " + << "be created beforehand).\n" + << "The parameters of this programs are names of files with persistence landscapes on grid.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<Persistence_landscape_on_grid*> lands; + for (size_t i = 0; i != filenames.size(); ++i) { + Persistence_landscape_on_grid* l = new Persistence_landscape_on_grid; + l->load_landscape_from_file(filenames[i]); + lands.push_back(l); + } + + Persistence_landscape_on_grid av; + av.compute_average(lands); + + av.print_to_file("average.g_land"); + + for (size_t i = 0; i != filenames.size(); ++i) { + delete lands[i]; + } + + std::cout << "Average can be found in 'average.g_land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp b/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp new file mode 100644 index 00000000..fd0fcd15 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp @@ -0,0 +1,93 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + std::cout << "This program computes distance of persistence landscapes on grid stored in files (the files needs to " + << "be created beforehand).\n" + << "The first parameter of a program is an integer p. The program compute L^p distance of the two heat " + << "maps. For L^infty distance choose p = -1. \n" + << "The remaining parameters of this program are names of files with persistence landscapes on grid.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + int pp = atoi(argv[1]); + double p = std::numeric_limits<double>::max(); + if (pp != -1) { + p = pp; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_landscape_on_grid> landscaspes; + landscaspes.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_landscape_on_grid l; + l.load_landscape_from_file(filenames[file_no]); + landscaspes.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > distance(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + distance[i] = v; + } + + // and now we can compute the scalar product: + for (size_t i = 0; i != landscaspes.size(); ++i) { + for (size_t j = i; j != landscaspes.size(); ++j) { + distance[i][j] = distance[j][i] = compute_distance_of_landscapes_on_grid(landscaspes[i], landscaspes[j], p); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("distance.g_land"); + for (size_t i = 0; i != distance.size(); ++i) { + for (size_t j = 0; j != distance.size(); ++j) { + std::cout << distance[i][j] << " "; + out << distance[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'distance.g_land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp b/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp new file mode 100644 index 00000000..01de3dee --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp @@ -0,0 +1,85 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <sstream> +#include <vector> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + std::cout + << "This program computes scalar product of persistence landscapes on grid stored in a file (the file needs to " + << "be created beforehand). \n" + << "The parameters of this programs are names of files with persistence landscapes on grid.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Persistence_landscape_on_grid> landscaspes; + landscaspes.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Persistence_landscape_on_grid l; + l.load_landscape_from_file(filenames[file_no]); + landscaspes.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > scalar_product(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + scalar_product[i] = v; + } + + // and now we can compute the scalar product: + for (size_t i = 0; i != landscaspes.size(); ++i) { + for (size_t j = i; j != landscaspes.size(); ++j) { + scalar_product[i][j] = scalar_product[j][i] = compute_inner_product(landscaspes[i], landscaspes[j]); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("scalar_product.g_land"); + for (size_t i = 0; i != scalar_product.size(); ++i) { + for (size_t j = 0; j != scalar_product.size(); ++j) { + std::cout << scalar_product[i][j] << " "; + out << scalar_product[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'scalar_product.g_land' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp b/utilities/Persistence_representations/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp new file mode 100644 index 00000000..78e8ef57 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp @@ -0,0 +1,79 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence landscapes on grid files (*.g_land) of persistence diagrams files " + << "(*.pers) provided as an input.\n" + << "The first parameter of a program is an integer, a size of a grid.\n" + << "The second and third parameters are min and max of the grid. If you want those numbers to be computed " + << "based on the data, set them both to -1 \n" + << "The fourth parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a fourth parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the fourth parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 6) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + size_t size_of_grid = (size_t)atoi(argv[1]); + double min_ = atof(argv[2]); + double max_ = atof(argv[3]); + int dim = atoi(argv[4]); + unsigned dimension = std::numeric_limits<unsigned>::max(); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 5; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + for (size_t i = 0; i != filenames.size(); ++i) { + std::cout << "Creating persistence landscape on a grid based on a file : " << filenames[i] << std::endl; + Persistence_landscape_on_grid l; + if ((min_ != -1) || (max_ != -1)) { + l = Persistence_landscape_on_grid(filenames[i], min_, max_, size_of_grid, dimension); + } else { + // (min_ == -1) && (max_ == -1), in this case the program will find min_ and max_ based on the data. + l = Persistence_landscape_on_grid(filenames[i], size_of_grid, dimension); + } + std::stringstream ss; + ss << filenames[i] << ".g_land"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp b/utilities/Persistence_representations/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp new file mode 100644 index 00000000..dddb3615 --- /dev/null +++ b/utilities/Persistence_representations/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp @@ -0,0 +1,44 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_landscape_on_grid.h> + +#include <iostream> +#include <sstream> + +using Persistence_landscape_on_grid = Gudhi::Persistence_representations::Persistence_landscape_on_grid; + +int main(int argc, char** argv) { + std::cout << "This program creates a gnuplot script from a persistence landscape on grid stored in a file (the file " + << "needs to be created beforehand). Please call the code with the name of a single landscape on grid file" + << ".\n"; + if (argc != 2) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + Persistence_landscape_on_grid l; + l.load_landscape_from_file(argv[1]); + l.plot(argv[1]); + + return 0; +} diff --git a/utilities/Persistence_representations/persistence_vectors/CMakeLists.txt b/utilities/Persistence_representations/persistence_vectors/CMakeLists.txt new file mode 100644 index 00000000..a401c955 --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/CMakeLists.txt @@ -0,0 +1,10 @@ +cmake_minimum_required(VERSION 2.6) +project(Persistence_vectors_utilities) + +add_persistence_representation_creation_utility(create_persistence_vectors "-1") + +add_persistence_representation_plot_utility(plot_persistence_vectors ".vect") + +add_persistence_representation_function_utility(average_persistence_vectors ".vect") +add_persistence_representation_function_utility(compute_distance_of_persistence_vectors ".vect" "1") +add_persistence_representation_function_utility(compute_scalar_product_of_persistence_vectors ".vect") diff --git a/utilities/Persistence_representations/persistence_vectors/average_persistence_vectors.cpp b/utilities/Persistence_representations/persistence_vectors/average_persistence_vectors.cpp new file mode 100644 index 00000000..0144e76f --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/average_persistence_vectors.cpp @@ -0,0 +1,65 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <vector> + +using Euclidean_distance = Gudhi::Euclidean_distance; +using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; + +int main(int argc, char** argv) { + std::cout << "This program computes average of persistence vectors stored in files (the files needs to " + << "be created beforehand).\n" + << "The parameters of this programs are names of files with persistence vectors.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + std::vector<Vector_distances_in_diagram*> lands; + for (size_t i = 0; i != filenames.size(); ++i) { + Vector_distances_in_diagram* l = new Vector_distances_in_diagram; + l->load_from_file(filenames[i]); + lands.push_back(l); + } + + Vector_distances_in_diagram av; + av.compute_average(lands); + + av.print_to_file("average.vect"); + + for (size_t i = 0; i != filenames.size(); ++i) { + delete lands[i]; + } + + std::cout << "Done \n"; + + return 0; +} diff --git a/utilities/Persistence_representations/persistence_vectors/compute_distance_of_persistence_vectors.cpp b/utilities/Persistence_representations/persistence_vectors/compute_distance_of_persistence_vectors.cpp new file mode 100644 index 00000000..7e66d25e --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/compute_distance_of_persistence_vectors.cpp @@ -0,0 +1,94 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Euclidean_distance = Gudhi::Euclidean_distance; +using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; + +int main(int argc, char** argv) { + std::cout << "This program compute distance of persistence vectors stored in a file (the file needs to be created " + "beforehand). \n"; + std::cout << "The first parameter of a program is an integer p. The program compute l^p distance of the vectors. For " + "l^infty distance choose p = -1. \n"; + std::cout << "The remaining parameters of this programs are names of files with persistence vectors.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + int pp = atoi(argv[1]); + double p = std::numeric_limits<double>::max(); + if (pp != -1) { + p = pp; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Vector_distances_in_diagram> vectors; + vectors.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Vector_distances_in_diagram l; + l.load_from_file(filenames[file_no]); + vectors.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > distance(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + distance[i] = v; + } + + // and now we can compute the distances: + for (size_t i = 0; i != vectors.size(); ++i) { + for (size_t j = i + 1; j != vectors.size(); ++j) { + distance[i][j] = distance[j][i] = vectors[i].distance(vectors[j], p); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("distance.vect"); + for (size_t i = 0; i != distance.size(); ++i) { + for (size_t j = 0; j != distance.size(); ++j) { + std::cout << distance[i][j] << " "; + out << distance[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'distance.vect' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp b/utilities/Persistence_representations/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp new file mode 100644 index 00000000..303c6e3e --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp @@ -0,0 +1,86 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Euclidean_distance = Gudhi::Euclidean_distance; +using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; + +int main(int argc, char** argv) { + std::cout << "This program computes scalar product of persistence vectors stored in a file (the file needs to " + << "be created beforehand). \n" + << "The parameters of this programs are names of files with persistence vectors.\n"; + + if (argc < 3) { + std::cout << "Wrong number of parameters, the program will now terminate \n"; + return 1; + } + + std::vector<const char*> filenames; + for (int i = 1; i < argc; ++i) { + filenames.push_back(argv[i]); + } + std::vector<Vector_distances_in_diagram> vectors; + vectors.reserve(filenames.size()); + for (size_t file_no = 0; file_no != filenames.size(); ++file_no) { + Vector_distances_in_diagram l; + l.load_from_file(filenames[file_no]); + vectors.push_back(l); + } + + // and now we will compute the scalar product of landscapes. + + // first we prepare an array: + std::vector<std::vector<double> > scalar_product(filenames.size()); + for (size_t i = 0; i != filenames.size(); ++i) { + std::vector<double> v(filenames.size(), 0); + scalar_product[i] = v; + } + + // and now we can compute the scalar product: + for (size_t i = 0; i != vectors.size(); ++i) { + for (size_t j = i; j != vectors.size(); ++j) { + scalar_product[i][j] = scalar_product[j][i] = vectors[i].compute_scalar_product(vectors[j]); + } + } + + // and now output the result to the screen and a file: + std::ofstream out; + out.open("scalar_product.vect"); + for (size_t i = 0; i != scalar_product.size(); ++i) { + for (size_t j = 0; j != scalar_product.size(); ++j) { + std::cout << scalar_product[i][j] << " "; + out << scalar_product[i][j] << " "; + } + std::cout << std::endl; + out << std::endl; + } + out.close(); + + std::cout << "Distance can be found in 'scalar_product.vect' file\n"; + return 0; +} diff --git a/utilities/Persistence_representations/persistence_vectors/create_persistence_vectors.cpp b/utilities/Persistence_representations/persistence_vectors/create_persistence_vectors.cpp new file mode 100644 index 00000000..cc5e5393 --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/create_persistence_vectors.cpp @@ -0,0 +1,69 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <sstream> +#include <limits> +#include <vector> + +using Euclidean_distance = Gudhi::Euclidean_distance; +using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; + +int main(int argc, char** argv) { + std::cout << "This program creates persistence vectors files (*.vect) of persistence diagrams files (*.pers) " + << "provided as an input.\n" + << "The first parameter of this program is a dimension of persistence that will be used in creation of " + << "the persistence heat maps." + << "If your input files contains persistence pairs of various dimension, as a first parameter of the " + << "procedure please provide the dimension of persistence you want to use." + << "If in your files there are only birth-death pairs of the same dimension, set the first parameter to " + << "-1.\n" + << "The remaining parameters are the names of files with persistence diagrams. \n"; + + if (argc < 3) { + std::cout << "Wrong parameter list, the program will now terminate \n"; + return 1; + } + + std::cout << "The remaining parameters are the names of files with persistence diagrams. \n"; + int dim = atoi(argv[1]); + unsigned dimension = std::numeric_limits<unsigned>::max(); + if (dim >= 0) { + dimension = (unsigned)dim; + } + + std::vector<const char*> filenames; + for (int i = 2; i < argc; ++i) { + filenames.push_back(argv[i]); + } + + for (size_t i = 0; i != filenames.size(); ++i) { + std::cerr << "Creating persistence vectors based on a file : " << filenames[i] << std::endl; + Vector_distances_in_diagram l(filenames[i], dimension); + std::stringstream ss; + ss << filenames[i] << ".vect"; + l.print_to_file(ss.str().c_str()); + } + return 0; +} diff --git a/utilities/Persistence_representations/persistence_vectors/plot_persistence_vectors.cpp b/utilities/Persistence_representations/persistence_vectors/plot_persistence_vectors.cpp new file mode 100644 index 00000000..aa33107d --- /dev/null +++ b/utilities/Persistence_representations/persistence_vectors/plot_persistence_vectors.cpp @@ -0,0 +1,43 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Pawel Dlotko + * + * Copyright (C) 2016 INRIA (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#include <gudhi/Persistence_vectors.h> + +#include <iostream> +#include <sstream> + +using Euclidean_distance = Gudhi::Euclidean_distance; +using Vector_distances_in_diagram = Gudhi::Persistence_representations::Vector_distances_in_diagram<Euclidean_distance>; + +int main(int argc, char** argv) { + std::cout << "This program create a Gnuplot script to plot persistence vector. Please call this program with the " + "name of file with persistence vector. \n"; + if (argc != 2) { + std::cout << "Wrong number of parameters, the program will now terminate. \n"; + return 1; + } + Vector_distances_in_diagram l; + l.load_from_file(argv[1]); + l.plot(argv[1]); + + return 0; +} diff --git a/utilities/Rips_complex/CMakeLists.txt b/utilities/Rips_complex/CMakeLists.txt new file mode 100644 index 00000000..baa571fa --- /dev/null +++ b/utilities/Rips_complex/CMakeLists.txt @@ -0,0 +1,21 @@ +cmake_minimum_required(VERSION 2.6) +project(Rips_complex_utilities) + +add_executable(rips_distance_matrix_persistence rips_distance_matrix_persistence.cpp) +target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + +add_executable(rips_persistence rips_persistence.cpp) +target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + +if (TBB_FOUND) + target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) + target_link_libraries(rips_persistence ${TBB_LIBRARIES}) +endif() + +add_test(NAME Rips_complex_utility_from_rips_distance_matrix COMMAND $<TARGET_FILE:rips_distance_matrix_persistence> + "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" "-r" "1.0" "-d" "3" "-p" "3" "-m" "0") +add_test(NAME Rips_complex_utility_from_rips_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") + +install(TARGETS rips_distance_matrix_persistence DESTINATION bin) +install(TARGETS rips_persistence DESTINATION bin) diff --git a/example/Persistent_cohomology/rips_distance_matrix_persistence.cpp b/utilities/Rips_complex/rips_distance_matrix_persistence.cpp index d38808c7..ca3c0327 100644 --- a/example/Persistent_cohomology/rips_distance_matrix_persistence.cpp +++ b/utilities/Rips_complex/rips_distance_matrix_persistence.cpp @@ -1,5 +1,5 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ * library for computational topology. * * Author(s): Pawel Dlotko, Vincent Rouvreau @@ -36,18 +36,13 @@ using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persis using Filtration_value = Simplex_tree::Filtration_value; using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp >; +using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp>; using Distance_matrix = std::vector<std::vector<Filtration_value>>; -void program_options(int argc, char * argv[] - , std::string & csv_matrix_file - , std::string & filediag - , Filtration_value & threshold - , int & dim_max - , int & p - , Filtration_value & min_persistence); +void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag, + Filtration_value& threshold, int& dim_max, int& p, Filtration_value& min_persistence); -int main(int argc, char * argv[]) { +int main(int argc, char* argv[]) { std::string csv_matrix_file; std::string filediag; Filtration_value threshold; @@ -88,33 +83,28 @@ int main(int argc, char * argv[]) { return 0; } -void program_options(int argc, char * argv[] - , std::string & csv_matrix_file - , std::string & filediag - , Filtration_value & threshold - , int & dim_max - , int & p - , Filtration_value & min_persistence) { +void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag, + Filtration_value& threshold, int& dim_max, int& p, Filtration_value& min_persistence) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&csv_matrix_file), - "Name of file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'."); + hidden.add_options()( + "input-file", po::value<std::string>(&csv_matrix_file), + "Name of file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'."); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-edge-length,r", - po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), - "Maximal length of an edge for the Rips complex construction.") - ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), - "Maximal dimension of the Rips complex we want to compute.") - ("field-charac,p", po::value<int>(&p)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals"); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-edge-length,r", + po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal length of an edge for the Rips complex construction.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Rips complex we want to compute.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); po::positional_options_description pos; pos.add("input-file", 1); @@ -123,8 +113,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { diff --git a/example/Persistent_cohomology/rips_persistence.cpp b/utilities/Rips_complex/rips_persistence.cpp index d504798b..8405c014 100644 --- a/example/Persistent_cohomology/rips_persistence.cpp +++ b/utilities/Rips_complex/rips_persistence.cpp @@ -1,5 +1,5 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ * library for computational topology. * * Author(s): Clément Maria @@ -37,19 +37,14 @@ using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persis using Filtration_value = Simplex_tree::Filtration_value; using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; -using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp >; +using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp>; using Point = std::vector<double>; using Points_off_reader = Gudhi::Points_off_reader<Point>; -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & filediag - , Filtration_value & threshold - , int & dim_max - , int & p - , Filtration_value & min_persistence); +void program_options(int argc, char* argv[], std::string& off_file_points, std::string& filediag, + Filtration_value& threshold, int& dim_max, int& p, Filtration_value& min_persistence); -int main(int argc, char * argv[]) { +int main(int argc, char* argv[]) { std::string off_file_points; std::string filediag; Filtration_value threshold; @@ -91,33 +86,27 @@ int main(int argc, char * argv[]) { return 0; } -void program_options(int argc, char * argv[] - , std::string & off_file_points - , std::string & filediag - , Filtration_value & threshold - , int & dim_max - , int & p - , Filtration_value & min_persistence) { +void program_options(int argc, char* argv[], std::string& off_file_points, std::string& filediag, + Filtration_value& threshold, int& dim_max, int& p, Filtration_value& min_persistence) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&off_file_points), - "Name of an OFF file containing a point set.\n"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of an OFF file containing a point set.\n"); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-edge-length,r", - po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), - "Maximal length of an edge for the Rips complex construction.") - ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), - "Maximal dimension of the Rips complex we want to compute.") - ("field-charac,p", po::value<int>(&p)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals"); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-edge-length,r", + po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()), + "Maximal length of an edge for the Rips complex construction.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Rips complex we want to compute.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); po::positional_options_description pos; pos.add("input-file", 1); @@ -126,8 +115,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { diff --git a/utilities/Rips_complex/ripscomplex.md b/utilities/Rips_complex/ripscomplex.md new file mode 100644 index 00000000..4291fae7 --- /dev/null +++ b/utilities/Rips_complex/ripscomplex.md @@ -0,0 +1,49 @@ + + +# Rips complex # + +## rips_persistence ## +This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points, using Euclidean distance. The output diagram contains one bar per line, written with the convention: + +`p dim birth death` + +where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number). + +**Usage** + +`rips_persistence [options] <OFF input file>` + +**Allowed options** + +* `-h [ --help ]` Produce help message +* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output. +* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction. +* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute. +* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology. +* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals. + +Beware: this program may use a lot of RAM and take a lot of time if `max-edge-length` is set to a large value. + +**Example 1 with Z/2Z coefficients** + +`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2` + +**Example 2 with Z/3Z coefficients** + +`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3` + + +## rips_distance_matrix_persistence ## + +Same as `rips_persistence` but taking a distance matrix as input. + +**Usage** + +`rips_persistence [options] <CSV input file>` + +where +`<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'. + +**Example** + +`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0` diff --git a/utilities/Witness_complex/CMakeLists.txt b/utilities/Witness_complex/CMakeLists.txt new file mode 100644 index 00000000..125a41ff --- /dev/null +++ b/utilities/Witness_complex/CMakeLists.txt @@ -0,0 +1,28 @@ +cmake_minimum_required(VERSION 2.6) +project(Witness_complex_utilities) + +# CGAL and Eigen3 are required for Euclidean version of Witness +if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) + + add_executable ( Witness_complex_strong_witness_persistence strong_witness_persistence.cpp ) + target_link_libraries(Witness_complex_strong_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + add_executable ( Witness_complex_weak_witness_persistence weak_witness_persistence.cpp ) + target_link_libraries(Witness_complex_weak_witness_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + + if (TBB_FOUND) + target_link_libraries(Witness_complex_strong_witness_persistence ${TBB_LIBRARIES}) + target_link_libraries(Witness_complex_weak_witness_persistence ${TBB_LIBRARIES}) + endif() + + add_test(NAME Witness_complex_strong_test_torus_persistence + COMMAND $<TARGET_FILE:Witness_complex_strong_witness_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") + add_test(NAME Witness_complex_weak_test_torus_persistence + COMMAND $<TARGET_FILE:Witness_complex_weak_witness_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-l" "20" "-a" "0.5") + + install(TARGETS Witness_complex_strong_witness_persistence DESTINATION bin) + install(TARGETS Witness_complex_weak_witness_persistence DESTINATION bin) + +endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) diff --git a/example/Witness_complex/example_strong_witness_persistence.cpp b/utilities/Witness_complex/strong_witness_persistence.cpp index f786fe7b..2fba631b 100644 --- a/example/Witness_complex/example_strong_witness_persistence.cpp +++ b/utilities/Witness_complex/strong_witness_persistence.cpp @@ -25,6 +25,7 @@ #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_off_io.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <boost/program_options.hpp> @@ -46,16 +47,10 @@ using Filtration_value = SimplexTree::Filtration_value; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<SimplexTree, Field_Zp>; -void program_options(int argc, char * argv[] - , int & nbL - , std::string & file_name - , std::string & filediag - , Filtration_value & max_squared_alpha - , int & p - , int & dim_max - , Filtration_value & min_persistence); +void program_options(int argc, char* argv[], int& nbL, std::string& file_name, std::string& filediag, + Filtration_value& max_squared_alpha, int& p, int& dim_max, Filtration_value& min_persistence); -int main(int argc, char * argv[]) { +int main(int argc, char* argv[]) { std::string file_name; std::string filediag; Filtration_value max_squared_alpha; @@ -69,19 +64,20 @@ int main(int argc, char * argv[]) { Point_vector witnesses, landmarks; Gudhi::Points_off_reader<Point_d> off_reader(file_name); if (!off_reader.is_valid()) { - std::cerr << "Witness complex - Unable to read file " << file_name << "\n"; - exit(-1); // ----- >> + std::cerr << "Witness complex - Unable to read file " << file_name << "\n"; + exit(-1); // ----- >> } witnesses = Point_vector(off_reader.get_point_cloud()); std::cout << "Successfully read " << witnesses.size() << " points.\n"; std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; - // Choose landmarks from witnesses - Gudhi::subsampling::pick_n_random_points(witnesses, nbL, std::back_inserter(landmarks)); + // 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, + std::back_inserter(landmarks)); // Compute witness complex - Strong_witness_complex strong_witness_complex(landmarks, - witnesses); + Strong_witness_complex strong_witness_complex(landmarks, witnesses); strong_witness_complex.create_complex(simplex_tree, max_squared_alpha, lim_d); @@ -110,37 +106,28 @@ int main(int argc, char * argv[]) { return 0; } -void program_options(int argc, char * argv[] - , int & nbL - , std::string & file_name - , std::string & filediag - , Filtration_value & max_squared_alpha - , int & p - , int & dim_max - , Filtration_value & min_persistence) { +void program_options(int argc, char* argv[], int& nbL, std::string& file_name, std::string& filediag, + Filtration_value& max_squared_alpha, int& p, int& dim_max, Filtration_value& min_persistence) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&file_name), - "Name of file containing a point set in off format."); + hidden.add_options()("input-file", po::value<std::string>(&file_name), + "Name of file containing a point set in off format."); po::options_description visible("Allowed options", 100); Filtration_value default_alpha = std::numeric_limits<Filtration_value>::infinity(); - visible.add_options() - ("help,h", "produce help message") - ("landmarks,l", po::value<int>(&nbL), - "Number of landmarks to choose from the point cloud.") - ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), - "Maximal squared relaxation parameter.") - ("field-charac,p", po::value<int>(&p)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence)->default_value(0), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals") - ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(std::numeric_limits<int>::max()), - "Maximal dimension of the strong witness complex we want to compute."); + visible.add_options()("help,h", "produce help message")("landmarks,l", po::value<int>(&nbL), + "Number of landmarks to choose from the point cloud.")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), + "Maximal squared relaxation parameter.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence)->default_value(0), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals")("cpx-dimension,d", po::value<int>(&dim_max)->default_value(std::numeric_limits<int>::max()), + "Maximal dimension of the strong witness complex we want to compute."); po::positional_options_description pos; pos.add("input-file", 1); @@ -149,8 +136,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { @@ -168,4 +154,3 @@ void program_options(int argc, char * argv[] std::abort(); } } - diff --git a/example/Witness_complex/example_witness_complex_persistence.cpp b/utilities/Witness_complex/weak_witness_persistence.cpp index a1146922..23fa93aa 100644 --- a/example/Witness_complex/example_witness_complex_persistence.cpp +++ b/utilities/Witness_complex/weak_witness_persistence.cpp @@ -25,6 +25,7 @@ #include <gudhi/Persistent_cohomology.h> #include <gudhi/Points_off_io.h> #include <gudhi/pick_n_random_points.h> +#include <gudhi/choose_n_farthest_points.h> #include <boost/program_options.hpp> @@ -46,16 +47,10 @@ using Filtration_value = SimplexTree::Filtration_value; using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<SimplexTree, Field_Zp>; -void program_options(int argc, char * argv[] - , int & nbL - , std::string & file_name - , std::string & filediag - , Filtration_value & max_squared_alpha - , int & p - , int & dim_max - , Filtration_value & min_persistence); +void program_options(int argc, char* argv[], int& nbL, std::string& file_name, std::string& filediag, + Filtration_value& max_squared_alpha, int& p, int& dim_max, Filtration_value& min_persistence); -int main(int argc, char * argv[]) { +int main(int argc, char* argv[]) { std::string file_name; std::string filediag; Filtration_value max_squared_alpha; @@ -69,19 +64,20 @@ int main(int argc, char * argv[]) { Point_vector witnesses, landmarks; Gudhi::Points_off_reader<Point_d> off_reader(file_name); if (!off_reader.is_valid()) { - std::cerr << "Witness complex - Unable to read file " << file_name << "\n"; - exit(-1); // ----- >> + std::cerr << "Witness complex - Unable to read file " << file_name << "\n"; + exit(-1); // ----- >> } witnesses = Point_vector(off_reader.get_point_cloud()); std::cout << "Successfully read " << witnesses.size() << " points.\n"; std::cout << "Ambient dimension is " << witnesses[0].dimension() << ".\n"; - // Choose landmarks from witnesses - Gudhi::subsampling::pick_n_random_points(witnesses, nbL, std::back_inserter(landmarks)); + // 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, + std::back_inserter(landmarks)); // Compute witness complex - Witness_complex witness_complex(landmarks, - witnesses); + Witness_complex witness_complex(landmarks, witnesses); witness_complex.create_complex(simplex_tree, max_squared_alpha, lim_d); @@ -110,38 +106,28 @@ int main(int argc, char * argv[]) { return 0; } - -void program_options(int argc, char * argv[] - , int & nbL - , std::string & file_name - , std::string & filediag - , Filtration_value & max_squared_alpha - , int & p - , int & dim_max - , Filtration_value & min_persistence) { +void program_options(int argc, char* argv[], int& nbL, std::string& file_name, std::string& filediag, + Filtration_value& max_squared_alpha, int& p, int& dim_max, Filtration_value& min_persistence) { namespace po = boost::program_options; po::options_description hidden("Hidden options"); - hidden.add_options() - ("input-file", po::value<std::string>(&file_name), - "Name of file containing a point set in off format."); + hidden.add_options()("input-file", po::value<std::string>(&file_name), + "Name of file containing a point set in off format."); Filtration_value default_alpha = std::numeric_limits<Filtration_value>::infinity(); po::options_description visible("Allowed options", 100); - visible.add_options() - ("help,h", "produce help message") - ("landmarks,l", po::value<int>(&nbL), - "Number of landmarks to choose from the point cloud.") - ("output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), - "Name of file in which the persistence diagram is written. Default print in std::cout") - ("max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), - "Maximal squared relaxation parameter.") - ("field-charac,p", po::value<int>(&p)->default_value(11), - "Characteristic p of the coefficient field Z/pZ for computing homology.") - ("min-persistence,m", po::value<Filtration_value>(&min_persistence)->default_value(0), - "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length intervals") - ("cpx-dimension,d", po::value<int>(&dim_max)->default_value(std::numeric_limits<int>::max()), - "Maximal dimension of the weak witness complex we want to compute."); + visible.add_options()("help,h", "produce help message")("landmarks,l", po::value<int>(&nbL), + "Number of landmarks to choose from the point cloud.")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "max-sq-alpha,a", po::value<Filtration_value>(&max_squared_alpha)->default_value(default_alpha), + "Maximal squared relaxation parameter.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence)->default_value(0), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals")("cpx-dimension,d", po::value<int>(&dim_max)->default_value(std::numeric_limits<int>::max()), + "Maximal dimension of the weak witness complex we want to compute."); po::positional_options_description pos; pos.add("input-file", 1); @@ -150,8 +136,7 @@ void program_options(int argc, char * argv[] all.add(visible).add(hidden); po::variables_map vm; - po::store(po::command_line_parser(argc, argv). - options(all).positional(pos).run(), vm); + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); po::notify(vm); if (vm.count("help") || !vm.count("input-file")) { diff --git a/utilities/Witness_complex/witnesscomplex.md b/utilities/Witness_complex/witnesscomplex.md new file mode 100644 index 00000000..2341759b --- /dev/null +++ b/utilities/Witness_complex/witnesscomplex.md @@ -0,0 +1,66 @@ +
+
+# Witness complex #
+
+
+For more details about the witness complex, please read the [user manual of the package](/doc/latest/group__witness__complex.html).
+
+## weak_witness_persistence ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
+
+`weak_witness_persistence [options] <OFF input file>`
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+**Example**
+
+`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
+
+
+## strong_witness_persistence ##
+
+This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
+
+`strong_witness_persistence [options] <OFF input file>`
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+**Example**
+
+`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
diff --git a/utilities/common/README b/utilities/common/README deleted file mode 100644 index dc841521..00000000 --- a/utilities/common/README +++ /dev/null @@ -1,19 +0,0 @@ -======================= off_file_from_shape_generator ================================== - -Example of use : - -*** on|in sphere|cube|curve|torus|klein generator - -./off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2 - - => generates a onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2 - -./off_file_from_shape_generator in sphere inSphere.off 100 2 - - => generates a inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default) - -./off_file_from_shape_generator in cube inCube.off 10000 3 5.8 - - => generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8 - -!! Warning: hypegenerator on cube is not available !! diff --git a/utilities/common/pointsetgenerator.md b/utilities/common/pointsetgenerator.md new file mode 100644 index 00000000..284715d4 --- /dev/null +++ b/utilities/common/pointsetgenerator.md @@ -0,0 +1,33 @@ + + +# common # + +## off_file_from_shape_generator ## + +Generates a pointset and save it in an OFF file. Command-line is: + +``` +off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>... +``` + +Warning: "on cube" generator is not available! + +**Examples** + +``` +off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2 +``` + +* Generates an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2. + +``` +off_file_from_shape_generator in sphere inSphere.off 100 2 +``` + +* Generates an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default). + +``` +off_file_from_shape_generator in cube inCube.off 10000 3 5.8 +``` + +* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8. |
