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author | Marc Glisse <marc.glisse@inria.fr> | 2020-01-20 16:59:13 +0100 |
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committer | Marc Glisse <marc.glisse@inria.fr> | 2020-01-20 16:59:13 +0100 |
commit | 463043e08e9c8938af96d7220a6d5bb77fc064d8 (patch) | |
tree | 2b150676eeb8cfa2a0dc3f2a7a1344c1501f87b0 /src/python/doc | |
parent | a7f3167ffb465bd6d1e3b9e40bc6f1c35daf87fc (diff) | |
parent | 653b8ff129a9676d1bc69ee5231cf12f9aadd7e9 (diff) |
Merge remote-tracking branch 'origin/master' into ext
Diffstat (limited to 'src/python/doc')
-rw-r--r-- | src/python/doc/alpha_complex_sum.inc | 6 | ||||
-rw-r--r-- | src/python/doc/alpha_complex_user.rst | 19 | ||||
-rw-r--r-- | src/python/doc/cubical_complex_user.rst | 3 | ||||
-rw-r--r-- | src/python/doc/installation.rst | 7 | ||||
-rw-r--r-- | src/python/doc/persistence_graphical_tools_user.rst | 2 | ||||
-rw-r--r-- | src/python/doc/reader_utils_ref.rst | 2 | ||||
-rw-r--r-- | src/python/doc/rips_complex_user.rst | 3 | ||||
-rw-r--r-- | src/python/doc/wasserstein_distance_sum.inc | 6 | ||||
-rw-r--r-- | src/python/doc/wasserstein_distance_user.rst | 2 | ||||
-rw-r--r-- | src/python/doc/witness_complex_user.rst | 2 |
10 files changed, 29 insertions, 23 deletions
diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index c5ba9dc7..a1184663 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -9,9 +9,9 @@ | | circumradius of the simplex if the circumsphere is empty (the simplex | :Copyright: MIT (`GPL v3 </licensing/>`_) | | | is then said to be Gabriel), and as the minimum of the filtration | | | | values of the codimension 1 cofaces that make it not Gabriel | :Requires: `Eigen <installation.html#eigen>`__ :math:`\geq` 3.1.0 and `CGAL <installation.html#cgal>`__ :math:`\geq` 4.11.0 | - | | otherwise. All simplices that have a filtration value strictly | | - | | greater than a given alpha squared value are not inserted into the | | - | | complex. | | + | | otherwise. All simplices that have a filtration value | | + | | :math:`> \alpha^2` are removed from the Delaunay complex | | + | | when creating the simplicial complex if it is specified. | | | | | | | | This package requires having CGAL version 4.7 or higher (4.8.1 is | | | | advised for better performance). | | diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index b7e69e12..60319e84 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -16,7 +16,8 @@ Definition Remarks ^^^^^^^ -When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha`, the complex is a Delaunay complex (with special filtration values). +When an :math:`\alpha`-complex is constructed with an infinite value of :math:`\alpha^2`, +the complex is a Delaunay complex (with special filtration values). Example from points ------------------- @@ -137,19 +138,20 @@ sets the filtration value (0 in case of a vertex - propagation will have no effe Non decreasing filtration values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -As the squared radii computed by CGAL are an approximation, it might happen that these alpha squared values do not -quite define a proper filtration (i.e. non-decreasing with respect to inclusion). +As the squared radii computed by CGAL are an approximation, it might happen that these +:math:`\alpha^2` values do not quite define a proper filtration (i.e. non-decreasing with +respect to inclusion). We fix that up by calling :func:`~gudhi.SimplexTree.make_filtration_non_decreasing` (cf. `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). Prune above given filtration value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The simplex tree is pruned from the given maximum alpha squared value (cf. +The simplex tree is pruned from the given maximum :math:`\alpha^2` value (cf. :func:`~gudhi.SimplexTree.prune_above_filtration`). Note that this does not provide any kind of speed-up, since we always first build the full filtered complex, so it is recommended not to use :paramref:`~gudhi.AlphaComplex.create_simplex_tree.max_alpha_square`. -In the following example, a threshold of 59 is used. +In the following example, a threshold of :math:`\alpha^2 = 32.0` is used. Example from OFF file @@ -166,7 +168,7 @@ Then, it is asked to display information about the alpha complex: import gudhi alpha_complex = gudhi.AlphaComplex(off_file=gudhi.__root_source_dir__ + \ '/data/points/alphacomplexdoc.off') - simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=59.0) + simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=32.0) result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ repr(simplex_tree.num_vertices()) + ' vertices.' @@ -179,7 +181,7 @@ the program output is: .. testoutput:: - Alpha complex is of dimension 2 - 23 simplices - 7 vertices. + Alpha complex is of dimension 2 - 20 simplices - 7 vertices. [0] -> 0.00 [1] -> 0.00 [2] -> 0.00 @@ -200,9 +202,6 @@ the program output is: [4, 6] -> 22.74 [4, 5, 6] -> 22.74 [3, 6] -> 30.25 - [2, 6] -> 36.50 - [2, 3, 6] -> 36.50 - [2, 4, 6] -> 37.24 CGAL citations ============== diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index b13b500e..56cf0170 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -142,8 +142,7 @@ 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_cc = pcc(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.' diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 50a697c7..40f3f44b 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -257,6 +257,13 @@ The :doc:`Wasserstein distance </wasserstein_distance_user>` module requires `POT <https://pot.readthedocs.io/>`_, a library that provides several solvers for optimization problems related to Optimal Transport. +Scikit-learn +============ + +The :doc:`persistence representations </representations>` module require +`scikit-learn <https://scikit-learn.org/>`_, a Python-based ecosystem of +open-source software for machine learning. + SciPy ===== diff --git a/src/python/doc/persistence_graphical_tools_user.rst b/src/python/doc/persistence_graphical_tools_user.rst index f41a926b..80002db6 100644 --- a/src/python/doc/persistence_graphical_tools_user.rst +++ b/src/python/doc/persistence_graphical_tools_user.rst @@ -24,7 +24,7 @@ This function can display the persistence result as a barcode: import gudhi off_file = gudhi.__root_source_dir__ + '/data/points/tore3D_300.off' - point_cloud = gudhi.read_off(off_file=off_file) + point_cloud = gudhi.read_points_from_off_file(off_file=off_file) rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.7) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diff --git a/src/python/doc/reader_utils_ref.rst b/src/python/doc/reader_utils_ref.rst index f3ecebad..b8977a5a 100644 --- a/src/python/doc/reader_utils_ref.rst +++ b/src/python/doc/reader_utils_ref.rst @@ -6,7 +6,7 @@ Reader utils reference manual ============================= -.. autofunction:: gudhi.read_off +.. autofunction:: gudhi.read_points_from_off_file .. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index a8659542..a27573e8 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -136,7 +136,8 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off') + off_file = gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off' + point_cloud = gudhi.read_points_from_off_file(off_file = off_file) rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=12.0) simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index ffd4d312..a97f428d 100644 --- a/src/python/doc/wasserstein_distance_sum.inc +++ b/src/python/doc/wasserstein_distance_sum.inc @@ -2,12 +2,12 @@ :widths: 30 50 20 +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ - | .. figure:: | The p-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | + | .. figure:: | The q-Wasserstein distance measures the similarity between two | :Author: Theo Lacombe | | ../../doc/Bottleneck_distance/perturb_pd.png | persistence diagrams. It's the minimum value c that can be achieved | | | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Introduced in: GUDHI 3.1.0 | | | diagonal points), where the value of a matching is defined as the | | - | Wasserstein distance is the p-th root of the sum of the | p-th root of the sum of all edge lengths to the power p. Edge lengths| :Copyright: MIT | - | edge lengths to the power p. | are measured in norm q, for :math:`1 \leq q \leq \infty`. | | + | Wasserstein distance is the q-th root of the sum of the | q-th root of the sum of all edge lengths to the power q. Edge lengths| :Copyright: MIT | + | edge lengths to the power q. | are measured in norm p, for :math:`1 \leq p \leq \infty`. | | | | | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | * :doc:`wasserstein_distance_user` | | diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index 355ad247..648cc568 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -39,7 +39,7 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) diag2 = np.array([[2.8, 4.45],[9.5, 14.1]]) - message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, q=2., p=1.) + message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, order=1., internal_p=2.) print(message) The output is: diff --git a/src/python/doc/witness_complex_user.rst b/src/python/doc/witness_complex_user.rst index 45ba5b3b..7087fa98 100644 --- a/src/python/doc/witness_complex_user.rst +++ b/src/python/doc/witness_complex_user.rst @@ -101,7 +101,7 @@ Let's start with a simple example, which reads an off point file and computes a print("#####################################################################") print("EuclideanWitnessComplex creation from points read in a OFF file") - witnesses = gudhi.read_off(off_file=args.file) + witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) message = "EuclideanWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ |