diff options
Diffstat (limited to 'src/python/doc')
| -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 |
7 files changed, 16 insertions, 8 deletions
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 a049cfb5..32999a0c 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -30,7 +30,7 @@ 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) + \ |