From e1c8edc4b148331083f53c7c3d34766190bb6d99 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Tue, 17 Mar 2020 22:16:23 +0100 Subject: Another proposal to fix #248 --- src/python/doc/point_cloud_sum.inc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'src/python/doc/point_cloud_sum.inc') diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index 85d52de7..77245e86 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -1,5 +1,5 @@ .. table:: - :widths: 30 50 20 + :widths: 30 40 30 +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, etc. | :Author: Vincent Rouvreau | -- cgit v1.2.3 From cf29f4a485d06469d17c6d12d306901fa3c5ab36 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Mon, 23 Mar 2020 18:11:15 +0100 Subject: Shorter headers in sphinx: Introduced in -> Since and Copyright -> License --- src/python/doc/alpha_complex_sum.inc | 4 ++-- src/python/doc/bottleneck_distance_sum.inc | 4 ++-- src/python/doc/cubical_complex_sum.inc | 4 ++-- src/python/doc/cubical_complex_user.rst | 2 +- src/python/doc/nerve_gic_complex_sum.inc | 4 ++-- src/python/doc/persistence_graphical_tools_sum.inc | 4 ++-- src/python/doc/persistent_cohomology_sum.inc | 4 ++-- src/python/doc/persistent_cohomology_user.rst | 2 +- src/python/doc/point_cloud_sum.inc | 4 ++-- src/python/doc/representations_sum.inc | 4 ++-- src/python/doc/rips_complex_sum.inc | 4 ++-- src/python/doc/rips_complex_user.rst | 2 +- src/python/doc/simplex_tree_sum.inc | 4 ++-- src/python/doc/tangential_complex_sum.inc | 4 ++-- src/python/doc/wasserstein_distance_sum.inc | 4 ++-- src/python/doc/witness_complex_sum.inc | 4 ++-- 16 files changed, 29 insertions(+), 29 deletions(-) (limited to 'src/python/doc/point_cloud_sum.inc') diff --git a/src/python/doc/alpha_complex_sum.inc b/src/python/doc/alpha_complex_sum.inc index 00c35155..9e6414d0 100644 --- a/src/python/doc/alpha_complex_sum.inc +++ b/src/python/doc/alpha_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | .. figure:: | Alpha complex is a simplicial complex constructed from the finite | :Author: Vincent Rouvreau | | ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | | - | :alt: Alpha complex representation | | :Introduced in: GUDHI 2.0.0 | + | :alt: Alpha complex representation | | :Since: GUDHI 2.0.0 | | :figclass: align-center | The filtration value of each simplex is computed as the **square** of | | - | | the circumradius of the simplex if the circumsphere is empty (the | :Copyright: MIT (`GPL v3 `_) | + | | the circumradius of the simplex if the circumsphere is empty (the | :License: MIT (`GPL v3 `_) | | | simplex is then said to be Gabriel), and as the minimum of the | | | | filtration values of the codimension 1 cofaces that make it not | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | | | Gabriel otherwise. | | diff --git a/src/python/doc/bottleneck_distance_sum.inc b/src/python/doc/bottleneck_distance_sum.inc index a01e7f04..0de4625c 100644 --- a/src/python/doc/bottleneck_distance_sum.inc +++ b/src/python/doc/bottleneck_distance_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | .. figure:: | Bottleneck distance measures the similarity between two persistence | :Author: François Godi | | ../../doc/Bottleneck_distance/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | | - | :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | :Since: GUDHI 2.0.0 | | | diagonal points) such that any couple of matched points are at | | - | Bottleneck distance is the length of | distance at most b, where the distance between points is the sup | :Copyright: MIT (`GPL v3 `_) | + | Bottleneck distance is the length of | distance at most b, where the distance between points is the sup | :License: MIT (`GPL v3 `_) | | the longest edge | norm in :math:`\mathbb{R}^2`. | | | | | :Requires: `CGAL `__ :math:`\geq` 4.11.0 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index ab6388e5..28bf8e94 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -4,9 +4,9 @@ +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ | .. figure:: | The cubical complex is an example of a structured complex useful in | :Author: Pawel Dlotko | | ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | | - | :alt: Cubical complex representation | analysis. | :Introduced in: GUDHI 2.0.0 | + | :alt: Cubical complex representation | analysis. | :Since: GUDHI 2.0.0 | | :figclass: align-center | | | - | | | :Copyright: MIT | + | | | :License: MIT | | | | | +--------------------------------------------------------------------------+----------------------------------------------------------------------+-----------------------------+ | * :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst index 56cf0170..93ca6b24 100644 --- a/src/python/doc/cubical_complex_user.rst +++ b/src/python/doc/cubical_complex_user.rst @@ -8,7 +8,7 @@ Definition ---------- ===================================== ===================================== ===================================== -:Author: Pawel Dlotko :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 +:Author: Pawel Dlotko :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 ===================================== ===================================== ===================================== +---------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/python/doc/nerve_gic_complex_sum.inc b/src/python/doc/nerve_gic_complex_sum.inc index d5356eca..7fe55aff 100644 --- a/src/python/doc/nerve_gic_complex_sum.inc +++ b/src/python/doc/nerve_gic_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+------------------------------------------------------------------+ | .. figure:: | Nerves and Graph Induced Complexes are cover complexes, i.e. | :Author: Mathieu Carrière | | ../../doc/Nerve_GIC/gicvisu.jpg | simplicial complexes that provably contain topological information | | - | :alt: Graph Induced Complex of a point cloud. | about the input data. They can be computed with a cover of the data, | :Introduced in: GUDHI 2.3.0 | + | :alt: Graph Induced Complex of a point cloud. | about the input data. They can be computed with a cover of the data, | :Since: GUDHI 2.3.0 | | :figclass: align-center | that comes i.e. from the preimage of a family of intervals covering | | - | | the image of a scalar-valued function defined on the data. | :Copyright: MIT (`GPL v3 `_) | + | | the image of a scalar-valued function defined on the data. | :License: MIT (`GPL v3 `_) | | | | | | | | :Requires: `CGAL `__ :math:`\geq` 4.11.0 | | | | | diff --git a/src/python/doc/persistence_graphical_tools_sum.inc b/src/python/doc/persistence_graphical_tools_sum.inc index 723c0f78..b68d3d7e 100644 --- a/src/python/doc/persistence_graphical_tools_sum.inc +++ b/src/python/doc/persistence_graphical_tools_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ | .. figure:: | These graphical tools comes on top of persistence results and allows | :Author: Vincent Rouvreau, Theo Lacombe | | img/graphical_tools_representation.png | the user to display easily persistence barcode, diagram or density. | | - | | | :Introduced in: GUDHI 2.0.0 | + | | | :Since: GUDHI 2.0.0 | | | Note that these functions return the matplotlib axis, allowing | | - | | for further modifications (title, aspect, etc.) | :Copyright: MIT | + | | for further modifications (title, aspect, etc.) | :License: MIT | | | | | | | | :Requires: matplotlib, numpy and scipy | +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ diff --git a/src/python/doc/persistent_cohomology_sum.inc b/src/python/doc/persistent_cohomology_sum.inc index 9c29bfaa..0effb50f 100644 --- a/src/python/doc/persistent_cohomology_sum.inc +++ b/src/python/doc/persistent_cohomology_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+-----------------------------------------------------------------------+-----------------------------------------------+ | .. figure:: | The theory of homology consists in attaching to a topological space | :Author: Clément Maria | | ../../doc/Persistent_cohomology/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | | - | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | :Since: GUDHI 2.0.0 | | | homology studies the evolution -- birth, life and death -- of these | | - | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | :Copyright: MIT | + | Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | :License: MIT | | Torus | theory is essentially composed of three elements: topological spaces, | | | | their homology groups and an evolution scheme. | | | | | | diff --git a/src/python/doc/persistent_cohomology_user.rst b/src/python/doc/persistent_cohomology_user.rst index de83cda1..5f931b3a 100644 --- a/src/python/doc/persistent_cohomology_user.rst +++ b/src/python/doc/persistent_cohomology_user.rst @@ -7,7 +7,7 @@ Persistent cohomology user manual Definition ---------- ===================================== ===================================== ===================================== -:Author: Clément Maria :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 +:Author: Clément Maria :Since: GUDHI PYTHON 2.0.0 :License: GPL v3 ===================================== ===================================== ===================================== +-----------------------------------------------------------------+-----------------------------------------------------------------------+ diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index 77245e86..0a159680 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, etc. | :Author: Vincent Rouvreau | | | :math:`(y_1, y_2, \ldots, y_d)` | | | - | | | :Introduced in: GUDHI 2.0.0 | + | | | :Since: GUDHI 2.0.0 | | | | | - | | | :Copyright: MIT (`GPL v3 `_) | + | | | :License: MIT (`GPL v3 `_) | | | Parts of this package require CGAL. | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | | | | | diff --git a/src/python/doc/representations_sum.inc b/src/python/doc/representations_sum.inc index edb8a448..eac89b9d 100644 --- a/src/python/doc/representations_sum.inc +++ b/src/python/doc/representations_sum.inc @@ -4,9 +4,9 @@ +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ | .. figure:: | Vectorizations, distances and kernels that work on persistence | :Author: Mathieu Carrière | | img/sklearn-tda.png | diagrams, compatible with scikit-learn. | | - | | | :Introduced in: GUDHI 3.1.0 | + | | | :Since: GUDHI 3.1.0 | | | | | - | | | :Copyright: MIT | + | | | :License: MIT | | | | | | | | :Requires: scikit-learn | +------------------------------------------------------------------+----------------------------------------------------------------+-----------------------------------------------+ diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index a1f0e469..6feb74cd 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------+ | .. figure:: | Rips complex is a simplicial complex constructed from a one skeleton | :Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse | | ../../doc/Rips_complex/rips_complex_representation.png | graph. | | - | :figclass: align-center | | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | | :Since: GUDHI 2.0.0 | | | The filtration value of each edge is computed from a user-given | | - | | distance function and is inserted until a user-given threshold | :Copyright: MIT | + | | distance function and is inserted until a user-given threshold | :License: MIT | | | value. | | | | | | | | This complex can be built from a point cloud and a distance function, | | diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index a27573e8..8efb12e6 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -8,7 +8,7 @@ Definition ---------- ==================================================================== ================================ ====================== -:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 +:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse :Since: GUDHI 2.0.0 :License: GPL v3 ==================================================================== ================================ ====================== +-------------------------------------------+----------------------------------------------------------------------+ diff --git a/src/python/doc/simplex_tree_sum.inc b/src/python/doc/simplex_tree_sum.inc index 3c637b8c..a8858f16 100644 --- a/src/python/doc/simplex_tree_sum.inc +++ b/src/python/doc/simplex_tree_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ | .. figure:: | The simplex tree is an efficient and flexible data structure for | :Author: Clément Maria | | ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | | - | :alt: Simplex tree representation | | :Introduced in: GUDHI 2.0.0 | + | :alt: Simplex tree representation | | :Since: GUDHI 2.0.0 | | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica` | :Copyright: MIT | + | | :cite:`boissonnatmariasimplextreealgorithmica` | :License: MIT | | | | | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ | * :doc:`simplex_tree_user` | * :doc:`simplex_tree_ref` | diff --git a/src/python/doc/tangential_complex_sum.inc b/src/python/doc/tangential_complex_sum.inc index ddc3e609..45ce2a66 100644 --- a/src/python/doc/tangential_complex_sum.inc +++ b/src/python/doc/tangential_complex_sum.inc @@ -4,9 +4,9 @@ +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ | .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | :Author: Clément Jamin | | ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | | - | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | :Introduced in: GUDHI 2.0.0 | + | :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | :Since: GUDHI 2.0.0 | | | an unknown manifold. The running time depends only linearly on the | | - | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :Copyright: MIT (`GPL v3 `_) | + | | extrinsic dimension :math:`d` and exponentially on the intrinsic | :License: MIT (`GPL v3 `_) | | | dimension :math:`k`. | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ diff --git a/src/python/doc/wasserstein_distance_sum.inc b/src/python/doc/wasserstein_distance_sum.inc index 1632befa..0ff22035 100644 --- a/src/python/doc/wasserstein_distance_sum.inc +++ b/src/python/doc/wasserstein_distance_sum.inc @@ -4,9 +4,9 @@ +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ | .. 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 | + | :figclass: align-center | by a perfect matching between the points of the two diagrams (+ all | :Since: GUDHI 3.1.0 | | | diagonal points), where the value of a matching is defined as the | | - | 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 | + | 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| :License: 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 | +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+ diff --git a/src/python/doc/witness_complex_sum.inc b/src/python/doc/witness_complex_sum.inc index f9c009ab..34d4df4a 100644 --- a/src/python/doc/witness_complex_sum.inc +++ b/src/python/doc/witness_complex_sum.inc @@ -4,9 +4,9 @@ +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ | .. figure:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | :Author: Siargey Kachanovich | | ../../doc/Witness_complex/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | | - | :alt: Witness complex representation | | :Introduced in: GUDHI 2.0.0 | + | :alt: Witness complex representation | | :Since: GUDHI 2.0.0 | | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica`. | :Copyright: MIT (`GPL v3 `_ for Euclidean versions only) | + | | :cite:`boissonnatmariasimplextreealgorithmica`. | :License: MIT (`GPL v3 `_ for Euclidean versions only) | | | | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 for Euclidean versions only | +-------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+ -- cgit v1.2.3 From 7120b186471828a9570fdeef37900bd8b98d0d31 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Thu, 26 Mar 2020 23:06:06 +0100 Subject: license --- src/python/doc/point_cloud_sum.inc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'src/python/doc/point_cloud_sum.inc') diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index 0a159680..ecc18951 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -6,7 +6,7 @@ | | :math:`(y_1, y_2, \ldots, y_d)` | | | | | | :Since: GUDHI 2.0.0 | | | | | - | | | :License: MIT (`GPL v3 `_) | + | | | :License: MIT (`GPL v3 `_, BSD-3-Clause, Apache-2.0) | | | Parts of this package require CGAL. | | | | | :Requires: `Eigen `__ :math:`\geq` 3.1.0 and `CGAL `__ :math:`\geq` 4.11.0 | | | | | -- cgit v1.2.3 From 35a12b553c85af8ce31629b90a27a7071b0cc379 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Sat, 28 Mar 2020 11:48:43 +0100 Subject: Doc tweaks, default DTM exponent --- src/python/doc/point_cloud.rst | 6 ++++-- src/python/doc/point_cloud_sum.inc | 4 ++-- src/python/gudhi/point_cloud/dtm.py | 17 ++++++++++++----- src/python/gudhi/point_cloud/knn.py | 6 +++--- 4 files changed, 21 insertions(+), 12 deletions(-) (limited to 'src/python/doc/point_cloud_sum.inc') diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst index 351b0786..192f70db 100644 --- a/src/python/doc/point_cloud.rst +++ b/src/python/doc/point_cloud.rst @@ -28,11 +28,12 @@ Time Delay Embedding :members: :special-members: __call__ -Nearest neighbors ------------------ +K nearest neighbors +------------------- .. automodule:: gudhi.point_cloud.knn :members: + :undoc-members: :special-members: __init__ Distance to measure @@ -40,4 +41,5 @@ Distance to measure .. automodule:: gudhi.point_cloud.dtm :members: + :undoc-members: :special-members: __init__ diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc index ecc18951..d4761aba 100644 --- a/src/python/doc/point_cloud_sum.inc +++ b/src/python/doc/point_cloud_sum.inc @@ -2,8 +2,8 @@ :widths: 30 40 30 +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------+ - | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, etc. | :Author: Vincent Rouvreau | - | | :math:`(y_1, y_2, \ldots, y_d)` | | | + | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi | + | | :math:`(y_1, y_2, \ldots, y_d)` | find neighbors, embed time series in higher dimension, etc. | | | | | :Since: GUDHI 2.0.0 | | | | | | | | :License: MIT (`GPL v3 `_, BSD-3-Clause, Apache-2.0) | diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index e4096c5e..520cbea8 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -15,10 +15,11 @@ class DTM: Class to compute the distance to the empirical measure defined by a point set. """ - def __init__(self, k, q=2, **kwargs): + def __init__(self, k, q=None, **kwargs): """ Args: - q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'. + k (int): number of neighbors (possibly including the point itself). + q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if metric is "neighbors" or "distance_matrix". kwargs: Same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors. """ self.k = k @@ -31,7 +32,7 @@ class DTM: def fit(self, X, y=None): """ Args: - X (numpy.array): coordinates for mass points + X (numpy.array): coordinates for mass points. """ if self.params.setdefault("metric", "euclidean") != "neighbors": # KNN gives sorted distances, which is unnecessary here. @@ -45,11 +46,17 @@ class DTM: Args: X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored). """ + q = self.q + if q is None: + if self.params["metric"] in {"neighbors", "precomputed"}: + q = 2 + else: + q = len(X[0]) if self.params["metric"] == "neighbors": distances = X[:, : self.k] else: distances = self.knn.transform(X) - distances = distances ** self.q + distances = distances ** q dtm = distances.sum(-1) / self.k - dtm = dtm ** (1.0 / self.q) + dtm = dtm ** (1.0 / q) return dtm diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 02448530..31e4fc9f 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -18,7 +18,7 @@ class KNN: def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs): """ Args: - k (int): number of neighbors (including the point itself). + k (int): number of neighbors (possibly including the point itself). return_index (bool): if True, return the index of each neighbor. return_distance (bool): if True, return the distance to each neighbor. implementation (str): Choice of the library that does the real work. @@ -68,7 +68,7 @@ class KNN: def fit(self, X, y=None): """ Args: - X (numpy.array): coordinates for reference points + X (numpy.array): coordinates for reference points. """ self.ref_points = X if self.params["implementation"] == "ckdtree": @@ -105,7 +105,7 @@ class KNN: def transform(self, X): """ Args: - X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed" + X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed". """ metric = self.metric k = self.k -- cgit v1.2.3