diff options
author | Vincent Rouvreau <10407034+VincentRouvreau@users.noreply.github.com> | 2021-11-02 16:47:39 +0100 |
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committer | GitHub <noreply@github.com> | 2021-11-02 16:47:39 +0100 |
commit | 58c09b0547749604260ea2584b4d2a3a9ea9735b (patch) | |
tree | a499da7f859ffcec0dc88e5d0a589b9ae5aab9d1 /src/python/gudhi | |
parent | dc4f34632e2532ae8aa5a9efa50c428369a94965 (diff) | |
parent | 93df8a0622836ab03ada2eac075132388708d2c4 (diff) |
Merge pull request #490 from Hind-M/generate_points_torus_python
First version of points generation on torus
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/datasets/generators/_points.cc | 120 | ||||
-rw-r--r-- | src/python/gudhi/datasets/generators/points.cc | 68 | ||||
-rw-r--r-- | src/python/gudhi/datasets/generators/points.py | 56 |
3 files changed, 176 insertions, 68 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc new file mode 100644 index 00000000..70ce4925 --- /dev/null +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -0,0 +1,120 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Hind Montassif + * + * Copyright (C) 2021 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#include <pybind11/pybind11.h> +#include <pybind11/numpy.h> + +#include <gudhi/random_point_generators.h> +#include <gudhi/Debug_utils.h> + +#include <CGAL/Epick_d.h> + +namespace py = pybind11; + + +typedef CGAL::Epick_d< CGAL::Dynamic_dimension_tag > Kern; + +py::array_t<double> generate_points_on_sphere(size_t n_samples, int ambient_dim, double radius, std::string sample) { + + if (sample != "random") { + throw pybind11::value_error("This sample type is not supported"); + } + + py::array_t<double> points({n_samples, (size_t)ambient_dim}); + + py::buffer_info buf = points.request(); + double *ptr = static_cast<double *>(buf.ptr); + + GUDHI_CHECK(n_samples == buf.shape[0], "Py array first dimension not matching n_samples on sphere"); + GUDHI_CHECK(ambient_dim == buf.shape[1], "Py array second dimension not matching the ambient space dimension"); + + + std::vector<typename Kern::Point_d> points_generated; + + { + py::gil_scoped_release release; + points_generated = Gudhi::generate_points_on_sphere_d<Kern>(n_samples, ambient_dim, radius); + } + + for (size_t i = 0; i < n_samples; i++) + for (int j = 0; j < ambient_dim; j++) + ptr[i*ambient_dim+j] = points_generated[i][j]; + + return points; +} + +py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, std::string sample) { + + if ( (sample != "random") && (sample != "grid")) { + throw pybind11::value_error("This sample type is not supported"); + } + + std::vector<typename Kern::Point_d> points_generated; + + { + py::gil_scoped_release release; + points_generated = Gudhi::generate_points_on_torus_d<Kern>(n_samples, dim, sample); + } + + size_t npoints = points_generated.size(); + + GUDHI_CHECK(2*dim == points_generated[0].size(), "Py array second dimension not matching the double torus dimension"); + + py::array_t<double> points({npoints, (size_t)2*dim}); + + py::buffer_info buf = points.request(); + double *ptr = static_cast<double *>(buf.ptr); + + for (size_t i = 0; i < npoints; i++) + for (int j = 0; j < 2*dim; j++) + ptr[i*(2*dim)+j] = points_generated[i][j]; + + return points; +} + +PYBIND11_MODULE(_points, m) { + m.attr("__license__") = "LGPL v3"; + + m.def("sphere", &generate_points_on_sphere, + py::arg("n_samples"), py::arg("ambient_dim"), + py::arg("radius") = 1., py::arg("sample") = "random", + R"pbdoc( + Generate random i.i.d. points uniformly on a (d-1)-sphere in R^d + + :param n_samples: The number of points to be generated. + :type n_samples: integer + :param ambient_dim: The ambient dimension d. + :type ambient_dim: integer + :param radius: The radius. Default value is `1.`. + :type radius: float + :param sample: The sample type. Default and only available value is `"random"`. + :type sample: string + :rtype: numpy array of float + :returns: the generated points on a sphere. + )pbdoc"); + + m.def("ctorus", &generate_points_on_torus, + py::arg("n_samples"), py::arg("dim"), py::arg("sample") = "random", + R"pbdoc( + Generate random i.i.d. points on a d-torus in R^2d or as a grid + + :param n_samples: The number of points to be generated. + :type n_samples: integer + :param dim: The dimension of the torus on which points would be generated in R^2*dim. + :type dim: integer + :param sample: The sample type. Available values are: `"random"` and `"grid"`. Default value is `"random"`. + :type sample: string + :rtype: numpy array of float. + The shape of returned numpy array is : + if sample is 'random' : (n_samples, 2*dim). + if sample is 'grid' : (⌊n_samples**(1./dim)⌋**dim, 2*dim), where shape[0] is rounded down to the closest perfect 'dim'th power. + :returns: the generated points on a torus. + )pbdoc"); +} diff --git a/src/python/gudhi/datasets/generators/points.cc b/src/python/gudhi/datasets/generators/points.cc deleted file mode 100644 index d658946b..00000000 --- a/src/python/gudhi/datasets/generators/points.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. - * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. - * Author(s): Hind Montassif - * - * Copyright (C) 2021 Inria - * - * Modification(s): - * - YYYY/MM Author: Description of the modification - */ - -#include <pybind11/pybind11.h> -#include <pybind11/numpy.h> - -#include <gudhi/random_point_generators.h> -#include <gudhi/Debug_utils.h> - -#include <CGAL/Epick_d.h> - -namespace py = pybind11; - - -typedef CGAL::Epick_d< CGAL::Dynamic_dimension_tag > Kern; - -py::array_t<double> generate_points_on_sphere(size_t n_samples, int ambient_dim, double radius, std::string sample) { - - if (sample != "random") { - throw pybind11::value_error("This sample type is not supported"); - } - - py::array_t<double> points({n_samples, (size_t)ambient_dim}); - - py::buffer_info buf = points.request(); - double *ptr = static_cast<double *>(buf.ptr); - - GUDHI_CHECK(n_samples == buf.shape[0], "Py array first dimension not matching n_samples on sphere"); - GUDHI_CHECK(ambient_dim == buf.shape[1], "Py array second dimension not matching the ambient space dimension"); - - - py::gil_scoped_release release; - auto points_generated = Gudhi::generate_points_on_sphere_d<Kern>(n_samples, ambient_dim, radius); - - for (size_t i = 0; i < n_samples; i++) - for (int j = 0; j < ambient_dim; j++) - ptr[i*ambient_dim+j] = points_generated[i][j]; - - return points; -} - -PYBIND11_MODULE(points, m) { - m.attr("__license__") = "LGPL v3"; - m.def("sphere", &generate_points_on_sphere, - py::arg("n_samples"), py::arg("ambient_dim"), - py::arg("radius") = 1., py::arg("sample") = "random", - R"pbdoc( - Generate random i.i.d. points uniformly on a (d-1)-sphere in R^d - - :param n_samples: The number of points to be generated. - :type n_samples: integer - :param ambient_dim: The ambient dimension d. - :type ambient_dim: integer - :param radius: The radius. Default value is `1.`. - :type radius: float - :param sample: The sample type. Default and only available value is `"random"`. - :type sample: string - :rtype: numpy array of float - :returns: the generated points on a sphere. - )pbdoc"); -} diff --git a/src/python/gudhi/datasets/generators/points.py b/src/python/gudhi/datasets/generators/points.py new file mode 100644 index 00000000..cf97777d --- /dev/null +++ b/src/python/gudhi/datasets/generators/points.py @@ -0,0 +1,56 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Hind Montassif +# +# Copyright (C) 2021 Inria +# +# Modification(s): +# - YYYY/MM Author: Description of the modification + +import numpy as np + +from ._points import ctorus +from ._points import sphere + +def _generate_random_points_on_torus(n_samples, dim): + + # Generate random angles of size n_samples*dim + alpha = 2*np.pi*np.random.rand(n_samples*dim) + + # Based on angles, construct points of size n_samples*dim on a circle and reshape the result in a n_samples*2*dim array + array_points = np.column_stack([np.cos(alpha), np.sin(alpha)]).reshape(-1, 2*dim) + + return array_points + +def _generate_grid_points_on_torus(n_samples, dim): + + # Generate points on a dim-torus as a grid + n_samples_grid = int((n_samples+.5)**(1./dim)) # add .5 to avoid rounding down with numerical approximations + alpha = np.linspace(0, 2*np.pi, n_samples_grid, endpoint=False) + + array_points = np.column_stack([np.cos(alpha), np.sin(alpha)]) + array_points_idx = np.empty([n_samples_grid]*dim + [dim], dtype=int) + for i, x in enumerate(np.ix_(*([np.arange(n_samples_grid)]*dim))): + array_points_idx[...,i] = x + return array_points[array_points_idx].reshape(-1, 2*dim) + +def torus(n_samples, dim, sample='random'): + """ + Generate points on a flat dim-torus in R^2dim either randomly or on a grid + + :param n_samples: The number of points to be generated. + :param dim: The dimension of the torus on which points would be generated in R^2*dim. + :param sample: The sample type of the generated points. Can be 'random' or 'grid'. + :returns: numpy array containing the generated points on a torus. + The shape of returned numpy array is: + if sample is 'random' : (n_samples, 2*dim). + if sample is 'grid' : (⌊n_samples**(1./dim)⌋**dim, 2*dim), where shape[0] is rounded down to the closest perfect 'dim'th power. + """ + if sample == 'random': + # Generate points randomly + return _generate_random_points_on_torus(n_samples, dim) + elif sample == 'grid': + # Generate points on a grid + return _generate_grid_points_on_torus(n_samples, dim) + else: + raise ValueError("Sample type '{}' is not supported".format(sample)) |