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-rw-r--r--src/python/gudhi/datasets/__init__.py0
-rw-r--r--src/python/gudhi/datasets/generators/__init__.py0
-rw-r--r--src/python/gudhi/datasets/generators/_points.cc121
-rw-r--r--src/python/gudhi/datasets/generators/points.py59
4 files changed, 180 insertions, 0 deletions
diff --git a/src/python/gudhi/datasets/__init__.py b/src/python/gudhi/datasets/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/src/python/gudhi/datasets/__init__.py
diff --git a/src/python/gudhi/datasets/generators/__init__.py b/src/python/gudhi/datasets/generators/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/src/python/gudhi/datasets/generators/__init__.py
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc
new file mode 100644
index 00000000..82fea25b
--- /dev/null
+++ b/src/python/gudhi/datasets/generators/_points.cc
@@ -0,0 +1,121 @@
+/* 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
+ :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
+ :returns: 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.
+ )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..9bb2799d
--- /dev/null
+++ b/src/python/gudhi/datasets/generators/points.py
@@ -0,0 +1,59 @@
+# 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))
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-02 07:36:42 +0000