diff options
author | Hind-M <hind.montassif@gmail.com> | 2021-06-07 17:07:55 +0200 |
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committer | Hind-M <hind.montassif@gmail.com> | 2021-06-07 17:07:55 +0200 |
commit | b9160fb8410bbb999913b0b4e91f1aa1ff58d2cd (patch) | |
tree | 410795193a098deeb42ddc120e51d0a7250ecb4c /src/python/gudhi | |
parent | b04759faf8f474ff98e9e229c41d85ff3bf009da (diff) |
Replace 'uniform' flag of torus generation with 'sample' taking two possible values: 'grid'(i.e uniform==True) or 'random' (i.e uniform==False)
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/datasets/generators/_points.cc | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc index 55b21b2b..6bbdf284 100644 --- a/src/python/gudhi/datasets/generators/_points.cc +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -46,13 +46,13 @@ py::array_t<double> generate_points_on_sphere(size_t n_samples, int ambient_dim, return points; } -py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, bool uniform) { +py::array_t<double> generate_points_on_torus(size_t n_samples, int dim, std::string sample) { 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, uniform); + points_generated = Gudhi::generate_points_on_torus_d<Kern>(n_samples, dim, sample); } size_t npoints = points_generated.size(); @@ -93,7 +93,7 @@ PYBIND11_MODULE(_points, m) { )pbdoc"); m.def("torus", &generate_points_on_torus, - py::arg("n_samples"), py::arg("dim"), py::arg("uniform") = false, + 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 @@ -101,8 +101,8 @@ PYBIND11_MODULE(_points, m) { :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 uniform: A flag to define if the points generation is uniform (i.e generated as a grid). - :type uniform: bool + :param sample: The sample type. Available values are: `"random"` and `"grid"`. Default value is `"random"`. + :type sample: string :rtype: numpy array of float :returns: the generated points on a torus. )pbdoc"); |