From b9160fb8410bbb999913b0b4e91f1aa1ff58d2cd Mon Sep 17 00:00:00 2001 From: Hind-M Date: Mon, 7 Jun 2021 17:07:55 +0200 Subject: Replace 'uniform' flag of torus generation with 'sample' taking two possible values: 'grid'(i.e uniform==True) or 'random' (i.e uniform==False) --- src/python/doc/datasets_generators.rst | 8 ++++---- src/python/gudhi/datasets/generators/_points.cc | 10 +++++----- 2 files changed, 9 insertions(+), 9 deletions(-) (limited to 'src/python') diff --git a/src/python/doc/datasets_generators.rst b/src/python/doc/datasets_generators.rst index ef21c9d2..2802eccd 100644 --- a/src/python/doc/datasets_generators.rst +++ b/src/python/doc/datasets_generators.rst @@ -52,9 +52,9 @@ First module : **_points** """""""""""""""""""""""""" The user should provide the number of points to be generated on the torus :code:`n_samples`, and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`. -The flag :code:`uniform` is optional and is set to **False** by default, meaning that the points will be generated randomly. +The :code:`sample` argument is optional and is set to **'random'** by default. In this case, the returned generated points would be an array of shape :math:`(n\_samples, 2*dim)`. -Otherwise, if set to **True**, the points are generated as a grid and would be given as an array of shape : +Otherwise, if set to **'grid'**, the points are generated on a grid and would be given as an array of shape : .. math:: @@ -70,7 +70,7 @@ Example gen_points = _points.torus(n_samples = 50, dim = 3) # Generate 27 points on a torus as a grid in R^6 - gen_points = _points.torus(n_samples = 50, dim = 3, uniform = True) + gen_points = _points.torus(n_samples = 50, dim = 3, sample = 'grid') .. autofunction:: gudhi.datasets.generators._points.torus @@ -79,7 +79,7 @@ Second module : **points** The user should provide the number of points to be generated on the torus :code:`n_samples` and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`. The :code:`sample` argument is optional and is set to **'random'** by default. -The other allowed value of sample type is **'grid'** and is equivalent to :code:`uniform = True` in the first module. +The other allowed value of sample type is **'grid'**. Example """"""" 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 generate_points_on_sphere(size_t n_samples, int ambient_dim, return points; } -py::array_t generate_points_on_torus(size_t n_samples, int dim, bool uniform) { +py::array_t generate_points_on_torus(size_t n_samples, int dim, std::string sample) { std::vector points_generated; { py::gil_scoped_release release; - points_generated = Gudhi::generate_points_on_torus_d(n_samples, dim, uniform); + points_generated = Gudhi::generate_points_on_torus_d(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"); -- cgit v1.2.3