diff options
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/datasets/generators/_points.cc | 11 | ||||
-rw-r--r-- | src/python/gudhi/datasets/generators/points.py | 19 |
2 files changed, 17 insertions, 13 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc index 70ce4925..82fea25b 100644 --- a/src/python/gudhi/datasets/generators/_points.cc +++ b/src/python/gudhi/datasets/generators/_points.cc @@ -96,7 +96,6 @@ PYBIND11_MODULE(_points, m) { :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"); @@ -111,10 +110,12 @@ PYBIND11_MODULE(_points, m) { :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. + + 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 index cf97777d..9bb2799d 100644 --- a/src/python/gudhi/datasets/generators/points.py +++ b/src/python/gudhi/datasets/generators/points.py @@ -19,15 +19,15 @@ def _generate_random_points_on_torus(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))): @@ -35,16 +35,19 @@ def _generate_grid_points_on_torus(n_samples, dim): 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. + + 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 |