1 files changed, 42 insertions, 0 deletions
diff --git a/src/python/gudhi/datasets/generators/points.py b/src/python/gudhi/datasets/generators/points.py
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+++ b/src/python/gudhi/datasets/generators/points.py
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+# 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
+import itertools
+
+def _generate_random_points(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(n_samples, dim):
+    
+    n_samples_grid = int(n_samples**(1./dim))
+    alpha = np.linspace(0, 2*np.pi, n_samples_grid, endpoint=False)
+    
+    array_points_inter = np.column_stack([np.cos(alpha), np.sin(alpha)])
+    array_points = np.array(list(itertools.product(array_points_inter, repeat=dim))).reshape(-1, 2*dim)
+    
+    return array_points
+
+def torus(n_samples, dim, sample='random'):
+    if sample == 'random':
+        print("Sample is random")
+        return _generate_random_points(n_samples, dim)
+    elif sample == 'grid':
+        print("Sample is grid")
+        return _generate_grid_points(n_samples, dim)
+    else:
+        raise Exception("Sample type '{}' is not supported".format(sample))
+        return