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Diffstat (limited to 'cython/test/test_subsampling.py')
| -rwxr-xr-x | cython/test/test_subsampling.py | 133 |
1 files changed, 133 insertions, 0 deletions
diff --git a/cython/test/test_subsampling.py b/cython/test/test_subsampling.py new file mode 100755 index 00000000..2caf4ddb --- /dev/null +++ b/cython/test/test_subsampling.py @@ -0,0 +1,133 @@ +import gudhi + +"""This file is part of the Gudhi Library. The Gudhi library + (Geometric Understanding in Higher Dimensions) is a generic C++ + library for computational topology. + + Author(s): Vincent Rouvreau + + Copyright (C) 2016 INRIA + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see <http://www.gnu.org/licenses/>. +""" + +__author__ = "Vincent Rouvreau" +__copyright__ = "Copyright (C) 2016 INRIA" +__license__ = "GPL v3" + + +def test_write_off_file_for_tests(): + file = open("subsample.off", "w") + file.write("nOFF\n") + file.write("2 7 0 0\n") + file.write("1.0 1.0\n") + file.write("7.0 0.0\n") + file.write("4.0 6.0\n") + file.write("9.0 6.0\n") + file.write("0.0 14.0\n") + file.write("2.0 19.0\n") + file.write("9.0 17.0\n") + file.close() + +def test_simple_choose_n_farthest_points_with_a_starting_point(): + point_set = [[0,1], [0,0], [1,0], [1,1]] + i = 0 + for point in point_set: + # The iteration starts with the given starting point + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 1, starting_point = i) + assert sub_set[0] == point_set[i] + i = i + 1 + + # The iteration finds then the farthest + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 1) + assert sub_set[1] == point_set[3] + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 3) + assert sub_set[1] == point_set[1] + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 0) + assert sub_set[1] == point_set[2] + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 2) + assert sub_set[1] == point_set[0] + + # Test the limits + assert gudhi.choose_n_farthest_points(points = [], nb_points = 0, starting_point = 0) == [] + assert gudhi.choose_n_farthest_points(points = [], nb_points = 1, starting_point = 0) == [] + assert gudhi.choose_n_farthest_points(points = [], nb_points = 0, starting_point = 1) == [] + assert gudhi.choose_n_farthest_points(points = [], nb_points = 1, starting_point = 1) == [] + + # From off file test + for i in range (0, 7): + assert len(gudhi.choose_n_farthest_points(off_file = 'subsample.off', nb_points = i, starting_point = i)) == i + +def test_simple_choose_n_farthest_points_randomed(): + point_set = [[0,1], [0,0], [1,0], [1,1]] + # Test the limits + assert gudhi.choose_n_farthest_points(points = [], nb_points = 0) == [] + assert gudhi.choose_n_farthest_points(points = [], nb_points = 1) == [] + assert gudhi.choose_n_farthest_points(points = point_set, nb_points = 0) == [] + + # Go furter than point set on purpose + for iter in range(1,10): + sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = iter) + for sub in sub_set: + found = False + for point in point_set: + if point == sub: + found = True + # Check each sub set point is existing in the point set + assert found == True + + # From off file test + for i in range (0, 7): + assert len(gudhi.choose_n_farthest_points(off_file = 'subsample.off', nb_points = i)) == i + +def test_simple_pick_n_random_points(): + point_set = [[0,1], [0,0], [1,0], [1,1]] + # Test the limits + assert gudhi.pick_n_random_points(points = [], nb_points = 0) == [] + assert gudhi.pick_n_random_points(points = [], nb_points = 1) == [] + assert gudhi.pick_n_random_points(points = point_set, nb_points = 0) == [] + + # Go furter than point set on purpose + for iter in range(1,10): + sub_set = gudhi.pick_n_random_points(points = point_set, nb_points = iter) + print(5) + for sub in sub_set: + found = False + for point in point_set: + if point == sub: + found = True + # Check each sub set point is existing in the point set + assert found == True + + # From off file test + for i in range (0, 7): + assert len(gudhi.pick_n_random_points(off_file = 'subsample.off', nb_points = i)) == i + +def test_simple_sparsify_points(): + point_set = [[0,1], [0,0], [1,0], [1,1]] + # Test the limits + # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == [] + # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == [] + assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 0.0) == point_set + assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 1.0) == point_set + assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 2.0) == [[0,1], [1,0]] + assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 2.01) == [[0,1]] + + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 0.0)) == 7 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 30.0)) == 5 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 40.0)) == 4 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 90.0)) == 3 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 100.0)) == 2 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 325.0)) == 2 + assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 325.01)) == 1 |