diff options
Diffstat (limited to 'src/python/test')
| -rwxr-xr-x | src/python/test/test_alpha_complex.py | 83 | ||||
| -rwxr-xr-x | src/python/test/test_tomato.py | 65 |
2 files changed, 129 insertions, 19 deletions
diff --git a/src/python/test/test_alpha_complex.py b/src/python/test/test_alpha_complex.py index 943ad2c4..a4ee260b 100755 --- a/src/python/test/test_alpha_complex.py +++ b/src/python/test/test_alpha_complex.py @@ -8,7 +8,7 @@ - YYYY/MM Author: Description of the modification """ -from gudhi import AlphaComplex, SimplexTree +import gudhi as gd import math import numpy as np import pytest @@ -25,17 +25,16 @@ __license__ = "MIT" def _empty_alpha(precision): - alpha_complex = AlphaComplex(points=[[0, 0]], precision = precision) + alpha_complex = gd.AlphaComplex(points=[[0, 0]], precision = precision) assert alpha_complex.__is_defined() == True def test_empty_alpha(): - _empty_alpha('fast') - _empty_alpha('safe') - _empty_alpha('exact') + for precision in ['fast', 'safe', 'exact']: + _empty_alpha(precision) def _infinite_alpha(precision): point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - alpha_complex = AlphaComplex(points=point_list, precision = precision) + alpha_complex = gd.AlphaComplex(points=point_list, precision = precision) assert alpha_complex.__is_defined() == True simplex_tree = alpha_complex.create_simplex_tree() @@ -84,13 +83,12 @@ def _infinite_alpha(precision): assert False def test_infinite_alpha(): - _infinite_alpha('fast') - _infinite_alpha('safe') - _infinite_alpha('exact') + for precision in ['fast', 'safe', 'exact']: + _infinite_alpha(precision) def _filtered_alpha(precision): point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - filtered_alpha = AlphaComplex(points=point_list, precision = precision) + filtered_alpha = gd.AlphaComplex(points=point_list, precision = precision) simplex_tree = filtered_alpha.create_simplex_tree(max_alpha_square=0.25) @@ -128,9 +126,8 @@ def _filtered_alpha(precision): assert simplex_tree.get_cofaces([0], 1) == [([0, 1], 0.25), ([0, 2], 0.25)] def test_filtered_alpha(): - _filtered_alpha('fast') - _filtered_alpha('safe') - _filtered_alpha('exact') + for precision in ['fast', 'safe', 'exact']: + _filtered_alpha(precision) def _safe_alpha_persistence_comparison(precision): #generate periodic signal @@ -144,10 +141,10 @@ def _safe_alpha_persistence_comparison(precision): embedding2 = [[signal[i], delayed[i]] for i in range(len(time))] #build alpha complex and simplex tree - alpha_complex1 = AlphaComplex(points=embedding1, precision = precision) + alpha_complex1 = gd.AlphaComplex(points=embedding1, precision = precision) simplex_tree1 = alpha_complex1.create_simplex_tree() - alpha_complex2 = AlphaComplex(points=embedding2, precision = precision) + alpha_complex2 = gd.AlphaComplex(points=embedding2, precision = precision) simplex_tree2 = alpha_complex2.create_simplex_tree() diag1 = simplex_tree1.persistence() @@ -165,7 +162,7 @@ def test_safe_alpha_persistence_comparison(): def _delaunay_complex(precision): point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - filtered_alpha = AlphaComplex(points=point_list, precision = precision) + filtered_alpha = gd.AlphaComplex(points=point_list, precision = precision) simplex_tree = filtered_alpha.create_simplex_tree(default_filtration_value = True) @@ -197,6 +194,54 @@ def _delaunay_complex(precision): assert math.isnan(filtered_value[1]) def test_delaunay_complex(): - _delaunay_complex('fast') - _delaunay_complex('safe') - _delaunay_complex('exact') + for precision in ['fast', 'safe', 'exact']: + _delaunay_complex(precision) + +def _3d_points_on_a_plane(precision, default_filtration_value): + alpha = gd.AlphaComplex(off_file=gd.__root_source_dir__ + '/data/points/alphacomplexdoc.off', + precision = precision) + + simplex_tree = alpha.create_simplex_tree(default_filtration_value = default_filtration_value) + assert simplex_tree.dimension() == 2 + assert simplex_tree.num_vertices() == 7 + assert simplex_tree.num_simplices() == 25 + +def test_3d_points_on_a_plane(): + for default_filtration_value in [True, False]: + for precision in ['fast', 'safe', 'exact']: + _3d_points_on_a_plane(precision, default_filtration_value) + +def _3d_tetrahedrons(precision): + points = 10*np.random.rand(10, 3) + alpha = gd.AlphaComplex(points=points, precision = precision) + st_alpha = alpha.create_simplex_tree(default_filtration_value = False) + # New AlphaComplex for get_point to work + delaunay = gd.AlphaComplex(points=points, precision = precision) + st_delaunay = delaunay.create_simplex_tree(default_filtration_value = True) + + delaunay_tetra = [] + for sk in st_delaunay.get_skeleton(4): + if len(sk[0]) == 4: + tetra = [delaunay.get_point(sk[0][0]), + delaunay.get_point(sk[0][1]), + delaunay.get_point(sk[0][2]), + delaunay.get_point(sk[0][3]) ] + delaunay_tetra.append(sorted(tetra, key=lambda tup: tup[0])) + + alpha_tetra = [] + for sk in st_alpha.get_skeleton(4): + if len(sk[0]) == 4: + tetra = [alpha.get_point(sk[0][0]), + alpha.get_point(sk[0][1]), + alpha.get_point(sk[0][2]), + alpha.get_point(sk[0][3]) ] + alpha_tetra.append(sorted(tetra, key=lambda tup: tup[0])) + + # Check the tetrahedrons from one list are in the second one + assert len(alpha_tetra) == len(delaunay_tetra) + for tetra_from_del in delaunay_tetra: + assert tetra_from_del in alpha_tetra + +def test_3d_tetrahedrons(): + for precision in ['fast', 'safe', 'exact']: + _3d_tetrahedrons(precision) diff --git a/src/python/test/test_tomato.py b/src/python/test/test_tomato.py new file mode 100755 index 00000000..ecab03c4 --- /dev/null +++ b/src/python/test/test_tomato.py @@ -0,0 +1,65 @@ +""" 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): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.clustering.tomato import Tomato +import numpy as np +import pytest +import matplotlib.pyplot as plt + +# Disable graphics for testing purposes +plt.show = lambda: None + + +def test_tomato_1(): + a = [(1, 2), (1.1, 1.9), (0.9, 1.8), (10, 0), (10.1, 0.05), (10.2, -0.1), (5.4, 0)] + t = Tomato(metric="euclidean", n_clusters=2, k=4, n_jobs=-1, eps=0.05) + assert np.array_equal(t.fit_predict(a), [1, 1, 1, 0, 0, 0, 0]) # or with swapped 0 and 1 + assert np.array_equal(t.children_, [[0, 1]]) + + t = Tomato(density_type="KDE", r=1, k=4) + t.fit(a) + assert np.array_equal(t.leaf_labels_, [1, 1, 1, 0, 0, 0, 0]) # or with swapped 0 and 1 + assert t.n_clusters_ == 2 + t.merge_threshold_ = 10 + assert t.n_clusters_ == 1 + assert (t.labels_ == 0).all() + + t = Tomato(graph_type="radius", r=0.1, metric="cosine", k=3) + assert np.array_equal(t.fit_predict(a), [1, 1, 1, 0, 0, 0, 0]) # or with swapped 0 and 1 + + t = Tomato(metric="euclidean", graph_type="radius", r=4.7, k=4) + t.fit(a) + assert t.max_weight_per_cc_.size == 2 + assert np.array_equal(t.neighbors_, [[0, 1, 2], [0, 1, 2], [0, 1, 2], [3, 4, 5, 6], [3, 4, 5], [3, 4, 5], [3, 6]]) + t.plot_diagram() + + t = Tomato(graph_type="radius", r=4.7, k=4, symmetrize_graph=True) + t.fit(a) + assert t.max_weight_per_cc_.size == 2 + assert [set(i) for i in t.neighbors_] == [{1, 2}, {0, 2}, {0, 1}, {4, 5, 6}, {3, 5}, {3, 4}, {3}] + + t = Tomato(n_clusters=2, k=4, symmetrize_graph=True) + t.fit(a) + assert [set(i) for i in t.neighbors_] == [ + {1, 2, 6}, + {0, 2, 6}, + {0, 1, 6}, + {4, 5, 6}, + {3, 5, 6}, + {3, 4, 6}, + {0, 1, 2, 3, 4, 5}, + ] + t.plot_diagram() + + t = Tomato(k=6, metric="manhattan") + t.fit(a) + assert t.diagram_.size == 0 + assert t.max_weight_per_cc_.size == 1 + t.plot_diagram() |