diff options
Diffstat (limited to 'src/python/test')
| -rwxr-xr-x | src/python/test/test_cubical_complex.py | 25 | ||||
| -rwxr-xr-x | src/python/test/test_representations.py | 37 |
2 files changed, 62 insertions, 0 deletions
diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py index d0e4e9e8..29d559b3 100755 --- a/src/python/test/test_cubical_complex.py +++ b/src/python/test/test_cubical_complex.py @@ -174,3 +174,28 @@ def test_periodic_cofaces_of_persistence_pairs_when_pd_has_no_paired_birth_and_d assert np.array_equal(pairs[1][0], np.array([0])) assert np.array_equal(pairs[1][1], np.array([0, 1])) assert np.array_equal(pairs[1][2], np.array([1])) + +def test_cubical_persistence_intervals_in_dimension(): + cub = CubicalComplex( + dimensions=[3, 3], + top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9], + ) + cub.compute_persistence() + H0 = cub.persistence_intervals_in_dimension(0) + assert np.array_equal(H0, np.array([[ 1., float("inf")]])) + assert cub.persistence_intervals_in_dimension(1).shape == (0, 2) + +def test_periodic_cubical_persistence_intervals_in_dimension(): + cub = PeriodicCubicalComplex( + dimensions=[3, 3], + top_dimensional_cells=[1, 2, 3, 4, 5, 6, 7, 8, 9], + periodic_dimensions = [True, True] + ) + cub.compute_persistence() + H0 = cub.persistence_intervals_in_dimension(0) + assert np.array_equal(H0, np.array([[ 1., float("inf")]])) + H1 = cub.persistence_intervals_in_dimension(1) + assert np.array_equal(H1, np.array([[ 3., float("inf")], [ 7., float("inf")]])) + H2 = cub.persistence_intervals_in_dimension(2) + assert np.array_equal(H2, np.array([[ 9., float("inf")]])) + assert cub.persistence_intervals_in_dimension(3).shape == (0, 2) diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index cda1a15b..c1f4df12 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -6,6 +6,12 @@ import pytest from sklearn.cluster import KMeans +from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ + TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ + PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ + PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ + SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance) + def test_representations_examples(): # Disable graphics for testing purposes @@ -98,3 +104,34 @@ def test_infinity(): assert c[1] == 0 assert c[7] == 3 assert c[9] == 2 + +def pow(n): + return lambda x: np.power(x[1]-x[0],n) + +def test_vectorization_empty_diagrams(): + empty_diag = np.empty(shape = [0, 2]) + Landscape(resolution=1000)(empty_diag) + Silhouette(resolution=1000, weight=pow(2))(empty_diag) + BettiCurve(resolution=1000)(empty_diag) + ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag) + TopologicalVector(threshold=-1)(empty_diag) + PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag) + #Entropy(mode="scalar")(empty_diag) + #Entropy(mode="vector", normalized=False)(empty_diag) + +#def arctan(C,p): +# return lambda x: C*np.arctan(np.power(x[1], p)) +# +#def test_kernel_empty_diagrams(): +# empty_diag = np.empty(shape = [0, 2]) +# PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag) +# PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag) +# PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag) +# PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag) +# SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag) +# SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag) +# WassersteinDistance(order=2, internal_p=2, mode="pot")(empty_diag, empty_diag) +# WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001)(empty_diag, empty_diag) +# BottleneckDistance(epsilon=.001)(empty_diag, empty_diag) +# PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)(empty_diag, empty_diag) +# PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag) |