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author | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2021-10-19 21:20:01 +0200 |
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committer | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2021-10-19 21:20:01 +0200 |
commit | 0c8e1e2b69c7658c153df99931e3407ec18c1332 (patch) | |
tree | 281878cf4817d7618d3462b3a900d53caf94fbdc /src/python/test | |
parent | ec06a9b9ae0a9ff1897249dcbc2b497764f54aaf (diff) |
Add empty diags tests
Diffstat (limited to 'src/python/test')
-rwxr-xr-x | src/python/test/test_representations.py | 60 |
1 files changed, 39 insertions, 21 deletions
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index c1f4df12..b888b7f1 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -6,9 +6,16 @@ import pytest from sklearn.cluster import KMeans -from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ - TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ - PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ +# Vectorization +from gudhi.representations import (Landscape, Silhouette, BettiCurve, ComplexPolynomial,\ + TopologicalVector, PersistenceImage, Entropy) + +# Preprocessing +from gudhi.representations import (BirthPersistenceTransform, Clamping, DiagramScaler, Padding, ProminentPoints, \ + DiagramSelector) + +# Kernel +from gudhi.representations import (PersistenceWeightedGaussianKernel, \ PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance) @@ -105,33 +112,44 @@ def test_infinity(): assert c[7] == 3 assert c[9] == 2 + +def test_preprocessing_empty_diagrams(): + empty_diag = np.empty(shape = [0, 2]) + assert not np.any(BirthPersistenceTransform()(empty_diag)) + assert not np.any(Clamping().fit_transform(empty_diag)) + assert not np.any(DiagramScaler()(empty_diag)) + assert not np.any(Padding()(empty_diag)) + assert not np.any(ProminentPoints()(empty_diag)) + assert not np.any(DiagramSelector()(empty_diag)) + 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)) + assert not np.any(Landscape(resolution=1000)(empty_diag)) + assert not np.any(Silhouette(resolution=1000, weight=pow(2))(empty_diag)) + assert not np.any(BettiCurve(resolution=1000)(empty_diag)) + assert not np.any(ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag)) + assert not np.any(TopologicalVector(threshold=-1)(empty_diag)) + assert not np.any(PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag)) + assert not np.any(Entropy(mode="scalar")(empty_diag)) + assert not np.any(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]) +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) + assert SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag) == 0. + assert SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag) == 1. + assert WassersteinDistance(mode="hera", delta=0.0001)(empty_diag, empty_diag) == 0. + assert WassersteinDistance(mode="pot")(empty_diag, empty_diag) == 0. + assert BottleneckDistance(epsilon=.001)(empty_diag, empty_diag) == 0. + assert BottleneckDistance()(empty_diag, empty_diag) == 0. # 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) |