diff options
Diffstat (limited to 'src/python/test/test_representations.py')
| -rwxr-xr-x | src/python/test/test_representations.py | 71 |
1 files changed, 71 insertions, 0 deletions
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index cda1a15b..93461f1e 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -3,9 +3,23 @@ import sys import matplotlib.pyplot as plt import numpy as np import pytest +import random from sklearn.cluster import KMeans +# 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) + def test_representations_examples(): # Disable graphics for testing purposes @@ -98,3 +112,60 @@ def test_infinity(): assert c[1] == 0 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]) + random_resolution = random.randint(50,100)*10 # between 500 and 1000 + print("resolution = ", random_resolution) + lsc = Landscape(resolution=random_resolution)(empty_diag) + assert not np.any(lsc) + assert lsc.shape[0]%random_resolution == 0 + slt = Silhouette(resolution=random_resolution, weight=pow(2))(empty_diag) + assert not np.any(slt) + assert slt.shape[0] == random_resolution + btc = BettiCurve(resolution=random_resolution)(empty_diag) + assert not np.any(btc) + assert btc.shape[0] == random_resolution + cpp = ComplexPolynomial(threshold=random_resolution, polynomial_type="T")(empty_diag) + assert not np.any(cpp) + assert cpp.shape[0] == random_resolution + tpv = TopologicalVector(threshold=random_resolution)(empty_diag) + assert tpv.shape[0] == random_resolution + assert not np.any(tpv) + prmg = PersistenceImage(resolution=[random_resolution,random_resolution])(empty_diag) + assert not np.any(prmg) + assert prmg.shape[0] == random_resolution * random_resolution + sce = Entropy(mode="scalar", resolution=random_resolution)(empty_diag) + assert not np.any(sce) + assert sce.shape[0] == 1 + scv = Entropy(mode="vector", normalized=False, resolution=random_resolution)(empty_diag) + assert not np.any(scv) + assert scv.shape[0] == random_resolution + +def test_kernel_empty_diagrams(): + empty_diag = np.empty(shape = [0, 2]) + 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. +# 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) +# 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) |