How to use the gudhi.representations.PersistenceFisherKernel function in gudhi
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GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github 
SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
X = SW.fit(diags)
Y = SW.transform(diags2)
print("SW kernel is " + str(Y[0][0]))
W = BottleneckDistance(epsilon=.001)
X = W.fit(diags)
Y = W.transform(diags2)
print("Bottleneck distance is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
X = PF.fit(diags)
Y = PF.transform(diags2)
print("PF kernel is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PF.fit(diags)
Y = PF.transform(diags2)
print("Approximate PF kernel is " + str(Y[0][0]))SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
X = SW.fit(diags)
Y = SW.transform(diags2)
print("SW kernel is " + str(Y[0][0]))
W = BottleneckDistance(epsilon=.001)
X = W.fit(diags)
Y = W.transform(diags2)
print("Bottleneck distance is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
X = PF.fit(diags)
Y = PF.transform(diags2)
print("PF kernel is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PF.fit(diags)
Y = PF.transform(diags2)
print("Approximate PF kernel is " + str(Y[0][0]))sW = SlicedWassersteinDistance(num_directions=100)
X = sW.fit(diags)
Y = sW.transform(diags2)
print("SW distance is " + str(Y[0][0]))
SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
X = SW.fit(diags)
Y = SW.transform(diags2)
print("SW kernel is " + str(Y[0][0]))
W = BottleneckDistance(epsilon=.001)
X = W.fit(diags)
Y = W.transform(diags2)
print("Bottleneck distance is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
X = PF.fit(diags)
Y = PF.transform(diags2)
print("PF kernel is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PF.fit(diags)
Y = PF.transform(diags2)
print("Approximate PF kernel is " + str(Y[0][0]))sW = SlicedWassersteinDistance(num_directions=100)
X = sW.fit(diags)
Y = sW.transform(diags2)
print("SW distance is " + str(Y[0][0]))
SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
X = SW.fit(diags)
Y = SW.transform(diags2)
print("SW kernel is " + str(Y[0][0]))
W = BottleneckDistance(epsilon=.001)
X = W.fit(diags)
Y = W.transform(diags2)
print("Bottleneck distance is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
X = PF.fit(diags)
Y = PF.transform(diags2)
print("PF kernel is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PF.fit(diags)
Y = PF.transform(diags2)
print("Approximate PF kernel is " + str(Y[0][0]))gudhi
The Gudhi library is an open source library for Computational Topology and Topological Data Analysis (TDA).
Package Health Score
75 / 100Popular gudhi functions
- gudhi.AlphaComplex
- gudhi.bottleneck_distance
- gudhi.CoverComplex
- gudhi.CubicalComplex
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- gudhi.MiniSimplexTree
- gudhi.PeriodicCubicalComplex
- gudhi.PersistenceLandscapes
- gudhi.pick_n_random_points
- gudhi.plot_persistence_barcode
- gudhi.plot_persistence_diagram
- gudhi.read_lower_triangular_matrix_from_csv_file
- gudhi.read_off
- gudhi.representations.Entropy
- gudhi.representations.PersistenceFisherKernel
- gudhi.representations.PersistenceScaleSpaceKernel
- gudhi.representations.PersistenceWeightedGaussianKernel
- gudhi.RipsComplex
- gudhi.SimplexTree