# How to use the gudhi.representations.PersistenceScaleSpaceKernel function in gudhi

## To help you get started, we’ve selected a few gudhi examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("PWG kernel is " + str(Y[0][0]))

PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("Approximate PWG kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("PSS kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("Approximate PSS 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)``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````plt.show()

def arctan(C,p):
return lambda x: C*np.arctan(np.power(x[1], p))

PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("PWG kernel is " + str(Y[0][0]))

PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("Approximate PWG kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("PSS kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("Approximate PSS 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)``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("PWG kernel is " + str(Y[0][0]))

PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("Approximate PWG kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("PSS kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("Approximate PSS 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)``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````plt.show()

def arctan(C,p):
return lambda x: C*np.arctan(np.power(x[1], p))

PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("PWG kernel is " + str(Y[0][0]))

PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("Approximate PWG kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("PSS kernel is " + str(Y[0][0]))

PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("Approximate PSS 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)``````

## gudhi

The Gudhi library is an open source library for Computational Topology and Topological Data Analysis (TDA).

MIT