How to use the gudhi.representations.PersistenceWeightedGaussianKernel function in gudhi

plt.scatter(D[:,0],D[:,1])
D = diags2[0]
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,1.],[0.,1.])
plt.title("Test Persistence Diagrams for kernel methods")
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)

plt.scatter(D[:,0],D[:,1])
D = diags2[0]
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,1.],[0.,1.])
plt.title("Test Persistence Diagrams for kernel methods")
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)

diags2 = [D]

diags2 = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags2)

D = diags[0]
plt.scatter(D[:,0],D[:,1])
D = diags2[0]
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,1.],[0.,1.])
plt.title("Test Persistence Diagrams for kernel methods")
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)

diags2 = [D]

diags2 = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags2)

D = diags[0]
plt.scatter(D[:,0],D[:,1])
D = diags2[0]
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,1.],[0.,1.])
plt.title("Test Persistence Diagrams for kernel methods")
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)