Move cython directory stuff to python directory. Make example work. Fix CMake for sktda. Make sktda a submodule of gudhi package.

1 files changed, 106 insertions, 0 deletions

diff --git a/src/python/example/ex_diagrams.py b/src/python/example/ex_diagrams.py
new file mode 100644
index 00000000..3dc6fe84
--- /dev/null
+++ b/src/python/example/ex_diagrams.py
@@ -0,0 +1,106 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.kernel_approximation import RBFSampler
+
+from gudhi.sktda import Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
+  PersistenceImage, PersistenceWeightedGaussianKernel,\
+  PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
+  SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel
+
+D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
+plt.scatter(D[:,0],D[:,1])
+plt.plot([0.,10.],[0.,10.])
+plt.show()
+
+diags = [D]
+
+LS = Landscape(resolution = 1000)
+L = LS.fit_transform(diags)
+plt.plot(L[0][:1000])
+plt.plot(L[0][1000:2000])
+plt.plot(L[0][2000:3000])
+plt.show()
+
+def pow(n):
+  return lambda x: np.power(x[1]-x[0],n)
+
+SH = Silhouette(resolution=1000, weight=pow(2))
+sh = SH.fit_transform(diags)
+plt.plot(sh[0])
+plt.show()
+
+BC = BettiCurve(resolution=1000)
+bc = BC.fit_transform(diags)
+plt.plot(bc[0])
+plt.show()
+
+CP = ComplexPolynomial(threshold=-1, F="T")
+cp = CP.fit_transform(diags)
+print("Complex polynomial is " + str(cp[0,:]))
+
+TV = TopologicalVector(threshold=-1)
+tv = TV.fit_transform(diags)
+print("Topological vector is " + str(tv[0,:]))
+
+#diagsT = DiagramPreprocessor(use=True, scalers=[([0,1], BirthPersistenceTransform())]).fit_transform(diags)
+#PI = PersistenceImage(bandwidth=1., weight=lambda x: x[1], im_range=[0,10,0,10], resolution=[100,100])
+#pi = PI.fit_transform(diagsT)
+#plt.imshow(np.flip(np.reshape(pi[0], [100,100]), 0))
+#plt.show()
+
+plt.scatter(D[:,0],D[:,1])
+D = np.array([[1.,5.],[3.,6.],[2.,7.]])
+plt.scatter(D[:,0],D[:,1])
+plt.plot([0.,10.],[0.,10.])
+plt.show()
+
+diags2 = [D]
+
+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)
+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]))