From b2d81dd8ee2ed7e1269eb16816f9af6794305046 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Fri, 15 Nov 2019 22:23:21 +0100
Subject: Run example as a test, without graphics

---
 .../diagram_vectorizations_distances_kernels.py    | 133 +++++++++++++++++++++
 1 file changed, 133 insertions(+)
 create mode 100755 src/python/example/diagram_vectorizations_distances_kernels.py

(limited to 'src/python/example/diagram_vectorizations_distances_kernels.py')

diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py
new file mode 100755
index 00000000..a6a36b7c
--- /dev/null
+++ b/src/python/example/diagram_vectorizations_distances_kernels.py
@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.kernel_approximation import RBFSampler
+from sklearn.preprocessing import MinMaxScaler
+
+from gudhi.sktda import DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
+  PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \
+  PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
+  SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel
+
+D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]])
+diags = [D]
+
+diags = DiagramSelector(use=True, point_type="finite").fit_transform(diags)
+diags = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags)
+diags = DiagramScaler(use=True, scalers=[([1], Clamping(limit=.9))]).fit_transform(diags)
+
+D = diags[0]
+plt.scatter(D[:,0],D[:,1])
+plt.plot([0.,1.],[0.,1.])
+plt.title("Test Persistence Diagram for vector methods")
+plt.show()
+
+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.title("Landscape")
+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.title("Silhouette")
+plt.show()
+
+BC = BettiCurve(resolution=1000)
+bc = BC.fit_transform(diags)
+plt.plot(bc[0])
+plt.title("Betti Curve")
+plt.show()
+
+CP = ComplexPolynomial(threshold=-1, polynomial_type="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,:]))
+
+PI = PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])
+pi = PI.fit_transform(diags)
+plt.imshow(np.flip(np.reshape(pi[0], [100,100]), 0))
+plt.title("Persistence Image")
+plt.show()
+
+ET = Entropy(mode="scalar")
+et = ET.fit_transform(diags)
+print("Entropy statistic is " + str(et[0,:]))
+
+ET = Entropy(mode="vector", normalized=False)
+et = ET.fit_transform(diags)
+plt.plot(et[0])
+plt.title("Entropy function")
+plt.show()
+
+D = np.array([[1.,5.],[3.,6.],[2.,7.]])
+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)
+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]))
-- 
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