# How to use the gudhi.representations.Entropy 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.

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GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````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")``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````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")``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````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])``````
GUDHI / gudhi-devel / src / python / example / diagram_vectorizations_distances_kernels.py View on Github
``````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])``````

## gudhi

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

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