affinity.py

import pandas as pd
import numpy as np

from sklearn.datasets import make_blobs
from matplotlib import pyplot as plt
from sklearn.cluster import AffinityPropagation
from sklearn import metrics

# generazione dei dati casuali
X, y = make_blobs(n_samples=40, centers=4, cluster_std=0.80)
plt.scatter(X[:, 0], X[:, 1], cmap='Accent')

afprop  = AffinityPropagation(preference=-50)
afprop.fit(X)

labels = afprop.predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='Accent')

centro_cluster = afprop.cluster_centers_indices_
numero_cluster = len(centro_cluster)
print('Numero di cluster: %d' % numero_cluster)
afprop.cluster_centers_

print("Omogeneità: %0.4f" % metrics.homogeneity_score(y, labels))
print("Completezza: %0.4f" % metrics.completeness_score(y, labels))