58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
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import numpy as np
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from matplotlib import pyplot as plt
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_agg import FigureCanvas
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from sklearn.cluster import KMeans
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from sklearn.datasets.samples_generator import make_blobs
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from sklearn.metrics import silhouette_score
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import cv2
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import glob
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k=5
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cluster_std=1.30
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n_samples=300
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fig, ((ax1, ax2), (ax3, ax4))=plt.subplots(2, 2)
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canvas=FigureCanvas(fig)
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fig.set_size_inches(12, 8)
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X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std)
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while 1:
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ax1.cla()
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ax1.plot(X[:,0], X[:,1], "+", c="#FF0000")
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ax1.set_title('Données')
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wcss=[]
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tab_silhouette=[]
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for i in range(2, 11):
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kmeans=KMeans(n_clusters=i)
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cluster_labels=kmeans.fit_predict(X)
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wcss.append(kmeans.inertia_)
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tab_silhouette.append(silhouette_score(X, cluster_labels))
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ax2.cla()
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ax2.plot(range(2, 11), wcss, c="#FF0000")
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ax2.set_title('WCSS pour "elbow method"')
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ax3.cla()
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ax3.plot(range(2, 11), tab_silhouette, c="#FF0000")
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ax3.set_title('Coefficient silhouette')
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kmeans=KMeans(n_clusters=np.argmax(tab_silhouette)+2)
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pred_y=kmeans.fit_predict(X)
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ax4.cla()
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ax4.scatter(X[:,0], X[:,1], c=pred_y, marker='+')
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ax4.set_title('Données + centre clusters')
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ax4.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c="#0000FF")
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canvas.draw()
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img=np.array(canvas.renderer.buffer_rgba())
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cv2.putText(img, "[r] reset [q] quit".format(k), (450, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)
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cv2.imshow("plot", img)
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key=cv2.waitKey()&0xFF
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if key==ord('r'):
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X, y=make_blobs(n_samples=n_samples, centers=k, cluster_std=cluster_std)
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if key==ord('q'):
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quit()
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