33 lines
832 B
Python
33 lines
832 B
Python
import utils
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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def kmeans(n_clusters, slab_width, slab_height):
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slabs, i_min, i_max = utils.load_slabs(slab_width, slab_height)
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X = utils.as_np_array(slabs)
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f, axes = plt.subplots(1, 3)
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n_freqs, n_pixels = X.shape[0], X.shape[1]
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X = X.T
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km_estimator = KMeans(n_clusters=n_clusters)
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km_estimator.fit(X)
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centers = km_estimator.cluster_centers_
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labels = km_estimator.labels_.reshape((slab_width, slab_height))
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for i in range(X.shape[0]):
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axes[0].plot(utils.energies, X[i,:], c='blue', alpha=0.1)
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for i in range(centers.shape[0]):
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axes[0].plot(utils.energies, centers[i,:], c='black', alpha=1)
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axes[1].imshow(slabs[15]["data"], vmin=i_min, vmax=i_max)
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axes[2].imshow(labels)
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plt.show()
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