43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
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"""
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===========================================================
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An example of K-Means++ initialization
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===========================================================
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An example to show the output of the :func:`sklearn.cluster.kmeans_plusplus`
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function for generating initial seeds for clustering.
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K-Means++ is used as the default initialization for :ref:`k_means`.
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"""
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import matplotlib.pyplot as plt
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from sklearn.cluster import kmeans_plusplus
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from sklearn.datasets import make_blobs
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# Generate sample data
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n_samples = 4000
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n_components = 4
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X, y_true = make_blobs(
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n_samples=n_samples, centers=n_components, cluster_std=0.60, random_state=0
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)
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X = X[:, ::-1]
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# Calculate seeds from k-means++
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centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)
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# Plot init seeds along side sample data
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plt.figure(1)
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colors = ["#4EACC5", "#FF9C34", "#4E9A06", "m"]
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for k, col in enumerate(colors):
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cluster_data = y_true == k
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plt.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=".", s=10)
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plt.scatter(centers_init[:, 0], centers_init[:, 1], c="b", s=50)
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plt.title("K-Means++ Initialization")
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plt.xticks([])
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plt.yticks([])
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plt.show()
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