1 | import os |
2 | import numpy as np |
3 | import matplotlib.pyplot as plt |
4 | from sklearn import datasets |
5 | from sklearn import metrics |
6 | from sklearn import cluster |
7 | os.chdir(r'D:\projects\wordpress\ex49') |
8 | os.getcwd() |
9 | iris = datasets.load_iris() |
10 | X, y = iris.data, iris.target |
11 | np.random.seed(123) |
12 | n_clusters = 3 |
13 | c = cluster.KMeans(n_clusters=n_clusters) |
14 | c.fit(X) |
15 | y_pred = c.predict(X) |
16 | print(y_pred[::8]) |
17 | print(y[::8]) |
18 | idx_0, idx_1, idx_2 = (np.where(y_pred == n) for n in range(3)) |
19 | y_pred[idx_0], y_pred[idx_1], y_pred[idx_2] = 2, 0, 1 |
20 | print(y_pred[::8]) |
21 | print(metrics.confusion_matrix(y, y_pred)) |
22 | N = X.shape[1] |
23 | fig, axes = plt.subplots(N, N, figsize=(12, 12), sharex=True, sharey=True) |
24 | colors = ["coral", "blue", "green"] |
25 | markers = ["^", "v", "o"] |
26 | for m in range(N): |
27 | for n in range(N): |
28 | for p in range(n_clusters): |
29 | mask = y_pred == p |
30 | axes[m, n].scatter(X[:, m][mask], X[:, n][mask], |
31 | marker=markers[p], s=30, |
32 | color=colors[p], alpha=0.25) |
33 |
34 | [crayon-67f18d234b79c204146585/] |
35 |
36 | fig.tight_layout() |
37 | plt.savefig("example49.png", dpi=100) |
38 | plt.show() |
39 | plt.close() |
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