回答編集履歴
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追記
test
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すべてのmap(...)をlist(map(...))のようにlistで囲むと解決する可能性が高いです。
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---
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追記
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古いlambda関数の挙動を初めて知りました。
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簡潔な解決法はpython2.xを使用することでした。
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以下にpython3.xで動くコードを載せます。
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import scipy.spatial.distance as dist
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%matplotlib inline
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def label_cluster_num(means, mesh_points, metrics):
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def label(point):
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cluster_label = np.argmin(list(map(lambda mean: metrics(mean, point), means)))
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return point, cluster_label
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return list(map(label, mesh_points))
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c_means = np.array([[1, 2], [-3, 4], [-5, -6], [7, -8]])
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xs = np.linspace(-10, 10, 100)
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ys = np.linspace(-10, 10, 100)
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xx, yy = np.meshgrid(xs, ys)
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mesh_points = np.c_[xx.ravel(), yy.ravel()]
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def show_volonoi_with_metrics(metrics):
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labeled_mesh_points = label_cluster_num(c_means, mesh_points, metrics=metrics)
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plt.figure()
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fig, ax = plt.subplots()
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ax.set_aspect('equal')
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ax.grid(True, which='both')
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ax.axhline(y=0, color='k')
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ax.axvline(x=0, color='k')
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ax.set_xlim([-10, 10])
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ax.set_ylim([-10, 10])
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for i in range(0, len(c_means)):
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cluster_points = list(map(lambda p: p[0], filter(lambda p: p[1] == i, labeled_mesh_points)))
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xs = list(map(lambda p: p[0], cluster_points))
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ys = list(map(lambda p: p[1], cluster_points))
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ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')
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ax.scatter(list(map(lambda p: p[0], c_means)), list(map(lambda p: p[1], c_means)), color="g", marker='o')
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plt.show()
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show_volonoi_with_metrics(dist.euclidean)
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```
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読みやすいバージョン
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import scipy.spatial.distance as dist
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%matplotlib inline
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def label_cluster_num(means, mesh_points, metrics):
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def label(point):
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cluster_label = np.argmin([metrics(p, point) for p in means])
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return cluster_label
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return np.array([label(p) for p in mesh_points])
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c_means = np.array([[1, 2], [-3, 4], [-5, -6], [7, -8]])
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xs = np.linspace(-10, 10, 100)
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ys = np.linspace(-10, 10, 100)
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xx, yy = np.meshgrid(xs, ys)
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mesh_points = np.c_[xx.ravel(), yy.ravel()]
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def show_volonoi_with_metrics(metrics):
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labels = label_cluster_num(c_means, mesh_points, metrics=metrics)
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fig, ax = plt.subplots()
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ax.set_aspect('equal')
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ax.grid(True, which='both')
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ax.axhline(y=0, color='k')
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ax.axvline(x=0, color='k')
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ax.set_xlim([-10, 10])
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ax.set_ylim([-10, 10])
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for i in range(0, len(c_means)):
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cluster_points = mesh_points[labels==i]
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xs = cluster_points[:, 0]
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ys = cluster_points[:, 1]
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ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')
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ax.scatter(c_means[:, 0], c_means[:, 0], color="g", marker='o')
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plt.show()
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show_volonoi_with_metrics(dist.euclidean)
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```
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