pythonでのRuntimeError

度々すみません
これの最後のshow_volonoi_with_metrics(dist.euclidean)でエラーが出てしまいます。
なにか良い方法はありますでしょうか
以下エラーです

RuntimeError Traceback (most recent call last)
<ipython-input-62-670a63d7ce7e> in <module>()
----> 1 show_volonoi_with_metrics(dist.euclidean)

<ipython-input-61-e1fd4304de1a> in show_volonoi_with_metrics(metrics)
15 xs = map(lambda p: p[0], cluster_points)
16 ys = map(lambda p: p[1], cluster_points)
---> 17 ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')
18
19 ax.scatter(map(lambda p: p[0], c_means), map(lambda p: p[1], c_means), color="g", marker='o')

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/init.py in inner(ax, *args, **kwargs)
1708 warnings.warn(msg % (label_namer, func.name),
1709 RuntimeWarning, stacklevel=2)
-> 1710 return func(ax, *args, **kwargs)
1711 pre_doc = inner.doc
1712 if pre_doc is None:

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
4007 edgecolors = 'face'
4008
-> 4009 self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
4010 x = self.convert_xunits(x)
4011 y = self.convert_yunits(y)

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/axes/_base.py in _process_unit_info(self, xdata, ydata, kwargs)
1967 # we only need to update if there is nothing set yet.
1968 if not self.xaxis.have_units():
-> 1969 self.xaxis.update_units(xdata)
1970
1971 if ydata is not None:

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/axis.py in update_units(self, data)
1430 """
1431
-> 1432 converter = munits.registry.get_converter(data)
1433 if converter is None:
1434 return False

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/units.py in get_converter(self, x)
160 if converter is None:
161 try:
--> 162 thisx = safe_first_element(x)
163 except (TypeError, StopIteration):
164 pass

~/anaconda3/envs/styletrans/lib/python3.6/site-packages/matplotlib/cbook/init.py in safe_first_element(obj)
2310 except TypeError:
2311 pass
-> 2312 raise RuntimeError("matplotlib does not support generators "
2313 "as input")
2314 return next(iter(obj))

RuntimeError: matplotlib does not support generators as input

以下コードです

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import scipy.spatial.distance as dist
%matplotlib inline

def label_cluster_num(means, mesh_points, metrics):
def label(point):
cluster_label = np.argmin(map(lambda mean: metrics(mean, point), means))
return point, cluster_label
return map(label, mesh_points)

c_means = np.array([[1, 2], [-3, 4], [-5, -6], [7, -8]])
xs = np.linspace(-10, 10, 100)
ys = np.linspace(-10, 10, 100)
xx, yy = np.meshgrid(xs, ys)
mesh_points = np.c_[xx.ravel(), yy.ravel()]

def show_volonoi_with_metrics(metrics):
labeled_mesh_points = label_cluster_num(c_means, mesh_points, metrics=metrics)
plt.figure()
fig, ax = plt.subplots()

ax.set_aspect('equal')
ax.grid(True, which='both')
ax.axhline(y=0, color='k')
ax.axvline(x=0, color='k')
ax.set_xlim([-10, 10])
ax.set_ylim([-10, 10])

for i in range(0, len(c_means)):
    cluster_points = map(lambda p, label: p, filter(lambda p, label: label == i, labeled_mesh_points))
    xs = map(lambda p: p[0], cluster_points)
    ys = map(lambda p: p[1], cluster_points)
    ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')

ax.scatter(map(lambda p: p[0], c_means), map(lambda p: p[1], c_means), color="g", marker='o')

plt.show()

show_volonoi_with_metrics(dist.euclidean)

mkgrei

2017/11/24 06:13

高度の嫌がらせですか…RuntimeErrorの詳細がないと、手元で実行しないとデバッグは無理ですよ。

LouiS0616

2017/11/24 06:36

ネットで拾ってきたコードを、ろくに調べず／考えず動かそうとして失敗するのは当たり前です。拾い食いして腹を壊すようなものです。

行動規範の内容に同意します

回答1件

ベストアンサー

仮説ですが、オリジナルのコードはPython2.xでmapがlistを返していたのです。
そして今、Python3.xを使っているせいで、mapがイテレータを返してきているので、matplotlibは困惑している状況です。

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。

追記
古いlambda関数の挙動を初めて知りました。
簡潔な解決法はpython2.xを使用することでした。

以下にpython3.xで動くコードを載せます。

python
1import numpy as np
2import matplotlib.pyplot as plt
3import matplotlib.cm as cm
4import scipy.spatial.distance as dist
5%matplotlib inline
6
7def label_cluster_num(means, mesh_points, metrics):
8    def label(point):
9        cluster_label = np.argmin(list(map(lambda mean: metrics(mean, point), means)))
10        return point, cluster_label
11    return list(map(label, mesh_points))
12
13c_means = np.array([[1, 2], [-3, 4], [-5, -6], [7, -8]])
14xs = np.linspace(-10, 10, 100)
15ys = np.linspace(-10, 10, 100)
16xx, yy = np.meshgrid(xs, ys)
17mesh_points = np.c_[xx.ravel(), yy.ravel()]
18
19def show_volonoi_with_metrics(metrics):
20    labeled_mesh_points = label_cluster_num(c_means, mesh_points, metrics=metrics)
21    plt.figure()
22    fig, ax = plt.subplots()
23
24    ax.set_aspect('equal')
25    ax.grid(True, which='both')
26    ax.axhline(y=0, color='k')
27    ax.axvline(x=0, color='k')
28    ax.set_xlim([-10, 10])
29    ax.set_ylim([-10, 10])
30
31    for i in range(0, len(c_means)):
32        cluster_points = list(map(lambda p: p[0], filter(lambda p: p[1] == i, labeled_mesh_points)))
33        xs = list(map(lambda p: p[0], cluster_points))
34        ys = list(map(lambda p: p[1], cluster_points))
35        ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')
36
37    ax.scatter(list(map(lambda p: p[0], c_means)), list(map(lambda p: p[1], c_means)), color="g", marker='o')
38
39    plt.show()
40
41show_volonoi_with_metrics(dist.euclidean)

読みやすいバージョン

python
1import numpy as np
2import matplotlib.pyplot as plt
3import matplotlib.cm as cm
4import scipy.spatial.distance as dist
5%matplotlib inline
6
7def label_cluster_num(means, mesh_points, metrics):
8    def label(point):
9        cluster_label = np.argmin([metrics(p, point) for p in means])
10        return cluster_label
11    return np.array([label(p) for p in mesh_points])
12
13c_means = np.array([[1, 2], [-3, 4], [-5, -6], [7, -8]])
14xs = np.linspace(-10, 10, 100)
15ys = np.linspace(-10, 10, 100)
16xx, yy = np.meshgrid(xs, ys)
17mesh_points = np.c_[xx.ravel(), yy.ravel()]
18
19def show_volonoi_with_metrics(metrics):
20    labels = label_cluster_num(c_means, mesh_points, metrics=metrics)
21    fig, ax = plt.subplots()
22
23    ax.set_aspect('equal')
24    ax.grid(True, which='both')
25    ax.axhline(y=0, color='k')
26    ax.axvline(x=0, color='k')
27    ax.set_xlim([-10, 10])
28    ax.set_ylim([-10, 10])
29
30    for i in range(0, len(c_means)):
31        cluster_points = mesh_points[labels==i]
32        xs = cluster_points[:, 0]
33        ys = cluster_points[:, 1]
34        ax.scatter(xs, ys, color=cm.prism(i / float(len(c_means))), marker='.')
35
36    ax.scatter(c_means[:, 0], c_means[:, 0], color="g", marker='o')
37
38    plt.show()
39
40show_volonoi_with_metrics(dist.euclidean)