我正在使用混淆矩陣來衡量分類器的性能。這個例子就很好地工作,我(its from here),但我得到的全部時間TypeError: Invalid dimensions for image data
混亂矩陣錯誤:圖像數據的尺寸無效
from numpy import *
import matplotlib.pyplot as plt
from pylab import *
conf_arr = [[50.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [3.0, 26.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0], [4.0, 1.0, 0.0, 5.0, 0.0, 0.0, 0.0], [3.0, 0.0, 1.0, 0.0, 6.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 47.0, 0.0], [2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0]]
norm_conf = []
for i in conf_arr:
a = 0
tmp_arr = []
a = sum(i,0)
for j in i:
tmp_arr.append(float(j)/float(a))
norm_conf.append(tmp_arr)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest')
cb = fig.colorbar(res)
savefig("confmat.png", format="png")
我是新來的python和matplotlib。任何幫助?
Matplot版本是1.1.1。這裏是完整的回溯:RES後
= ...我得到
TypeError Traceback (most recent call last)
C:\Python27\lib\site-packages\SimpleCV\Shell\Shell.pyc in <module>()
----> 1 res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest')
C:\Python27\lib\site-packages\matplotlib\axes.pyc in imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filter
rad, imlim, resample, url, **kwargs)
6794 filterrad=filterrad, resample=resample, **kwargs)
6795
-> 6796 im.set_data(X)
6797 im.set_alpha(alpha)
6798 self._set_artist_props(im)
C:\Python27\lib\site-packages\matplotlib\image.pyc in set_data(self, A)
409 if (self._A.ndim not in (2, 3) or
410 (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
--> 411 raise TypeError("Invalid dimensions for image data")
412
413 self._imcache =None
TypeError: Invalid dimensions for image data
SimpleCV:105> cb = fig.colorbar(res)
對於打印norm_conf我得到現在的結果:[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],...]]
。我糾正了縮進問題。但我的困惑.png是相當扭曲的。另外,我應該如何繼續在矩陣中標記正方形?
@ user1665514:好的,所以看起來我們看到了同樣的錯誤。你可以運行'python -tt yourprogramname.py'來檢查空白問題嗎? – DSM
@ user1665514:您也可以在'plt.clf()'之前添加'print norm_conf'來查看它的值。 – DSM
print norm_conf給出[] – user1665514