Python 绘制混淆矩阵（Confusion Matrix）

import numpy as np
import matplotlib.pyplot as plt
from pylab import *
conf_arr=[[13,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,5,0,1,0,0,],
[0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,1,0,2,1,2,0,0,0,0,0,0,0,1,0,10,5,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,],
[0,0,9,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,1,1,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,4,0,1,0,0,0,2,],
[1,0,0,12,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,1,0,1,0,0,],
[0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,1,0,1,0,1,0,0,0,0,6,2,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,3,0,2,0,2,],
[0,0,0,1,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,8,0,2,0,1,0,0,0,0,],
[1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,2,0,0,0,0,0,1,0,0,0,0,0,5,0,3,1,1,0,0,0,0,1,0,0,0,0,7,0,1,0,0,0,0,3,1,],
[0,0,0,0,0,0,0,10,0,0,0,0,5,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,4,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,1,],
[0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,4,0,1,3,6,],
[0,0,2,0,0,0,0,0,1,6,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,3,0,0,0,0,8,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,1,0,0,1,0,],
[0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,1,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,1,0,3,0,1,0,2,0,6,0,2,2,0,],
[0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,1,3,0,0,4,0,2,0,2,0,5,0,0,1,2,],
[0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,3,0,1,0,2,1,0,],
[1,0,0,0,0,0,0,0,0,1,0,0,2,2,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,4,1,1,0,0,1,1,0,1,0,0,0,1,0,9,0,0,0,0,2,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,1,0,0,0,0,0,2,0,1,0,4,10,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,],
[0,0,0,0,0,3,0,0,1,2,0,0,0,0,0,13,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,1,],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,1,0,0,0,0,0,0,1,0,0,3,3,1,3,0,0,2,0,1,2,0,0,0,0,0,0,3,0,2,0,0,1,1,],
[0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,6,1,0,2,1,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,2,0,3,1,0,0,0,0,1,0,3,0,0,1,0,],
[0,0,0,0,0,1,0,1,0,1,0,0,2,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,2,1,0,0,0,0,0,3,0,0,0,0,3,0,],
[0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,1,1,0,3,0,2,0,0,0,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,6,0,2,0,1,4,1,],
[0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,3,1,1,0,0,0,0,0,2,0,0,3,5,1,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,4,0,],
[0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,9,0,0,1,0,0,0,0,0,2,0,0,0,0,2,1,1,0,4,0,1,0,0,0,4,0,1,0,0,],
[0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,3,0,0,1,0,0,0,0,3,1,1,0,7,0,1,0,2,0,3,0,0,1,0,],
[0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,1,4,2,0,0,0,1,0,0,0,0,1,4,0,3,0,3,0,0,1,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,16,0,0,0,1,0,0,0,0,1,1,1,0,2,0,1,0,0,0,0,0,6,0,0,0,0,],
[0,0,0,2,0,5,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,17,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,],
[0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,10,0,2,0,1,5,1,],
[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,9,1,1,2,0,0,0,0,1,1,1,0,3,0,0,0,5,0,0,0,1,2,0,],
[0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,17,0,0,0,0,1,0,0,0,0,0,3,0,0,0,1,0,2,0,0,0,0,],
[0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,15,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,20,1,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,2,0,],
[0,0,0,0,0,0,0,3,0,0,0,0,4,0,1,2,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,4,0,],
[0,0,2,1,0,0,0,0,1,2,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,1,1,1,2,2,0,0,0,8,1,0,0,0,0,0,0,1,0,1,0,0,1,0,],
[2,0,8,0,0,0,0,0,5,2,0,0,1,0,0,0,0,0,3,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,22,0,0,0,0,],
[0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,3,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,3,0,0,0,1,12,0,0,0,0,0,0,0,0,0,0,0,0,2,0,],
[0,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,3,0,0,0,1,0,1,0,1,],
[3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,1,0,1,0,14,0,0,1,1,0,0,0,3,0,0,0,0,],
[1,0,0,4,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,1,0,6,2,1,0,2,0,1,0,0,0,0,0,3,0,0,],
[1,0,0,1,0,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,8,0,1,0,1,0,5,0,1,0,0,],
[0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,2,0,3,4,0,0,3,0,0,2,0,0,0,4,0,0,2,0,0,0,1,3,0,],
[0,0,1,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,15,0,0,0,0,0,3,1,0,],
[0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,0,3,0,0,0,0,1,3,1,0,1,0,0,2,0,0,0,0,2,0,8,0,0,0,0,2,0,],
[0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,2,1,0,1,0,1,2,0,0,0,1,0,0,1,0,0,2,0,0,1,0,2,2,0,0,0,0,0,0,8,0,0,0,1,0,1,],
[1,0,0,0,0,0,0,0,4,1,0,1,1,0,0,2,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,2,2,0,0,0,0,0,0,1,0,0,0,4,2,2,],
[0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,18,0,1,0,0,],
[1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,3,0,0,0,0,0,0,1,1,0,2,1,0,0,1,2,2,0,0,0,0,0,0,0,5,0,5,0,0,0,1,],
[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,1,0,7,9,1,],
[1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,1,0,3,0,5,0,1,0,1,11,0,],
[0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,3,3,0,0,0,0,2,0,2,0,1,0,3,1,3,],
];
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)
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet,
                interpolation='nearest')
width = len(conf_arr)
height = len(conf_arr[0])
##for x in xrange(width):
##    for y in xrange(height):
##        ax.annotate(str(conf_arr[x][y]), xy=(y, x),
##                    horizontalalignment='center',
##                    verticalalignment='center')
cb = fig.colorbar(res)
alphabet = ['brush_hair','cartwheel','catch','chew','clap','climb','climb_stairs','dive','draw_sword','dribble','drink','eat','fall_floor','fencing',
'flic_flac','golf','handstand','hit','hug','jump','kick','kick_ball','kiss','laugh','pick','pour','pullup','punch','push','pushup','ride_bike',
'ride_horse','run','shake_hands','shoot_ball','shoot_bow','shoot_gun','sit','situp','smile','smoke','somersault','stand','swing_baseball','sword',
'sword_exercise','talk','throw','turn','walk','wave'];
plt.xticks(fontsize=7)
plt.yticks(fontsize=7)
locs, labels = plt.xticks(range(width), alphabet[:width])
for t in labels:
     t.set_rotation(90)
#plt.xticks('orientation', 'vertical')
#locs, labels = xticks([1,2,3,4], ['Frogs', 'Hogs', 'Bogs', 'Slogs'])
#setp(alphabet, 'rotation', 'vertical')
plt.yticks(range(height), alphabet[:height])
plt.savefig('confusion_matrix.png', format='png')

from http://blog.sina.com.cn/s/blog_4db1685d010189qj.html