#confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
# classes = ['A','B','C','D','E']
# confusion_matrix = np.array([(9,1,3,4,0),(2,13,1,3,4),(1,4,10,0,13),(3,1,1,17,0),(0,0,0,1,14)],dtype=np.float64)


# 标签
classes=['Left','Front','Right']

# 标签的个数
classNamber=3 #表情的数量

# 在标签中的矩阵
confusion_matrix = np.array([
    (66,0, 0),
    (0,100,0),
    (0,0,98)
    ])

plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Oranges)  #按照像素显示出矩阵
plt.title('Confusion matrix')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=-45)
plt.yticks(tick_marks, classes)

thresh = confusion_matrix.max() / 2.
#iters = [[i,j] for i in range(len(classes)) for j in range((classes))]
#ij配对，遍历矩阵迭代器
iters = np.reshape([[[i,j] for j in range(classNamber)] for i in range(classNamber)],(confusion_matrix.size,2))
for i, j in iters:
    plt.text(j, i, format(confusion_matrix[i, j]),va='center',ha='center')   #显示对应的数字

plt.ylabel('Real label')
plt.xlabel('Prediction')
plt.tight_layout()
plt.show()


