# confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
classes = ['0','1','2','3','4','5','6','7','8','9']
confusion_matrix = np.array([(150,0,0,0,0,0,0,0,0,0),
(0,150,0,0,0,0,0,0,0,0),
(0,0,150,0,0,0,0,0,0,0),
(1,0,0,149,0,0,0,0,0,0),
(1,0,0,0,146,0,0,3,0,0),
(0,0,0,0,0,150,0,0,0,0),
(0,0,1,0,0,0,149,0,0,0),
(0,0,0,0,3,0,0,147,0,0),
(1,0,1,0,1,0,1,0,146,0),
(0,2,0,0,0,0,0,0,0,148)])
classNamber = 10;
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()
python画混淆矩阵
最新推荐文章于 2025-04-29 14:07:38 发布