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@sparkydogX
Last active November 21, 2018 13:10
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使用matplot绘制混淆矩阵
import itertools
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif']=['Tahoma', 'DejaVu Sans',
'Lucida Grande', 'Verdana']
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
if __name__ == '__main__':
cnf_matrix = np.load('/data2/guoyu/workspace/horizon_classfication/results/confusion_matrix.npy')
# cnf_matrix.shape == (3,3) in this example
class_names = ['run','stand', 'walk']
# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')
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