import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
import _pickle as pickle
import matplotlib.pyplot as plt
mnist = load_digits()
x,test_x,y,test_y = train_test_split(mnist.data,mnist.target,test_size=0.25,random_state=40)
from sklearn.tree import DecisionTreeClassifier, export_graphviz
model = DecisionTreeClassifier(criterion="entropy")
model.fit(x,y)
z=model.predict(test_x)
print('准确率：',np.sum(z==test_y)/z.size)
#学习后识别99到105六张图片并给出预测
model.predict(mnist.data[99:105])
#实际的99到105代表的数
mnist.target[99:105]
#显示99到105数字图片
plt.subplot(321)
plt.imshow(mnist.images[99],cmap=plt.cm.gray_r,interpolation='nearest')
plt.subplot(322)
plt.imshow(mnist.images[100],cmap=plt.cm.gray_r,interpolation='nearest')
plt.subplot(323)
plt.imshow(mnist.images[101],cmap=plt.cm.gray_r,interpolation='nearest')
plt.subplot(324)
plt.imshow(mnist.images[102],cmap=plt.cm.gray_r,interpolation='nearest')
plt.subplot(325)
plt.imshow(mnist.images[103],cmap=plt.cm.gray_r,interpolation='nearest')
plt.subplot(326)
plt.imshow(mnist.images[104],cmap=plt.cm.gray_r,interpolation='nearest')