# coding:utf-8

import numpy as np

import matplotlib.pyplot as plt

from sklearn.neighbors import KNeighborsClassifier

# 1、数据读取

x_tain =[]

x_test =[]

y_tain=[]

y_test=[]

for i in range(0,10):

    for j in range(1,501):

        if j < 451: #将数据保存到训练数据中

            x_tain.append(plt.imread('./data/%d/%d_%d.bmp'%(i,i,j)).reshape(-1) )  #reshape 可以降维也就是矩阵变化

            y_tain.append(i)  #append 是读进来的数据进行存储的意思

        else: #保存到预测数据中

            x_test.append(plt.imread('./data/%d/%d_%d.bmp'%(i,i,j)).reshape(-1))

            y_test.append(i)

# 2、数据转换成

x_tain,y_tain= np.array(x_tain),np.array(y_tain)

# print(x_tain.shape,len(y_tain),len(x_test))

# 3、机器学习

knn = KNeighborsClassifier() #构造分类器

knn.fit(x_tain,y_tain)

y_ = knn.predict(x_test)  #进行预测的结果

# print(len(y_[::10]),'\n',y_test[::10])

gl=knn.score(x_test,y_test)

print('准确率为：',gl)

# 3、图片绘制

plt.figure(figsize=(13,15))

img = x_test[::10]

img1 = y_test[::10]

yimg = y_[::10]

for i in range(50):

    plt.subplot(5,10,i+1)

    plt.imshow(img[i].reshape(28,28),cmap='gray')

    plt.title('预测数据：%d'%(yimg[i])+'\n真实数据：%d'%(img1[i]))

plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置字体为SimHei显示中文

plt.rcParams['axes.unicode_minus'] = False  # 设置正常显示符号

plt.show()