基于传统机器学习(KNN+SVM)的MNIST手写数字识别系统源码+数据集+项目说明.zip

# -*- coding: UTF-8 -*- # @time: 2022/10/29 # ==================================================================================================== import numpy as np from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier, export_graphviz import pylab mnist = load_digits() x,test_x,y,test_y = train_test_split(mnist.data,mnist.target,test_size=0.25,random_state=40) 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 print(model.predict(mnist.data[109:115])) # 实际的99到105代表的数 var = mnist.target[109:115] # 显示99到105数字图片 fig = pylab.gcf() fig.canvas.set_window_title('decision tree') plt.subplot(321) plt.imshow(mnist.images[109],cmap=plt.cm.gray_r,interpolation='nearest') plt.subplot(322) plt.imshow(mnist.images[110],cmap=plt.cm.gray_r,interpolation='nearest') plt.subplot(323) plt.imshow(mnist.images[111],cmap=plt.cm.gray_r,interpolation='nearest') plt.subplot(324) plt.imshow(mnist.images[112],cmap=plt.cm.gray_r,interpolation='nearest') plt.subplot(325) plt.imshow(mnist.images[113],cmap=plt.cm.gray_r,interpolation='nearest') plt.subplot(326) plt.imshow(mnist.images[114],cmap=plt.cm.gray_r,interpolation='nearest') plt.show() import pandas as pd x = pd.DataFrame(x) with open("../Dataset\JueCetree.dot", 'w') as f: f = export_graphviz(model, feature_names = x.columns, out_file = f)