机器学习的一些学习代码.zip

# -*- coding: utf-8 -*- import pandas as pd import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap import numpy as np from Perceptron import Perceptron def plot_decision_regions(X, y, classifier, resolution=0.02): #画图划线分割 markers = ['s', 'x', 'o', 'v']; colors= ['red', 'blue', 'lightred', 'gray', 'cyan'] cmap = ListedColormap(colors[:len(np.unique(y))]); x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max(); x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max(); #print(x1_min, x1_max, x2_min, x2_max); # 根据数据最大最小值构建向量,差值resolution xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution)) #print(np.arange(x1_min, x1_max, resolution).shape, np.arange(x1_min, x1_max, resolution), xx1.shape, xx1) z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T); z = z.reshape(xx1.shape); plt.contourf(xx1, xx2, z, alpha=0.4, cmap=cmap) plt.xlim(xx1.min(), xx1.max()) plt.ylim(xx2.min(), xx2.max()) for idx,cl in enumerate(np.unique(y)): plt.scatter(x=X[y==cl, 0], y=X[y==cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl) pass plt.xlabel('花瓣长度'); plt.ylabel('花径长度'); plt.legend(loc='upper left'); plt.show(); pass # 读取文件 file = './Perceptron/examples.csv'; df = pd.read_csv(file, header=None); # print(df.head(10)) # 处理第4列表 y = df.loc[0: 100, 4].values; y = np.where(y == 'Tris-setosa', -1, 1); # print(y) # 讲第0和2列取出来分析 X = df.iloc[0: 100, [0, 2]].values; # print(X) # 对数据可视化 """ plt.scatter(X[:5, 0], X[:5, 1], color='red', marker='o', label='setosa'),; plt.scatter(X[5:10, 0], X[5:10, 1], color='blue', marker='x', label='versicolor'); plt.xlabel('花瓣长度'); plt.ylabel('花径长度'); plt.legend(loc='upper left'); plt.show(); """ # 训练 ppn = Perceptron(eta=0.1, n_iter=10); ppn.fit(X, y) """ # 训练输出 plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o'); plt.xlabel('Epochs'); plt.ylabel('错误分类次数'); plt.show(); """ # 绘制分割图 plot_decision_regions(X, y, ppn, resolution=0.02)