机器学习之感知机python实现机器学习

机器学习之感知机python实现 【机器学习之感知机python实现】

机器学习之感知机python实现
- 一理论基础
  - - 损失函数
    - 更新参数
- 二 python实现
  - - 代码
    - 结果

一. 理论基础 1. 损失函数
L(w,b)=?∑i=0myi(wxi+b)
2. 更新参数
w=w+α∑i=0mxiyi
b=b+α∑i=0myi
二. python实现 1. 代码 Perceptron.py

#encoding=utf-8 ''' implements the perceptron '''import numpy as np import matplotlib.pyplot as plt import pandas as pdclass Perceptron: def __init__(self, alpha=0.1, iterator_num=100): self.alpha = alpha self.iterator_num = iterator_numdef train(self, x_train, y_train): x_train = np.mat(x_train) y_train = np.mat(y_train) [m, n] = x_train.shape self.theta = np.mat(np.zeros((n, 1))) self.b = 0 self.__stochastic_gradient_decent__(x_train, y_train)def __gradient_decent__(self, x_train, y_train): x_train = np.mat(x_train) y_train = np.mat(y_train) for i in xrange(self.iterator_num): self.theta = self.theta + self.alpha * x_train.T * y_train self.b = self.b + self.alpha * np.sum(y_train)def __stochastic_gradient_decent__(self, x_train, y_train): x_train = np.mat(x_train) y_train = np.mat(y_train) [m, n] = x_train.shape for i in xrange(self.iterator_num): for j in xrange(m): self.theta = self.theta + self.alpha * x_train[j].T * y_train[j] self.b = self.b + self.alpha * y_train[j]def main(): ''' test unit ''' print "step 1: load data..." data = https://www.it610.com/article/pd.read_csv('/home/LiuYao/Documents/MarchineLearning/data.csv') # data = https://www.it610.com/article/data.ix[0:30, :]x = np.mat(data[['x', 'y']].values) y = np.mat(data['label'].values).T y[y == 0] = -1 print y[y == 1] print "positive samples : ", y[y == 1].shape print "nagetive samples : ", y[y == -1].shape## step 2: training... print "step 2: training..." perceptron = Perceptron(alpha=0.1,iterator_num=100) perceptron.train(x, y)## step 3: show the decision boundary print "step 3: show the decision boundary..." print perceptron.theta x_min = np.min(x[:, 0]) x_max = np.max(x[:, 0]) y_min = (-perceptron.b - perceptron.theta[0] * x_min) / perceptron.theta[1] y_max = (-perceptron.b - perceptron.theta[0] * x_max) / perceptron.theta[1] plt.plot([x_min, x_max], [y_min[0,0], y_max[0,0]]) plt.scatter(x[:, 0].getA(), x[:, 1].getA(), c=y.getA()) plt.show()if __name__ == '__main__': main()

2. 结果最终结果会有一些问题，不知道为什么，请知道的大神解答一下,梯度下降和随机梯度下降都试了，结果一样。
* 用前三十个数据的时候，分界面正确；
* 用前60个数据的时候，分界面就离谱了。
* 所有数据的时候分界面明显有一些偏差；

前30个数据

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前60个数据

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所有数据

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数据如下：

x,y,label 10.6,13.5,0 12.55,12.1,0 12.05,13.95,0 10.85,15.05,0 7.5,12.75,0 9.45,11.25,0 8.95,13.3,0 8.45,15.5,0 12.15,12.2,0 5.15,8.25,0 17.45,6.0,0 18.55,5.8,1 16.1,4.45,1 13.95,6.75,1 15.4,7.85,1 17.7,9.25,1 19.3,9.8,1 20.5,8.1,1 8.15,2.05,1 11.7,4.9,1 21.1,4.6,1 21.1,9.75,1 17.65,11.4,1 6.95,9.9,1 5.8,12.05,1 7.35,10.0,0 8.15,11.05,0 7.4,11.65,0 4.55,11.35,0 4.4,15.2,0 4.2,16.6,0 7.85,17.1,0 13.45,18.95,0 15.35,18.9,0 18.35,17.1,0 16.85,15.75,0 15.75,10.8,0 13.95,9.25,0 10.25,10.7,0 9.85,12.05,0 14.25,17.45,0 10.15,17.55,0 7.0,14.1,0 4.85,11.8,0 4.75,8.6,0 3.25,6.65,0 1.9,9.55,0 2.1,14.75,0 1.5,10.9,0 5.75,9.65,0 7.65,8.1,0 9.6,9.1,0 10.1,2.0,1 12.2,2.75,1 8.0,6.3,1 6.8,5.1,1 7.35,3.65,1 9.5,4.65,1 13.05,7.7,1 17.85,5.15,1 24.35,7.4,1 20.4,13.1,1 14.55,15.4,1 24.95,11.05,1 22.15,11.15,1 22.85,5.85,1 22.5,4.15,1 19.3,1.6,1 15.6,0.25,1 14.5,1.55,1 14.5,3.95,1 10.35,7.1,1 13.65,6.75,1 14.0,5.55,1 12.15,4.8,1 10.5,4.15,1 22.95,8.75,1 21.25,7.05,1 17.05,7.9,1 17.05,7.9,1