机器学习之线性分类器(机器学习算法原理与实践）郑捷 (P166-p168)

 

# -*- coding: utf-8 -*-
import os
import sys
import numpy as np
import operator
from numpy import *
from common_libs import *
import matplotlib.pyplot as plt

 

def hardlim(dataSet):
dataSet[nonzero(dataSet.A>0)[0]]=1
dataSet[nonzero(dataSet.A<=0)[0]]=0
return dataSet
# 输入数据
Input = file2matrix("testSet.txt","\t")
print(Input)
print('-------')
target = Input[:,-1] #获取分类标签列表
[m,n] = shape(Input)

# 按分类绘制散点图
drawScatterbyLabel(plt,Input)

# 构建x+b 系数矩阵：b这里默认为1
dataMat = buildMat(Input)
print dataMat

 

alpha = 0.001 # 步长
steps = 500 # 迭代次数

weights = ones((n,1))# 初始化权重向量
# 主程序
for k in xrange(steps):
gradient = dataMat*mat(weights) # 梯度
output = hardlim(gradient) # 硬限幅函数
errors = target-output # 计算误差
weights = weights + alpha*dataMat.T*errors

print weights # 输出权重

X = np.linspace(-5,5,10)
#y=w*x+b: b:weights[0]/weights[2]; w:weights[1]/weights[2]
Y = -(double(weights[0])+X*(double(weights[1])))/double(weights[2])
plt.plot(X,Y)
plt.show()