import numpy as np
import as plt
"""装载函数"""
def loadDataSet():
dataMat = []
labelMat = []
fr = open('')
for line in (): # 按行取数据
lineArr = ().split() # 按空格切分
([1.0, float(lineArr[0]), float(lineArr[1])]) # 每一行的前两个数据存入特征集
(int(lineArr[2])) # 每一行的最后一个数据存入标签集
return dataMat, labelMat
"""Sigmoid函数"""
def sigmoid(inX):
return 1.0 / (1 + (-inX))
"""梯度上升发"""
def gradAscent(dataMatIn, classLabels):
dataMatrix = (dataMatIn) # 转换成numpy的mat
labelMat = (classLabels).transpose() # 转换成numpy的mat,并进行转置
m, n = (dataMatrix) # 返回dataMatrix的大小。m为行数,n为列数。
alpha = 0.001 # 移动步长,也就是学习速率,控制更新的幅度。
maxCycles = 500 # 最大迭代次数
weights = ((n, 1)) # 权重，含有一列向量，并且元素都为1的矩阵
for k in range(maxCycles): # 迭代500次
h = sigmoid(dataMatrix * weights) # 梯度上升矢量化公式
error = labelMat - h
weights = weights + alpha * () * error
return ()
"""绘制拟合曲线"""
def plotBestFit(weights):
dataMat, labelMat = loadDataSet()
dataArr = (dataMat)
n = (dataMat)[0] # 数据个数
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
for i in range(n):
if int(labelMat[i]) == 1: # 红色的点
(dataArr[i, 1])
(dataArr[i, 2])
else: # 绿色的点
(dataArr[i, 1])
(dataArr[i, 2])
fig = () # 画布大小默认
ax = fig.add_subplot(111) # 设置子图
(xcord1, ycord1, s=30, c='red', marker='s')
(xcord2, ycord2, s=30, c='green')
x = (-3.0, 3.0, 0.1) # 绘制你和曲线，x轴方向每次增加0.1个点
y = (-weights[0] - weights[1] * x) / weights[2] # 绘制你和曲线 y轴方向每次增加幅度
(x, y)
('BestFit')
('X1')
('X2')
()
if __name__ == '__main__':
dataMat, labelMat = loadDataSet()
weights = gradAscent(dataMat, labelMat)
plotBestFit(weights)