#!python
#coding=UTF-8
# logistic
# sigmod
# O(z)=1/(1+e(-z))
# w:=w+af(w)
#Logistic
from numpy import *
def loadDataSet():
dataMat = [];labelMat = []
fr = open('./testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0,float(lineArr[0]),float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return mat(dataMat),mat(labelMat)
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def gradAscent(dataMatIn,classLabels):
dataMatrix = mat(dataMatIn)
labelMat = mat(classLabels).transpose()
m,n = shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = ones((n,1))
for k in range(maxCycles):
h = sigmoid(dataMatrix*weights)
error = (labelMat - h)
weights = weights + alpha * dataMatrix.transpose()*error
return weights
#画出分割线
def plotBestFit(wei):
import matplotlib.pyplot as plt
weights = wei.getA()
dataMat,labelMat = loadDataSet()
dataArr = array(dataMat)
n = shape(dataArr)[0]
xcord1 = []; ycord1 = []
xcord2 = []; ycord2 = []
for i in range(n):
if int(labelMat[i]) == 1:
xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])
else:
xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
ax.scatter(xcord2, ycord2, s=30, c='green')
x = arange(-3.0, 3.0, 0.1)
y = (-weights[0] - weights[1]*x)/weights[2]
ax.plot(x,y)
plt.xlabel('x1');plt.ylabel('x2');
plt.show()
#随机梯度上升算法
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n)
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights));
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
#改进的随机梯度上升算法
def stocGradAscent1(dataMatrix,classLabels,numIter=150):
m,n = shape(dataMatrix)
weights = ones(n)
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.01
randIndex = int(random.uniform(0,len(dataIndex)))
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
weights = weights + alpha * error * dataMatrix[randIndex]
del(dataIndex[randIndex])
return weights
Logistic回归numpy版本
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转载自blog.csdn.net/mengjiexu_cn/article/details/83019093
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