# -- coding: utf-8 --
from numpy import*
from math import log
import operator
import matplotlib.pyplot as plt
def loadDataSet():
path = r'E:\file\python\test\test\logisticRession_data\testSet.txt'
dataMat = []
labelMat = []
fr = open(path)
for line in fr.readlines():
lineArr = line.strip().split() #移除字符串 头尾 中间的空格
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])]) #矩阵增广
labelMat.append(int(lineArr[2]))
return dataMat, labelMat
def sigmoid(inX):
return 1.0/(1 + exp(-inX))
#训练权值
#梯度上升 批处理方法 离线
def gradAscent(dataMatIn, classLabels):
dataMatrix = mat(dataMatIn) #构造矩阵 100*3
labelMat = mat(classLabels).transpose() #transpose() 转置 变成列向量 100*1
m, n = shape(dataMatrix) #m为行数100 n为列数3
alpha = 0.001 #下降速率参数
maxCycles = 500 #迭代次数
weights = ones((n,1)) #构造n，1矩阵 3*1
for k in range(maxCycles):
h = sigmoid(dataMatrix * weights)
error = (labelMat - h)
weights = weights + alpha*dataMatrix.transpose()*error #批处理方式
return weights
#画出决策边界
def plotBestFit(wei):
# weight = wei.getA() #将numpy矩阵转换为数组
weight = wei
dataMat , labelMat = loadDataSet()
dataArr = array(dataMat)
n = shape(dataArr)[0]
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
for i in range(n):
if 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) #与range()类似，返回一个array对象 （起点，终点，步长）
y = (-weight[0]-weight[1]*x)/weight[2]
ax.plot(x,y)
plt.xlabel('X1')
plt.xlabel('X2')
plt.show()
#随机梯度上升法 在线算法
def stoGradAscent0(dataMatIn, classLabels):
m, n = shape(dataMatIn) # m为行数100 n为列数3
alpha = 0.01 # 下降速率参数
weights = ones(n) # 构造n维向量 数组array
for i in range(m):
h = sigmoid(sum(dataMatIn[i] * weights))
error = classLabels[i] - h
weights = weights + alpha* error * dataMatIn[i] #列表list 数组array 矩阵mat 三者不一样 数组可以a*data【i】 列表不可以
return weights
#随机梯度上升法 改进
def stoGradAscent1(dataMatIn, classLabels, numIter = 150):
m, n = shape(dataMatIn) # m为行数100 n为列数3
weights = ones(n) # 构造n维向量 数组array
for j in range(numIter): #循环150
dataIndex = range(m) #范围0-99
for i in range(m): #循环100次
alpha = 4/(1.0+j+i) + 0.01
randIndex = int(random.uniform(0, len(dataIndex)))
h = sigmoid(sum(dataMatIn[randIndex] * weights))
error = classLabels[randIndex] - h
weights = weights + alpha* error * dataMatIn[randIndex]
del(dataIndex[randIndex])
return weights
#分类函数
def classisfyVector(inX, weights):
prob = sigmoid(sum(inX*weights))
if prob > 0.5:
return 1.0
else:
return 0.0
path1 = r'E:\file\python\test\test\logisticRession_data\horseColicTest.txt'
path2 = r'E:\file\python\test\test\logisticRession_data\horseColicTraining.txt'
#使用logistic ression 算法
def colicTest():
frTrain = open(path2)
frTest = open(path1)
trainSet = []
labelSet = []
for lines in frTrain.readlines():
curline = lines.strip().split()
lineArr = []
for i in range(21):
lineArr.append(float(curline[i]))
trainSet.append(lineArr)
labelSet.append(float(curline[21]))
wegiht = stoGradAscent1(array(trainSet), labelSet, 500)
errcount = 0.0
numtest = 0.0
for lines in frTest.readlines():
numtest += 1.0
curline = lines.strip().split()
Arr = []
for i in range(21):
Arr.append(float(curline[i]))
if int(classisfyVector(array(Arr), wegiht)) != int(curline[21]):
errcount += 1.0
errorrate = errcount/numtest
print('the error rate is :', errorrate)
return errorrate
def multitext():
errorsum = 0.0
for k in range(10):
errorsum += colicTest()
print(errorsum/float(10))
# -- coding: utf-8 --
from numpy import*
from logRegres import*
import matplotlib.pyplot as plt
dataArr, labelMat = loadDataSet()
W = gradAscent(dataArr, labelMat)
print(W)
we0 = stoGradAscent0(array(dataArr), labelMat) #将dataArr从列表变为数组再带入函数
we1 = stoGradAscent1(array(dataArr), labelMat)
#plotBestFit(we1)
multitext()
