In [183]:
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 dataMat,labelMat
In [184]:
def sigmoid(inX):
return 1.0/(1+exp(-inX))
批量梯度下降
In [185]:
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) # h是一个矩阵
error = (labelMat - h)
weights = weights + alpha * dataMatrix.transpose() * error
return weights
随机梯度下降
In [186]:
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n)
#weights = [0.1,0.1,0.1]
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))# h是一个数值
print dataMatrix[i]
print weights
print dataMatrix[i]*weights
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
sum()的参数是一个list 下面是改进的随机梯度上升算法：
In [187]:
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = shape(dataMatrix)
weights = ones(n)
#weights = [0.1,0.1,0.1]
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))# h是一个数值
error = classLabels[randIndex] - h
weights = weights + alpha * error * dataMatrix[randIndex]
del(dataIndex[randIndex])
return weights
In [188]:
#import logRegres
In [189]:
dataArr,labelMat = loadDataSet()
In [190]:
#weights=gradAscent(dataArr,labelMat)
weights=stocGradAscent1(array(dataArr),labelMat,500)
In [191]:
def plotBestFit(wei):
import matplotlib.pyplot as plt
weights = wei
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()
h = subplot(m,n,p)/subplot(mnp) 将figure划分为m×n块，在第p块创建坐标系，并返回它的句柄。当m,n,p<10时，可以简化为subplot(mnp)或者subplot mnp （注：subplot（m,n,p）或者subplot（mnp）此函数最常用：subplot是将多个图画到一个平面上的工具。其中，m表示是图排成m行，n表示图排成n列，也就是整个figure中有n个图是排成一行的，一共m行，如果第一个数字是2就是表示2行图。p是指你现在要把曲线画到figure中哪个图上，最后一个如果是1表示是从左到右第一个位置。 ）
In [192]:
from numpy import *
#reload
print weights
plotBestFit(weights)