看了半个礼拜的朴素贝叶斯，依然没有理解，想想还是跳过先看逻辑回归吧。
前面巴拉巴拉的话就不说了，下面直接贴代码。
5.2.2 训练算法
from math import *
from numpy import *
import os
os.chdir('D:\xx\machinelearning\MLiA_SourceCode')
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
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数据依然还是作者给的，用之前需要提前加载。
上面的代码没有什么大问题，就是在gradAscent函数中，作者给出了梯度计算
dataMatrix.transpose() * error但是作者卖了个关子，原话是“最后还需说明一点，你可能对公式中的前两行觉得陌生。此处略去了一个简单的数学推导，我把它留给有兴趣的读者。”
显然作者高估了我的水平，我倒是挺有兴趣，但是没能力啊。有哪位大佬知道推导过程请联系我，感激不尽，临表涕零。
接着，照葫芦画瓢，测试一下：
5.2.3 画出决策边界
代码直接贴上：
def plotBestFit(weights):
import matplotlib.pyplot as plt
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()测试一下：
reload(logRegres) weights = logRegres.gradAscent(dataArr,labelMat)#注意原书上有有坑，labelMat敲成了LabelMat logRegres.plotBestFit(weights.getA())看一下结果：
5.2.4 训练算法：随机梯度上升
代码是：
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看下结果：
这个分类器错分了三分之一的样本。
接下来进行改进：
def stocGradAscent1(dataMatrix,classLabels,numIter = 150):
m,n = shape(dataMatrix)
weights = ones(n)
for j in range(numIter):
dataIndex = list(range(m))#python3中range不返回数组对象，而是返回range对象，所以注意与原书的区别
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看下结果：
这个结果与gradAscent()差不多的效果，所用计算也得到减少。
5.3 从疝气病症预测病马的死亡率
5.3.1 处理数据中的缺失值
1.使用0来替换缺失值
2.类别标签缺失，将该条数据丢弃
5.3.2测试算法，使用Logistic回归进行分类
这块代码比较简单，直接贴上代码：
def classifyVector(inX,weights):
prob = sigmoid(sum(inX * weights))
if prob > 0.5:
return 1.0
else:
return 0.0
def colicTest():
frTrain = open('horseColicTraining.txt')
frTest = open('horseColictest.txt')
trainingSet = []; trainingLabels = []
for line in frTrain.readlines():
currLine = line.strip().split('\t')
lineArr = []
for i in range(21):
lineArr.append(float(currLine[i]))
trainingSet.append(lineArr)
trainingLabels.append(float(currLine[21]))
trainWeights = stocGradAscent1(array(trainingSet),trainingLabels,500)
errorCount = 0;numTestVec = 0.0
for line in frTest.readlines():
numTestVec += 1.0
currLine = line.strip().split('\t')
lineArr = []
for i in range(21):
lineArr.append(float(currLine[i]))
if int(classifyVector(array(lineArr),trainWeights)) != int(currLine[21]):
errorCount += 1
errorRate = (float(errorCount)/numTestVec)
print('the error rate of this test is: %f' %errorRate)
return errorRate
def multiTest():
numTests = 10;errorSum = 0.0
for k in range(numTests):
errorSum += colicTest()
print('after %d iterations the average error rate is:'
'%f' %(numTests,errorSum/float(numTests)))
测试结果为：
logRegres.multiTest() the error rate of this test is: 0.283582 the error rate of this test is: 0.388060 the error rate of this test is: 0.298507 the error rate of this test is: 0.388060 the error rate of this test is: 0.447761 the error rate of this test is: 0.313433 the error rate of this test is: 0.283582 the error rate of this test is: 0.402985 the error rate of this test is: 0.343284 the error rate of this test is: 0.417910 after 10 iterations the average error rate is:0.356716这章逻辑回归的内容还是比较简单的，个人感觉数据预处理阶段实际上应该还有很多有技巧的手段，只有数据合适，后续的算法建立才能顺利进行。