每周一搏，提升自我。

这段时间对python的应用，对python的理解越来越深。摸索中修改网上实例代码，有了自己的理解。

c45是ID3算法的升级版，比ID3高级。个人建议，用CART算法，感觉比C45好。

下面是c45代码，其中显示决策树结构的代码，下篇博文发布。

#!/usr/bin/python
#coding:utf-8

import operator
from math import log
import time
import os,sys
import string

#已文件为数据源
def createDataSet(trainDataFile):
	print trainDataFile
	dataSet=[]
	try:
		fin=open(trainDataFile)
		for line in fin:
			line=line.strip('\n')  #清除行皆为换行符
			cols=line.split(',')  #逗号分割行信息
			row =[cols[1],cols[2],cols[3],cols[4],cols[5],cols[6],cols[7],cols[8],cols[9],cols[10],cols[0]]
			dataSet.append(row)
			#print row
	except:
		print 'Usage xxx.py trainDataFilePath'
		sys.exit()
	labels=['cip1', 'cip2', 'cip3', 'cip4', 'sip1', 'sip2', 'sip3', 'sip4', 'sport', 'domain']
	print 'dataSetlen',len(dataSet)
	return dataSet,labels

#c4.5 信息熵算法
def calcShannonEntOfFeature(dataSet,feat):
	numEntries=len(dataSet)
	labelCounts={}
	for feaVec in dataSet:
		currentLabel=feaVec[feat]
		if currentLabel not in labelCounts:
			labelCounts[currentLabel]=0
		labelCounts[currentLabel]+=1
	shannonEnt=0.0
	for key in labelCounts:
		prob=float(labelCounts[key])/numEntries
		shannonEnt-=prob * log(prob,2)
	return shannonEnt

def splitDataSet(dataSet,axis,value):
	retDataSet=[]
	for featVec in dataSet:
		if featVec[axis] ==value:
			reducedFeatVec=featVec[:axis]
			reducedFeatVec.extend(featVec[axis+1:])
			retDataSet.append(reducedFeatVec)
	return retDataSet

def chooseBestFeatureToSplit(dataSet):
	numFeatures=len(dataSet[0])-1
	baseEntropy=calcShannonEntOfFeature(dataSet,-1)
	bestInfoGainRate=0.0
	bestFeature=-1
	for i in range(numFeatures):
		featList=[example[i] for example in dataSet]
		uniqueVals=set(featList)
		newEntropy=0.0
		for value in uniqueVals:
			subDataSet=splitDataSet(dataSet,i,value)
			prob=len(subDataSet) / float(len(dataSet))
			newEntropy+=prob * calcShannonEntOfFeature(subDataSet,-1)
		infoGain=baseEntropy- newEntropy
		iv = calcShannonEntOfFeature(dataSet,i)
		if(iv == 0):
			continue
		infoGainRate= infoGain /iv
		if infoGainRate > bestInfoGainRate:
			bestInfoGainRate = infoGainRate
			bestFeature = i
	return bestFeature

def majorityCnt(classList):
	classCount={}
	for vote in classList:
		if vote not in classCount.keys():
			classCount[vote]=0
		classCount[vote] +=1
	return max(classCount)


def createTree(dataSet,labels):
	classList= [example[-1] for example in dataSet]
	if classList.count(classList[0]) == len(classList):
		return classList[0]
	if len(dataSet[0]) == 1:
		return majorityCnt(classList)
	bestFeat = chooseBestFeatureToSplit(dataSet)
	bestFeatLabel = labels[bestFeat]
	if(bestFeat == -1): #特征一样，但类别不一样，即类别与特征不相关，随机选第一个类别分类结果
		return classList[0]
	myTree={bestFeatLabel:{}}
	del(labels[bestFeat])
	featValues =  [example[bestFeat] for example in dataSet]
	uniqueVals =set(featValues)
	for value in uniqueVals:
		subLabels = labels [:]
		myTree[bestFeatLabel][value]=createTree(splitDataSet(dataSet,bestFeat,value),subLabels)
	return myTree


#创建简单的数据集   武器类型（0 步枪 1机枪），子弹（0 少 1多），血量（0 少，1多）  fight战斗 1逃跑 
def createDataSet():
	dataSet =[[1,1,0,'fight'],[1,0,1,'fight'],[1,0,1,'fight'],[1,0,1,'fight'],[0,0,1,'run'],[0,1,0,'fight'],[0,1,1,'run']]
	lables=['weapon','bullet','blood']
	return dataSet,lables

#按行打印数据集
def printData(myData):
	for item in myData:
		print '%s' %(item)

#使用决策树分类
def classify(inputTree,featLabels,testVec):
	firstStr=inputTree.keys()[0]
	secondDict=inputTree[firstStr]
	featIndex=featLabels.index(firstStr)
	for key in secondDict.keys():
		if testVec[featIndex] ==key:
			if type(secondDict[key]).__name__=='dict':
				classLabel=classify(secondDict[key],featLabels,testVec)
			else:classLabel=secondDict[key]
	return classLabel

#存储决策树
def storeTree(inputTree,filename):
	import pickle
	fw=open(filename,'w')
	pickle.dump(inputTree,fw)
	fw.close()


#获取决策树
def grabTree(filename):
	import pickle
	fr=open(filename)
	return pickle.load(fr)



def main():
	data,label =createDataSet()
	myTree=createTree(data,label)
	print(myTree)

	#打印决策树
	import showTree as show
	show.createPlot(myTree)


if __name__ == '__main__':
	main()