此演算法需要更改的地方出除了上篇寫到的loadDataSet函式，在課本中getBestFeat（）函式中資訊增益計算公式處給出的矩陣相除在py3無法執行需要改為dot（A,B.T）形式

具體程式碼

 def getBestFeat(self,dataSet):
        Num_Feats=len(dataSet[0][:-1])
        totality=len(dataSet)
        BaseEntropy=self.computeEntropy(dataSet)
        ConditionEntropy=[]
        splitInfo=[]
        allFeatVList=[]
        for f in range(Num_Feats):
            featList=[example[f] for example in dataSet]
            [splitI,featureValueList]=self.computeSplitInfo(featList)
            allFeatVList.append(featureValueList)
            splitInfo.append(splitI)
            resultGain=0.0
            for value in featureValueList:
                subSet=self.splitDataSet(dataSet,f,value)
                appearNum=float(len(subSet))
                subEntropy=self.computeEntropy(subSet)
                resultGain+=(appearNum/totality)*subEntropy
            ConditionEntropy.append(resultGain)
        infoGainArray=BaseEntropy*ones(Num_Feats)-array(ConditionEntropy)
       # infoGainRatio=infoGainArray/array(splitInfo)#py2可以這樣做但是py3不行
        infoGainRatio=dot(infoGainArray,array(splitInfo).T)#py3這種用法更貼近線性代數中矩陣除法形式
        bestFeatureIndex=argsort(-infoGainRatio)[0]
        return bestFeatureIndex,allFeatVList[bestFeatureIndex]