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客官°小女子只卖身不卖艺 2021-09-15 06:34 474阅读 0赞

#### 熵的定义 ####

![5057999-5702853710d12e87.png][]

计算给定数据集的熵

def calcShannonEnt(dataSet):
        numEntires = len(dataSet)                        #返回数据集的行数
        labelCounts = {}                                #保存每个标签(Label)出现次数的字典
        for featVec in dataSet:                            #对每组特征向量进行统计
            currentLabel = featVec[-1]                    #提取标签(Label)信息
            if currentLabel not in labelCounts.keys():    #如果标签(Label)没有放入统计次数的字典,添加进去
                labelCounts[currentLabel] = 0
            labelCounts[currentLabel] += 1                #Label计数
        shannonEnt = 0.0                                #经验熵(香农熵)
        for key in labelCounts:                            #计算香农熵
            prob = float(labelCounts[key]) /numEntires
            #选择该标签(Label)的概率
            shannonEnt -= prob * log(prob, 2)            #利用公式计算
        return shannonEnt                                #返回经验熵(香农熵)

数据集格式

![5057999-cd5960ca5e3eb8d1.png][]

def createDataSet():
        dataSet=[[1,1,'yes'],[1,1,'yes'],[1,0,'no'],[0,1,'no'],[0,1,'no']]
        labels=['no surfacing','flippers']
        return dataSet,labels

进行测试计算，添加了第三个名为maybe的分类，熵增加。（熵越高，则混合的数据越多）

from decisionTree import *
    
    myDat,labels=createDataSet()
    print(myDat)
    print(labels)
    print(calcShannonEnt(myDat))
    
    myDat[0][-1]='maybe'
    print(myDat)
    print(calcShannonEnt(myDat))

![5057999-3f32bc2d66071141.png][]

--------------------

### 按照给定特征划分数据集 ###

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

测试及结果

print(splitDataSet(myDat,0,1))
    #[[1, 'maybe'], [1, 'yes'], [0, 'no']]
    print(splitDataSet(myDat,0,0))
    #[[1, 'no'], [1, 'no']]

def chooseBestFeatureToSplit(dataSet):
        numFeatures = len(dataSet[0]) - 1                    #特征数量
        baseEntropy = calcShannonEnt(dataSet)                 #计算数据集的香农熵
        bestInfoGain = 0.0                                  #信息增益
        bestFeature = -1                                    #最优特征的索引值
        for i in range(numFeatures):                         #遍历所有特征
            #获取dataSet的第i个所有特征
            featList = [example[i] for example in dataSet]
            uniqueVals = set(featList)                         #创建set集合{},元素不可重复
            newEntropy = 0.0                                  #经验条件熵
            for value in uniqueVals:                         #计算信息增益
                subDataSet = splitDataSet(dataSet, i, value)         #subDataSet划分后的子集
                prob = len(subDataSet) / float(len(dataSet))           #计算子集的概率
                newEntropy += prob * calcShannonEnt(subDataSet)     #根据公式计算经验条件熵
            infoGain = baseEntropy - newEntropy                     #信息增益
            print("第%d个特征的增益为%.3f" % (i, infoGain))            #打印每个特征的信息增益
            if (infoGain > bestInfoGain):                             #计算信息增益
                bestInfoGain = infoGain                             #更新信息增益，找到最大的信息增益
                bestFeature = i                                     #记录信息增益最大的特征的索引值
        return bestFeature                                             #返回信息增益最大的特征的索引值

[5057999-5702853710d12e87.png]: /images/20210811/fe2e38229a92499197a6cbecee35f066.png
[5057999-cd5960ca5e3eb8d1.png]: /images/20210811/4ea840624b6640228b309fe621268533.png
[5057999-3f32bc2d66071141.png]: /images/20210811/8861b38b916f430aa251d2f0247aea54.png