《机器学习实战》第3章决策树程序清单3-1 计算给定数据集的香农熵calcShannonEnt()运行过程

from math import log

def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    print("样本总数：" + str(numEntries))

    labelCounts = {} #记录每一类标签的数量

    #定义特征向量featVec
    for featVec in dataSet:
        
        currentLabel = featVec[-1] #最后一列是类别标签

        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0;

        labelCounts[currentLabel] += 1 #标签currentLabel出现的次数
        print("当前labelCounts状态：" + str(labelCounts))

    shannonEnt = 0.0

    for key in labelCounts:
        
        prob = float(labelCounts[key]) / numEntries #每一个类别标签出现的概率

        print(str(key) + "类别的概率：" + str(prob))
        print(prob * log(prob, 2) )
        shannonEnt -= prob * log(prob, 2) 
        print("熵值：" + str(shannonEnt))

    return shannonEnt


def createDataSet():
    dataSet = [
        # [1, 1, ‘yes‘],
        # [1, 0, ‘yes‘],
        # [1, 1, ‘no‘],
        # [0, 1, ‘no‘],
        # [0, 1, ‘no‘],
        # #以下随意添加，用于测试熵的变化，越混乱越冲突，熵越大
        # [1, 1, ‘no‘],
        # [1, 1, ‘no‘],
        # [1, 1, ‘no‘],
        # [1, 1, ‘no‘],
        # [1, 1, ‘maybe‘],
        # [1, 1, ‘maybe1‘]
        # 用下面的8个比较极端的例子看得会更清楚。
        [1,1,‘1‘],
        [1,1,‘2‘],
        [1,1,‘3‘],
        [1,1,‘4‘],
        [1,1,‘5‘],
        [1,1,‘6‘],
        [1,1,‘7‘],
        [1,1,‘8‘],


    ]

    labels = [‘no surfacing‘, ‘flippers‘]

    return dataSet, labels

def testCalcShannonEnt():

    myDat, labels = createDataSet()
    print(calcShannonEnt(myDat))

if __name__ == ‘__main__‘:
    testCalcShannonEnt()
    print(log(0.000002, 2))