决策树（decision tree)

Posted 2020-10-21 卷珠帘

代码还好懂，但是后面选择更好的划分数据集的方法，有点不知道为什么那样选。

还要好好理解推导。

from math import log
#计算香农熵
def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCount = {}
    for featVector in dataSet:
        currentlabel = featVector[-1]
        labelCount[currentlabel] = labelCount.get(currentlabel,0) + 1
        shannonEnt = 0.0
        for key in labelCount:
            prob = float(labelCount[key])/numEntries
            shannonEnt -= prob * log(prob, 2)
    return shannonEnt
#训练样本
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
#按照给定特征划分数据集
def splitDataSet(dataSet,axis,value):
    retDataSet = []
    for featVec in dataSet:
        if(featVec[0]==value):
            reducedFeatVec = featVec[:axis] #这个变量干嘛的？
            reducedFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reducedFeatVec)
    return retDataSet
def main():
    dataSet,labels = createDataSet()
    # shannonEnt = calcShannonEnt(dataSet) #香农熵
    # print(shannonEnt)
    print(splitDataSet(dataSet,0,1))
    print(splitDataSet(dataSet,0,0))
main()

append和extend区别：

a = [1,2,3]
c = [1,2,3]
b = [4,5,6]
a.append(b)
c.extend(b)
print(a)
print(c)

[1, 2, 3, [4, 5, 6]]
[1, 2, 3, 4, 5, 6]