决策树的简单实现

来自机器学习实战简单的实例

#!/usr/bin/python# coding:utf-8from math import logdef createDataset():    #加载数据返回标签和数据集    dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [        1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no']]    labels = ['no surfacing', 'flippers']    return labels, dataSetdef calcShannonEnt(dataSet):#计算香侬熵    numEntries = len(dataSet)    labelCounts = {}    for featVec in dataSet:        currentLabel = featVec[-1]        if currentLabel not in labelCounts.keys():            labelCounts[currentLabel] = 0        labelCounts[currentLabel] += 1    shannonEnt = 0.0    for key in labelCounts:        prob = float(labelCounts[key]) / numEntries        shannonEnt -= prob * log(prob, 2)    return shannonEntdef splitDataSet(dataSet, axis, value):#划分数据集，    retDataSet = []    for featVec in dataSet:        if featVec[axis] == value:            retDataSet.append(featVec[axis + 1:])    return retDataSetdef chooseBestFeatureToSplit(dataSet):#计算有最大香侬熵的特征    numFeature = len(dataSet[0]) - 1    for i in range(numFeature):        1        feature = [example[i] for example in dataSet]        print(feature)labels, dataSet = createDataset()calcShannonEnt(dataSet)chooseBestFeatureToSplit(dataSet)