学步园

from math import log
import operator

def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts= {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0
        labelCounts[currentLabel] += 1
    print labelCounts
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key]) / numEntries
        shannonEnt -= prob * log(prob,2)
    return shannonEnt

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

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)
    #      if featVec[axis] == value:
 #            retDataSet.append(featVec)
    return retDataSet

def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    print numFeatures
    baseEntropy = calcShannonEnt(dataSet)
    print "baseEntropy = %s" % baseEntropy
    bestInfoGain = 0.0; bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]  #链表推导式 []：取出所有dataSet中的数据的第i个元素组成一个列表
        print "featList: %s" % featList
        uniqueVals  = set(featList)  #去重，不重的放在uniqueVals
        newEntropy = 0.0
        for value in uniqueVals:
            #print "i = %d, value = %d" % (i, value)
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet) / float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
            print newEntropy
        infoGain = baseEntropy - newEntropy
        print "infoGain = %s" % infoGain
        if infoGain > bestInfoGain:
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature 

def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        if vote not in classCount.keys():
            classCount[vote] = 0
        classCount[vote] += 1
    sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse = True)
    return sortedClassCount[0][0]

def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]  #dataSet里每个列表的的最右边元素组成一个新的列表clasList
    if classList.count(classList[0]) == len(classList):   #如果同一类，return类标签
        return classList[0]
    if len(dataSet[0]) == 1:                              #不是classList[0]，就是说第一行只有一个特征了，
        return majorityCnt(classList)                      #return站多数的标签
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)

    return myTree

def classify(inputTree, featLabels, testVec):
    firstStr = inputTree.keys()[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    for key in secondDict.keys():
        if testVec[featIndex] == key:
            if type(secondDict[key]).__name__ == 'dict':
                classLabel = classify(secondDict[key], featLabels, testVec)
            else:
                classLabel = secondDict[key]
    return classLabel