from math import log

import operator


"""程序清单3-1 计算给定数据集的香农熵"""

def calcShannonEnt(dataSet):

    numEntries = len(dataSet)

    labelCounts = {}

    for featVec in dataSet:  # the the number of unique elements and their occurance

        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)  # log base 2

    return shannonEnt


"""程序清单3-2 按照给定的特征划分数据集"""

"""dataSet待划分数据集 axis划分数据集的特征 value特征的返回值"""

def splitDataSet(dataSet, axis, value):

    retDataSet = []

    for featVec in dataSet:

        if featVec[axis] == value:

            reducedFeatVec = featVec[:axis]     #chop out axis used for splitting

            reducedFeatVec.extend(featVec[axis+1:])

            retDataSet.append(reducedFeatVec)

    return retDataSet


"""程序清单3-3 选择最好的数据集划分方式"""

def chooseBestFeatureToSplit(dataSet):

    numFeatures = len(dataSet[0]) - 1      #the last column is used for the labels

    baseEntropy = calcShannonEnt(dataSet)

    bestInfoGain = 0.0; bestFeature = -1

    for i in range(numFeatures):        #iterate over all the features

        featList = [example[i] for example in dataSet]#create a list of all the examples of this feature

        uniqueVals = set(featList)       #get a set of unique values

        newEntropy = 0.0

        for value in uniqueVals:

            subDataSet = splitDataSet(dataSet, i, value)

            prob = len(subDataSet)/float(len(dataSet))

            newEntropy += prob * calcShannonEnt(subDataSet)

        infoGain = baseEntropy - newEntropy     #calculate the info gain; ie reduction in entropy

        if (infoGain > bestInfoGain):       #compare this to the best gain so far

            bestInfoGain = infoGain         #if better than current best, set to best

            bestFeature = i

    return bestFeature                      #returns an integer


"""程序清单3-4 创建树的代码"""

"""dataSet是数据集 labels是标签列表"""

def createTree(dataSet,labels):

    classList = [example[-1] for example in dataSet]

    if classList.count(classList[0]) == len(classList):

        return classList[0]#stop splitting when all of the classes are equal

    if len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSet

        return majorityCnt(classList)

    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[:]       #copy all of labels, so trees don't mess up existing labels

        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)

    return myTree


def createDataSet():

    dataSet = [[1, 1, 'yes'],

               [1, 1, 'yes'],

               [1, 0, 'no'],

               [0, 1, 'no'],

               [0, 1, 'no']]

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

    #change to discrete values

    return dataSet, labels


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]


if __name__ == "__main__":

    myDat, labels = createDataSet()

    # print(myDat)

    # print(labels)

    """输出熵,熵越高则混合的数据越多"""

    #print(calcShannonEnt(myDat))

    """0.9709505944546686"""

    # myDat[0][-1] = "maybe"

    # print(myDat)

    # print(calcShannonEnt(myDat))

    """1.3709505944546687"""

    #print(splitDataSet(myDat, 0, 1))

    """[[1, 'yes'], [1, 'yes'], [0, 'no']]"""

    #print(splitDataSet(myDat, 0, 0))

    """[[1, 'no'], [1, 'no']]"""

    #print(chooseBestFeatureToSplit(myDat))

    """0"""

    """第0特征是最好的用于划分数据集的特征"""

    mytree = createTree(myDat, labels)

    print(mytree)