#!/usr/bin/env python
# coding: utf-8

from math import log


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


# 初始值
def createDataSet():
    dataSet = [[1, 1, 'yes'],
               [1, 0, 'yes'],
               [1, 0, 'no'],
               [0, 1, 'no'],
               [0, 1, 'no']]
    label = ['no surfaceing', 'flippers']
    return dataSet, label


# 按照给定特征划分数据集
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)
    return retDataSet


# 选择最好数据集的划分方式
def choooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet)
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]
        uniqyeVals = set(featList)
        newEntropy = 0.0
        for value in uniqyeVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet)/float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
        infoGain = baseEntropy - newEntropy
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

"""
myData, labels = createDataSet()
print(myData)
calcShannonEnt(myData)
myData[0][-1] = 'maybe'
print(myData)
shannonEnt = calcShannonEnt(myData)
print(shannonEnt)
myData[0][-1] = 'yes'
data = splitDataSet(myData, 0, 1)
print(data)
data = splitDataSet(myData, 0, 0)
print(data)
"""