#coding=utf-8
import operator
from math import log
import time

def createDataSet():
  dataSet=[[1,1,'yes'],
      [1,1,'yes'],
      [1,0,'no'],
      [0,1,'no'],
      [0,1,'no']]
  labels = ['no surfaceing','flippers']
  return dataSet, labels

#计算香农熵
def calcShannonEnt(dataSet):
  numEntries = len(dataSet)
  labelCounts = {}
  for feaVec in dataSet:
    currentLabel = feaVec[-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

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
  
def chooseBestFeatureToSplit(dataSet):
  numFeatures = len(dataSet[0]) - 1#因为数据集的最后一项是标签
  baseEntropy = calcShannonEnt(dataSet)
  bestInfoGain = 0.0
  bestFeature = -1
  for i in range(numFeatures):
    featList = [example[i] for example in dataSet]
    uniqueVals = set(featList)
    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
    if infoGain > bestInfoGain:
      bestInfoGain = infoGain
      bestFeature = i
  return bestFeature
      
#因为我们递归构建决策树是根据属性的消耗进行计算的，所以可能会存在最后属性用完了，但是分类
#还是没有算完，这时候就会采用多数表决的方式计算节点分类
def majorityCnt(classList):
  classCount = {}
  for vote in classList:
    if vote not in classCount.keys():
      classCount[vote] = 0
    classCount[vote] += 1
  return max(classCount)     
  
def createTree(dataSet, labels):
  classList = [example[-1] for example in dataSet]
  if classList.count(classList[0]) ==len(classList):#类别相同则停止划分
    return classList[0]
  if len(dataSet[0]) == 1:#所有特征已经用完
    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[:]#为了不改变原始列表的内容复制了一下
    myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, 
                    bestFeat, value),subLabels)
  return myTree
  
def main():
  data,label = createDataSet()
  t1 = time.clock()
  myTree = createTree(data,label)
  t2 = time.clock()
  print myTree
  print 'execute for ',t2-t1
if __name__=='__main__':
  main()