#!/usr/bin/python
# coding=utf-8
from numpy import *
# 过滤网站的恶意留言 侮辱性：1   非侮辱性：0
# 创建一个实验样本
def loadDataSet():
  postingList = [['my','dog','has','flea','problems','help','please'],
          ['maybe','not','take','him','to','dog','park','stupid'],
          ['my','dalmation','is','so','cute','I','love','him'],
          ['stop','posting','stupid','worthless','garbage'],
          ['mr','licks','ate','my','steak','how','to','stop','him'],
          ['quit','buying','worthless','dog','food','stupid']]
  classVec = [0,1,0,1,0,1]
  return postingList, classVec
# 创建一个包含在所有文档中出现的不重复词的列表
def createVocabList(dataSet):
  vocabSet = set([])   # 创建一个空集
  for document in dataSet:
    vocabSet = vocabSet | set(document)  # 创建两个集合的并集
  return list(vocabSet)
# 将文档词条转换成词向量
def setOfWords2Vec(vocabList, inputSet):
  returnVec = [0]*len(vocabList)    # 创建一个其中所含元素都为0的向量
  for word in inputSet:
    if word in vocabList:
      # returnVec[vocabList.index(word)] = 1   # index函数在字符串里找到字符第一次出现的位置 词集模型
      returnVec[vocabList.index(word)] += 1   # 文档的词袋模型  每个单词可以出现多次
    else: print "the word: %s is not in my Vocabulary!" % word
  return returnVec
# 朴素贝叶斯分类器训练函数  从词向量计算概率
def trainNB0(trainMatrix, trainCategory):
  numTrainDocs = len(trainMatrix)
  numWords = len(trainMatrix[0])
  pAbusive = sum(trainCategory)/float(numTrainDocs)
  # p0Num = zeros(numWords); p1Num = zeros(numWords)
  # p0Denom = 0.0; p1Denom = 0.0
  p0Num = ones(numWords);  # 避免一个概率值为0,最后的乘积也为0
  p1Num = ones(numWords);  # 用来统计两类数据中，各词的词频
  p0Denom = 2.0; # 用于统计0类中的总数
  p1Denom = 2.0 # 用于统计1类中的总数
  for i in range(numTrainDocs):
    if trainCategory[i] == 1:
      p1Num += trainMatrix[i]
      p1Denom += sum(trainMatrix[i])
    else:
      p0Num += trainMatrix[i]
      p0Denom += sum(trainMatrix[i])
      # p1Vect = p1Num / p1Denom
      # p0Vect = p0Num / p0Denom
  p1Vect = log(p1Num / p1Denom)  # 在类1中，每个次的发生概率
  p0Vect = log(p0Num / p0Denom)   # 避免下溢出或者浮点数舍入导致的错误  下溢出是由太多很小的数相乘得到的
  return p0Vect, p1Vect, pAbusive
# 朴素贝叶斯分类器
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
  p1 = sum(vec2Classify*p1Vec) + log(pClass1)
  p0 = sum(vec2Classify*p0Vec) + log(1.0-pClass1)
  if p1 > p0:
    return 1
  else:
    return 0
def testingNB():
  listOPosts, listClasses = loadDataSet()
  myVocabList = createVocabList(listOPosts)
  trainMat = []
  for postinDoc in listOPosts:
    trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
  p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses))
  testEntry = ['love','my','dalmation']
  thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
  print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)
  testEntry = ['stupid','garbage']
  thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
  print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)
# 调用测试方法----------------------------------------------------------------------
testingNB()