#encoding:utf-8
'''''
Created on 2015年9月6日
@author: ZHOUMEIXU204
樸素貝葉斯實現過程
'''
#在該算法中類標簽為1和0，如果是多標簽稍微改動代碼既可
import numpy as np
path=u"D:\\Users\\zhoumeixu204\Desktop\\python語言機器學習\\機器學習實戰(zhàn)代碼  python\\機器學習實戰(zhàn)代碼\\machinelearninginaction\\Ch04\\"
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]  #1 is abusive, 0 not
  return postingList,classVec
def createVocabList(dataset):
  vocabSet=set([])
  for document in dataset:
    vocabSet=vocabSet|set(document)
  return list(vocabSet)
def setOfWordseVec(vocabList,inputSet):
  returnVec=[0]*len(vocabList)
  for word in inputSet:
    if word in vocabList:
      returnVec[vocabList.index(word)]=1  #vocabList.index() 函數獲取vocabList列表某個元素的位置，這段代碼得到一個只包含0和1的列表
    else:
      print("the word :%s is not in my Vocabulary!"%word)
  return returnVec
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
print(len(myVocabList))
print(myVocabList)
print(setOfWordseVec(myVocabList, listOPosts[0]))
print(setOfWordseVec(myVocabList, listOPosts[3]))
#上述代碼是將文本轉化為向量的形式，如果出現則在向量中為1，若不出現 ，則為0
def trainNB0(trainMatrix,trainCategory):  #創(chuàng)建樸素貝葉斯分類器函數
  numTrainDocs=len(trainMatrix)
  numWords=len(trainMatrix[0])
  pAbusive=sum(trainCategory)/float(numTrainDocs)
  p0Num=np.ones(numWords);p1Num=np.ones(numWords)
  p0Deom=2.0;p1Deom=2.0
  for i in range(numTrainDocs):
    if trainCategory[i]==1:
      p1Num+=trainMatrix[i]
      p1Deom+=sum(trainMatrix[i])
    else:
      p0Num+=trainMatrix[i]
      p0Deom+=sum(trainMatrix[i])
  p1vect=np.log(p1Num/p1Deom)  #change to log
  p0vect=np.log(p0Num/p0Deom)  #change to log
  return p0vect,p1vect,pAbusive
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
  trainMat.append(setOfWordseVec(myVocabList, postinDoc))
p0V,p1V,pAb=trainNB0(trainMat, listClasses)
if __name__!='__main__':
  print("p0的概況")
  print (p0V)
  print("p1的概率")
  print (p1V)
  print("pAb的概率")
  print (pAb)

# -*- coding:utf-8 -*-
#!python2
#構建樣本分類器testEntry=['love','my','dalmation'] testEntry=['stupid','garbage']到底屬于哪個類別
import numpy as np
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]  #1 is abusive, 0 not
  return postingList,classVec
def createVocabList(dataset):
  vocabSet=set([])
  for document in dataset:
    vocabSet=vocabSet|set(document)
  return list(vocabSet)
def setOfWordseVec(vocabList,inputSet):
  returnVec=[0]*len(vocabList)
  for word in inputSet:
    if word in vocabList:
      returnVec[vocabList.index(word)]=1  #vocabList.index() 函數獲取vocabList列表某個元素的位置，這段代碼得到一個只包含0和1的列表
    else:
      print("the word :%s is not in my Vocabulary!"%word)
  return returnVec
def trainNB0(trainMatrix,trainCategory):  #創(chuàng)建樸素貝葉斯分類器函數
  numTrainDocs=len(trainMatrix)
  numWords=len(trainMatrix[0])
  pAbusive=sum(trainCategory)/float(numTrainDocs)
  p0Num=np.ones(numWords);p1Num=np.ones(numWords)
  p0Deom=2.0;p1Deom=2.0
  for i in range(numTrainDocs):
    if trainCategory[i]==1:
      p1Num+=trainMatrix[i]
      p1Deom+=sum(trainMatrix[i])
    else:
      p0Num+=trainMatrix[i]
      p0Deom+=sum(trainMatrix[i])
  p1vect=np.log(p1Num/p1Deom)  #change to log
  p0vect=np.log(p0Num/p0Deom)  #change to log
  return p0vect,p1vect,pAbusive
def  classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
  p1=sum(vec2Classify*p1Vec)+np.log(pClass1)
  p0=sum(vec2Classify*p0Vec)+np.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(setOfWordseVec(myVocabList, postinDoc))
  p0V,p1V,pAb=trainNB0(np.array(trainMat),np.array(listClasses))
  print("p0V={0}".format(p0V))
  print("p1V={0}".format(p1V))
  print("pAb={0}".format(pAb))
  testEntry=['love','my','dalmation']
  thisDoc=np.array(setOfWordseVec(myVocabList, testEntry))
  print(thisDoc)
  print("vec2Classify*p0Vec={0}".format(thisDoc*p0V))
  print(testEntry,'classified as :',classifyNB(thisDoc, p0V, p1V, pAb))
  testEntry=['stupid','garbage']
  thisDoc=np.array(setOfWordseVec(myVocabList, testEntry))
  print(thisDoc)
  print(testEntry,'classified as :',classifyNB(thisDoc, p0V, p1V, pAb))
if __name__=='__main__':
  testingNB()

# -*- coding:utf-8 -*-
#! python2
#使用樸素貝葉斯過濾垃圾郵件
# 1.收集數據：提供文本文件
# 2.準備數據：講文本文件見習成詞條向量
# 3.分析數據：檢查詞條確保解析的正確性
# 4.訓練算法：使用我們之前簡歷的trainNB0()函數
# 5.測試算法：使用classifyNB()，并且對建一個新的測試函數來計算文檔集的錯誤率
# 6.使用算法，構建一個完整的程序對一組文檔進行分類，將錯分的文檔輸出到屏幕上
# import re
# mySent='this book is the best book on python or M.L. I hvae ever laid eyes upon.'
# print(mySent.split())
# regEx=re.compile('\\W*')
# print(regEx.split(mySent))
# emailText=open(path+"email\\ham\\6.txt").read()
import numpy as np
path=u"C:\\py\\jb51PyDemo\\src\\Demo\\Ch04\\"
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]  #1 is abusive, 0 not
  return postingList,classVec
def createVocabList(dataset):
  vocabSet=set([])
  for document in dataset:
    vocabSet=vocabSet|set(document)
  return list(vocabSet)
def setOfWordseVec(vocabList,inputSet):
  returnVec=[0]*len(vocabList)
  for word in inputSet:
    if word in vocabList:
      returnVec[vocabList.index(word)]=1  #vocabList.index() 函數獲取vocabList列表某個元素的位置，這段代碼得到一個只包含0和1的列表
    else:
      print("the word :%s is not in my Vocabulary!"%word)
  return returnVec
def trainNB0(trainMatrix,trainCategory):  #創(chuàng)建樸素貝葉斯分類器函數
  numTrainDocs=len(trainMatrix)
  numWords=len(trainMatrix[0])
  pAbusive=sum(trainCategory)/float(numTrainDocs)
  p0Num=np.ones(numWords);p1Num=np.ones(numWords)
  p0Deom=2.0;p1Deom=2.0
  for i in range(numTrainDocs):
    if trainCategory[i]==1:
      p1Num+=trainMatrix[i]
      p1Deom+=sum(trainMatrix[i])
    else:
      p0Num+=trainMatrix[i]
      p0Deom+=sum(trainMatrix[i])
  p1vect=np.log(p1Num/p1Deom)  #change to log
  p0vect=np.log(p0Num/p0Deom)  #change to log
  return p0vect,p1vect,pAbusive
def  classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
  p1=sum(vec2Classify*p1Vec)+np.log(pClass1)
  p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)
  if p1>p0:
    return 1
  else:
    return 0
def textParse(bigString):
  import re
  listOfTokens=re.split(r'\W*',bigString)
  return [tok.lower() for tok in listOfTokens if len(tok)>2]
def spamTest():
  docList=[];classList=[];fullText=[]
  for i in range(1,26):
    wordList=textParse(open(path+"email\\spam\\%d.txt"%i).read())
    docList.append(wordList)
    fullText.extend(wordList)
    classList.append(1)
    wordList=textParse(open(path+"email\\ham\\%d.txt"%i).read())
    docList.append(wordList)
    fullText.extend(wordList)
    classList.append(0)
  vocabList=createVocabList(docList)
  trainingSet=range(50);testSet=[]
  for i in range(10):
    randIndex=int(np.random.uniform(0,len(trainingSet)))
    testSet.append(trainingSet[randIndex])
    del (trainingSet[randIndex])
  trainMat=[];trainClasses=[]
  for  docIndex in trainingSet:
    trainMat.append(setOfWordseVec(vocabList, docList[docIndex]))
    trainClasses.append(classList[docIndex])
  p0V,p1V,pSpam=trainNB0(np.array(trainMat),np.array(trainClasses))
  errorCount=0
  for  docIndex in testSet:
    wordVector=setOfWordseVec(vocabList, docList[docIndex])
    if classifyNB(np.array(wordVector), p0V, p1V, pSpam)!=classList[docIndex]:
      errorCount+=1
  print 'the error rate is :',float(errorCount)/len(testSet)
if __name__=='__main__':
  spamTest()