修改4.NaiveBayes的 朴素贝叶斯.md 文件和 bayes.py

src/python/4.NaiveBayes/bayes.py

@@ -11,7 +11,7 @@ from numpy import *
 p(xy)=p(x|y)p(y)=p(y|x)p(x)
 p(x|y)=p(y|x)p(x)/p(y)
 """
-
+# 项目案例1: 屏蔽社区留言板的侮辱性言论
 
 def loadDataSet():
     """
@@ -21,7 +21,7 @@ def loadDataSet():
     postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], #[0,0,1,1,1......]
                    ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
                    ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
-                   ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
+                   ['stop', 'posting', 'stupid', 'worthless', 'gar e'],
                    ['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
@@ -154,7 +154,7 @@ def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
     # 计算公式  log(P(F1|C))+log(P(F2|C))+....+log(P(Fn|C))+log(P(C))
     # 使用 NumPy 数组来计算两个向量相乘的结果，这里的相乘是指对应元素相乘，即先将两个向量中的第一个元素相乘，然后将第2个元素相乘，以此类推。
     # 我的理解是：这里的 vec2Classify * p1Vec 的意思就是将每个词与其对应的概率相关联起来
-    # 可以理解为 1.单词在词汇表中的条件下，文件是good 类别的概率 也可以理解为 2.在整个空间下，文件既在词汇表中又是good类别的概率
+    # 可以理解为 1.单词在词汇表中的条件下，文件是good 类别的概率 也可以理解为 2.在整个空间下，文件既在词汇表中又是good类别的概率
     p1 = sum(vec2Classify * p1Vec) + log(pClass1)
     p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
     if p1 > p0:
@@ -195,5 +195,137 @@ def testingNB():
     print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)
 
 
+# ------------------------------------------------------------------------------------------
+# 项目案例2: 使用朴素贝叶斯过滤垃圾邮件
+
+# 切分文本
+def textParse(bigString):
+    import re
+    # 使用正则表达式来切分句子，其中分隔符是除单词、数字外的任意字符串
+    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('input/4.NaiveBayes/email/spam/%d.txt' % i).read())
+        docList.append(wordList)
+        classList.append(1)
+        wordList = textParse(open('input/4.NaiveBayes/email/ham/%d.txt' % i).read())
+        docList.append(wordList)
+        fullText.extend(wordList)
+        classList.append(0)
+    # 创建词汇表    
+    vocabList = createVocabList(docList)
+    trainingSet = range(50)
+    testSet = []
+    # 随机取 10 个邮件用来测试
+    for i in range(10):
+        # random.uniform(x, y) 随机生成一个范围为 x - y 的实数
+        randIndex = int(random.uniform(0, len(trainingSet)))
+        testSet.append(trainingSet[randIndex])
+        del(trainingSet[randIndex])
+    trainMat = []
+    trainClasses = []
+    for docIndex in trainingSet:
+        trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
+        trainClasses.append(classList[docIndex])
+    p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
+    errorCount = 0
+    for docIndex in testSet:
+        wordVector = setOfWords2Vec(vocabList, docList[docIndex])
+        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
+            errorCount += 1
+    print 'the errorCount is: ', errorCount
+    print 'the testSet length is :', len(testSet)
+    print 'the error rate is :', float(errorCount)/len(testSet)
+
+
+def testParseTest():
+    print textParse(open('input/4.NaiveBayes/email/ham/1.txt').read())
+
+# -----------------------------------------------------------------------------------
+# 项目案例3: 使用朴素贝叶斯从个人广告中获取区域倾向
+
+# 将文本文件解析成 词条向量
+def setOfWords2VecMN(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
+    return returnVec
+
+
+#文件解析
+def textParse(bigString):
+    import re
+    listOfTokens=re.split(r'\W*',bigString)
+    return [tok.lower() for tok in listOfTokens if len(tok)>2]
+
+#RSS源分类器及高频词去除函数
+def calcMostFreq(vocabList,fullText):
+    import operator
+    freqDict={}
+    for token in vocabList:  #遍历词汇表中的每个词
+        freqDict[token]=fullText.count(token)  #统计每个词在文本中出现的次数
+    sortedFreq=sorted(freqDict.iteritems(),key=operator.itemgetter(1),reverse=True)  #根据每个词出现的次数从高到底对字典进行排序
+    return sortedFreq[:30]   #返回出现次数最高的30个单词
+def localWords(feed1,feed0):
+    import feedparser
+    docList=[];classList=[];fullText=[]
+    minLen=min(len(feed1['entries']),len(feed0['entries']))
+    for i in range(minLen):
+        wordList=textParse(feed1['entries'][i]['summary'])   #每次访问一条RSS源
+        docList.append(wordList)
+        fullText.extend(wordList)
+        classList.append(1)
+        wordList=textParse(feed0['entries'][i]['summary'])
+        docList.append(wordList)
+        fullText.extend(wordList)
+        classList.append(0)
+    vocabList=createVocabList(docList)
+    top30Words=calcMostFreq(vocabList,fullText)
+    for pairW in top30Words:
+        if pairW[0] in vocabList:vocabList.remove(pairW[0])    #去掉出现次数最高的那些词
+    trainingSet=range(2*minLen);testSet=[]
+    for i in range(20):
+        randIndex=int(random.uniform(0,len(trainingSet)))
+        testSet.append(trainingSet[randIndex])
+        del(trainingSet[randIndex])
+    trainMat=[];trainClasses=[]
+    for docIndex in trainingSet:
+        trainMat.append(bagOfWords2VecMN(vocabList,docList[docIndex]))
+        trainClasses.append(classList[docIndex])
+    p0V,p1V,pSpam=trainNBO(array(trainMat),array(trainClasses))
+    errorCount=0
+    for docIndex in testSet:
+        wordVector=bagOfWords2VecMN(vocabList,docList[docIndex])
+        if classifyNB(array(wordVector),p0V,p1V,pSpam)!=classList[docIndex]:
+            errorCount+=1
+    print 'the error rate is:',float(errorCount)/len(testSet)
+    return vocabList,p0V,p1V
+
+
+# 最具表征性的词汇显示函数
+def getTopWords(ny,sf):
+    import operator
+    vocabList,p0V,p1V=localWords(ny,sf)
+    topNY=[];topSF=[]
+    for i in range(len(p0V)):
+        if p0V[i]>-6.0:topSF.append((vocabList[i],p0V[i]))
+        if p1V[i]>-6.0:topNY.append((vocabList[i],p1V[i]))
+    sortedSF=sorted(topSF,key=lambda pair:pair[1],reverse=True)
+    print "SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**"
+    for item in sortedSF:
+        print item[0]
+    sortedNY=sorted(topNY,key=lambda pair:pair[1],reverse=True)
+    print "NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**"
+    for item in sortedNY:
+        print item[0]
+
 if __name__ == "__main__":
-    testingNB()
+    # testingNB()
+    spamTest()
+    # laTest()