按照惯例，先把代码粘到这里

from numpy import *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 notreturn postingList, classVecdef CreateVocabList(dataSet):vocabSet = set([])  # create empty setfor document in dataSet:vocabSet = vocabSet | set(document)  # union of the two setsreturn list(vocabSet)def SetOfWords2Vec(vocabList, inputSet):returnVec = [0] * len(vocabList)for word in inputSet:if word in vocabList:returnVec[vocabList.index(word)] = 1else:print ("the word: %s is not in my Vocabulary!" % word)return returnVecdef BagOfWords2VecMN(vocabList, inputSet):returnVec = [0] * len(vocabList)for word in inputSet:if word in vocabList:returnVec[vocabList.index(word)] += 1return returnVecdef trainNB0(trainMatrix, trainCategory):numTrainDocs = len(trainMatrix)numWords = len(trainMatrix[0])pAbusive = sum(trainCategory) / float(numTrainDocs)p0Num = ones(numWords)p1Num = ones(numWords)            #change to ones() to avoid product to be zerop0Denom = 2.0; p1Denom = 2.0      #change to 2.0for i in range(numTrainDocs):if trainCategory[i] == 1:p1Num += trainMatrix[i]p1Denom += sum(trainMatrix[i])else:p0Num += trainMatrix[i]p0Denom += sum(trainMatrix[i])p1Vect = log(p1Num/p1Denom)          #change to log()  to avoid down-overflowp0Vect = log(p0Num/p0Denom)          #change to log()return p0Vect, p1Vect, pAbusivedef ClassifyNB(vec2classify, p0Vec, p1Vec, pClass1):p1 = sum(vec2classify * p1Vec) + log(pClass1)p0 = sum(vec2classify * p0Vec) + log(1.0 - pClass1)if p1 > p0:return 1else:return 0def TextParse(bigString):   # input is big string, #output is word listimport relistOfTokens = 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('machinelearninginaction\Ch04\email\spam\%d.txt' % i).read())docList.append(wordList)fullText.extend(wordList)classList.append(1)wordList = TextParse(open('machinelearninginaction\Ch04\email\ham\%d.txt' % i).read())docList.append(wordList)fullText.extend(wordList)classList.append(0)vocabList = CreateVocabList(docList)  # create vocabularytrainingSet = list(range(50))testSet = []  # create test setfor i in range(10):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del (trainingSet[randIndex])trainMat = []trainClasses = []for docIndex in trainingSet:  # train the classifier (get probs) trainNB0trainMat.append(BagOfWords2VecMN(vocabList, docList[docIndex]))trainClasses.append(classList[docIndex])p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))errorCount = 0for docIndex in testSet:  # classify the remaining itemswordVector = BagOfWords2VecMN(vocabList, docList[docIndex])if ClassifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:errorCount += 1print("classification error", docList[docIndex])print('the error rate is: ', float(errorCount) / len(testSet))# return vocabList,fullText# def CalcMostFreq(vocabList, fullText):
#     import operator
#     freqDict = {}
#     for token in vocabList:
#         freqDict[token] = fullText.count(token)
#     sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)
#     return sortedFreq[:30]
#
#
# 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'])
#         docList.append(wordList)
#         fullText.extend(wordList)
#         classList.append(1)  # NY is class 1
#         wordList = TextParse(feed0['entries'][i]['summary'])
#         docList.append(wordList)
#         fullText.extend(wordList)
#         classList.append(0)
#     vocabList = CreateVocabList(docList)  # create vocabulary
#     top30Words = calcMostFreq(vocabList, fullText)  # remove top 30 words
#     for pairW in top30Words:
#         if pairW[0] in vocabList: vocabList.remove(pairW[0])
#     trainingSet = range(2 * minLen);
#     testSet = []  # create test set
#     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:  # train the classifier (get probs) trainNB0
#         trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
#         trainClasses.append(classList[docIndex])
#     p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
#     errorCount = 0
#     for docIndex in testSet:  # classify the remaining items
#         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
#
#
# 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**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**NY**NY**"
#     for item in sortedNY:
#         print
#         item[0]

编译过程中遇到的错误

错误1：

UnicodeDecodeError: 'gbk' codec can't decode byte 0xae in position 199: illegal multibyte sequence

这个错误意思是python无法读取某个txt文件，实为编码问题，解决方法是按照错误跳转到相应程序行，在spam或者ham中查找有问题的txt，看看是否编码错误。如果编码错误，可以手动修改编码，或者你的编译器可以自动选择编码模式，修改一下就可以了。

错误2：

TypeError: 'range' object doesn't support item deletion

将

trainingSet = range(50) 

改为

trainingSet = list(range(50))

即可

关于机器学习的源代码以及数据集在这里：
http://blog.csdn.net/iamoldpan/article/details/78010329