bayespython 源代码

#coding: utf-8 #date: 2016-07-10 #mail: artorius.mailbox@qq.com #author: xinwangzhong -version 0.1 from numpy import * def trainNB0(trainMatrix,trainCatergory): #适用于二分类问题，其中一类的标签为1 #return #p0Vect：标签为0的样本中，出现某个特征对应的概率 #p1Vect：标签为1的样本中，出现某个特征对应的概率 #pAbusive：标签为1的样本出现的概率 numTrainDoc = len(trainMatrix) numWords = len(trainMatrix[0]) pAbusive = sum(trainCatergory)/float(numTrainDoc) #防止多个概率的成绩当中的一个为0 #p0Num: 在训练样本标签为0的数据中，所有特征的对应value值之和，为矩阵 #p1Num: 在训练样本标签为1的数据中，所有特征的对应value值之和，为矩阵 p0Num = ones(numWords) p1Num = ones(numWords) #p0Denom：在训练样本标签为0的数据中，所有特征的value值之和，为标量 #p1Denom：在训练样本标签为1的数据中，所有特征的value值之和，为标量 #为什么初始化为2？？ p0Denom = 2.0 p1Denom = 2.0 for i in range(numTrainDoc): if trainCatergory[i] == 1: p1Num +=trainMatrix[i] p1Denom += sum(trainMatrix[i]) else: p0Num +=trainMatrix[i] p0Denom += sum(trainMatrix[i]) #出于精度的考虑，否则很可能到限归零，change to log() p1Vect = log(p1Num/p1Denom) p0Vect = log(p0Num/p0Denom) return p0Vect,p1Vect,pAbusive def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): #element-wise mult,只算分子的log值，因为只需比较大小，所以正负无关 p1 = sum(vec2Classify * p1Vec) + log(pClass1) p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1) if p1 > p0: return 1 else: return 0 ####################3 #from numpy import * #import os #os.chdir(r"/home/luogan/lg/Python728/bayes/classical-machine-learning-algorithm-master/bayesian") #import bayes 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([]) #create empty set for document in dataSet: vocabSet = vocabSet | set(document) #union of the two sets return list(vocabSet) def setOfWords2Vec(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] = 1 else: print ("the word: %s is not in my Vocabulary!" % word) return returnVec 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)) def bagOfWords2VecMN(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] += 1 return returnVec def textParse(bigString): #input is big string, #output is word list import re listOfTokens = re.split(r'\W*', bigString) return [tok.lower() for tok in listOfTokens if len(tok) > 2] if __name__ == "__main__": listOPosts,listClasses = loadDataSet() myVocabList = createVocabList(listOPosts) #print (myVocabList) trainMat = [] for postinDoc in listOPosts: trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) p0V,p1V,pAb = trainNB0(trainMat, listClasses) testingNB() # spamTest()