朴素叶贝斯算法

111

# coding=utf-8
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 not
    return postingList, classVec


# 创建一个带有所有单词的列表
def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)


def setOfWords2Vec(vocabList, inputSet):
    retVocabList = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            retVocabList[vocabList.index(word)] = 1
        else:
            print ('word ', word, 'not in dict')
    return retVocabList


# 另一种模型
def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec


def trainNB0(trainMatrix, trainCatergory):
    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 = ones(numWords)
    p1Num = ones(numWords)
    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])
    p1Vect = log(p1Num / p1Denom)  # 处于精度的考虑，否则很可能到限归零
    p0Vect = log(p0Num / p0Denom)
    return p0Vect, p1Vect, pAbusive



def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + log(pClass1)  # element-wise mult
    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
    if p1 > p0:
        return 1
    else:
        return 0


def testingNB():
    listOPosts, listClasses = loadDataSet()  ##这个是模型数据及结果，例子中有6行
    myVocabList = createVocabList(listOPosts) ##将模型中的所有数据转换为一个list ['park', 'so', 'take', 'food', 'mr'...]
    trainMat = []
    for postinDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) ##先设置一个全为零的和上面一样的长的list，将有词的位置赋值为1 ，循环6次,所以得到6条很长的
    p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses)) ##主要运算的地方
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print (testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)) ##主要运算的地方
    testEntry = ['stupid', 'garbage']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print (testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))


def main():
    testingNB()


if __name__ == '__main__':
    main()