Python-常用機器學習算法-貝葉斯算法

# 輸入trainMatrix是樣本資料向量化之後的結果，trainCategory是樣本資料的類别标簽(0,1,0,0,1,0,1,1.....)

def trainNB0(trainMatrix,trainCategory):

    numTrainDocs = len(trainMatrix)

    numWords = len(trainMatrix[0])

    # 求A類别的總機率

    pAbusive = sum(trainCategory)/float(numTrainDocs)

    p0Num = ones(numWords); p1Num = ones(numWords)      #change to ones()

    p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0

    for 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()

    p0Vect = log(p0Num/p0Denom)          #change to log()

    return p0Vect,p1Vect,pAbusive

#樸素貝葉斯分類器

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