numpy手写朴素贝叶斯

numpy手写朴素贝叶斯

import numpy as np



# 1 构建词向量矩阵
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 cal_prob(trainMat, labelMat):
    p0 = sum(np.array(labelMat) == 0) / len(labelMat)
    p1 = 1 - p0
    pA_vec, pB_vec = np.zeros((1, len(trainMat[0]))), np.zeros((1, len(trainMat[0])))
    pA_Num, pB_Num = 0, 0
    for i in range(len(trainMat)):
        if labelMat[i] == 1:
            pA_vec += trainMat[i]
            pA_Num += sum(trainMat[i])
        elif labelMat[i] == 0:
            pB_Num += sum(trainMat[i])
            pB_vec += trainMat[i]
    pA = pA_vec + 1 / pA_Num + 2
    pB = pB_vec + 1/ pB_Num + 2
    return pA, pB, p1

def classify(classvec, pA, pB, p1):
    classvec = np.array(classvec).reshape(-1, len(pA[0]))
    s0 = np.sum(np.log(np.multiply(classvec, pA)), axis=1) + np.log(1 - p1)
    s1 = np.sum(np.log(np.multiply(classvec, pB)), axis=1) + np.log(p1)
    return 0 if s0 > s1 else 1






if __name__ == '__main__':
    postingList, classVec = loadDataSet()
    wordset = createVocabList(postingList)

    # word_vec0 = setOfWords2Vec(wordset, postingList[0])

    trainmat = []
    for input_text in postingList:
        vec = setOfWords2Vec(wordset, input_text)
        trainmat.append(vec)
    test = setOfWords2Vec(wordset, postingList[1])
    pA, pB, p1 = cal_prob(trainmat, classVec)
    print(classify(test, pA, pB, p1))