朴素贝叶斯(Naive Bayes)实战：垃圾邮件过滤

#读取所有训练数据并按照空格分隔，保存在一个列表里返回
def load_file(path):
    cab=[]
    for i in range(1, 25):
        data=open(path %i)
        for line in data.readlines():
            cab.append(line.strip().split(','))
    cab_f=[]
    for i in range(len(cab)):
        for j in range(len(cab[i])):
            if cab[i][j]!='':
                cab_f.append(cab[i][j].strip())
    cab_final=[]
    for i in cab_f:
        for j in i.split(' '):
            cab_final.append(j)
    return cab_final

#朴素贝叶斯分类器
def bayes(test):
    path1='Emails/Training/normal/%d.txt'
    path2='Emails/Training/spam/%d.txt'
    normal_data=load_file(path1)
    spam_data=load_file(path2)
    # 计算p(x|C1)=p1与p(x|C2)=p2
    p1=1.0;p2=1.0
    for i in range(len(test)):
        x=0.0
        for j in normal_data:
            if test[i]==j:
                x=x+1.0
        p1=p1*((x+1.0)/(len(normal_data)+2.0))   #拉普拉斯平滑

    for i in range(len(test)):
        x=0.0
        for j in spam_data:
            if test[i]==j:
                x=x+1.0
        p2=p2*((x+1.0)/(len(spam_data)+2.0))   #拉普拉斯平滑

    pc1 = len(normal_data) / (len(normal_data)+len(spam_data))
    pc2 = 1 - pc1

    if p1*pc1>p2*pc2:
        return 'normal'
    else:
        return 'spam'


#测试
def test(path):
    data=open(path)
    cab=[]
    for line in data.readlines():
        cab.append(line.strip().split(','))
    cab_f = []
    for i in range(len(cab)):
        for j in range(len(cab[i])):
            if cab[i][j] != '':
                cab_f.append(cab[i][j].strip())
    cab_final = []
    for i in cab_f:
        for j in i.split(' '):
            cab_final.append(j)
    return bayes(cab_final)

if __name__=='__main__':
    print(test('Emails/test/normal.txt'))
    print(test('Emails/test/spam.txt'))
    sum1=0;sum2=0
    #再试试训练集
    for i in range(1,25):
        if test('Emails/Training/normal/%d.txt' %i)=='normal':
            sum1=sum1+1
    for i in range(1,25):
        if test('Emails/Training/spam/%d.txt' %i)== 'spam':
            sum2=sum2+1
    print('normal分类正确率：',sum1/24)
    print('spam分类正确率：', sum2/24)

normal 
spam 
normal分类正确率：0.9583333333333334 
spam分类正确率：1.0