前不久简单学习了python,写了一个朴素贝叶斯算法：

#coding=gbk#Naive Bayes #Calculate the Prob. of class:clsdef P(data,cls_val,cls_name="class"):    cnt = 0.0    for e in data:        if e[cls_name] == cls_val:            cnt += 1     return cnt/len(data) #Calculate the Prob(attr|cls)def PT(data,cls_val,attr_name,attr_val,cls_name="class"):    cnt1 = 0.0    cnt2 = 0.0    for e in data:        if e[cls_name] == cls_val:            cnt1 += 1            if e[attr_name] == attr_val:                cnt2 += 1     return cnt2/cnt1 #Calculate the NBdef NB(data,test,cls_y,cls_n):    PY = P(data,cls_y)    PN = P(data,cls_n)    for key,val in test.items():        print (key,val)        PY *= PT(data,cls_y,key,val)        PN *= PT(data,cls_n,key,val)    return {cls_y:PY,cls_n:PN}     if __name__ == "__main__":     #data    data = [        {
    "outlook":"sunny", "temp":"hot", "humidity":"high", "wind":"weak", "class":"no" },        {
    "outlook":"sunny", "temp":"hot", "humidity":"high", "wind":"strong", "class":"no" },        {
    "outlook":"overcast", "temp":"hot", "humidity":"high", "wind":"weak", "class":"yes" },        {
    "outlook":"rain", "temp":"mild", "humidity":"high", "wind":"weak", "class":"yes" },        {
    "outlook":"rain", "temp":"cool", "humidity":"normal", "wind":"weak", "class":"yes" },        {
    "outlook":"rain", "temp":"cool", "humidity":"normal", "wind":"strong", "class":"no" },        {
    "outlook":"overcast", "temp":"cool", "humidity":"normal", "wind":"strong", "class":"yes" },        {
    "outlook":"sunny", "temp":"mild", "humidity":"high", "wind":"weak", "class":"no" },        {
    "outlook":"sunny", "temp":"cool", "humidity":"normal", "wind":"weak", "class":"yes" },        {
    "outlook":"rain", "temp":"mild", "humidity":"normal", "wind":"weak", "class":"yes" },        {
    "outlook":"sunny", "temp":"mild", "humidity":"normal", "wind":"strong", "class":"yes" },        {
    "outlook":"overcast", "temp":"mild", "humidity":"high", "wind":"strong", "class":"yes" },        {
    "outlook":"overcast", "temp":"hot", "humidity":"normal", "wind":"weak", "class":"yes" },        {
    "outlook":"rain", "temp":"mild", "humidity":"high", "wind":"strong", "class":"no" },        ]        #calculate    print (NB(data,{
   "outlook":"sunny","temp":"cool","humidity":"high","wind":"strong"},"yes","no"))

这是数据挖掘书本上的一个例子的运行结果：