基础的机器学习实例，朴素贝叶斯分类

这是我能网上所能找到的最易于入门的实例了（代码拷贝于网络）

例子是关于天气案例（天气，温感，湿度，风量，是否正常气候）

通过读取测试集的数据，算出P(C1|X)*P(C1)/P(X),P(C2|X)*P(C2)/P(X),考虑到P(X)相同，省去

具体的是P(C1|X)=P(X1|C1)*P(X2|C1)*P(X3|C1)......P(C1)

同理算出P(C2|X)，比较两个值，大的值就是最有可能的结果

#Calculate the Prob. of class:cls  求P（"yes"）,p("no")def 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)  求P(Xn|Cn)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 NB主函数def NB(data,test,cls_y,cls_n):    PY = P(data,cls_y)    PN = P(data,cls_n)    for key,val in test.items():#我记得是key是索引，val是值        print (key,val)        PY *= PT(data,cls_y,key,val)        PN *= PT(data,cls_n,key,val)    return {cls_y:PY,cls_n:PN}#以字典返回值     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"))