python学习---朴素贝叶斯算法的简单实现

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:cls
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)
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
def 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：