贝叶斯分类器（续近邻分类器）

#A Naive Bayesian Classifiertotal ={} #类训练实例,存储对应类的出现的次数histo ={} #存储对应类中的对应特征的值得频率train = open("D:\\iris.trn",'r')for line in train:    f = line.rstrip().split(',')        label = f.pop()        if not total.has_key(label):        total[label] =0        histo[label]=[{},{},{},{}]        total[label] +=1        for i in range(4):        histo[label][i][f[i]] = 1 +histo[label][i].get(f[i],0.0)train.close()        #读取测试集并且评估可能性，选出最大可能性的类hit , miss = 0,0test = open("D:\\iris.tst")for line in test:    f = line.rstrip().split(',')    true = f.pop()        p = {} #类的可能性        for label in total.keys():        p[label] =1         for i in range(4):            p[label] *= histo[label][i].get(f[i],0.0)/total[label] #计算出类中的对应的属性的频率的乘积    mx ,predicted = 0,-1;    for k in p.keys():#找出最大的概率        if p[k] >=mx:            mx,predicted=p[k],k        if true == predicted:        flag ='+'        hit +=1    else:        flag ='-'        miss +=1    print flag ,"\t",true,"\t",predicted,"\t",        for label in p.keys():        print label,":",p[label],"\t",    printprintprint hit,"out of ",hit+miss,"correct-Accuracy: ",hit/(hit+miss+0.0)test.close()

结果

+ Iris-setosa Iris-setosa Iris-virginica : 0.0 Iris-setosa : 0.000764069733796 Iris-versicolor : 0.0
+ Iris-setosa Iris-setosa Iris-virginica : 0.0 Iris-setosa : 0.000377136983989 Iris-versicolor : 0.0
+ Iris-versicolor Iris-versicolor Iris-virginica : 0.0 Iris-setosa : 0.0 Iris-versicolor : 2.88e-05
+ Iris-versicolor Iris-versicolor Iris-virginica : 0.0 Iris-setosa : 0.0 Iris-versicolor : 0.0004368
+ Iris-virginica Iris-virginica Iris-virginica : 1.728e-05Iris-setosa : 0.0 Iris-versicolor : 0.0


5 out of  5 correct-Accuracy:  1.0