Python实现朴素贝叶斯分类器

# -*-coding:utf-8-*-'''    朴素贝叶斯算法'''from __future__ import division global classNameclassName = "class"def calc_class(train, classValue):    # 计算分类的概率    _num_cls = 0    _num_trains = len(train)    for t in train:        if t[className] == classValue:            _num_cls += 1    return  _num_cls / _num_trains    def calc_attr(train, classValue, attrName, attrValue):    # 计算属性的概率    _num_cls  = 0    _num_attr = 0    for a in train:        if a[className] == classValue:            _num_cls += 1            if a[attrName] == attrValue:                _num_attr += 1                    if _num_attr == 0:        _num_attr = 1    return _num_attr / _num_clsdef bayes(train, test, classY, classN):        _prob_Y = calc_class(train, classY)    _prob_N = calc_class(train, classN)    for key,value in test.items():        _prob_Y *= calc_attr(train, classY, key, value)        _prob_N *= calc_attr(train, classN, key, value)        return {classY:_prob_Y,classN:_prob_N}    if __name__=='__main__':    # 训练数据    train = [               {"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" },               ]     # 测试数据     test = {"outlook":"overcast","temp":"cool","humidity":"high","wind":"strong"}          print bayes(train, test, "yes", "no")