Python实现的朴素贝叶斯分类器示例

Python  /  管理员 发布于 7年前   148

本文实例讲述了Python实现的朴素贝叶斯分类器。分享给大家供大家参考，具体如下：

因工作中需要，自己写了一个朴素贝叶斯分类器。

对于未出现的属性，采取了拉普拉斯平滑，避免未出现的属性的概率为零导致整个条件概率都为零的情况出现。

朴素贝叶斯的基本原理网上很容易查到，这里不再叙述，直接附上代码

因工作中需要，自己写了一个朴素贝叶斯分类器。对于未出现的属性，采取了拉普拉斯平滑，避免未出现的属性的概率为零导致整个条件概率都为零的情况出现。

class NBClassify(object):  def __init__(self, fillNa = 1):    self.fillNa = 1    pass  def train(self, trainSet):    # 计算每种类别的概率    # 保存所有tag的所有种类，及它们出现的频次    dictTag = {}    for subTuple in trainSet:      dictTag[str(subTuple[1])] = 1 if str(subTuple[1]) not in dictTag.keys() else dictTag[str(subTuple[1])] + 1    # 保存每个tag本身的概率    tagProbablity = {}    totalFreq = sum([value for value in dictTag.values()])    for key, value in dictTag.items():      tagProbablity[key] = value / totalFreq    # print(tagProbablity)    self.tagProbablity = tagProbablity    ##############################################################################    # 计算特征的条件概率    # 保存特征属性基本信息{特征1:{值1:出现5次, 值2:出现1次}, 特征2:{值1:出现1次, 值2:出现5次}}    dictFeaturesBase = {}    for subTuple in trainSet:      for key, value in subTuple[0].items():        if key not in dictFeaturesBase.keys():          dictFeaturesBase[key] = {value:1}        else:          if value not in dictFeaturesBase[key].keys():dictFeaturesBase[key][value] = 1          else:dictFeaturesBase[key][value] += 1    # dictFeaturesBase = {      # '职业': {'农夫': 1, '教师': 2, '建筑工人': 2, '护士': 1},      # '症状': {'打喷嚏': 3, '头痛': 3}      # }    dictFeatures = {}.fromkeys([key for key in dictTag])    for key in dictFeatures.keys():      dictFeatures[key] = {}.fromkeys([key for key in dictFeaturesBase])    for key, value in dictFeatures.items():      for subkey in value.keys():        value[subkey] = {}.fromkeys([x for x in dictFeaturesBase[subkey].keys()])    # dictFeatures = {    #  '感冒 ': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}},    #  '脑震荡': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}},    #  '过敏 ': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}}    #  }    # initialise dictFeatures    for subTuple in trainSet:      for key, value in subTuple[0].items():        dictFeatures[subTuple[1]][key][value] = 1 if dictFeatures[subTuple[1]][key][value] == None else dictFeatures[subTuple[1]][key][value] + 1    # print(dictFeatures)    # 将驯良样本中没有的项目，由None改为一个非常小的数值，表示其概率极小而并非是零    for tag, featuresDict in dictFeatures.items():      for featureName, fetureValueDict in featuresDict.items():        for featureKey, featureValues in fetureValueDict.items():          if featureValues == None:fetureValueDict[featureKey] = 1    # 由特征频率计算特征的条件概率P(feature|tag)    for tag, featuresDict in dictFeatures.items():      for featureName, fetureValueDict in featuresDict.items():        totalCount = sum([x for x in fetureValueDict.values() if x != None])        for featureKey, featureValues in fetureValueDict.items():          fetureValueDict[featureKey] = featureValues/totalCount if featureValues != None else None    self.featuresProbablity = dictFeatures    ##############################################################################  def classify(self, featureDict):    resultDict = {}    # 计算每个tag的条件概率    for key, value in self.tagProbablity.items():      iNumList = []      for f, v in featureDict.items():        if self.featuresProbablity[key][f][v]:          iNumList.append(self.featuresProbablity[key][f][v])      conditionPr = 1      for iNum in iNumList:        conditionPr *= iNum      resultDict[key] = value * conditionPr    # 对比每个tag的条件概率的大小    resultList = sorted(resultDict.items(), key=lambda x:x[1], reverse=True)    return resultList[0][0]if __name__ == '__main__':  trainSet = [    ({"症状":"打喷嚏", "职业":"护士"}, "感冒 "),    ({"症状":"打喷嚏", "职业":"农夫"}, "过敏 "),    ({"症状":"头痛", "职业":"建筑工人"}, "脑震荡"),    ({"症状":"头痛", "职业":"建筑工人"}, "感冒 "),    ({"症状":"打喷嚏", "职业":"教师"}, "感冒 "),    ({"症状":"头痛", "职业":"教师"}, "脑震荡"),  ]  monitor = NBClassify()  # trainSet is something like that [(featureDict, tag), ]  monitor.train(trainSet)  # 打喷嚏的建筑工人  # 请问他患上感冒的概率有多大？  result = monitor.classify({"症状":"打喷嚏", "职业":"建筑工人"})  print(result)

另：关于朴素贝叶斯算法详细说明还可参看本站前面一篇///article/129903.htm。

更多关于Python相关内容感兴趣的读者可查看本站专题：《Python数据结构与算法教程》、《Python加密解密算法与技巧总结》、《Python编码操作技巧总结》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》及《Python入门与进阶经典教程》

希望本文所述对大家Python程序设计有所帮助。