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NBStudy.py
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NBStudy.py
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# coding:UTF-8
import numpy as np
from collections import defaultdict
class NBClass():
def __init__(self):
self.dataMat=[]
self.labelMat=[]
self.testArr=[]
self.trainDic=defaultdict(int)
self.labelDic = defaultdict(int)
self.labelPDict=defaultdict(int)
self.trainDataPPDict=defaultdict(int)
self.NBDic = defaultdict(int)
self.argMaxVal = 0
self.argMaxKey = ''
#######################################
#加载数据
#input: path 训练数据
#output: dataMat(list)特征
# labelMat(list)标签
#######################################
def loadTxtData(self,path):
print("-----------loadTxtData-----------")
dataMat = []
labelMat = []
fr = open(path) # 打开文件
for line in fr.readlines():
lines = line.strip().split("\t")
lineArr = []
for i in range(len(lines) - 1):
lineArr.append(lines[i])
dataMat.append(lineArr)
labelMat.append(lines[-1]) # 转换成{-1,1}
fr.close()
return dataMat, labelMat
#######################################
#统计训练数据中,新样例所占数量
# eg :('Sunny', 'No'): 3
#input: testArr(list) 目标新列表
#output: testArr(list) 目标新列表
#output: trainDict(dic) 训练数据中,新样例所占数量
#######################################
def trainDataCount(self,testArr):
print("-----------trainDataCount-----------")
trainDict=defaultdict(int)
for i in range(len(self.getDataTrain())):
for (a,b) in zip(self.getDataTrain()[i],self.getLabelTrain()):
if a == testArr[i]:
trainDict[(a,b)]=trainDict[(a,b)]+1
self.setTestArr(testArr)
print(trainDict)
self.setTrainDic(trainDict)
#######################################
# 统计标签数量
# eg :({'No': 0.35714285714285715, 'Yes': 0.6428571428571429}
# output: labelDic(dic) 标签数量字典
#######################################
def labelDataCount(self):
print("-----------labelDataCount-----------")
labelSets=set(self.getLabelTrain())
labelDic={}
i=0
for labelSet in labelSets:
labelDic[labelSet]=len([item for item in self.getLabelTrain() if item == labelSet])
i=i+1
self.setLabelDic(labelDic)
#######################################
# 事件A的先验概率(prior probability)
# eg :({'No': 0.35714285714285715, 'Yes': 0.6428571428571429}
# output: labelDic(dic) 标签数量字典
#######################################
def calPriorProbability(self):
print("-----------calPriorProbability-----------")
labelPDict={}
for (key,value) in self.getLabelDic().items():
labelPDict[key]=self.getLabelDic().get(key)/self.getAllLabel()
self.setLabelPDict(labelPDict)
print(self.getLabelPDict())
#######################################
# 事件A的后验概率(prior probability)
# eg :{('Sunny', 'No'): 0.6, ('Sunny', 'Yes'): 0.2222222222222222, ('Cool', 'Yes'): 0.3333333333333333, ('Cool', 'No'): 0.2, ('High', 'No'): 0.8, ('High', 'Yes'): 0.3333333333333333, ('Strong', 'No'): 0.6, ('Strong', 'Yes'): 0.3333333333333333}
# eg: p('Sunny'|'No')=0.6
# output: trainDataPPDict(dic) 后验概率字典
#######################################
def calPosteriorProbability(self):
print("-----------calPosteriorProbability-----------")
trainDataPPDict={}
for ((key11,key12),value1) in self.getTrainDic().items():
#print(self.getLabelDic()[key12])
#print(((key11, key12), value1))
trainDataPPDict[(key11, key12)] = value1 / self.getLabelDic()[key12]
print(trainDataPPDict)
self.setTrainDataPPDict(trainDataPPDict)
#######################################
# 计算argmax, 即朴素贝叶斯概率最大的那个元素的下标
# output: argMaxVal(int) 最大概率的值
# output: argMaxKey(int) 最大概率的下标
#######################################
def getArgMax(self):
print("---------getArgMax--------")
NBDict=defaultdict(int)
labelSets = set(self.getLabelTrain())
for labelSet in labelSets:
mal=self.getLabelPDict()[labelSet]
for i in range(len(self.getTestArr())):
mal=mal*self.getTrainDataPPDict()[(self.getTestArr()[i],labelSet)]
NBDict[labelSet]=mal
if self.getArgMaxVal()<mal:
self.setArgMaxVal(mal)
self.setArgMaxKey(labelSet)
print(self.getArgMaxKey(),":",self.getArgMaxVal())
def main(self,path,testArr):
print("-----------main-----------")
self.dataTrain, self.labelTrain = self.loadTxtData(path)
self.setDataTrain(np.array(self.dataTrain).T)
self.labelDataCount()
self.trainDataCount(testArr)
self.calPriorProbability()
self.calPosteriorProbability()
self.getArgMax()
def getAllLabel(self):
return len(self.getLabelTrain())
def setDataTrain(self,dataTrain):
self.dataTrain=dataTrain
def getDataTrain(self):
return self.dataTrain
def setLabelTrain(self,labelTrain):
self.labelTrain=labelTrain
def getLabelTrain(self):
return self.labelTrain
def setTestArr(self,testArr):
self.testArr=testArr
def getTestArr(self):
return self.testArr
def setTrainDic(self,trainDic):
self.trainDic=trainDic
def getTrainDic(self):
return self.trainDic
def setLabelDic(self,labelDic):
self.labelDic=labelDic
def getLabelDic(self):
return self.labelDic
def setLabelPDict(self,labelPDict):
self.labelPDict=labelPDict
def getLabelPDict(self):
return self.labelPDict
def setTrainDataPPDict(self,trainDataPPDict):
self.trainDataPPDict=trainDataPPDict
def getTrainDataPPDict(self):
return self.trainDataPPDict
def setNBDict(self,NBDict):
self.NBDict=NBDict
def getNBDict(self):
return self.NBDict
def setArgMaxVal(self,argMalVal):
self.argMaxVal=argMalVal
def getArgMaxVal(self):
return self.argMaxVal
def setArgMaxKey(self,argMalKey):
self.argMalKey=argMalKey
def getArgMaxKey(self):
return self.argMalKey
if __name__ == "__main__":
nbObj=NBClass()
testArr=['Sunny','Cool','High','Strong']
nbObj.main("playTennis.txt",testArr)