朴素贝叶斯算法的Python实现
# Author :Wenxiang Cui# Date :2015/9/11
# Function: A classifier which using naive Bayesian algorithm
import math
class Bayesian:
def __init__(self):
self.dataS = [] # 训练样本集DataSource
self.attriList = [] # 属性集合
self.desClass = 0 # 分类目标属性在attriList中的位置
def loadDataS(self,fileName,decollator):
#input：
#fileName - DataSource 的文件名
#decollator - DataSource 中每个字段之间的分割符，有可能是空格或','
#function :
#从磁盘中读取数据并转化为较好处理的列表
items = []
fp = open(filename,'r')
lines = fp.readlines()
for line in lines:
line = line.strip('\n')
items.append(line)
fp.close()
i = 0
b = []
for i in range(len(items)):
b.append(items.split(decollator))
self.dataS = b[:]
def getAttriList(self,attributes):
#input：
#attributes - 训练数据集中的属性集合，必须与dataSource中的列相对应
#function:
#获得训练数据集的属性列表
self.attriList = attributes[:]
def getDesClass(self,loca):
#input:
#loca - 分类目标属性在attriList中的位置
#function:
#获得分类目标属性在attriList中的位置
self.desClass = loca
def calPriorProb(self):
#input:
#
#function:
#计算类的先验概率
dictFreq = {} # 构建频度表，用字典表示
desLabel = []
sampleNum = 0
for itemsin self.dataS:
sampleNum += 1
if not items in dictFreq:
dictFreq] = 1
desLabel.append(items)
else:
dictFreq] += 1
dictPriorP = {} # 构建先验概率表，用字典表示
for item in desLabel:
dictPriorP = float(dictFreq) / sampleNum
self.PriorP = dictPriorP[:]
self.classLabel = desLabel[:]
def calProb(self,type,loca):
#input:
#type - 定义属性是连续的还是离散的
# loca - 该属性在属性集中的位置
#output:
#dictPara - 连续属性的样本均值和方差（列表表示）
#dictProb - 离散属性的类条件概率
#function:
#计算某个属性的类条件概率密度
if type == 'continuous':
dictData = [] # 提取出样本的类别和当前属性值
dictPara = [] # 记录样本的类别和其对应的样本均值和方差
for item in self.classLabel:
dictData.append([])
dictPara.append([])
for items in self.dataS:
dataIndex = self.classLabel.index(items) # 返回当前样本类属性
dictData.append(float(items)) # 记录当前属性值及该样本的类属性
#计算类属性的样本均值和方差（可以用Numpy包来快速处理）
for i in range(len(self.classLabel)):
= self.calParam(dictData)
dictPara.append(a)
dictPara.append(b)
return dictPara
elif type == 'discrete':
dictFreq = {}
dictProb = {}
for item in self.classLabel:# 构建频度表，用字典表示
dictFreq = {}
dictProb = {}
label = []
for items in self.dataS:
if not items in label:
label.append(items)
dictFreq]] = 1
else:
dictFreq]] += 1
needLaplace = 0
for key in dictFreq.keys():
for ch in labels:
if ch not in dictFreq:
dictFreq = 0
needLaplace = 1
if needLaplace == 1: # 拉普拉斯平滑用于处理类条件概率为0的情况
dictFreq = self.LaplaceEstimator(dictFreq)
needLaplace = 0
for item in self.classLabel:
for ch in dictFreq:
dictProb = float(dictFreq) / self.dictFreq
return dictProb
else:
print 'Wrong type!'
def calParam(self,souList):
#input:
#souList - 待计算的列表
#output:
#meanVal - 列表元素的均值
# deviation - 列表元素的标准差
#function:
#计算某个属性的类条件概率密度
meanVal = sum(souList) / float(len(souList))
deviation = 0
tempt = 0
for val in souList:
tempt += (val - meanVal)**2
deviation = math.sqrt(float(tempt)/(len(souList)-1))
return meanVal,deviation
def LaplaceEstimator(self,souDict):
#input:
#souDict - 待计算的字典
#output:
#desDict - 平滑后的字典
#function:
#拉普拉斯平滑
desDict = souDict.copy()
for key in souDict:
desDict = souDict + 1
return desDict
class CarBayesian(Bayesian):
def __init__(self):
Bayesian.__init__(self)
self.buying = {}
self.maint = {}
self.doors = {}
self.persons = {}
self.lug_boot = {}
self.safety = {}
def tranning(self):
self.Prob = []
self.buying = Bayesian.calProb('discrete',0)
self.maint = Bayesian.calProb('discrete',1)
self.doors = Bayesian.calProb('discrete',2)
self.persons = Bayesian.calProb('discrete',3)
self.lug_boot = Bayesian.calProb('discrete',4)
self.safety = Bayesian.calProb('discrete',5)
self.Prob.append(self.buying)
self.Prob.append(self.maint)
self.Prob.append(self.doors)
self.Prob.append(self.persons)
self.Prob.append(self.lug_boot)
self.Prob.append(self.safety)
def classify(self,sample):
#input ：
# sample - 一个样本
#function:
# 判断输入的这个样本的类别
posteriorProb = {}
for item in self.classLabel:
posteriorProb = self.PriorP
for i in range(len(sample)-1):
posteriorProb *= self.Prob]
maxVal = posteriorProb]
i = 0
for item in posteriorProb:
i += 1
if posteriorProb > maxVal:
maxVal = posteriorProb
location = i
print "该样本属于的类别是：",self.classLabel
filename = "D:\MyDocuments-HnH\DataMining\DataSets\Car\Car_Data.txt"
MyCar = CarBayesian()
MyCar.loadDataS(filename,',')
attributes = ['buying','maint','doors','persons','lug_boot','safety']
MyCar.getAttriList(attributes)
MyCar.getDesClass(7-1)
MyCar.tranning()
sample = ['vhigh','vhigh','2','2','small','low']
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