注意：1、代码中的注释请不要放在源程序中运行，会报错。
2、代码中的数据集来源于http://archive.ics.uci.edu/ml/datasets/Car+Evaluation
3、对于朴素贝叶斯的原理，可以查看我的前面的博客
# 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[i].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 items in self.dataS:
sampleNum += 1
if not items[self.desClass] in dictFreq:
dictFreq[items[self.desClass]] = 1
desLabel.append(items[self.desClass])
else:
dictFreq[items[self.desClass]] += 1
dictPriorP = {} # 构建先验概率表，用字典表示
for item in desLabel:
dictPriorP[item] = float(dictFreq[item]) / 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[self.desLabel]) # 返回当前样本类属性
dictData[dataIndex].append(float(items[loca])) # 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)):
[a,b] = self.calParam(dictData[i])
dictPara[i].append(a)
dictPara[i].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[item] = {}
dictProb[item] = {}
label = []
for items in self.dataS:
if not items[loca] in label:
label.append(items[loca])
dictFreq[items[self.desClass]][items[loca]] = 1
else:
dictFreq[items[self.desClass]][items[loca]] += 1
needLaplace = 0
for key in dictFreq.keys():
for ch in labels:
if ch not in dictFreq[key]:
dictFreq[key][ch] = 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[key] = self.LaplaceEstimator(dictFreq[key])
needLaplace = 0
for item in self.classLabel:
for ch in dictFreq[item]:
dictProb[item][ch] = float(dictFreq[item][ch]) / self.dictFreq[item]
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[key] = souDict[key] + 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[item] = self.PriorP[item]
for i in range(len(sample)-1):
posteriorProb[item] *= self.Prob[i][item][sample[i]]
maxVal = posteriorProb[self.classLabel[0]]
i = 0
for item in posteriorProb:
i += 1
if posteriorProb[item] > maxVal:
maxVal = posteriorProb[item]
location = i
print "该样本属于的类别是：",self.classLabel[location]
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‘]本文出自 “路遥” 博客，请务必保留此出处http://cwxfly.blog.51cto.com/6113982/1694356
原文地址：http://cwxfly.blog.51cto.com/6113982/1694356
