# coding=utf-8
#朴素贝叶斯分类,P(ci|X)=P(X|ci)*P(ci)/P(X),因为P(X)与类无关,每个样本对各个类的概率一样。
#假设各特征相互独立,对一样本的不同类,可求P(X|ci)*P(ci)=P(X1|ci)P(X2|ci).。。P(Xn|ci)*P(ci)
#概率大着即为其类
#先根据所有样本,求所有特征的条件概率P(X1|ci)P(X2|ci).。。P(Xn|ci)和分类概率P(ci)
#样本X用0,1组成的向量表示,则dot(X,P(X1|ci)P(X2|ci).。。P(Xn|ci))即得到其对应的特征的条件概率,再*P(ci)就得到
#其对应的属于Ci类的概率,大则为其类
from numpy import *
def loadDataSet():
postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['stop', 'posting', 'stupid', 'worthless', 'garbage'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
classVec = [0,1,0,1,0,1] #1 is abusive, 0 not
return postingList,classVec
def createVocabList(dataSet): #'建立词汇表集合=n个特征'
vocabSet = set([]) #create empty set
for document in dataSet:
vocabSet = vocabSet | set(document) #union of the two sets
return list(vocabSet)
def setOfWords2Vec(vocabList, inputSet):#'将话语词汇与词汇表对照映射成0、1组成的n特征向量'
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] = 1
else: print("the word: %s is not in my Vocabulary!" % word)
return returnVec
def trainNB0(trainMatrix,trainCategory):#'训练样本得到P(X1|ci)P(X2|ci).。。P(Xn|ci),P(ci)
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory)/float(numTrainDocs) #P(c1) 脏话
p0Num = ones(numWords); p1Num = ones(numWords) #change zeros() to ones() '防止有P(Xj|Ci)=0使联合概率成0'
p0Denom = 2.0; p1Denom = 2.0 #change 0.0 to 2.0
for i in range(numTrainDocs):
if trainCategory[i] == 1:
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p1Vect = log(p1Num/p1Denom) #change to log() '进行对数变换,变*为+,ln(a*b)=lna+lnb防止很小的数连乘下溢出成0'
p0Vect = log(p0Num/p0Denom) #change to log() 'ln(f(x))和f(x)的极值点一致,走势一致'
return p0Vect,p1Vect,pAbusive
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify * p1Vec) + log(pClass1) #element-wise mult
p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
if p1 > p0:
return 1
else:
return 0
def bagOfWords2VecMN(vocabList, inputSet): #'文档词袋模型:当话语中的特征词汇出现多次,使对应的向量特征+1,而不是=1。增强其对分类的影响'
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] += 1
return returnVec
def testingNB():
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
testEntry = ['love', 'my', 'dalmation']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry = ['stupid', 'garbage']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
def textParse(bigString): #input is big string, #output is word list
import re
listOfTokens = re.split(r'\W*', bigString) #'分隔符为除单词数字外的任意字符串'
return [tok.lower() for tok in listOfTokens if len(tok) > 2] #'去掉空字符串并变小写'
testingNB()
机器学习-朴素贝叶斯分类
猜你喜欢
转载自blog.csdn.net/lijil168/article/details/69215158
今日推荐
周排行