朴素贝叶斯估计朴素贝叶斯是基于贝叶斯定理与特征条件独立分布假设的分类方法。首先根
朴素贝叶斯估计
朴素贝叶斯是基于贝叶斯定理与特征条件独立分布假设的分类方法。首先根据特征条件独立的假设学习输入/输出的联合概率分布，然后基于此模型，对给定的输入x，利用贝叶斯定理求出后验概率最大的输出y。 具体的，根据训练数据集，学习先验概率的极大似然估计分布
以及条件概率为
Xl表示第l个特征，由于特征条件独立的假设，可得
条件概率的极大似然估计为
根据贝叶斯定理
则由上式可以得到条件概率P(Y=ck|X=x)。
贝叶斯估计
用极大似然估计可能会出现所估计的概率为0的情况。后影响到后验概率结果的计算，使分类产生偏差。采用如下方法解决。 条件概率的贝叶斯改为
其中Sl表示第l个特征可能取值的个数。 同样，先验概率的贝叶斯估计改为
$$ P(Y=c_k) = \frac{\sum\limits_{i=1}^NI(y_i=c_k)+\lambda}{N+K\lambda} $K$
表示Y的所有可能取值的个数，即类型的个数。 具体意义是，给每种可能初始化出现次数为1，保证每种可能都出现过一次，来解决估计为0的情况。
文本分类
朴素贝叶斯分类器可以给出一个最有结果的猜测值，并给出估计概率。通常用于文本分类。 分类核心思想为选择概率最大的类别。贝叶斯公式如下：
词条：将每个词出现的次数作为特征。 假设每个特征相互独立，即每个词相互独立，不相关。则
完整代码如下;
import numpy as np
import re
import feedparser
import operator
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(data): #创建词向量
returnList = set([])
for subdata in data:
returnList = returnList | set(subdata)
return list(returnList)
def setofWords2Vec(vocabList,data): #将文本转化为词条
returnList = [0]*len(vocabList)
for vocab in data:
if vocab in vocabList:
returnList[vocabList.index(vocab)] += 1
return returnList
def trainNB0(trainMatrix,trainCategory): #训练，得到分类概率
pAbusive = sum(trainCategory)/len(trainCategory)
p1num = np.ones(len(trainMatrix[0]))
p0num = np.ones(len(trainMatrix[0]))
p1Denom = 2
p0Denom = 2
for i in range(len(trainCategory)):
if trainCategory[i] == 1:
p1num = p1num + trainMatrix[i]
p1Denom = p1Denom + sum(trainMatrix[i])
else:
p0num = p0num + trainMatrix[i]
p0Denom = p0Denom + sum(trainMatrix[i])
p1Vect = np.log(p1num/p1Denom)
p0Vect = np.log(p0num/p0Denom)
return p0Vect,p1Vect,pAbusive
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1): #分类
p0 = sum(vec2Classify*p0Vec)+np.log(1-pClass1)
p1 = sum(vec2Classify*p1Vec)+np.log(pClass1)
if p1 > p0:
return 1
else:
return 0
def textParse(bigString): #文本解析
splitdata = re.split(r'\W+',bigString)
splitdata = [token.lower() for token in splitdata if len(token) > 2]
return splitdata
def spamTest():
docList = []
classList = []
for i in range(1,26):
with open('spam/%d.txt'%i) as f:
doc = f.read()
docList.append(doc)
classList.append(1)
with open('ham/%d.txt'%i) as f:
doc = f.read()
docList.append(doc)
classList.append(0)
vocalList = createVocabList(docList)
trainList = list(range(50))
testList = []
for i in range(13):
num = int(np.random.uniform(0,len(docList))-10)
testList.append(trainList[num])
del(trainList[num])
docMatrix = []
docClass = []
for i in trainList:
subVec = setofWords2Vec(vocalList,docList[i])
docMatrix.append(subVec)
docClass.append(classList[i])
p0v,p1v,pAb = trainNB0(docMatrix,docClass)
errorCount = 0
for i in testList:
subVec = setofWords2Vec(vocalList,docList[i])
if classList[i] != classifyNB(subVec,p0v,p1v,pAb):
errorCount += 1
return errorCount/len(testList)
def calcMostFreq(vocabList,fullText):
count = {}
for vocab in vocabList:
count[vocab] = fullText.count(vocab)
sortedFreq = sorted(count.items(),key=operator.itemgetter(1),reverse=True)
return sortedFreq[:30]
def localWords(feed1,feed0):
docList = []
classList = []
fullText = []
numList = min(len(feed1['entries']),len(feed0['entries']))
for i in range(numList):
doc1 = feed1['entries'][i]['summary']
docList.append(doc1)
classList.append(1)
fullText.extend(doc1)
doc0 = feed0['entries'][i]['summary']
docList.append(doc0)
classList.append(0)
fullText.extend(doc0)
vocabList = createVocabList(docList)
top30Words = calcMostFreq(vocabList,fullText)
for word in top30Words:
if word[0] in vocabList:
vocabList.remove(word[0])
trainingSet = list(range(2*numList))
testSet = []
for i in range(20):
randnum = int(np.random.uniform(0,len(trainingSet)-5))
testSet.append(trainingSet[randnum])
del(trainingSet[randnum])
trainMat = []
trainClass = []
for i in trainingSet:
trainClass.append(classList[i])
trainMat.append(setofWords2Vec(vocabList,docList[i]))
p0V,p1V,pSpam = trainNB0(trainMat,trainClass)
errCount = 0
for i in testSet:
testData = setofWords2Vec(vocabList,docList[i])
if classList[i] != classifyNB(testData,p0V,p1V,pSpam):
errCount += 1
return errCount/len(testData)
if __name__=="__main__":
ny = feedparser.parse('http://newyork.craigslist.org/stp/index.rss')
sf = feedparser.parse('http://sfbay.craigslist.org/stp/index.rss')
print(localWords(ny,sf))
编程技巧：
1.两个集合的并集
vocab = vocab | set(document)
2.创建元素全为零的向量
vec = [0]*10
代码及数据集下载：贝叶斯
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
python 朴素贝叶斯 文本分类