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這篇文章主要講解了“Naive Bayes 怎么使用”，文中的講解內容簡單清晰，易于學習與理解，下面請大家跟著丸趣 TV 小編的思路慢慢深入，一起來研究和學習“Naive Bayes 怎么使用”吧！
一、概述
優點：在數據少的情況下仍然有效，可以處理多類別問題
缺點：對于輸入數據的準備方式較為敏感
適用數據類型：標稱型數據
二、原理
三、文檔分類
A,B,C,D.. 為文檔中單詞。假設總詞匯只有 A,B,C,D 四種。訓練樣本為 5 個
ABCD 類別文檔 100110 文檔 201110 文檔 310011 文檔 411001 文檔 511101 測試文檔 1010?
類別：C0,C1
測試文檔：W
求：max{P(C0|W),P(C1|W)} === max{log[P(C0|W)],log[P(C1|W)]}
P(C0|W) = P(W|C0) * P(C0) / P(W)
P(C0) = 2 / 5 == 2 個 0 類型的文檔，3 個 1 類型的文檔
P(W|C0) = P(A*B*C*D|C0) == Navie Bayes == P(A|C0) * P(B|C0) * P(C|C0) * P(D|C0)
P(A|C0)=(0 + 0)/(0 + 0 + 1 + 1 + 0 + 1 + 1 + 1)=0 == A 在類別 0 文檔中出現的次數 / 類別 0 文檔中的總詞匯量
P(B|C0)=(0 + 1)/(0 + 0 + 1 + 1 + 0 + 1 + 1 + 1)=1/5 == B 在類別 0 文檔中出現的次數 / 類別 0 文檔中的總詞匯量
P(C|C0)=(1 + 1)/(0 + 0 + 1 + 1 + 0 + 1 + 1 + 1)=2/5 == C 在類別 0 文檔中出現的次數 / 類別 0 文檔中的總詞匯量
P(D|C0)=(1 + 1)/(0 + 0 + 1 + 1 + 0 + 1 + 1 + 1)=2/5 == D 在類別 0 文檔中出現的次數 / 類別 0 文檔中的總詞匯量
因為相乘為存在 0 * == 0 取 log
log[P(W|C0) * P(C0)] = log[P(A|C0) * P(B|C0) * P(C|C0) * P(D|C0) * P(C0)]
=log[P(A|C0)] + log[P(B|C0)] + log[P(C|C0)] + log[P(D|C0) ] + log[P(C0)]
同理計算 log[P(W|C1) * P(C1)]
測試樣本：
log[P(C0|W)] = 0 * log(1/5) + 1 * log(2/5) + 0 * log(2/5) + log(2/5) =
log[P(C1|W)] = 1 * log(3/7) + 0 * log(2/7) + 1 * log(1/7) + 0 * log(1/7) + log(1 – 2/5) =
# -*- coding:UTF-8
from numpy import *
1. 伯努利模型 == 不考慮詞在文檔中出現的次數，只考慮出不出現。假定詞是等權重中的
2. 多項式模型
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]
return postingList,classVec
def createVocabList(dataSet):
vocaSet = set([])
for document in dataSet:
vocaSet = vocaSet | set(document)
return list(vocaSet)
vocabList = [, ,.....]
inputSet = [my , dog , has , flea , problems , help , please]
def setOfWords2Vec(vocabList,inputSet):
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
trainCategory=== [0,0,1,1,0] 標簽集合的向量
pAbusive = (0 + 0 + 1 + 1 + 0) / 5
A B C D category
0 0 1 1 0
0 1 1 1 0
1 0 0 1 1
1 1 0 0 1
1 1 1 0 1
1 0 1 0 ?
numTrainDocs = 5 = 5 個文檔
numWords = 4 = 4 個特征
pAbusive = (0 + 0 + 0 + 1 + 1) / 5 = 2/5 == 先驗概率
p0Num = [0,0,0,0]
p1Num = [0,0,0,0]
p0Denom = 0.0
p1Denom = 0.0
0 0 1 1 0 === p0Num=[0,0,1,1] p0Denom=1
0 1 1 1 0 === p0Num=[0,1,2,2] p0Denom=2
1 0 0 1 1 === p1Num=[1,0,0,1] p1Denom=1
1 1 0 0 1 === p1Num=[2,1,0,1] p1Denom=2
1 1 1 0 1 === p1Num=[3,2,1,1] p1Denom=3
P(C0|W) = P(W|C0) * P(C0) / P(W) = P(A*B*C*D|C0) * P(C0) / P(W) = P(A|C0) * P(B|C0) * P(C|C0) * P(D|C0) * P(C0) / P(W)
P(C1|W) = P(W|C1) * P(C1) / P(W) = P(A*B*C*D|C1) * P(C1) / P(W) = P(A|C1) * P(B|C1) * P(C|C1) * P(D|C1) * P(C1) / P(W)
P(W) == 無需再計算了
max{P(C0|W),P(C1|W)} === max{Log[P(C0|W)],Log[P(C1|W)]}
Log[P(C0|W)] = Log[P(A|C0)] + Log[P(B|C0)] + Log[P(C|C0)] + Log[P(D|C0)] + Log[P(C0)]
P(A|C0) = 0/(0+1+2+2) = 0/5
P(B|C0) = 1/(0+1+2+2) = 1/5
P(C|C0) = 2/(0+1+2+2) = 2/5
P(D|C0) = 2/(0+1+2+2) = 2/5
Log[P(C1|W)] = Log[P(A|C1)] + Log[P(B|C1)] + Log[P(B|C1)] + Log[P(B|C1)] + Log[P(C1)]
P(A|C1) = 3/(3+2+1+1) = 3/7
P(B|C1) = 2/(3+2+1+1) = 2/7
P(C|C1) = 1/(3+2+1+1) = 1/7
P(D|C1) = 1/(3+2+1+1) = 1/7
測試樣本 1 0 1 0 ?
Log[P(C0|W)] = 1 * Log[0/5] + 0 * Log[1/5] + 1 * Log[2/5] + 0 * Log[2/5] + Log[2/5]
Log[P(C1|W)] = 1 * Log[3/7] + 0 * Log[2/7] + 1 * Log[1/7]+ 0 * Log[1/7] + Log[1 - 2/5]
注意存在 Log[0] == 所有初始化，我們設置
p0Num = [1,1,1,1]
p1Num = [1,1,1,1]
p0Denom = 2.0
p1Denom = 2.0
def trainNB0(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory) / float(numTrainDocs)
p0Num = zeros(numWords)
p1Num = zeros(numWords)
p0Denom = 0.0
p1Denom = 0.0
for i in range(numTrainDocs):
if trainCategory[i] == 1:
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p1Vec = log(p1Num/p1Denom)
p0Vec = log(p0Num/p0Denom)
return p0Vec,p1Vec,pAbusive
def trainNB1(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory) / float(numTrainDocs)
p0Num = ones(numWords)
p1Num = ones(numWords)
p0Denom = 2.0
p1Denom = 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])
p1Vec = log(p1Num/p1Denom)
p0Vec = log(p0Num/p0Denom)
return p0Vec,p1Vec,pAbusive
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify * p1Vec) + log(pClass1)
p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
if p1 p0:
return 1
else:
return 0
def testingNB():
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
trainMat = []
for postingDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList, postingDoc))
p0V,p1V,pAb = trainNB0(trainMat, listClasses)
testEntry = [love , my , dalmation]
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print(testEntry, classified as: ,classifyNB(thisDoc,p0V,p1V,pAb))四、過濾垃圾郵件
def textParse(bigString):
import re
listOfTokens = re.split(r \W* , bigString) # 簡單空格分詞
return [tok.lower() for tok in listOfTokens if len(tok) 2] # 簡單過濾詞長 = 2 的詞
def spamTest():
docList = []
classList = []
#fullText = []
for i in range(1,26):
# 讀取所有的單詞
wordList = textParse(open( emial/spam/%d.txt % i).read())
docList.append(wordList)
#fullText.extend(wordList)
classList.append(1)
wordList = textParse(open( emial/ham/%d.txt % i).read())
docList.append(wordList)
#fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)
trainSet = range(50)
testSet = []
for i in range(10):
randIndex = int(random.uniform(0,len(trainSet)))
testSet.append(trainSet[randIndex])
del(trainSet[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainSet:
trainMat.append(setOfWords2Vec(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = trainNB0(trainMat, trainClasses)
errorCount = 0
for docIndex in testSet:
wordVector = setOfWords2Vec(vocabList, docList[docIndex])
if classifyNB(wordVector, p0V, p1V, pSpam) != classList[docIndex]:
errorCount += 1
print classification error ,docList[docIndex]
print the error rate is: ,float(errorCount) / len(testSet)感謝各位的閱讀，以上就是“Naive Bayes 怎么使用”的內容了，經過本文的學習后，相信大家對 Naive Bayes 怎么使用這一問題有了更深刻的體會，具體使用情況還需要大家實踐驗證。這里是丸趣 TV，丸趣 TV 小編將為大家推送更多相關知識點的文章，歡迎關注！
