【機器學習六】貝葉斯NB
阿新 • • 發佈:2018-11-30
程式碼先貼上,後續總結
from numpy import * # 過濾網站的惡意留言 侮辱性:1 非侮辱性:0 # 建立一個實驗樣本 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 # 建立一個包含在所有文件中出現的不重複詞的列表 def createVocabList(dataSet): vocabSet = set([]) # 建立一個空集 for document in dataSet: vocabSet = vocabSet | set(document) # 建立兩個集合的並集 return list(vocabSet) # 將文件詞條轉換成詞向量 def setOfWords2Vec(vocabList, inputSet): returnVec = [0]*len(vocabList) # 建立一個其中所含元素都為0的向量 for word in inputSet: if word in vocabList: # returnVec[vocabList.index(word)] = 1 # index函式在字串裡找到字元第一次出現的位置 詞集模型 returnVec[vocabList.index(word)] += 1 # 文件的詞袋模型 每個單詞可以出現多次 else: print( "the word: %s is not in my Vocabulary!" % word) return returnVec # 樸素貝葉斯分類器訓練函式 從詞向量計算概率 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 p0Num = ones(numWords); # 避免一個概率值為0,最後的乘積也為0 p1Num = ones(numWords); # 用來統計兩類資料中,各詞的詞頻 p0Denom = 2.0; # 用於統計0類中的總數 p1Denom = 2.0 # 用於統計1類中的總數 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 = p1Num / p1Denom # p0Vect = p0Num / p0Denom print(p1Num) print(p1Denom) p1Vect = log(p1Num / p1Denom) # 在類1中,每個次的發生概率 print(p1Vect) p0Vect = log(p0Num / p0Denom) # 避免下溢位或者浮點數舍入導致的錯誤 下溢位是由太多很小的數相乘得到的 return p0Vect, p1Vect, pAbusive # 樸素貝葉斯分類器 def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): # print("---------") # print(vec2Classify) # print(p1Vec) print(vec2Classify*p1Vec) 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 postinDoc in listOPosts: trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses)) testEntry = ['love']*101 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)) # 呼叫測試方法---------------------------------------------------------------------- testingNB()