代碼來源于：https://www.cnblogs.com/huangyc/p/10327209.html? ，本人只是簡介學習

1、?貝葉斯.py

import numpy as np from word_utils import *class NaiveBayesBase(object):def __init__(self):passdef fit(self, trainMatrix, trainCategory):'''樸素貝葉斯分類器訓練函數，求：p(Ci),基于詞匯表的p(w|Ci)Args:trainMatrix : 訓練矩陣，即向量化表示后的文檔（詞條集合）trainCategory : 文檔中每個詞條的列表標注Return:p0Vect : 屬于0類別的概率向量(p(w1|C0),p(w2|C0),...,p(wn|C0))p1Vect : 屬于1類別的概率向量(p(w1|C1),p(w2|C1),...,p(wn|C1))pAbusive : 屬于1類別文檔的概率'''numTrainDocs = len(trainMatrix)# 長度為詞匯表長度numWords = len(trainMatrix[0])# p(ci)self.pAbusive = sum(trainCategory) / float(numTrainDocs)# 由于后期要計算p(w|Ci)=p(w1|Ci)*p(w2|Ci)*...*p(wn|Ci)，若wj未出現，則p(wj|Ci)=0,因此p(w|Ci)=0，這樣顯然是不對的# 故在初始化時，將所有詞的出現數初始化為1，分母即出現詞條總數初始化為2p0Num = np.ones(numWords)p1Num = np.ones(numWords)p0Denom = 2.0p1Denom = 2.0for i in range(numTrainDocs):if trainCategory[i] == 1:p1Num += trainMatrix[i]p1Denom += sum(trainMatrix[i])else:p0Num += trainMatrix[i]p0Denom += sum(trainMatrix[i])# p(wi | c1)# 為了避免下溢出（當所有的p都很小時，再相乘會得到0.0，使用log則會避免得到0.0）self.p1Vect = np.log(p1Num / p1Denom)# p(wi | c2)self.p0Vect = np.log(p0Num / p0Denom)return selfdef predict(self, testX):'''樸素貝葉斯分類器Args:testX : 待分類的文檔向量（已轉換成array）p0Vect : p(w|C0)p1Vect : p(w|C1)pAbusive : p(C1)Return:1 : 為侮辱性文檔 (基于當前文檔的p(w|C1)*p(C1)=log(基于當前文檔的p(w|C1))+log(p(C1)))0 : 非侮辱性文檔 (基于當前文檔的p(w|C0)*p(C0)=log(基于當前文檔的p(w|C0))+log(p(C0)))'''p1 = np.sum(testX * self.p1Vect) + np.log(self.pAbusive)p0 = np.sum(testX * self.p0Vect) + np.log(1 - self.pAbusive)if p1 > p0:return 1else:return 0def loadDataSet():'''數據加載函數。這里是一個小例子'''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代表侮辱性文字，0代表正常言論，代表上面6個樣本的類別return postingList, classVecdef checkNB():'''測試'''listPosts, listClasses = loadDataSet()myVocabList = createVocabList(listPosts)trainMat = []for postDoc in listPosts:trainMat.append(setOfWord2Vec(myVocabList, postDoc))nb = NaiveBayesBase()nb.fit(np.array(trainMat), np.array(listClasses))testEntry1 = ['love', 'my', 'dalmation']thisDoc = np.array(setOfWord2Vec(myVocabList, testEntry1))print(testEntry1, 'classified as:', nb.predict(thisDoc))testEntry2 = ['stupid', 'garbage']thisDoc2 = np.array(setOfWord2Vec(myVocabList, testEntry2))print(testEntry2, 'classified as:', nb.predict(thisDoc2))if __name__ == "__main__":checkNB() View Code

2、word_utils.py

def createVocabList(dataSet):'''創建所有文檔中出現的不重復詞匯列表Args:dataSet: 所有文檔Return:包含所有文檔的不重復詞列表，即詞匯表'''vocabSet = set([])# 創建兩個集合的并集for document in dataSet:vocabSet = vocabSet | set(document)return list(vocabSet)# 詞袋模型(bag-of-words model):詞在文檔中出現的次數 def bagOfWords2Vec(vocabList, inputSet):'''依據詞匯表，將輸入文本轉化成詞袋模型詞向量Args:vocabList: 詞匯表inputSet: 當前輸入文檔Return:returnVec: 轉換成詞向量的文檔例子：vocabList = ['I', 'love', 'python', 'and', 'machine', 'learning']inputset = ['python', 'machine', 'learning', 'python', 'machine']returnVec = [0, 0, 2, 0, 2, 1]長度與詞匯表一樣長，出現了的位置為1，未出現為0，如果詞匯表中無該單詞則print'''returnVec = [0] * len(vocabList)for word in inputSet:if word in vocabList:returnVec[vocabList.index(word)] += 1else:print("the word: %s is not in my vocabulary!" % word)return returnVec# 詞集模型(set-of-words model):詞在文檔中是否存在，存在為1，不存在為0 def setOfWord2Vec(vocabList, inputSet):'''依據詞匯表，將輸入文本轉化成詞集模型詞向量Args:vocabList: 詞匯表inputSet: 當前輸入文檔Return:returnVec: 轉換成詞向量的文檔例子：vocabList = ['I', 'love', 'python', 'and', 'machine', 'learning']inputset = ['python', 'machine', 'learning']returnVec = [0, 0, 1, 0, 1, 1]長度與詞匯表一樣長，出現了的位置為1，未出現為0，如果詞匯表中無該單詞則print'''returnVec = [0] * len(vocabList)for word in inputSet:if word in vocabList:returnVec[vocabList.index(word)] = 1else:print("the word: %s is not in my vocabulary!" % word)return returnVec View Code

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轉載于:https://www.cnblogs.com/ywjfx/p/11045395.html

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