From 8080d0af99d6b1dc2c134f5e9b31699aa535f380 Mon Sep 17 00:00:00 2001 From: ljy <2441898885@qq.com> Date: Tue, 1 Dec 2020 15:43:13 +0800 Subject: [PATCH] test --- Naive_Bayes/bayes.py | 178 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 Naive_Bayes/bayes.py diff --git a/Naive_Bayes/bayes.py b/Naive_Bayes/bayes.py new file mode 100644 index 0000000..42059c3 --- /dev/null +++ b/Naive_Bayes/bayes.py @@ -0,0 +1,178 @@ +# -*- coding: UTF-8 -*- +import numpy as np +from functools import reduce + +""" +函数说明:创建实验样本 + +Parameters: + 无 +Returns: + postingList - 实验样本切分的词条 + classVec - 类别标签向量 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-11 +""" +def loadDataSet(): + postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], #切分的词条 + ['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] #类别标签向量，1代表侮辱性词汇，0代表不是 + return postingList,classVec #返回实验样本切分的词条和类别标签向量 + +""" +函数说明:将切分的实验样本词条整理成不重复的词条列表，也就是词汇表 + +Parameters: + dataSet - 整理的样本数据集 +Returns: + vocabSet - 返回不重复的词条列表，也就是词汇表 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-11 +""" +def createVocabList(dataSet): + vocabSet = set([]) #创建一个空的不重复列表 + for document in dataSet: + vocabSet = vocabSet | set(document) #取并集 + return list(vocabSet) + +""" +函数说明:根据vocabList词汇表，将inputSet向量化，向量的每个元素为1或0 + +Parameters: + vocabList - createVocabList返回的列表 + inputSet - 切分的词条列表 +Returns: + returnVec - 文档向量,词集模型 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-11 +""" +def setOfWords2Vec(vocabList, inputSet): + returnVec = [0] * len(vocabList) #创建一个其中所含元素都为0的向量 + for word in inputSet: #遍历每个词条 + if word in vocabList: #如果词条存在于词汇表中，则置1 + returnVec[vocabList.index(word)] = 1 + else: print("the word: %s is not in my Vocabulary!" % word) + return returnVec #返回文档向量 + + +""" +函数说明:朴素贝叶斯分类器训练函数 + +Parameters: + trainMatrix - 训练文档矩阵，即setOfWords2Vec返回的returnVec构成的矩阵 + trainCategory - 训练类别标签向量，即loadDataSet返回的classVec +Returns: + p0Vect - 非的条件概率数组 + p1Vect - 侮辱类的条件概率数组 + pAbusive - 文档属于侮辱类的概率 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-12 +""" +def trainNB0(trainMatrix,trainCategory): + numTrainDocs = len(trainMatrix) #计算训练的文档数目 + numWords = len(trainMatrix[0]) #计算每篇文档的词条数 + pAbusive = sum(trainCategory)/float(numTrainDocs) #文档属于侮辱类的概率 + p0Num = np.zeros(numWords); p1Num = np.zeros(numWords) #创建numpy.zeros数组, + p0Denom = 0.0; p1Denom = 0.0 #分母初始化为0.0 + for i in range(numTrainDocs): + if trainCategory[i] == 1: #统计属于侮辱类的条件概率所需的数据，即P(w0|1),P(w1|1),P(w2|1)··· + p1Num += trainMatrix[i] + p1Denom += sum(trainMatrix[i]) ## 该词条的总的词数目 这样求得每个词条出现的概率 P(w1),P(w2), P(w3)... + else: #统计属于非侮辱类的条件概率所需的数据，即P(w0|0),P(w1|0),P(w2|0)··· + p0Num += trainMatrix[i] + p0Denom += sum(trainMatrix[i]) + p1Vect = p1Num/p1Denom #相除 + p0Vect = p0Num/p0Denom + return p0Vect,p1Vect,pAbusive #返回属于侮辱类的条件概率数组，属于非侮辱类的条件概率数组，文档属于侮辱类的概率 + +""" +函数说明:朴素贝叶斯分类器分类函数 + +Parameters: + vec2Classify - 待分类的词条数组 + p0Vec - 非侮辱类的条件概率数组 + p1Vec -侮辱类的条件概率数组 + pClass1 - 文档属于侮辱类的概率 +Returns: + 0 - 属于非侮辱类 + 1 - 属于侮辱类 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-12 +""" +def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): + print("vec2Classify",vec2Classify) #测试数据 + print("p1Vec",p1Vec) #侮辱类时每个单词的条件概率 + print("vec2Classify * p1Vec",vec2Classify * p1Vec) #利用用测试样本运用计算好的条件概率P(w0|1),P(w1|1),P(w2|1)··· + print(reduce(lambda x,y:x*y, vec2Classify * p1Vec)) #计算P(w0|1)*P(w1|1)*P(w2|1)··· + p1 = reduce(lambda x, y: x * y, vec2Classify * p1Vec) * pClass1 # 对应元素相乘 这里需要好好理解一下 + p0 = reduce(lambda x,y:x*y, vec2Classify * p0Vec) * (1.0 - pClass1) + print('p0:',p0) + print('p1:',p1) + if p1 > p0: + return 1 + else: + return 0 + +""" +函数说明:测试朴素贝叶斯分类器 + +Parameters: + 无 +Returns: + 无 +Author: + Jack Cui +Blog: + http://blog.csdn.net/c406495762 +Modify: + 2017-08-12 +""" +def testingNB(): + listOPosts,listClasses = loadDataSet() #创建实验样本 + myVocabList = createVocabList(listOPosts) #创建词汇表 + print("myVocabList:\n",myVocabList) + trainMat=[] + for postinDoc in listOPosts: + trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) #将实验样本向量化 + print("trainMat:\n",trainMat) + p0V,p1V,pAb = trainNB0(np.array(trainMat),np.array(listClasses)) #训练朴素贝叶斯分类器 + testEntry = ['love', 'my', 'dalmation'] #测试样本1 + thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry)) #测试样本向量化 + if classifyNB(thisDoc,p0V,p1V,pAb): + print(testEntry,'属于侮辱类') #执行分类并打印分类结果 + else: + print(testEntry,'属于非侮辱类') #执行分类并打印分类结果 + testEntry = ['stupid', 'garbage'] #测试样本2 + + thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry)) #测试样本向量化 + if classifyNB(thisDoc,p0V,p1V,pAb): + print(testEntry,'属于侮辱类') #执行分类并打印分类结果 + else: + print(testEntry,'属于非侮辱类') #执行分类并打印分类结果 + +if __name__ == '__main__': + testingNB() -- 2.34.1