|
|
@@ -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() |