"""
朴素贝叶斯算法案例
"""
import numpy as np
#准备数据
def loadDataSets():
"""
加载数据集
:return: dataMatrix,labelList
"""
dataMatrix = [
["stop", "fuck", "you", "bitch", "garbage"],
["useless", "dog", "stupid", "worthless"],
["suck", "my", "dick", "bitch", "pig", "asshole"],
["son", "bitch", "hoocker", "happy"],
['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
]
labelList = [1, 1, 1, 1, 0, 0, 0]
return dataMatrix, labelList
#得到去重列表（词汇表）
def buildWordsSet(dataMatrix):
"""
创建单词集合
:param dataMatrix: 单词矩阵
:return:
"""
# 定义一个空set,因为set本身有去重的功能
wordsSet = set([])
# 遍历矩阵的每一行
for comment in dataMatrix:
# |表示取交集
wordsSet = wordsSet | set(comment)
return list(wordsSet)
#把dataMatrix转为one-hot
def getOneZeroVector(wordSet, comment):
"""
以wordSet这个训练集词频向量为依据构建comment的词频（0,1）向量
:param wordSet: 全集
:param comment: 单个评论的list
:return: 每个comment对应的(0,1)向量
"""
one_zero_vec = [0] * len(wordSet)
for word in comment:
if word in wordSet:
one_zero_vec[wordSet.index(word)] = 1
else:
print(" %s 没有收录" % word)
return one_zero_vec
#训练：
#计算bayes参数代码
def getBayesParams(one_zero_matrix, labelList):
"""
计算贝叶斯公式参数
:param one_zero_matrix: （0,1）词频矩阵
:param labelList: 每个评论的标签
:return: log（词频/好类总词数） ， log（词频/坏类总次数） ， 侮辱性评论占训练集总评论概率
"""
# 侮辱性评论概率
p_c1 = sum(labelList) / float(len(labelList))
# 训练集总词数
total_words_count = len(one_zero_matrix[0])
# 两种单词出现频率列表
p0List = np.ones(len(one_zero_matrix[0]))
p1List = np.ones(len(one_zero_matrix[0]))
# 计算两类词频
p0num = 1.0
p1num = 1.0
# 遍历所有测试集评论
for i in range(len(labelList)):
# 若该评论是侮辱性
if labelList[i] == 1:
p1List += one_zero_matrix[i]
p1num += sum(one_zero_matrix[i])
else:
p0List += one_zero_matrix[i]
p0num += sum(one_zero_matrix[i])
# 每个词词频列表/该类别词频 再取对数
p1vec = np.log(p1List / p1num) # 已知是侮辱性评论情况下，每个词出现的概率
p0vec = np.log(p0List / p0num) # 已知不是侮辱性评论情况下，每个词出现的概率
return p1vec, p0vec, p_c1
def classifyByBayes(p1vec, p0vec, p_c1, one_zero_vector):
"""
使用贝叶斯参数比较得出结果
:param p1vec:
:param p0vec:
:param p_c1:
:param one_zero_vector:
:return:
"""
# sum(one_zero_vector * p1vec) 对应元素相乘相加
# p_1 = sum(one_zero_vector * p1vec) + np.log(p_c1)
p_1 = sum(one_zero_vector * p1vec)
# p_0 = sum(one_zero_vector * p0vec) + np.log(1.0 - p_c1)
p_0 = sum(one_zero_vector * p0vec)
if p_1 > p_0:
return 1
else:
return 0
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