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贝叶斯学习方法中实用性很高的一种为朴素贝叶斯学习期，常被称为朴素贝叶斯分类器。在某些领域中与神经网络和决策树学习相当。虽然朴素贝叶斯分类器忽略单词间的依赖关系，即假设所有单词是条件独立的，但朴素贝叶斯分类在实际应用中有很出色的表现。
朴素贝叶斯文本分类算法伪代码：
朴素贝叶斯文本分类算法流程：
通过计算训练集中每个类别的概率与不同类别下每个单词的概率，然后利用朴素贝叶斯公式计算新文档被分类为各个类别的概率，最终输出概率最大的类别。
C++源码：
/*
Bayesian classifier for document classifiaction
15S103182
Ethan
2015.12.27
*/
#include <iostream>
#include <vector>
#include <iterator>
#include <map>
#include <fstream>
#include <iomanip>
#include <sstream>
using namespace std;
int stringToInteger(string a){
stringstream ss;
ss<<a;
int b;
ss>>b;
return b;
}
vector<int> openClassificationFile(const char* dataset){
fstream file;
file.open(dataset,ios::in);
if(!file)
{
cout <<"Open File Failed!" <<endl;
vector<int> a;
return a;
}
vector<int> data;
int i=1;
while(!file.eof()){
string temp;
file>>temp;
data.push_back(stringToInteger(temp));
}
file.close();
return data;
}
vector<string> openFile(const char* dataset){
fstream file;
file.open(dataset,ios::in);
if(!file)
{
cout <<"Open File Failed!" <<endl;
vector<string> a;
return a;
}
vector<string> data;
int i=1;
while(!file.eof()){
string temp;
file>>temp;
data.push_back(temp);
}
file.close();
for(int i=0;i<data.size();i++) cout<<data[i]<<"\t";
cout<<endl;
cout<<"Open file successfully!"<<endl;
return data;
}
vector<vector<string> > openFiles(const vector<char*> files){
vector<vector<string> > docs;
for(int i=0;i<files.size();i++){
vector<string> t = openFile(files[i]);
docs.push_back(t);
}
return docs;
}
void bayesian(vector<vector<string> > docs,vector<int> c,vector<string> d){
map<string,int> wordFrequency;//每个单词出现的个数
map<int,float> cWordProbability;//类别单词频率
map<int,int> cTotalFrequency;//类别单词个数
map<int,map<string,int> > cWordlTotalFrequency;//类别下单词个数
int totalWords=0;
for(int i=0;i<docs.size();i++){
totalWords += docs[i].size();
cWordProbability[c[i]] = cWordProbability[c[i]] + docs[i].size();
map<string,int> sn;
for(int j=0;j<docs[i].size();j++){
wordFrequency[docs[i][j]] = wordFrequency[docs[i][j]] + 1;
sn[docs[i][j]] = sn[docs[i][j]] + 1;
}
map<string,int>::iterator isn;
for(isn = sn.begin();isn!=sn.end();isn++){
cWordlTotalFrequency[c[i]][isn->first] = cWordlTotalFrequency[c[i]][isn->first] + isn->second;
}
}
int tw = wordFrequency.size();
map<int,float>::iterator icWordProbability;
for(icWordProbability=cWordProbability.begin();icWordProbability!=cWordProbability.end();icWordProbability++){
cTotalFrequency[icWordProbability->first] = icWordProbability->second;
cWordProbability[icWordProbability->first] = icWordProbability->second / totalWords;
}
cout<<"Word Frequency:"<<endl;
map<string,int>::iterator iwordFrequency;
for(iwordFrequency=wordFrequency.begin();iwordFrequency!=wordFrequency.end();iwordFrequency++){
cout<<setw(8)<<iwordFrequency->first<<"\tFrequency:"<<iwordFrequency->second<<endl;
}
cout<<"Conditional Probability:"<<endl;
map<string,int> dtw;//待分类文档词频
for(int i=0;i<d.size();i++) dtw[d[i]] = dtw[d[i]] + 1;
map<string,map<int,float> > cp;//单词类别概率
map<string,int>::iterator idtw;
for(idtw=dtw.begin();idtw!=dtw.end();idtw++){
map<int,float> cf;
for(int j=0;j<cTotalFrequency.size();j++){
float p=0;
p = (float)(cWordlTotalFrequency[j][idtw->first] +1) / (cTotalFrequency[j] + wordFrequency.size());
cf[j] = p;
cout<<"P("<<idtw->first<<"|"<<j<<") \t= "<<p<<endl;
}
cp[idtw->first] = cf;
}
cout<<"Classification Probability:"<<endl;
float mp = 0;
int classification=0;
for(int i=0;i<cTotalFrequency.size();i++){
float tcp=1;
for(int j=0;j<d.size();j++){
tcp = tcp * cp[d[j]][i];
}
tcp = tcp * cWordProbability[i];
cout<<"classification:"<<i<<"\t"<<"Probability:"<<tcp<<endl;
if(mp<tcp) {
mp = tcp;
classification = i;
}
}
cout<<"The new document classification is："<<classification<<endl;
}
int main(int argc, char** argv) {
vector<vector<string> > docs;
vector<int> c = openClassificationFile("classification.txt");
vector<char *> files;
files.push_back("1.txt");files.push_back("2.txt");files.push_back("3.txt");files.push_back("4.txt");files.push_back("5.txt");
cout<<"训练文档集："<<endl;
docs = openFiles(files);
vector<string> d;
cout<<"待分类文档："<<endl;
d = openFile("new.txt");
bayesian(docs,c,d);
return 0;
}
结论：
朴素贝叶斯分类器用于处理离散型的文本数据，能够有效对文本文档进行分类。在实验过程中，最困难的地方在于数据结构的设计，由于要统计每个文档类别的频数和每个文档类别下单词的概率，这个地方需要用到复杂映射与统计，在编码过程中经过不断的思考，最终通过多级映射的形式储存所需的数据，最终计算出新文档的类别。通过实验，成功将新的未分类文档输入例子分类为期待的文档类型，实验结果较为满意。
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原文地址：http://blog.csdn.net/k76853/article/details/50532195
