貝葉斯學習方法中有用性非常高的一種為樸素貝葉斯學習期，常被稱為樸素貝葉斯分類器。

在某些領域中與神經網絡和決策樹學習相當。盡管樸素貝葉斯分類器忽略單詞間的依賴關系。即如果全部單詞是條件獨立的，但樸素貝葉斯分類在實際應用中有非常出色的表現。

樸素貝葉斯文本分類算法偽代碼：

樸素貝葉斯文本分類算法流程：

通過計算訓練集中每一個類別的概率與不同類別下每一個單詞的概率，然後利用樸素貝葉斯公式計算新文檔被分類為各個類別的概率。終於輸出概率最大的類別。

C++源代碼：

/*
	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;//每一個單詞出現的個數 
	map<int,float> cWordProbability;//類別單詞頻率 
	map<int,int> cTotalFrequency;//類別單詞個數
	map<int,map<string,int> > cWordlTotalFrequency;//類別下單詞個數 
	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;//待分類文檔詞頻 
	for(int i=0;i<d.size();i++) dtw[d[i]] = dtw[d[i]] + 1;
	map<string,map<int,float> > cp;//單詞類別概率 
	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<<"訓練文檔集："<<endl;
	docs = openFiles(files);
	vector<string> d;
	cout<<"待分類文檔："<<endl; 
	d = openFile("new.txt");
	bayesian(docs,c,d);
	return 0;
}

結論：

樸素貝葉斯分類器用於處理離散型的文本數據，可以有效對文本文檔進行分類。在實驗過程中，最困難的地方在於數據結構的設計。因為要統計每一個文檔類別的頻數和每一個文檔類別下單詞的概率，這個地方須要用到復雜映射與統計。在編碼過程中經過不斷的思考，終於通過多級映射的形式儲存所需的數據，終於計算出新文檔的類別。通過實驗，成功將新的未分類文檔輸入樣例分類為期待的文檔類型。實驗結果較為愜意。

Naive Bayesian文本分類器