当前位置:牛骨文开发手册数据结构与算法小五的算法学习之路 》 Naive Bayesian文本分类器

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]<<"	";
	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<<"	Frequency:"<<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<<") 	= "<<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<<"	"<<"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;
}

效果展示:

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。