不知所云！

 

 

 

 

代码示例：

	for(i=0;i<totalcount;i++)
	{
		if(labels[indexes[i]]!=0) e1-=weights[indexes[i]]; else e1+=weights[indexes[i]];
		if(e1<min1) { min1=e1; pos1=i; }
		e2 = 1 - e1;
		if(e2<min2) { min2=e2; pos2=i; }
	}

 

 

#include<iostream>
#include<algorithm>
#include<functional>
#include<cmath>
#include<vector>

using std::vector;

using namespace std;
#define FCOUNT 100//特征数
#define CCOUNT 30//弱分类器个数
#define PCOUNT 200//正样本数
#define NCOUNT 300//负样本数
struct sample
{ 
	int features[FCOUNT];//特征
	int pos_neg;//正0，负1
	float weight;//权值
	int result;//分类器的识别结果
};
struct weakClassifier  
{
	int indexF;
	float threshold;
};   
struct MySortFunction 
{

	int m_n;
	MySortFunction(int n):m_n(n)
	{ } 
	bool operator()(sample&s1,sample&s2)const
	{
		return s1.features[m_n]<s2.features[m_n];
	} 
}; 
//创建正样本
void CreatePos(vector<sample> &a)
{ 
	int i,j;
	for(i=0;i<PCOUNT;i++)
	{ 
		sample temp; 
		temp.pos_neg=0;
		temp.weight=(float)1/(2*PCOUNT);
		temp.result =0;

		for(j=0;j<FCOUNT;j++)
			temp.features[j]=rand()%10;
		a.push_back(temp); 
	} 
} 

float min(float a,float b)
{
	return(a<=b?a:b);
} 

//创建负样本
void CreateNeg(vector<sample>&a)
{ 
	int	i,j;

	for(i=0;i<NCOUNT;i++)
	{ 
		sample	temp; 
		temp.pos_neg=1;
		temp.weight=(float)1/(2*NCOUNT);
		temp.result =1;

		for(j=0;j<FCOUNT;j++)
			temp.features[j]=rand()%10;
		a.push_back(temp); 
	}
} 
//Training classifier

void Training(vector<sample>&a,vector<weakClassifier>&b,float*factors)
{ 
	int i,j;
	vector<sample>::size_type id=0,tcount=a.size();

	for(i=0;i<CCOUNT;i++)
	{ 
		weakClassifier 	temp; 
		float totalWeights=0.0,totalPos=0.0,totalNeg=0.0,bPos=0,bNeg=0;//（当前样本之前的）正负样本权值和
		float e,thr,besterr=1.0;//训练单个分类器时用到的错误率，阈值，最小错误率
		float FThr[FCOUNT];//特征阈值
		float minErr=1.0;//所有特征的最小错误率
		float beta;//更新权值所需系数

		for(id=0;id<tcount;id++)
		{
			totalWeights+=a[id].weight; 
			if(a[id].pos_neg) 
				totalNeg+=a[id].weight	; 
			else 
				totalPos+=a[id].weight; 
		} 
		
		for(id=0;id<tcount;id++)
			a[id].weight /=totalWeights; //bhj:归一化

		for(j=0;j<FCOUNT;j++)
		{
			 //按特征j排序

			sort(a.begin(),a.end (),MySortFunction(j));
			besterr=1.0;

			 //求单个弱分类器的阈值

			for(id=0;id<tcount;id++)
			{

				if(a[id].pos_neg) 
					bNeg+=a[id].weight; 
				else 
					bPos+=a[id].weight; 
				e=min((bPos+totalNeg-bNeg),(bNeg+totalPos-bPos));

				if(id==0) 
					thr=a[id].features[j]-0.5;
				else 
				{ 
					if(id==tcount-1) 
						thr=a[a.size()-1].features[j]+0.5;
					else 
						thr=(a[id].features[j]+a[id-1].features[j])/2; 
				}

				if(e<besterr)
				{
					besterr=e;
					FThr[j]=thr; 
					//cout<<FThr[j]<<""<<j<<endl;
				}

			}
		} 
 

		for(j=0;j<FCOUNT;j++)
		{

			float serror=0.0;

			 for(id=0;id<tcount;id++)
			{

				if(a[id].features[j]<=FThr[j])
					a[id].result =0;//positive sample 
				else 
					a[id].result=1;
				serror+=a[id].weight*abs(a[id].result-a[id].pos_neg	);

			} 
			 if(serror<minErr)
			{
				minErr=serror;
				temp.indexF=j;
				temp.threshold=FThr[j];
			 

			}  
		} 

		b.push_back(temp);//选出一个弱分类器
		beta=minErr/(1-minErr);
		factors[i]=log(1/beta);

		for(id=0;id<tcount;id++)
		{
			if(a[id].pos_neg==a[id].result	)
				a[id].weight*=beta;
		 
		}
	}
//强分类器训练完毕
}
void main()
{
	vector<sample>a;
	vector<weakClassifier>b;

	float factors[CCOUNT];//at
	CreatePos(a);//创建正样本
	CreateNeg(a);//创建负样本
	Training(a,b,factors);//训练分类器

	//查看训练出的分类器的参数

	int i=0;

	for(i=0;i<CCOUNT;i++)
	{

		cout<<"系数"<<factors[i]<<"特征"<<b[i].indexF<<"阈值"<<b[i].threshold<<endl;
	}
}