Adaboost 算法的C实现

#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); 
    }
} 
floatmin(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;

        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;
    }
}