#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structsample{
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 classifiervoid 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; }}
转载于:https://www.cnblogs.com/PierreDelatour/archive/2011/11/12/2246633.html
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