144 lines
5.0 KiB
C++
144 lines
5.0 KiB
C++
#include "precomp.hpp"
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#undef ALEX_DEBUG
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#include "debug.hpp"
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namespace cv{namespace optim{
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#define SEC_METHOD_ITERATIONS 4
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#define INITIAL_SEC_METHOD_SIGMA 0.1
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class ConjGradSolverImpl : public ConjGradSolver
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{
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public:
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Ptr<Function> getFunction() const;
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void setFunction(const Ptr<Function>& f);
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TermCriteria getTermCriteria() const;
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ConjGradSolverImpl();
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void setTermCriteria(const TermCriteria& termcrit);
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double minimize(InputOutputArray x);
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protected:
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Ptr<Solver::Function> _Function;
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TermCriteria _termcrit;
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Mat_<double> d,r,buf_x,r_old;
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Mat_<double> minimizeOnTheLine_buf1,minimizeOnTheLine_buf2;
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private:
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static void minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
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};
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void ConjGradSolverImpl::minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
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Mat_<double>& buf2){
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double sigma=INITIAL_SEC_METHOD_SIGMA;
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buf1=0.0;
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buf2=0.0;
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dprintf(("before minimizeOnTheLine\n"));
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dprintf(("x:\n"));
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print_matrix(x);
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dprintf(("d:\n"));
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print_matrix(d);
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for(int i=0;i<SEC_METHOD_ITERATIONS;i++){
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_f->getGradient((double*)x.data,(double*)buf1.data);
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dprintf(("buf1:\n"));
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print_matrix(buf1);
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x=x+sigma*d;
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_f->getGradient((double*)x.data,(double*)buf2.data);
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dprintf(("buf2:\n"));
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print_matrix(buf2);
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double d1=buf1.dot(d), d2=buf2.dot(d);
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if((d1-d2)==0){
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break;
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}
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double alpha=-sigma*d1/(d2-d1);
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dprintf(("(buf2.dot(d)-buf1.dot(d))=%f\nalpha=%f\n",(buf2.dot(d)-buf1.dot(d)),alpha));
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x=x+(alpha-sigma)*d;
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sigma=-alpha;
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}
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dprintf(("after minimizeOnTheLine\n"));
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print_matrix(x);
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}
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double ConjGradSolverImpl::minimize(InputOutputArray x){
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CV_Assert(_Function.empty()==false);
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dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
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Mat x_mat=x.getMat();
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CV_Assert(MIN(x_mat.rows,x_mat.cols)==1);
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int ndim=MAX(x_mat.rows,x_mat.cols);
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CV_Assert(x_mat.type()==CV_64FC1);
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if(d.cols!=ndim){
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d.create(1,ndim);
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r.create(1,ndim);
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r_old.create(1,ndim);
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minimizeOnTheLine_buf1.create(1,ndim);
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minimizeOnTheLine_buf2.create(1,ndim);
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}
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Mat_<double> proxy_x;
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if(x_mat.rows>1){
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buf_x.create(1,ndim);
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Mat_<double> proxy(ndim,1,(double*)buf_x.data);
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x_mat.copyTo(proxy);
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proxy_x=buf_x;
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}else{
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proxy_x=x_mat;
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}
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_Function->getGradient((double*)proxy_x.data,(double*)d.data);
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d*=-1.0;
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d.copyTo(r);
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//here everything goes. check that everything is setted properly
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dprintf(("proxy_x\n"));print_matrix(proxy_x);
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dprintf(("d first time\n"));print_matrix(d);
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dprintf(("r\n"));print_matrix(r);
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double beta=0;
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for(int count=0;count<_termcrit.maxCount;count++){
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minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
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r.copyTo(r_old);
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_Function->getGradient((double*)proxy_x.data,(double*)r.data);
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r*=-1.0;
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double r_norm_sq=norm(r);
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if(_termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && r_norm_sq<_termcrit.epsilon){
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break;
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}
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r_norm_sq=r_norm_sq*r_norm_sq;
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beta=MAX(0.0,(r_norm_sq-r.dot(r_old))/r_norm_sq);
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d=r+beta*d;
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}
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if(x_mat.rows>1){
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Mat(ndim, 1, CV_64F, (double*)proxy_x.data).copyTo(x);
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}
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return _Function->calc((double*)proxy_x.data);
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}
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ConjGradSolverImpl::ConjGradSolverImpl(){
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_Function=Ptr<Function>();
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}
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Ptr<Solver::Function> ConjGradSolverImpl::getFunction()const{
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return _Function;
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}
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void ConjGradSolverImpl::setFunction(const Ptr<Function>& f){
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_Function=f;
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}
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TermCriteria ConjGradSolverImpl::getTermCriteria()const{
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return _termcrit;
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}
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void ConjGradSolverImpl::setTermCriteria(const TermCriteria& termcrit){
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CV_Assert((termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0) ||
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((termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0));
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_termcrit=termcrit;
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}
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// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
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Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit){
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ConjGradSolver *CG=new ConjGradSolverImpl();
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CG->setFunction(f);
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CG->setTermCriteria(termcrit);
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return Ptr<ConjGradSolver>(CG);
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}
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}}
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