/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#define dprintf(x)
#define print_matrix(x)
namespace cv
{
double MinProblemSolver::Function::getGradientEps() const { return 1e-3; }
void MinProblemSolver::Function::getGradient(const double* x, double* grad)
{
double eps = getGradientEps();
int i, n = getDims();
AutoBuffer<double> x_buf(n);
double* x_ = x_buf;
for( i = 0; i < n; i++ )
x_[i] = x[i];
for( i = 0; i < n; i++ )
{
x_[i] = x[i] + eps;
double y1 = calc(x_);
x_[i] = x[i] - eps;
double y0 = calc(x_);
grad[i] = (y1 - y0)/(2*eps);
x_[i] = x[i];
}
}
#define SEC_METHOD_ITERATIONS 4
#define INITIAL_SEC_METHOD_SIGMA 0.1
class ConjGradSolverImpl : public ConjGradSolver
{
public:
Ptr<Function> getFunction() const;
void setFunction(const Ptr<Function>& f);
TermCriteria getTermCriteria() const;
ConjGradSolverImpl();
void setTermCriteria(const TermCriteria& termcrit);
double minimize(InputOutputArray x);
protected:
Ptr<MinProblemSolver::Function> _Function;
TermCriteria _termcrit;
Mat_<double> d,r,buf_x,r_old;
Mat_<double> minimizeOnTheLine_buf1,minimizeOnTheLine_buf2;
private:
static void minimizeOnTheLine(Ptr<MinProblemSolver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
};
void ConjGradSolverImpl::minimizeOnTheLine(Ptr<MinProblemSolver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
Mat_<double>& buf2){
double sigma=INITIAL_SEC_METHOD_SIGMA;
buf1=0.0;
buf2=0.0;
dprintf(("before minimizeOnTheLine\n"));
dprintf(("x:\n"));
print_matrix(x);
dprintf(("d:\n"));
print_matrix(d);
for(int i=0;i<SEC_METHOD_ITERATIONS;i++){
_f->getGradient((double*)x.data,(double*)buf1.data);
dprintf(("buf1:\n"));
print_matrix(buf1);
x=x+sigma*d;
_f->getGradient((double*)x.data,(double*)buf2.data);
dprintf(("buf2:\n"));
print_matrix(buf2);
double d1=buf1.dot(d), d2=buf2.dot(d);
if((d1-d2)==0){
break;
}
double alpha=-sigma*d1/(d2-d1);
dprintf(("(buf2.dot(d)-buf1.dot(d))=%f\nalpha=%f\n",(buf2.dot(d)-buf1.dot(d)),alpha));
x=x+(alpha-sigma)*d;
sigma=-alpha;
}
dprintf(("after minimizeOnTheLine\n"));
print_matrix(x);
}
double ConjGradSolverImpl::minimize(InputOutputArray x){
CV_Assert(_Function.empty()==false);
dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
Mat x_mat=x.getMat();
CV_Assert(MIN(x_mat.rows,x_mat.cols)==1);
int ndim=MAX(x_mat.rows,x_mat.cols);
CV_Assert(x_mat.type()==CV_64FC1);
if(d.cols!=ndim){
d.create(1,ndim);
r.create(1,ndim);
r_old.create(1,ndim);
minimizeOnTheLine_buf1.create(1,ndim);
minimizeOnTheLine_buf2.create(1,ndim);
}
Mat_<double> proxy_x;
if(x_mat.rows>1){
buf_x.create(1,ndim);
Mat_<double> proxy(ndim,1,buf_x.ptr<double>());
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
proxy_x=x_mat;
}
_Function->getGradient(proxy_x.ptr<double>(),d.ptr<double>());
d*=-1.0;
d.copyTo(r);
//here everything goes. check that everything is setted properly
dprintf(("proxy_x\n"));print_matrix(proxy_x);
dprintf(("d first time\n"));print_matrix(d);
dprintf(("r\n"));print_matrix(r);
for(int count=0;count<_termcrit.maxCount;count++){
minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
r.copyTo(r_old);
_Function->getGradient(proxy_x.ptr<double>(),r.ptr<double>());
r*=-1.0;
double r_norm_sq=norm(r);
if(_termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && r_norm_sq<_termcrit.epsilon){
break;
}
r_norm_sq=r_norm_sq*r_norm_sq;
double beta=MAX(0.0,(r_norm_sq-r.dot(r_old))/r_norm_sq);
d=r+beta*d;
}
if(x_mat.rows>1){
Mat(ndim, 1, CV_64F, proxy_x.ptr<double>()).copyTo(x);
}
return _Function->calc(proxy_x.ptr<double>());
}
ConjGradSolverImpl::ConjGradSolverImpl(){
_Function=Ptr<Function>();
}
Ptr<MinProblemSolver::Function> ConjGradSolverImpl::getFunction()const{
return _Function;
}
void ConjGradSolverImpl::setFunction(const Ptr<Function>& f){
_Function=f;
}
TermCriteria ConjGradSolverImpl::getTermCriteria()const{
return _termcrit;
}
void ConjGradSolverImpl::setTermCriteria(const TermCriteria& termcrit){
CV_Assert((termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0) ||
((termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0));
_termcrit=termcrit;
}
// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
Ptr<ConjGradSolver> ConjGradSolver::create(const Ptr<MinProblemSolver::Function>& f, TermCriteria termcrit){
Ptr<ConjGradSolver> CG = makePtr<ConjGradSolverImpl>();
CG->setFunction(f);
CG->setTermCriteria(termcrit);
return CG;
}
}