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Finish implementing the Nonlinear Conjugate Gradient

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Now everything is prepared for the pull request.
This commit is contained in:
Alex Leontiev
2013-09-24 07:51:21 +08:00
parent 581d454536
commit 891bcd8491
5 changed files with 93 additions and 34 deletions
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78
modules/optim/src/conjugate_gradient.cpp
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@@ -1,8 +1,11 @@
#include "precomp.hpp"
#undef ALEX_DEBUG
#include "debug.hpp"
namespace cv{namespace optim{
#define SEC_METHOD_ITERATIONS 4
#define INITIAL_SEC_METHOD_SIGMA 0.1
class ConjGradSolverImpl : public ConjGradSolver
{
public:
@@ -16,9 +19,45 @@ namespace cv{namespace optim{
Ptr<Solver::Function> _Function;
TermCriteria _termcrit;
Mat_<double> d,r,buf_x,r_old;
Mat_<double> minimizeOnTheLine_buf1,minimizeOnTheLine_buf2;
private:
static void minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
};
void ConjGradSolverImpl::minimizeOnTheLine(Ptr<Solver::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));
@@ -28,9 +67,13 @@ namespace cv{namespace optim{
int ndim=MAX(x_mat.rows,x_mat.cols);
CV_Assert(x_mat.type()==CV_64FC1);
d.create(1,ndim);
r.create(1,ndim);
r_old.create(1,ndim);
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){
@@ -41,14 +84,40 @@ namespace cv{namespace optim{
}else{
proxy_x=x_mat;
}
_Function->getGradient((double*)proxy_x.data,(double*)d.data);
if(true){
d*=-1.0;
d.copyTo(r);
}else{((double*)d.data)[1]=42.0;}
//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);
double beta=0;
for(int count=0;count<_termcrit.maxCount;count++){
minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
r.copyTo(r_old);
_Function->getGradient((double*)proxy_x.data,(double*)r.data);
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;
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, (double*)proxy_x.data).copyTo(x);
}
return 0.0;
return _Function->calc((double*)proxy_x.data);
}
ConjGradSolverImpl::ConjGradSolverImpl(){
_Function=Ptr<Function>();
}
@@ -74,4 +143,3 @@ namespace cv{namespace optim{
return Ptr<ConjGradSolver>(CG);
}
}}