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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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23
modules/optim/test/test_conjugate_gradient.cpp
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@@ -24,38 +24,39 @@ public:
return x[0]*x[0]+x[1]*x[1]+x[2]*x[2]+x[3]*x[3];
}
void getGradient(const double* x,double* grad){
for(int i=0;i<4;i++,grad++,x++){
grad[0]=2*x[0];
for(int i=0;i<4;i++){
grad[i]=2*x[i];
}
}
};
//TODO: test transp/usual x
/*class RosenbrockF:public cv::optim::Solver::Function{
class RosenbrockF:public cv::optim::Solver::Function{
double calc(const double* x)const{
return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
}
};*/
void getGradient(const double* x,double* grad){
grad[0]=-2*(1-x[0])-400*(x[1]-x[0]*x[0])*x[0];
grad[1]=200*(x[1]-x[0]*x[0]);
}
};
TEST(Optim_ConjGrad, regression_basic){
cv::Ptr<cv::optim::ConjGradSolver> solver=cv::optim::createConjGradSolver();
#if 1
{
cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
cv::Mat x=(cv::Mat_<double>(1,2)<<1.0,1.0),
etalon_x=(cv::Mat_<double>(1,2)<<0.0,0.0);
cv::Mat x=(cv::Mat_<double>(4,1)<<50.0,10.0,1.0,-10.0),
etalon_x=(cv::Mat_<double>(1,4)<<0.0,0.0,0.0,0.0);
double etalon_res=0.0;
return;
mytest(solver,ptr_F,x,etalon_x,etalon_res);
}
#endif
#if 0
#if 1
{
cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
step=(cv::Mat_<double>(2,1)<<0.5,+0.5),
etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
double etalon_res=0.0;
mytest(solver,ptr_F,x,step,etalon_x,etalon_res);
mytest(solver,ptr_F,x,etalon_x,etalon_res);
}
#endif
}