Prepare Downhill Simplex for pull request

This is an implementation of so-called downhill simplex method
(https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)

Please, let me know if you have any comments, whoever you'd be.

modules/optim/test/test_downhill_simplex.cpp

@@ -0,0 +1,63 @@
+#include "test_precomp.hpp"
+#include <cstdlib>
+#include <cmath>
+#include <algorithm>
+
+static void mytest(cv::Ptr<cv::optim::DownhillSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,cv::Mat& step, 
+        cv::Mat& etalon_x,double etalon_res){
+    solver->setFunction(ptr_F);
+    int ndim=MAX(step.cols,step.rows);
+    solver->setInitStep(step);
+    cv::Mat settedStep;
+    solver->getInitStep(settedStep);
+    ASSERT_TRUE(settedStep.rows==1 && settedStep.cols==ndim);
+    ASSERT_TRUE(std::equal(step.begin<double>(),step.end<double>(),settedStep.begin<double>()));
+    std::cout<<"step setted:\n\t"<<step<<std::endl;
+    double res=solver->minimize(x);
+    std::cout<<"res:\n\t"<<res<<std::endl;
+    std::cout<<"x:\n\t"<<x<<std::endl;
+    std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
+    std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
+    double tol=solver->getTermCriteria().epsilon;
+    ASSERT_TRUE(std::abs(res-etalon_res)<tol);
+    /*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
+        ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
+    }*/
+    std::cout<<"--------------------------\n";
+}
+
+class SphereF:public cv::optim::Solver::Function{
+public:
+    double calc(const double* x)const{
+        return x[0]*x[0]+x[1]*x[1];
+    }
+};
+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]);
+    }
+};
+
+TEST(Optim_Downhill, regression_basic){
+    cv::Ptr<cv::optim::DownhillSolver> solver=cv::optim::createDownhillSolver();
+#if 1
+    {
+        cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
+        cv::Mat x=(cv::Mat_<double>(1,2)<<1.0,1.0),
+            step=(cv::Mat_<double>(2,1)<<-0.5,-0.5),
+            etalon_x=(cv::Mat_<double>(1,2)<<-0.0,0.0);
+        double etalon_res=0.0;
+        mytest(solver,ptr_F,x,step,etalon_x,etalon_res);
+    }
+#endif
+#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);
+    }
+#endif
+}