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Additional cleaning for simplex method, removing the parts that are
currently unused. Removing developer's notes. Trying to reach production
level.
This commit is contained in:
Alex Leontiev 2013-07-11 09:31:10 +03:00
parent a95650111f
commit ba537a95db
3 changed files with 0 additions and 91 deletions
modules/optim
include/opencv2
optim.hpp
src
lpsolver.cpp
test
test_lpsolver.cpp

57
modules/optim/include/opencv2/optim.hpp

@ -52,63 +52,6 @@
namespace cv{namespace optim
{
//! generic class for optimization algorithms */
class CV_EXPORTS Solver : public Algorithm /* Algorithm is the base OpenCV class */
{
public:
class CV_EXPORTS Function
{
public:
virtual ~Function(){}
virtual double calc(InputArray args) const = 0;
};
class CV_EXPORTS Constraints
{
public:
virtual ~Constraints(){}
};
//! could be reused for all the generic algorithms like downhill simplex. Return value is the maximum value of a function*/
virtual double solve(const Function& F,const Constraints& C, OutputArray result) const = 0;
/*virtual void setTermCriteria(const TermCriteria& criteria) = 0;
virtual TermCriteria getTermCriteria() = 0;*/
// more detailed API to be defined later ...
};
class CV_EXPORTS LPSolver : public Solver
{
public:
class CV_EXPORTS LPFunction:public Solver::Function
{
Mat z;
public:
//! Note, that this class is supposed to be immutable, so it's ok to make only a shallow copy of z_in.*/
LPFunction(Mat z_in):z(z_in){}
~LPFunction(){};
const Mat& getz()const{return z;}
double calc(InputArray args)const;
};
//!This class represents constraints for linear problem. There are two matrix stored: m-by-n matrix A and n-by-1 column-vector b.
//!What this represents is the set of constraints Ax\leq b and x\geq 0. It can be shown that any set of linear constraints can be converted
//!this form and **we shall create various constructors for this class that will perform these conversions**.
class CV_EXPORTS LPConstraints:public Solver::Constraints
{
Mat A,b;
public:
~LPConstraints(){};
//! Note, that this class is supposed to be immutable, so it's ok to make only a shallow copy of A_in and b_in.*/
LPConstraints(Mat A_in, Mat b_in):A(A_in),b(b_in){}
const Mat& getA()const{return A;}
const Mat& getb()const{return b;}
};
LPSolver(){}
double solve(const Function& F,const Constraints& C, OutputArray result)const;
};
//!the return codes for solveLP() function
enum
{

14
modules/optim/src/lpsolver.cpp

@ -16,16 +16,6 @@ const void dprintf(const char* format,...){
#endif
}
double LPSolver::solve(const Function& F,const Constraints& C, OutputArray result)const{
return 0.0;
}
double LPSolver::LPFunction::calc(InputArray args)const{
dprintf("call to LPFunction::calc()\n");
return 0.0;
}
void const print_matrix(const Mat& X){
#ifdef ALEX_DEBUG
dprintf("\ttype:%d vs %d,\tsize: %d-on-%d\n",X.type(),CV_64FC1,X.rows,X.cols);
@ -337,7 +327,3 @@ const inline void swap_columns(Mat_<double>& A,int col1,int col2){
}
}
}}
/*FIXME (possible optimizations)
* use iterator-style (as in ddc0010e7... commit version of this file)
* remove calls to pivot inside the while-loops
*/

20
modules/optim/test/test_lpsolver.cpp

@ -112,23 +112,3 @@ TEST(Optim_LpSolver, regression_cycling){
//ASSERT_EQ(res,1);
}
}
//TODO
// get optimal solution from initial (0,0,...,0) - DONE
// milestone: pass first test (wo initial solution) - DONE
//
// ??how_check_multiple_solutions & pass_test - DONE
// Blands_rule - DONE
// (assert, assign) - DONE
//
// (&1tests on cycling)
// make_more_clear
// wrap in OOP
//
// non-trivial tests
// pull-request
//
// study hill and other algos
//
// ??how to get smallest l2 norm
// FUTURE: compress&debug-> more_tests(Cormen) -> readNumRecipes-> fast&stable || hill_climbing