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581d454536
Some interface was refined (most notably, the method for returning Hessian was removed and the method for getting gradient was added as optional to base Solver::Function class) and basic code for setters/getters was added. Now is the time for the real work on an algorithm.
110 lines
4.9 KiB
C++
110 lines
4.9 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_OPTIM_HPP__
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#define __OPENCV_OPTIM_HPP__
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#include "opencv2/core.hpp"
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namespace cv{namespace optim
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{
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class CV_EXPORTS Solver : public Algorithm
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{
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public:
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class CV_EXPORTS Function
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{
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public:
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virtual ~Function() {}
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virtual double calc(const double* x) const = 0;
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virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
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};
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virtual Ptr<Function> getFunction() const = 0;
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virtual void setFunction(const Ptr<Function>& f) = 0;
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virtual TermCriteria getTermCriteria() const = 0;
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virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
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// x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
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// after getMat() will return) row-vector or column-vector. *It's size and should
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// be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
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virtual double minimize(InputOutputArray x) = 0;
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};
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//! downhill simplex class
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class CV_EXPORTS DownhillSolver : public Solver
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{
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public:
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//! returns row-vector, even if the column-vector was given
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virtual void getInitStep(OutputArray step) const=0;
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//!This should be called at least once before the first call to minimize() and step is assumed to be (something that
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//! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.*
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virtual void setInitStep(InputArray step)=0;
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};
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// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
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CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f=Ptr<Solver::Function>(),
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InputArray initStep=Mat_<double>(1,1,0.0),
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TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
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//! conjugate gradient method
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class CV_EXPORTS ConjGradSolver : public Solver{
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};
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CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(),
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TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
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//!the return codes for solveLP() function
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enum
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{
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SOLVELP_UNBOUNDED = -2, //problem is unbounded (target function can achieve arbitrary high values)
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SOLVELP_UNFEASIBLE = -1, //problem is unfeasible (there are no points that satisfy all the constraints imposed)
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SOLVELP_SINGLE = 0, //there is only one maximum for target function
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SOLVELP_MULTI = 1 //there are multiple maxima for target function - the arbitrary one is returned
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};
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CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z);
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CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
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}}// cv
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#endif
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