model_search.h

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file        |
   | See: http://www.mrpt.org/Authors - All rights reserved.                   |
   | Released under BSD License. See details in http://www.mrpt.org/License    |
   +---------------------------------------------------------------------------+ */

#ifndef math_modelsearch_h
#define math_modelsearch_h

#include <mrpt/utils/utils_defs.h>
#include <set>

namespace mrpt {
        namespace math {


        /** Model search implementations: RANSAC and genetic algorithm
          *
          *  The type  \a TModelFit is a user-supplied struct/class that implements this interface:
          *  - Types:
          *    - \a Real : The numeric type to use (typ: double, float)
          *    - \a Model : The type of the model to be fitted (for example: A matrix, a TLine2D, a TPlane3D, ...)
          *  - Methods:
          *    - size_t getSampleCount() const : return the number of samples. This should not change during a model search.
          *    - bool fitModel( const vector_size_t& useIndices, Model& model ) const : This function fits a model to the data selected by the indices. The return value indicates the success, hence false means a degenerate case, where no model was found.
          *    - Real testSample( size_t index, const Model& model ) const : return some value that indicates how well a sample fits to the model. This way the thresholding is moved to the searching procedure and the model just tells how good a sample is.
          *
          *  There are two methods provided in this class to fit a model:
          *    - \a ransacSingleModel (RANSAC): Just like mrpt::math::RANSAC_Template
          *
          *    - \a geneticSingleModel (Genetic): Provides a mixture of a genetic and the ransac algorithm.
          *         Instead of selecting a set of data in each iteration, it takes more (ex. 10) and order these model
          *         using some fitness function: the average error of the inliers scaled by the number of outliers (This
          *         fitness might require some fine tuning). Than the (ex 10) new kernel for the next iteration is created as follows:
          *             - Take the best kernels (as for the original ransac)
          *             - Select two kernels ( with a higher probability for the better models) and let the new kernel be a subset of the two original kernels ( additionally to leave the local minimums an additional random seed might appear - mutation)
          *             - Generate some new random samples.
          *
          *  For an example of usage, see "samples/model_search_test/"
          *  \sa mrpt::math::RANSAC_Template, another RANSAC implementation where models can be matrices only.
          *
          *  \author Zoltar Gaal
          * \ingroup ransac_grp
          */
        class BASE_IMPEXP ModelSearch {
        private:
                //! Select random (unique) indices from the 0..p_size sequence
                void pickRandomIndex( size_t p_size, size_t p_pick, vector_size_t& p_ind );

                /** Select random (unique) indices from the set.
                  *  The set is destroyed during pick */
                void pickRandomIndex( std::set<size_t> p_set, size_t p_pick, vector_size_t& p_ind );

        public:
                template<typename TModelFit>
                bool    ransacSingleModel( const TModelFit& p_state,
                                                                   size_t p_kernelSize,
                                                                   const typename TModelFit::Real& p_fitnessThreshold,
                                                                   typename TModelFit::Model& p_bestModel,
                                                                   vector_size_t& p_inliers );

        private:
                template<typename TModelFit>
                struct TSpecies {
                        typename TModelFit::Model       model;
                        vector_size_t                           sample;
                        vector_size_t                           inliers;
                        typename TModelFit::Real        fitness;

                        static bool compare( const TSpecies* p_a, const TSpecies* p_b )
                        {
                                return p_a->fitness < p_b->fitness;
                        }
                };

        public:
                template<typename TModelFit>
                bool    geneticSingleModel( const TModelFit& p_state,
                                                                        size_t p_kernelSize,
                                                                        const typename TModelFit::Real& p_fitnessThreshold,
                                                                        size_t p_populationSize,
                                                                        size_t p_maxIteration,
                                                                        typename TModelFit::Model& p_bestModel,
                                                                        vector_size_t& p_inliers );
        }; // end of class

        } // namespace math
} // namespace mrpt

// Template implementations:
#include "model_search_impl.h"

#endif // math_modelsearch_h