// Copyright (C) 2008 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_REDUCED_TRAINERs_ABSTRACT_ #ifdef DLIB_REDUCED_TRAINERs_ABSTRACT_ #include "../matrix.h" #include "../algs.h" #include "function_abstract.h" #include "kernel_abstract.h" #include "../optimization.h" namespace dlib { // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- template < typename trainer_type > class reduced_decision_function_trainer { /*! REQUIREMENTS ON trainer_type - trainer_type == some kind of batch trainer object (e.g. svm_nu_trainer) WHAT THIS OBJECT REPRESENTS This object represents an implementation of a reduced set algorithm. This object acts as a post processor for anything that creates decision_function objects. It wraps another trainer object and performs this reduced set post processing with the goal of representing the original decision function in a form that involves fewer basis vectors. !*/ public: typedef typename trainer_type::kernel_type kernel_type; typedef typename trainer_type::scalar_type scalar_type; typedef typename trainer_type::sample_type sample_type; typedef typename trainer_type::mem_manager_type mem_manager_type; typedef typename trainer_type::trained_function_type trained_function_type; reduced_decision_function_trainer ( ); /*! ensures - This object is in an uninitialized state. You must construct a real one with the other constructor and assign it to this instance before you use this object. !*/ reduced_decision_function_trainer ( const trainer_type& trainer, const unsigned long num_bv ); /*! requires - num_bv > 0 ensures - returns a trainer object that applies post processing to the decision_function objects created by the given trainer object with the goal of creating decision_function objects with fewer basis vectors. - The reduced decision functions that are output will have at most num_bv basis vectors. !*/ template < typename in_sample_vector_type, typename in_scalar_vector_type > const decision_function<kernel_type> train ( const in_sample_vector_type& x, const in_scalar_vector_type& y ) const; /*! ensures - trains a decision_function using the trainer that was supplied to this object's constructor and then finds a reduced representation for it and returns the reduced version. throws - std::bad_alloc - any exceptions thrown by the trainer_type object !*/ }; // ---------------------------------------------------------------------------------------- template < typename trainer_type > const reduced_decision_function_trainer<trainer_type> reduced ( const trainer_type& trainer, const unsigned long num_bv ) { return reduced_decision_function_trainer<trainer_type>(trainer, num_bv); } /*! requires - num_bv > 0 - trainer_type == some kind of batch trainer object that creates decision_function objects (e.g. svm_nu_trainer) ensures - returns a reduced_decision_function_trainer object that has been instantiated with the given arguments. !*/ // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- template < typename trainer_type > class reduced_decision_function_trainer2 { /*! REQUIREMENTS ON trainer_type - trainer_type == some kind of batch trainer object (e.g. svm_nu_trainer) - trainer_type::sample_type must be a dlib::matrix object - kernel_derivative<trainer_type::kernel_type> must be defined WHAT THIS OBJECT REPRESENTS This object represents an implementation of a reduced set algorithm. This object acts as a post processor for anything that creates decision_function objects. It wraps another trainer object and performs this reduced set post processing with the goal of representing the original decision function in a form that involves fewer basis vectors. This object's implementation is the same as that in the above reduced_decision_function_trainer object except it also performs a global gradient based optimization at the end to further improve the approximation to the original decision function object. !*/ public: typedef typename trainer_type::kernel_type kernel_type; typedef typename trainer_type::scalar_type scalar_type; typedef typename trainer_type::sample_type sample_type; typedef typename trainer_type::mem_manager_type mem_manager_type; typedef typename trainer_type::trained_function_type trained_function_type; reduced_decision_function_trainer2 ( ); /*! ensures - This object is in an uninitialized state. You must construct a real one with the other constructor and assign it to this instance before you use this object. !*/ reduced_decision_function_trainer2 ( const trainer_type& trainer, const unsigned long num_bv, double eps = 1e-3 ); /*! requires - num_bv > 0 - eps > 0 ensures - returns a trainer object that applies post processing to the decision_function objects created by the given trainer object with the goal of creating decision_function objects with fewer basis vectors. - The reduced decision functions that are output will have at most num_bv basis vectors. - the gradient based optimization will continue until the change in the objective function is less than eps. So smaller values of eps will give better results but take longer to compute. !*/ template < typename in_sample_vector_type, typename in_scalar_vector_type > const decision_function<kernel_type> train ( const in_sample_vector_type& x, const in_scalar_vector_type& y ) const; /*! ensures - trains a decision_function using the trainer that was supplied to this object's constructor and then finds a reduced representation for it and returns the reduced version. throws - std::bad_alloc - any exceptions thrown by the trainer_type object !*/ }; // ---------------------------------------------------------------------------------------- template < typename trainer_type > const reduced_decision_function_trainer2<trainer_type> reduced2 ( const trainer_type& trainer, const unsigned long num_bv, double eps = 1e-3 ) { return reduced_decision_function_trainer2<trainer_type>(trainer, num_bv, eps); } /*! requires - num_bv > 0 - trainer_type == some kind of batch trainer object that creates decision_function objects (e.g. svm_nu_trainer) - kernel_derivative<trainer_type::kernel_type> is defined - eps > 0 ensures - returns a reduced_decision_function_trainer2 object that has been instantiated with the given arguments. !*/ // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- } #endif // DLIB_REDUCED_TRAINERs_ABSTRACT_