// Copyright (C) 2008 Davis E. King (davisking@users.sourceforge.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_KRLs_ABSTRACT_ #ifdef DLIB_KRLs_ABSTRACT_ #include <cmath> #include "../matrix/matrix_abstract.h" #include "../algs.h" #include "../serialize.h" #include "kernel_abstract.h" namespace dlib { template < typename kernel_type > class krls { /*! REQUIREMENTS ON kernel_type is a kernel function object as defined in dlib/svm/kernel_abstract.h INITIAL VALUE - dictionary_size() == 0 WHAT THIS OBJECT REPRESENTS This is an implementation of the kernel recursive least squares algorithm described in the paper: The Kernel Recursive Least Squares Algorithm by Yaakov Engel. The long and short of this algorithm is that it is an online kernel based regression algorithm. You give it samples (x,y) and it learns the function f(x) == y. For a detailed description of the algorithm read the above paper. Also note that the algorithm internally keeps a set of "dictionary vectors" that are used to represent the regression function. You can force the algorithm to use no more than a set number of vectors by setting the 3rd constructor argument to whatever you want. However, note that doing this causes the algorithm to bias it's results towards more recent training examples. !*/ public: typedef typename kernel_type::scalar_type scalar_type; typedef typename kernel_type::sample_type sample_type; typedef typename kernel_type::mem_manager_type mem_manager_type; explicit krls ( const kernel_type& kernel_, scalar_type tolerance_ = 0.001, unsigned long max_dictionary_size_ = 1000000 ); /*! requires - tolerance >= 0 ensures - this object is properly initialized - #tolerance() == tolerance_ - #get_decision_function().kernel_function == kernel_ (i.e. this object will use the given kernel function) - #get_kernel() == kernel_ - #max_dictionary_size() == max_dictionary_size_ !*/ scalar_type tolerance( ) const; /*! ensures - returns the tolerance to use for the approximately linearly dependent test in the KRLS algorithm. This is a number which governs how accurately this object will approximate the decision function it is learning. Smaller values generally result in a more accurate estimate while also resulting in a bigger set of support vectors in the learned decision function. Bigger tolerances values result in a less accurate decision function but also in less support vectors. !*/ const kernel_type& get_kernel ( ) const; /*! ensures - returns a const reference to the kernel used by this object !*/ unsigned long max_dictionary_size( ) const; /*! ensures - returns the maximum number of dictionary vectors this object will use at a time. That is, dictionary_size() will never be greater than max_dictionary_size(). !*/ void clear_dictionary ( ); /*! ensures - clears out all learned data (e.g. #get_decision_function().support_vectors.size() == 0) !*/ scalar_type operator() ( const sample_type& x ) const; /*! ensures - returns the current y estimate for the given x !*/ void train ( const sample_type& x, scalar_type y ); /*! ensures - trains this object that the given x should be mapped to the given y - if (dictionary_size() == max_dictionary_size() and training would add another dictionary vector to this object) then - discards the oldest dictionary vector so that we can still add a new one and remain below the max number of dictionary vectors. !*/ void swap ( krls& item ); /*! ensures - swaps *this with item !*/ unsigned long dictionary_size ( ) const; /*! ensures - returns the number of "support vectors" in the dictionary. That is, returns a number equal to get_decision_function().support_vectors.size() !*/ decision_function<kernel_type> get_decision_function ( ) const; /*! ensures - returns a decision function F that represents the function learned by this object so far. I.e. it is the case that: - for all x: F(x) == (*this)(x) !*/ }; // ---------------------------------------------------------------------------------------- template < typename kernel_type > void swap( krls<kernel_type>& a, krls<kernel_type>& b ) { a.swap(b); } /*! provides a global swap function !*/ template < typename kernel_type > void serialize ( const krls<kernel_type>& item, std::ostream& out ); /*! provides serialization support for krls objects !*/ template < typename kernel_type > void deserialize ( krls<kernel_type>& item, std::istream& in ); /*! provides serialization support for krls objects !*/ // ---------------------------------------------------------------------------------------- } #endif // DLIB_KRLs_ABSTRACT_