File: svm_c_trainer_abstract.h

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// Copyright (C) 2007  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.
#undef DLIB_SVm_C_TRAINER_ABSTRACT_
#ifdef DLIB_SVm_C_TRAINER_ABSTRACT_

#include <cmath>
#include <limits>
#include <sstream>
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "function_abstract.h"
#include "kernel_abstract.h"
#include "../optimization/optimization_solve_qp3_using_smo_abstract.h"

namespace dlib
{

// ----------------------------------------------------------------------------------------

    template <
        typename K 
        >
    class svm_c_trainer
    {
        /*!
            REQUIREMENTS ON K 
                is a kernel function object as defined in dlib/svm/kernel_abstract.h 

            WHAT THIS OBJECT REPRESENTS
                This object implements a trainer for a C support vector machine for 
                solving binary classification problems.  It is implemented using the SMO
                algorithm.

                The implementation of the C-SVM training algorithm used by this object is based
                on the following paper:
                    - Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
                      machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

        !*/

    public:
        typedef K kernel_type;
        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;
        typedef decision_function<kernel_type> trained_function_type;

        svm_c_trainer (
        );
        /*!
            ensures
                - This object is properly initialized and ready to be used
                  to train a support vector machine.
                - #get_c_class1() == 1
                - #get_c_class2() == 1
                - #get_cache_size() == 200
                - #get_epsilon() == 0.001
        !*/

        svm_c_trainer (
            const kernel_type& kernel, 
            const scalar_type& C
        );
        /*!
            requires
                - 0 < C
            ensures
                - This object is properly initialized and ready to be used
                  to train a support vector machine.
                - #get_kernel() == kernel
                - #get_c_class1() == C
                - #get_c_class2() == C
                - #get_cache_size() == 200
                - #get_epsilon() == 0.001
        !*/

        void set_cache_size (
            long cache_size
        );
        /*!
            requires
                - cache_size > 0
            ensures
                - #get_cache_size() == cache_size 
        !*/

        const long get_cache_size (
        ) const;
        /*!
            ensures
                - returns the number of megabytes of cache this object will use
                  when it performs training via the this->train() function.
                  (bigger values of this may make training go faster but won't affect 
                  the result.  However, too big a value will cause you to run out of 
                  memory, obviously.)
        !*/

        void set_epsilon (
            scalar_type eps
        );
        /*!
            requires
                - eps > 0
            ensures
                - #get_epsilon() == eps 
        !*/

        const scalar_type get_epsilon (
        ) const;
        /*!
            ensures
                - returns the error epsilon that determines when training should stop.
                  Generally a good value for this is 0.001.  Smaller values may result
                  in a more accurate solution but take longer to execute.
        !*/

        void set_kernel (
            const kernel_type& k
        );
        /*!
            ensures
                - #get_kernel() == k 
        !*/

        const kernel_type& get_kernel (
        ) const;
        /*!
            ensures
                - returns a copy of the kernel function in use by this object
        !*/

        void set_c (
            scalar_type C 
        );
        /*!
            requires
                - C > 0
            ensures
                - #get_c_class1() == C 
                - #get_c_class2() == C 
        !*/

        const scalar_type get_c_class1 (
        ) const;
        /*!
            ensures
                - returns the SVM regularization parameter for the +1 class.  
                  It is the parameter that determines the trade off between
                  trying to fit the +1 training data exactly or allowing more errors 
                  but hopefully improving the generalization ability of the 
                  resulting classifier.  Larger values encourage exact fitting 
                  while smaller values of C may encourage better generalization. 
        !*/

        const scalar_type get_c_class2 (
        ) const;
        /*!
            ensures
                - returns the SVM regularization parameter for the -1 class.  
                  It is the parameter that determines the trade off between
                  trying to fit the -1 training data exactly or allowing more errors 
                  but hopefully improving the generalization ability of the 
                  resulting classifier.  Larger values encourage exact fitting 
                  while smaller values of C may encourage better generalization. 
        !*/

        void set_c_class1 (
            scalar_type C
        );
        /*!
            requires
                - C > 0
            ensures
                - #get_c_class1() == C
        !*/

        void set_c_class2 (
            scalar_type C
        );
        /*!
            requires
                - C > 0
            ensures
                - #get_c_class2() == C
        !*/

        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;
        /*!
            requires
                - is_binary_classification_problem(x,y) == true
                - x == a matrix or something convertible to a matrix via vector_to_matrix().
                  Also, x should contain sample_type objects.
                - y == a matrix or something convertible to a matrix via vector_to_matrix().
                  Also, y should contain scalar_type objects.
            ensures
                - trains a C support vector classifier given the training samples in x and 
                  labels in y.  Training is done when the error is less than get_epsilon().
                - returns a decision function F with the following properties:
                    - if (new_x is a sample predicted have +1 label) then
                        - F(new_x) >= 0
                    - else
                        - F(new_x) < 0
        !*/

        void swap (
            svm_c_trainer& item
        );
        /*!
            ensures
                - swaps *this and item
        !*/
    }; 

    template <typename K>
    void swap (
        svm_c_trainer<K>& a,
        svm_c_trainer<K>& b
    ) { a.swap(b); }
    /*!
        provides a global swap
    !*/

// ----------------------------------------------------------------------------------------

}

#endif // DLIB_SVm_C_TRAINER_ABSTRACT_