dlib C++ Library - svm_one_class_trainer.h

    // Copyright (C) 2010  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_SVm_ONE_CLASS_TRAINER_Hh_
    #define DLIB_SVm_ONE_CLASS_TRAINER_Hh_

    #include "svm_one_class_trainer_abstract.h"
    #include <cmath>
    #include <limits>
    #include <sstream>
    #include "../matrix.h"
    #include "../algs.h"

    #include "function.h"
    #include "kernel.h"
    #include "../optimization/optimization_solve_qp3_using_smo.h"

    namespace dlib
    {

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

        template <
            typename K
            >
        class svm_one_class_trainer
        {
        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_one_class_trainer (
            ) :
                nu(0.1),
                cache_size(200),
                eps(0.001)
            {
            }

            svm_one_class_trainer (
                const kernel_type& kernel_,
                const scalar_type& nu_
            ) :
                kernel_function(kernel_),
                nu(nu_),
                cache_size(200),
                eps(0.001)
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < nu && nu <= 1,
                    "\tsvm_one_class_trainer::svm_one_class_trainer(kernel,nu)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t nu: " << nu
                    );
            }

            void set_cache_size (
                long cache_size_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(cache_size_ > 0,
                    "\tvoid svm_one_class_trainer::set_cache_size(cache_size_)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t cache_size: " << cache_size_
                    );
                cache_size = cache_size_;
            }

            long get_cache_size (
            ) const
            {
                return cache_size;
            }

            void set_epsilon (
                scalar_type eps_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(eps_ > 0,
                    "\tvoid svm_one_class_trainer::set_epsilon(eps_)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t eps: " << eps_
                    );
                eps = eps_;
            }

            const scalar_type get_epsilon (
            ) const
            {
                return eps;
            }

            void set_kernel (
                const kernel_type& k
            )
            {
                kernel_function = k;
            }

            const kernel_type& get_kernel (
            ) const
            {
                return kernel_function;
            }

            void set_nu (
                scalar_type nu_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < nu_ && nu_ <= 1,
                    "\tvoid svm_one_class_trainer::set_nu(nu_)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t nu: " << nu_
                    );
                nu = nu_;
            }

            const scalar_type get_nu (
            ) const
            {
                return nu;
            }

            template <
                typename in_sample_vector_type
                >
            const decision_function<kernel_type> train (
                const in_sample_vector_type& x
            ) const
            {
                return do_train(mat(x));
            }

            void swap (
                svm_one_class_trainer& item
            )
            {
                exchange(kernel_function, item.kernel_function);
                exchange(nu,              item.nu);
                exchange(cache_size,      item.cache_size);
                exchange(eps,             item.eps);
            }

        private:

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

            template <
                typename in_sample_vector_type
                >
            const decision_function<kernel_type> do_train (
                const in_sample_vector_type& x
            ) const
            {
                typedef typename K::scalar_type scalar_type;
                typedef typename decision_function<K>::sample_vector_type sample_vector_type;
                typedef typename decision_function<K>::scalar_vector_type scalar_vector_type;

                // make sure requires clause is not broken
                DLIB_ASSERT(is_col_vector(x) && x.size() > 0,
                    "\tdecision_function svm_one_class_trainer::train(x)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t x.nr(): " << x.nr()
                    << "\n\t x.nc(): " << x.nc()
                    );

                scalar_vector_type alpha;

                solve_qp3_using_smo<scalar_vector_type> solver;

                solver(symmetric_matrix_cache<float>(kernel_matrix(kernel_function,x), cache_size),
                       zeros_matrix<scalar_type>(x.size(),1),
                       ones_matrix<scalar_type>(x.size(),1),
                       nu*x.size(),
                       1,
                       1,
                       alpha,
                       eps);

                scalar_type rho;
                calculate_rho(alpha,solver.get_gradient(),rho);

                // count the number of support vectors
                const long sv_count = (long)sum(alpha != 0);

                scalar_vector_type sv_alpha;
                sample_vector_type support_vectors;

                // size these column vectors so that they have an entry for each support vector
                sv_alpha.set_size(sv_count);
                support_vectors.set_size(sv_count);

                // load the support vectors and their alpha values into these new column matrices
                long idx = 0;
                for (long i = 0; i < alpha.nr(); ++i)
                {
                    if (alpha(i) != 0)
                    {
                        sv_alpha(idx) = alpha(i);
                        support_vectors(idx) = x(i);
                        ++idx;
                    }
                }

                // now return the decision function
                return decision_function<K> (sv_alpha, rho, kernel_function, support_vectors);
            }

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

            template <
                typename scalar_vector_type
                >
            void calculate_rho(
                const scalar_vector_type& alpha,
                const scalar_vector_type& df,
                scalar_type& rho
            ) const
            {
                long num_p_free = 0;
                scalar_type sum_p_free = 0;

                scalar_type upper_bound_p;
                scalar_type lower_bound_p;

                find_min_and_max(df, upper_bound_p, lower_bound_p);

                for(long i = 0; i < alpha.nr(); ++i)
                {
                    if(alpha(i) == 1)
                    {
                        if (df(i) > upper_bound_p)
                            upper_bound_p = df(i);
                    }
                    else if(alpha(i) == 0)
                    {
                        if (df(i) < lower_bound_p)
                            lower_bound_p = df(i);
                    }
                    else
                    {
                        ++num_p_free;
                        sum_p_free += df(i);
                    }
                }

                scalar_type r1;
                if(num_p_free > 0)
                    r1 = sum_p_free/num_p_free;
                else
                    r1 = (upper_bound_p+lower_bound_p)/2;

                rho = r1;
            }

            kernel_type kernel_function;
            scalar_type nu;
            long cache_size;
            scalar_type eps;
        }; // end of class svm_one_class_trainer

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

        template <typename K>
        void swap (
            svm_one_class_trainer<K>& a,
            svm_one_class_trainer<K>& b
        ) { a.swap(b); }

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

    }

    #endif // DLIB_SVm_ONE_CLASS_TRAINER_Hh_