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diff --git a/ml/dlib/dlib/svm/svm_nu_trainer.h b/ml/dlib/dlib/svm/svm_nu_trainer.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.
-#ifndef DLIB_SVm_NU_TRAINER_Hh_
-#define DLIB_SVm_NU_TRAINER_Hh_
-
-//#include "local/make_label_kernel_matrix.h"
-
-#include "svm_nu_trainer_abstract.h"
-#include <cmath>
-#include <limits>
-#include <sstream>
-#include "../matrix.h"
-#include "../algs.h"
-#include "../serialize.h"
-
-#include "function.h"
-#include "kernel.h"
-#include "../optimization/optimization_solve_qp2_using_smo.h"
-
-namespace dlib
-{
-
-// ----------------------------------------------------------------------------------------
-
- template <
- typename K
- >
- class svm_nu_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_nu_trainer (
- ) :
- nu(0.1),
- cache_size(200),
- eps(0.001)
- {
- }
-
- svm_nu_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_nu_trainer::svm_nu_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_nu_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_nu_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_nu_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,
- typename in_scalar_vector_type
- >
- const decision_function<kernel_type> train (
- const in_sample_vector_type& x,
- const in_scalar_vector_type& y
- ) const
- {
- return do_train(mat(x), mat(y));
- }
-
- void swap (
- svm_nu_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,
- typename in_scalar_vector_type
- >
- const decision_function<kernel_type> do_train (
- const in_sample_vector_type& x,
- const in_scalar_vector_type& y
- ) 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_binary_classification_problem(x,y) == true,
- "\tdecision_function svm_nu_trainer::train(x,y)"
- << "\n\t invalid inputs were given to this function"
- << "\n\t x.nr(): " << x.nr()
- << "\n\t y.nr(): " << y.nr()
- << "\n\t x.nc(): " << x.nc()
- << "\n\t y.nc(): " << y.nc()
- << "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
- );
-
-
- scalar_vector_type alpha;
-
- solve_qp2_using_smo<scalar_vector_type> solver;
-
- solver(symmetric_matrix_cache<float>((diagm(y)*kernel_matrix(kernel_function,x)*diagm(y)), cache_size),
- //solver(symmetric_matrix_cache<float>(make_label_kernel_matrix(kernel_matrix(kernel_function,x),y), cache_size),
- y,
- nu,
- alpha,
- eps);
-
- scalar_type rho, b;
- calculate_rho_and_b(y,alpha,solver.get_gradient(),rho,b);
- alpha = pointwise_multiply(alpha,y)/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, b, kernel_function, support_vectors);
- }
-
- // ------------------------------------------------------------------------------------
-
- template <
- typename scalar_vector_type,
- typename scalar_vector_type2,
- typename scalar_type
- >
- void calculate_rho_and_b(
- const scalar_vector_type2& y,
- const scalar_vector_type& alpha,
- const scalar_vector_type& df,
- scalar_type& rho,
- scalar_type& b
- ) const
- {
- using namespace std;
- long num_p_free = 0;
- long num_n_free = 0;
- scalar_type sum_p_free = 0;
- scalar_type sum_n_free = 0;
-
- scalar_type upper_bound_p = -numeric_limits<scalar_type>::infinity();
- scalar_type upper_bound_n = -numeric_limits<scalar_type>::infinity();
- scalar_type lower_bound_p = numeric_limits<scalar_type>::infinity();
- scalar_type lower_bound_n = numeric_limits<scalar_type>::infinity();
-
- for(long i = 0; i < alpha.nr(); ++i)
- {
- if(y(i) == 1)
- {
- 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);
- }
- }
- else
- {
- if(alpha(i) == 1)
- {
- if (df(i) > upper_bound_n)
- upper_bound_n = df(i);
- }
- else if(alpha(i) == 0)
- {
- if (df(i) < lower_bound_n)
- lower_bound_n = df(i);
- }
- else
- {
- ++num_n_free;
- sum_n_free += df(i);
- }
- }
- }
-
- scalar_type r1,r2;
- if(num_p_free > 0)
- r1 = sum_p_free/num_p_free;
- else
- r1 = (upper_bound_p+lower_bound_p)/2;
-
- if(num_n_free > 0)
- r2 = sum_n_free/num_n_free;
- else
- r2 = (upper_bound_n+lower_bound_n)/2;
-
- rho = (r1+r2)/2;
- b = (r1-r2)/2/rho;
- }
-
- // ------------------------------------------------------------------------------------
-
- kernel_type kernel_function;
- scalar_type nu;
- long cache_size;
- scalar_type eps;
- }; // end of class svm_nu_trainer
-
-// ----------------------------------------------------------------------------------------
-
- template <typename K>
- void swap (
- svm_nu_trainer<K>& a,
- svm_nu_trainer<K>& b
- ) { a.swap(b); }
-
-// ----------------------------------------------------------------------------------------
-
-}
-
-#endif // DLIB_SVm_NU_TRAINER_Hh_
-
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