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https://gitcode.com/gh_mirrors/ope/OpenFace.git
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327 lines
9.8 KiB
C++
327 lines
9.8 KiB
C++
// Copyright (C) 2007 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_SVm_NU_TRAINER_Hh_
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#define DLIB_SVm_NU_TRAINER_Hh_
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//#include "local/make_label_kernel_matrix.h"
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#include "svm_nu_trainer_abstract.h"
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#include <cmath>
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#include <limits>
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#include <sstream>
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#include "../matrix.h"
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#include "../algs.h"
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#include "../serialize.h"
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#include "function.h"
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#include "kernel.h"
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#include "../optimization/optimization_solve_qp2_using_smo.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename K
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>
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class svm_nu_trainer
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{
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public:
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef decision_function<kernel_type> trained_function_type;
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svm_nu_trainer (
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) :
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nu(0.1),
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cache_size(200),
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eps(0.001)
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{
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}
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svm_nu_trainer (
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const kernel_type& kernel_,
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const scalar_type& nu_
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) :
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kernel_function(kernel_),
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nu(nu_),
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cache_size(200),
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eps(0.001)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(0 < nu && nu <= 1,
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"\tsvm_nu_trainer::svm_nu_trainer(kernel,nu)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t nu: " << nu
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);
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}
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void set_cache_size (
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long cache_size_
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)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(cache_size_ > 0,
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"\tvoid svm_nu_trainer::set_cache_size(cache_size_)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t cache_size: " << cache_size_
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);
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cache_size = cache_size_;
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}
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long get_cache_size (
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) const
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{
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return cache_size;
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}
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void set_epsilon (
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scalar_type eps_
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)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(eps_ > 0,
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"\tvoid svm_nu_trainer::set_epsilon(eps_)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t eps: " << eps_
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);
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eps = eps_;
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}
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const scalar_type get_epsilon (
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) const
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{
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return eps;
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}
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void set_kernel (
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const kernel_type& k
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)
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{
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kernel_function = k;
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}
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const kernel_type& get_kernel (
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) const
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{
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return kernel_function;
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}
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void set_nu (
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scalar_type nu_
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)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(0 < nu_ && nu_ <= 1,
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"\tvoid svm_nu_trainer::set_nu(nu_)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t nu: " << nu_
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);
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nu = nu_;
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}
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const scalar_type get_nu (
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) const
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{
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return nu;
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}
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template <
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typename in_sample_vector_type,
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typename in_scalar_vector_type
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>
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const decision_function<kernel_type> train (
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const in_sample_vector_type& x,
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const in_scalar_vector_type& y
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) const
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{
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return do_train(mat(x), mat(y));
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}
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void swap (
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svm_nu_trainer& item
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)
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{
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exchange(kernel_function, item.kernel_function);
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exchange(nu, item.nu);
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exchange(cache_size, item.cache_size);
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exchange(eps, item.eps);
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}
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private:
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// ------------------------------------------------------------------------------------
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template <
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typename in_sample_vector_type,
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typename in_scalar_vector_type
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>
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const decision_function<kernel_type> do_train (
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const in_sample_vector_type& x,
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const in_scalar_vector_type& y
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) const
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{
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typedef typename K::scalar_type scalar_type;
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typedef typename decision_function<K>::sample_vector_type sample_vector_type;
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typedef typename decision_function<K>::scalar_vector_type scalar_vector_type;
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// make sure requires clause is not broken
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DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
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"\tdecision_function svm_nu_trainer::train(x,y)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t x.nr(): " << x.nr()
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<< "\n\t y.nr(): " << y.nr()
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<< "\n\t x.nc(): " << x.nc()
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<< "\n\t y.nc(): " << y.nc()
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<< "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
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);
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scalar_vector_type alpha;
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solve_qp2_using_smo<scalar_vector_type> solver;
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solver(symmetric_matrix_cache<float>((diagm(y)*kernel_matrix(kernel_function,x)*diagm(y)), cache_size),
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//solver(symmetric_matrix_cache<float>(make_label_kernel_matrix(kernel_matrix(kernel_function,x),y), cache_size),
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y,
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nu,
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alpha,
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eps);
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scalar_type rho, b;
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calculate_rho_and_b(y,alpha,solver.get_gradient(),rho,b);
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alpha = pointwise_multiply(alpha,y)/rho;
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// count the number of support vectors
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const long sv_count = (long)sum(alpha != 0);
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scalar_vector_type sv_alpha;
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sample_vector_type support_vectors;
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// size these column vectors so that they have an entry for each support vector
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sv_alpha.set_size(sv_count);
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support_vectors.set_size(sv_count);
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// load the support vectors and their alpha values into these new column matrices
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long idx = 0;
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for (long i = 0; i < alpha.nr(); ++i)
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{
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if (alpha(i) != 0)
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{
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sv_alpha(idx) = alpha(i);
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support_vectors(idx) = x(i);
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++idx;
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}
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}
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// now return the decision function
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return decision_function<K> (sv_alpha, b, kernel_function, support_vectors);
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}
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// ------------------------------------------------------------------------------------
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template <
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typename scalar_vector_type,
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typename scalar_vector_type2,
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typename scalar_type
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>
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void calculate_rho_and_b(
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const scalar_vector_type2& y,
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const scalar_vector_type& alpha,
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const scalar_vector_type& df,
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scalar_type& rho,
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scalar_type& b
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) const
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{
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using namespace std;
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long num_p_free = 0;
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long num_n_free = 0;
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scalar_type sum_p_free = 0;
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scalar_type sum_n_free = 0;
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scalar_type upper_bound_p = -numeric_limits<scalar_type>::infinity();
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scalar_type upper_bound_n = -numeric_limits<scalar_type>::infinity();
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scalar_type lower_bound_p = numeric_limits<scalar_type>::infinity();
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scalar_type lower_bound_n = numeric_limits<scalar_type>::infinity();
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for(long i = 0; i < alpha.nr(); ++i)
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{
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if(y(i) == 1)
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{
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if(alpha(i) == 1)
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{
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if (df(i) > upper_bound_p)
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upper_bound_p = df(i);
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}
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else if(alpha(i) == 0)
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{
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if (df(i) < lower_bound_p)
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lower_bound_p = df(i);
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}
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else
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{
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++num_p_free;
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sum_p_free += df(i);
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}
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}
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else
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{
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if(alpha(i) == 1)
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{
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if (df(i) > upper_bound_n)
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upper_bound_n = df(i);
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}
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else if(alpha(i) == 0)
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{
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if (df(i) < lower_bound_n)
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lower_bound_n = df(i);
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}
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else
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{
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++num_n_free;
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sum_n_free += df(i);
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}
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}
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}
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scalar_type r1,r2;
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if(num_p_free > 0)
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r1 = sum_p_free/num_p_free;
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else
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r1 = (upper_bound_p+lower_bound_p)/2;
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if(num_n_free > 0)
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r2 = sum_n_free/num_n_free;
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else
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r2 = (upper_bound_n+lower_bound_n)/2;
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rho = (r1+r2)/2;
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b = (r1-r2)/2/rho;
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}
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// ------------------------------------------------------------------------------------
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kernel_type kernel_function;
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scalar_type nu;
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long cache_size;
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scalar_type eps;
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}; // end of class svm_nu_trainer
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// ----------------------------------------------------------------------------------------
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template <typename K>
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void swap (
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svm_nu_trainer<K>& a,
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svm_nu_trainer<K>& b
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) { a.swap(b); }
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_SVm_NU_TRAINER_Hh_
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