coco
360 lines
10 KiB
C++
360 lines
10 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_C_TRAINER_Hh_
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#define DLIB_SVm_C_TRAINER_Hh_
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//#include "local/make_label_kernel_matrix.h"
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#include "svm_c_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 "function.h"
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#include "kernel.h"
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#include "../optimization/optimization_solve_qp3_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_c_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_c_trainer (
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) :
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Cpos(1),
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Cneg(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_c_trainer (
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const kernel_type& kernel_,
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const scalar_type& C_
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) :
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kernel_function(kernel_),
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Cpos(C_),
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Cneg(C_),
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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 < C_,
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"\tsvm_c_trainer::svm_c_trainer(kernel,C)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t C_: " << C_
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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_c_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_c_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_c (
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scalar_type C
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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(C > 0,
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"\t void svm_c_trainer::set_c()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cpos = C;
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Cneg = C;
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}
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const scalar_type get_c_class1 (
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) const
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{
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return Cpos;
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}
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const scalar_type get_c_class2 (
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) const
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{
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return Cneg;
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}
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void set_c_class1 (
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scalar_type C
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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(C > 0,
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"\t void svm_c_trainer::set_c_class1()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cpos = C;
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}
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void set_c_class2 (
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scalar_type C
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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(C > 0,
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"\t void svm_c_trainer::set_c_class2()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cneg = C;
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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_c_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(Cpos, item.Cpos);
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exchange(Cneg, item.Cneg);
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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_c_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_qp3_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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uniform_matrix<scalar_type>(y.size(),1,-1),
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y,
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0,
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Cpos,
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Cneg,
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alpha,
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eps);
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scalar_type b;
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calculate_b(y,alpha,solver.get_gradient(),Cpos,Cneg,b);
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alpha = pointwise_multiply(alpha,y);
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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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>
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void calculate_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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const scalar_type& Cpos,
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const scalar_type& Cneg,
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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_free = 0;
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scalar_type sum_free = 0;
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scalar_type upper_bound = -numeric_limits<scalar_type>::infinity();
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scalar_type lower_bound = 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) == Cpos)
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{
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if (df(i) > upper_bound)
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upper_bound = 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)
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lower_bound = df(i);
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}
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else
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{
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++num_free;
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sum_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) == Cneg)
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{
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if (-df(i) < lower_bound)
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lower_bound = -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) > upper_bound)
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upper_bound = -df(i);
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}
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else
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{
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++num_free;
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sum_free -= df(i);
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}
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}
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}
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if(num_free > 0)
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b = sum_free/num_free;
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else
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b = (upper_bound+lower_bound)/2;
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}
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// ------------------------------------------------------------------------------------
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kernel_type kernel_function;
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scalar_type Cpos;
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scalar_type Cneg;
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long cache_size;
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scalar_type eps;
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}; // end of class svm_c_trainer
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// ----------------------------------------------------------------------------------------
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template <typename K>
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void swap (
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svm_c_trainer<K>& a,
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svm_c_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_C_TRAINER_Hh_
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