coco
285 lines
8.0 KiB
C++
285 lines
8.0 KiB
C++
// Copyright (C) 2010 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_ONE_CLASS_TRAINER_Hh_
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#define DLIB_SVm_ONE_CLASS_TRAINER_Hh_
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#include "svm_one_class_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_one_class_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_one_class_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_one_class_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_one_class_trainer::svm_one_class_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_one_class_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_one_class_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_one_class_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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>
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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
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{
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return do_train(mat(x));
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}
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void swap (
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svm_one_class_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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>
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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
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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_col_vector(x) && x.size() > 0,
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"\tdecision_function svm_one_class_trainer::train(x)"
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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 x.nc(): " << x.nc()
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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>(kernel_matrix(kernel_function,x), cache_size),
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zeros_matrix<scalar_type>(x.size(),1),
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ones_matrix<scalar_type>(x.size(),1),
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nu*x.size(),
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1,
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1,
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alpha,
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eps);
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scalar_type rho;
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calculate_rho(alpha,solver.get_gradient(),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, rho, 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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>
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void calculate_rho(
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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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) 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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scalar_type sum_p_free = 0;
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scalar_type upper_bound_p;
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scalar_type lower_bound_p;
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find_min_and_max(df, upper_bound_p, lower_bound_p);
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for(long i = 0; i < alpha.nr(); ++i)
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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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scalar_type r1;
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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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rho = r1;
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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_one_class_trainer
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// ----------------------------------------------------------------------------------------
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template <typename K>
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void swap (
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svm_one_class_trainer<K>& a,
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svm_one_class_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_ONE_CLASS_TRAINER_Hh_
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