// 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
{
using namespace std;
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_