// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SVm_EPSILON_REGRESSION_TRAINER_Hh_
#define DLIB_SVm_EPSILON_REGRESSION_TRAINER_Hh_
#include "svr_trainer_abstract.h"
#include <cmath>
#include <limits>
#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 svr_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;
svr_trainer (
) :
C(1),
eps_insensitivity(0.1),
cache_size(200),
eps(0.001)
{
}
void set_cache_size (
long cache_size_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(cache_size_ > 0,
"\tvoid svr_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 svr_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_epsilon_insensitivity (
scalar_type eps_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
"\tvoid svr_trainer::set_epsilon_insensitivity(eps_)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t eps_: " << eps_
);
eps_insensitivity = eps_;
}
const scalar_type get_epsilon_insensitivity (
) const
{
return eps_insensitivity;
}
void set_kernel (
const kernel_type& k
)
{
kernel_function = k;
}
const kernel_type& get_kernel (
) const
{
return kernel_function;
}
void set_c (
scalar_type C_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(C_ > 0,
"\t void svr_trainer::set_c()"
<< "\n\t C must be greater than 0"
<< "\n\t C_: " << C_
<< "\n\t this: " << this
);
C = C_;
}
const scalar_type get_c (
) const
{
return C;
}
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 (
svr_trainer& item
)
{
exchange(kernel_function, item.kernel_function);
exchange(C, item.C);
exchange(eps_insensitivity, item.eps_insensitivity);
exchange(cache_size, item.cache_size);
exchange(eps, item.eps);
}
private:
// ------------------------------------------------------------------------------------
template <typename M>
struct op_quad
{
explicit op_quad(
const M& m_
) : m(m_) {}
const M& m;
typedef typename M::type type;
typedef type const_ret_type;
const static long cost = M::cost + 2;
inline const_ret_type apply ( long r, long c) const
{
if (r < m.nr())
{
if (c < m.nc())
{
return m(r,c);
}
else
{
return -m(r,c-m.nc());
}
}
else
{
if (c < m.nc())
{
return -m(r-m.nr(),c);
}
else
{
return m(r-m.nr(),c-m.nc());
}
}
}
const static long NR = 2*M::NR;
const static long NC = 2*M::NC;
typedef typename M::mem_manager_type mem_manager_type;
typedef typename M::layout_type layout_type;
long nr () const { return 2*m.nr(); }
long nc () const { return 2*m.nc(); }
template <typename U> bool aliases ( const matrix_exp<U>& item) const
{ return m.aliases(item); }
template <typename U> bool destructively_aliases ( const matrix_exp<U>& item) const
{ return m.aliases(item); }
};
template <
typename EXP
>
const matrix_op<op_quad<EXP> > make_quad (
const matrix_exp<EXP>& m
) const
/*!
ensures
- returns the following matrix:
m -m
-m m
- I.e. returns a matrix that is twice the size of m and just
contains copies of m and -m
!*/
{
typedef op_quad<EXP> op;
return matrix_op<op>(op(m.ref()));
}
// ------------------------------------------------------------------------------------
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_learning_problem(x,y) == true,
"\tdecision_function svr_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()
);
scalar_vector_type alpha;
solve_qp3_using_smo<scalar_vector_type> solver;
solver(symmetric_matrix_cache<float>(make_quad(kernel_matrix(kernel_function,x)), cache_size),
uniform_matrix<scalar_type>(2*x.size(),1, eps_insensitivity) + join_cols(y,-y),
join_cols(uniform_matrix<scalar_type>(x.size(),1,1), uniform_matrix<scalar_type>(x.size(),1,-1)),
0,
C,
C,
alpha,
eps);
scalar_type b;
calculate_b(alpha,solver.get_gradient(),C,b);
alpha = -rowm(alpha,range(0,x.size()-1)) + rowm(alpha,range(x.size(), alpha.size()-1));
// 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
>
void calculate_b(
const scalar_vector_type& alpha,
const scalar_vector_type& df,
const scalar_type& C,
scalar_type& b
) const
{
using namespace std;
long num_free = 0;
scalar_type sum_free = 0;
scalar_type upper_bound = -numeric_limits<scalar_type>::infinity();
scalar_type lower_bound = numeric_limits<scalar_type>::infinity();
find_min_and_max(df, upper_bound, lower_bound);
for(long i = 0; i < alpha.nr(); ++i)
{
if(i < alpha.nr()/2)
{
if(alpha(i) == C)
{
if (df(i) > upper_bound)
upper_bound = df(i);
}
else if(alpha(i) == 0)
{
if (df(i) < lower_bound)
lower_bound = df(i);
}
else
{
++num_free;
sum_free += df(i);
}
}
else
{
if(alpha(i) == C)
{
if (-df(i) < lower_bound)
lower_bound = -df(i);
}
else if(alpha(i) == 0)
{
if (-df(i) > upper_bound)
upper_bound = -df(i);
}
else
{
++num_free;
sum_free -= df(i);
}
}
}
if(num_free > 0)
b = sum_free/num_free;
else
b = (upper_bound+lower_bound)/2;
}
// ------------------------------------------------------------------------------------
kernel_type kernel_function;
scalar_type C;
scalar_type eps_insensitivity;
long cache_size;
scalar_type eps;
}; // end of class svr_trainer
// ----------------------------------------------------------------------------------------
template <typename K>
void swap (
svr_trainer<K>& a,
svr_trainer<K>& b
) { a.swap(b); }
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_EPSILON_REGRESSION_TRAINER_Hh_