// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
#ifdef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
#include <cmath>
#include <limits>
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "function_abstract.h"
#include "kernel_abstract.h"
#include "../optimization/optimization_solve_qp3_using_smo_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename K
>
class svr_trainer
{
/*!
REQUIREMENTS ON K
is a kernel function object as defined in dlib/svm/kernel_abstract.h
WHAT THIS OBJECT REPRESENTS
This object implements a trainer for performing epsilon-insensitive support
vector regression. It is implemented using the SMO algorithm.
The implementation of the eps-SVR training algorithm used by this object is based
on the following paper:
- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
!*/
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 (
);
/*!
ensures
- This object is properly initialized and ready to be used
to train a support vector machine.
- #get_c() == 1
- #get_epsilon_insensitivity() == 0.1
- #get_cache_size() == 200
- #get_epsilon() == 0.001
!*/
void set_cache_size (
long cache_size
);
/*!
requires
- cache_size > 0
ensures
- #get_cache_size() == cache_size
!*/
const long get_cache_size (
) const;
/*!
ensures
- returns the number of megabytes of cache this object will use
when it performs training via the this->train() function.
(bigger values of this may make training go faster but won't affect
the result. However, too big a value will cause you to run out of
memory, obviously.)
!*/
void set_epsilon (
scalar_type eps
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
const scalar_type get_epsilon (
) const;
/*!
ensures
- returns the error epsilon that determines when training should stop.
Generally a good value for this is 0.001. Smaller values may result
in a more accurate solution but take longer to execute.
!*/
void set_epsilon_insensitivity (
scalar_type eps
);
/*!
requires
- eps > 0
ensures
- #get_epsilon_insensitivity() == eps
!*/
const scalar_type get_epsilon_insensitivity (
) const;
/*!
ensures
- This object tries to find a function which minimizes the
regression error on a training set. This error is measured
in the following way:
- if (abs(predicted_value - true_labeled_value) < eps) then
- The error is 0. That is, any function which gets within
eps of the correct output is good enough.
- else
- The error grows linearly once it gets bigger than eps
So epsilon-insensitive regression means we do regression but
stop trying to fit a data point once it is "close enough".
This function returns that eps value which controls what we
mean by "close enough".
!*/
void set_kernel (
const kernel_type& k
);
/*!
ensures
- #get_kernel() == k
!*/
const kernel_type& get_kernel (
) const;
/*!
ensures
- returns a copy of the kernel function in use by this object
!*/
void set_c (
scalar_type C
);
/*!
requires
- C > 0
ensures
- #get_c() == C
!*/
const scalar_type get_c (
) const;
/*!
ensures
- returns the SVR regularization parameter. It is the parameter that
determines the trade-off between trying to reduce the training error
or allowing more errors but hopefully improving the generalization
of the resulting decision_function. Larger values encourage exact
fitting while smaller values of C may encourage better generalization.
!*/
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;
/*!
requires
- is_learning_problem(x,y) == true
- x == a matrix or something convertible to a matrix via mat().
Also, x should contain sample_type objects.
- y == a matrix or something convertible to a matrix via mat().
Also, y should contain scalar_type objects.
ensures
- performs support vector regression given the training samples in x and
target values in y.
- returns a decision_function F with the following properties:
- F(new_x) == predicted y value
!*/
void swap (
svr_trainer& item
);
/*!
ensures
- swaps *this and item
!*/
};
template <typename K>
void swap (
svr_trainer<K>& a,
svr_trainer<K>& b
) { a.swap(b); }
/*!
provides a global swap
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_