coco
279 lines
8.7 KiB
C++
279 lines
8.7 KiB
C++
// Copyright (C) 2008 Davis E. King (davis@dlib.net)
|
|
// License: Boost Software License See LICENSE.txt for the full license.
|
|
#undef DLIB_RVm_ABSTRACT_
|
|
#ifdef DLIB_RVm_ABSTRACT_
|
|
|
|
#include <cmath>
|
|
#include <limits>
|
|
#include "../matrix.h"
|
|
#include "../algs.h"
|
|
#include "function.h"
|
|
#include "kernel.h"
|
|
|
|
namespace dlib
|
|
{
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <
|
|
typename kern_type
|
|
>
|
|
class rvm_trainer
|
|
{
|
|
/*!
|
|
REQUIREMENTS ON kern_type
|
|
is a kernel function object as defined in dlib/svm/kernel_abstract.h
|
|
|
|
WHAT THIS OBJECT REPRESENTS
|
|
This object implements a trainer for a relevance vector machine for
|
|
solving binary classification problems.
|
|
|
|
The implementation of the RVM training algorithm used by this object is based
|
|
on the following excellent paper:
|
|
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation
|
|
for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings
|
|
of the Ninth International Workshop on Artificial Intelligence and Statistics,
|
|
Key West, FL, Jan 3-6.
|
|
!*/
|
|
|
|
public:
|
|
typedef kern_type 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;
|
|
|
|
rvm_trainer (
|
|
);
|
|
/*!
|
|
ensures
|
|
- This object is properly initialized and ready to be used
|
|
to train a relevance vector machine.
|
|
- #get_epsilon() == 0.001
|
|
- #get_max_iterations() == 2000
|
|
!*/
|
|
|
|
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_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
|
|
!*/
|
|
|
|
unsigned long get_max_iterations (
|
|
) const;
|
|
/*!
|
|
ensures
|
|
- returns the maximum number of iterations the RVM optimizer is allowed to
|
|
run before it is required to stop and return a result.
|
|
!*/
|
|
|
|
void set_max_iterations (
|
|
unsigned long max_iter
|
|
);
|
|
/*!
|
|
ensures
|
|
- #get_max_iterations() == max_iter
|
|
!*/
|
|
|
|
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_binary_classification_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
|
|
- trains a relevance vector classifier given the training samples in x and
|
|
labels in y.
|
|
- returns a decision function F with the following properties:
|
|
- if (new_x is a sample predicted have +1 label) then
|
|
- F(new_x) >= 0
|
|
- else
|
|
- F(new_x) < 0
|
|
throws
|
|
- std::bad_alloc
|
|
!*/
|
|
|
|
void swap (
|
|
rvm_trainer& item
|
|
);
|
|
/*!
|
|
ensures
|
|
- swaps *this and item
|
|
!*/
|
|
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename K>
|
|
void swap (
|
|
rvm_trainer<K>& a,
|
|
rvm_trainer<K>& b
|
|
) { a.swap(b); }
|
|
/*!
|
|
provides a global swap
|
|
!*/
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
// ----------------------------------------------------------------------------------------
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <
|
|
typename kern_type
|
|
>
|
|
class rvm_regression_trainer
|
|
{
|
|
/*!
|
|
REQUIREMENTS ON kern_type
|
|
is a kernel function object as defined in dlib/svm/kernel_abstract.h
|
|
|
|
WHAT THIS OBJECT REPRESENTS
|
|
This object implements a trainer for a relevance vector machine for
|
|
solving regression problems.
|
|
|
|
The implementation of the RVM training algorithm used by this object is based
|
|
on the following excellent paper:
|
|
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation
|
|
for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings
|
|
of the Ninth International Workshop on Artificial Intelligence and Statistics,
|
|
Key West, FL, Jan 3-6.
|
|
!*/
|
|
|
|
public:
|
|
typedef kern_type 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;
|
|
|
|
rvm_regression_trainer (
|
|
);
|
|
/*!
|
|
ensures
|
|
- This object is properly initialized and ready to be used
|
|
to train a relevance vector machine.
|
|
- #get_epsilon() == 0.001
|
|
!*/
|
|
|
|
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_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
|
|
!*/
|
|
|
|
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
|
|
- 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.
|
|
- is_learning_problem(x,y) == true
|
|
- x.size() > 0
|
|
ensures
|
|
- trains a RVM given the training samples in x and
|
|
labels in y and returns the resulting decision_function.
|
|
throws
|
|
- std::bad_alloc
|
|
!*/
|
|
|
|
void swap (
|
|
rvm_regression_trainer& item
|
|
);
|
|
/*!
|
|
ensures
|
|
- swaps *this and item
|
|
!*/
|
|
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename K>
|
|
void swap (
|
|
rvm_regression_trainer<K>& a,
|
|
rvm_regression_trainer<K>& b
|
|
) { a.swap(b); }
|
|
/*!
|
|
provides a global swap
|
|
!*/
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
}
|
|
|
|
#endif // DLIB_RVm_ABSTRACT_
|
|
|