/usr/include/dlib/svm/rvm_abstract.h is in libdlib-dev 18.18-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 | // 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_
|