File: svr_trainer_abstract.h

package info (click to toggle)
mldemos 0.5.1-3
  • links: PTS, VCS
  • area: main
  • in suites: jessie, jessie-kfreebsd
  • size: 32,224 kB
  • ctags: 46,525
  • sloc: cpp: 306,887; ansic: 167,718; ml: 126; sh: 109; makefile: 2
file content (209 lines) | stat: -rw-r--r-- 6,917 bytes parent folder | download | duplicates (4)
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
// 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 vector_to_matrix().
                  Also, x should contain sample_type objects.
                - y == a matrix or something convertible to a matrix via vector_to_matrix().
                  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_