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
|
// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_H__
#define DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_H__
#include "structural_assignment_trainer_abstract.h"
#include "../algs.h"
#include "../optimization.h"
#include "structural_svm_assignment_problem.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename feature_extractor
>
class structural_assignment_trainer
{
public:
typedef typename feature_extractor::lhs_element lhs_element;
typedef typename feature_extractor::rhs_element rhs_element;
typedef std::pair<std::vector<lhs_element>, std::vector<rhs_element> > sample_type;
typedef std::vector<long> label_type;
typedef assignment_function<feature_extractor> trained_function_type;
structural_assignment_trainer (
)
{
set_defaults();
}
explicit structural_assignment_trainer (
const feature_extractor& fe_
) : fe(fe_)
{
set_defaults();
}
const feature_extractor& get_feature_extractor (
) const { return fe; }
void set_num_threads (
unsigned long num
)
{
num_threads = num;
}
unsigned long get_num_threads (
) const
{
return num_threads;
}
void set_epsilon (
double eps_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
"\t void structural_assignment_trainer::set_epsilon()"
<< "\n\t eps_ must be greater than 0"
<< "\n\t eps_: " << eps_
<< "\n\t this: " << this
);
eps = eps_;
}
double get_epsilon (
) const { return eps; }
void set_max_cache_size (
unsigned long max_size
)
{
max_cache_size = max_size;
}
unsigned long get_max_cache_size (
) const
{
return max_cache_size;
}
void be_verbose (
)
{
verbose = true;
}
void be_quiet (
)
{
verbose = false;
}
void set_oca (
const oca& item
)
{
solver = item;
}
const oca get_oca (
) const
{
return solver;
}
void set_c (
double C_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(C_ > 0,
"\t void structural_assignment_trainer::set_c()"
<< "\n\t C_ must be greater than 0"
<< "\n\t C_: " << C_
<< "\n\t this: " << this
);
C = C_;
}
double get_c (
) const
{
return C;
}
bool forces_assignment(
) const { return force_assignment; }
void set_forces_assignment (
bool new_value
)
{
force_assignment = new_value;
}
const assignment_function<feature_extractor> train (
const std::vector<sample_type>& samples,
const std::vector<label_type>& labels
) const
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
if (force_assignment)
{
DLIB_ASSERT(is_forced_assignment_problem(samples, labels),
"\t assignment_function structural_assignment_trainer::train()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_forced_assignment_problem(samples,labels): " << is_forced_assignment_problem(samples,labels)
<< "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
<< "\n\t is_learning_problem(samples,labels): " << is_learning_problem(samples,labels)
);
}
else
{
DLIB_ASSERT(is_assignment_problem(samples, labels),
"\t assignment_function structural_assignment_trainer::train()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
<< "\n\t is_learning_problem(samples,labels): " << is_learning_problem(samples,labels)
);
}
#endif
structural_svm_assignment_problem<feature_extractor> prob(samples,labels, fe, force_assignment, num_threads);
if (verbose)
prob.be_verbose();
prob.set_c(C);
prob.set_epsilon(eps);
prob.set_max_cache_size(max_cache_size);
matrix<double,0,1> weights;
solver(prob, weights);
return assignment_function<feature_extractor>(weights,fe,force_assignment);
}
private:
bool force_assignment;
double C;
oca solver;
double eps;
bool verbose;
unsigned long num_threads;
unsigned long max_cache_size;
void set_defaults ()
{
force_assignment = false;
C = 100;
verbose = false;
eps = 0.1;
num_threads = 2;
max_cache_size = 40;
}
feature_extractor fe;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_ASSiGNMENT_TRAINER_H__
|