// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_ABSTRACT_Hh_
#ifdef DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_ABSTRACT_Hh_
#include "../matrix.h"
#include <vector>
#include "structural_svm_problem_threaded_abstract.h"
#include "assignment_function_abstract.h"
// ----------------------------------------------------------------------------------------
namespace dlib
{
template <
typename feature_extractor
>
class structural_svm_assignment_problem : noncopyable,
public structural_svm_problem_threaded<matrix<double,0,1>,
typename feature_extractor::feature_vector_type >
{
/*!
REQUIREMENTS ON feature_extractor
It must be an object that implements an interface compatible with
the example_feature_extractor defined in dlib/svm/assignment_function_abstract.h.
WHAT THIS OBJECT REPRESENTS
This object is a tool for learning the parameters needed to use an
assignment_function object. It learns the parameters by formulating the
problem as a structural SVM problem.
!*/
public:
typedef matrix<double,0,1> matrix_type;
typedef typename feature_extractor::feature_vector_type feature_vector_type;
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;
structural_svm_assignment_problem(
const std::vector<sample_type>& samples,
const std::vector<label_type>& labels,
const feature_extractor& fe,
bool force_assignment,
unsigned long num_threads,
const double loss_per_false_association,
const double loss_per_missed_association
);
/*!
requires
- loss_per_false_association > 0
- loss_per_missed_association > 0
- is_assignment_problem(samples,labels) == true
- if (force_assignment) then
- is_forced_assignment_problem(samples,labels) == true
ensures
- This object attempts to learn a mapping from the given samples to the
given labels. In particular, it attempts to learn to predict labels[i]
based on samples[i]. Or in other words, this object can be used to learn
a parameter vector and bias, w and b, such that an assignment_function declared as:
assignment_function<feature_extractor> assigner(w,b,fe,force_assignment)
results in an assigner object which attempts to compute the following mapping:
labels[i] == labeler(samples[i])
- This object will use num_threads threads during the optimization
procedure. You should set this parameter equal to the number of
available processing cores on your machine.
- When solving the structural SVM problem, we will use
loss_per_false_association as the loss for incorrectly associating
objects that shouldn't be associated.
- When solving the structural SVM problem, we will use
loss_per_missed_association as the loss for failing to associate to
objects that are supposed to be associated with each other.
!*/
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_ABSTRACT_Hh_