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