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Diffstat (limited to 'ml/dlib/dlib/svm/structural_svm_assignment_problem_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/svm/structural_svm_assignment_problem_abstract.h | 87 |
1 files changed, 0 insertions, 87 deletions
diff --git a/ml/dlib/dlib/svm/structural_svm_assignment_problem_abstract.h b/ml/dlib/dlib/svm/structural_svm_assignment_problem_abstract.h deleted file mode 100644 index c06190726..000000000 --- a/ml/dlib/dlib/svm/structural_svm_assignment_problem_abstract.h +++ /dev/null @@ -1,87 +0,0 @@ -// 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_ - - - |