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Diffstat (limited to 'ml/dlib/dlib/svm/structural_svm_assignment_problem.h')
| -rw-r--r-- | ml/dlib/dlib/svm/structural_svm_assignment_problem.h | 288 |
1 files changed, 0 insertions, 288 deletions
diff --git a/ml/dlib/dlib/svm/structural_svm_assignment_problem.h b/ml/dlib/dlib/svm/structural_svm_assignment_problem.h deleted file mode 100644 index 963af1631..000000000 --- a/ml/dlib/dlib/svm/structural_svm_assignment_problem.h +++ /dev/null @@ -1,288 +0,0 @@ -// Copyright (C) 2011 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#ifndef DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_Hh_ -#define DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_Hh_ - - -#include "structural_svm_assignment_problem_abstract.h" -#include "../matrix.h" -#include <vector> -#include <iterator> -#include "structural_svm_problem_threaded.h" - -// ---------------------------------------------------------------------------------------- - -namespace dlib -{ - template <long n, typename T> - struct column_matrix_static_resize - { - typedef T type; - }; - - template <long n, typename T, long NR, long NC, typename MM, typename L> - struct column_matrix_static_resize<n, matrix<T,NR,NC,MM,L> > - { - typedef matrix<T,NR+n,NC,MM,L> type; - }; - - template <long n, typename T, long NC, typename MM, typename L> - struct column_matrix_static_resize<n, matrix<T,0,NC,MM,L> > - { - typedef matrix<T,0,NC,MM,L> type; - }; - - template <typename T> - struct add_one_to_static_feat_size - { - typedef typename column_matrix_static_resize<1,typename T::feature_vector_type>::type type; - }; - -// ---------------------------------------------------------------------------------------- - - template < - typename feature_extractor - > - class structural_svm_assignment_problem : noncopyable, - public structural_svm_problem_threaded<matrix<double,0,1>, typename add_one_to_static_feat_size<feature_extractor>::type > - { - public: - typedef matrix<double,0,1> matrix_type; - typedef typename add_one_to_static_feat_size<feature_extractor>::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_ - ) : - structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads), - samples(samples_), - labels(labels_), - fe(fe_), - force_assignment(force_assignment_), - loss_per_false_association(loss_per_false_association_), - loss_per_missed_association(loss_per_missed_association_) - { - // make sure requires clause is not broken -#ifdef ENABLE_ASSERTS - DLIB_ASSERT(loss_per_false_association > 0 && loss_per_missed_association > 0, - "\t structural_svm_assignment_problem::structural_svm_assignment_problem()" - << "\n\t invalid inputs were given to this function" - << "\n\t loss_per_false_association: " << loss_per_false_association - << "\n\t loss_per_missed_association: " << loss_per_missed_association - << "\n\t this: " << this - ); - if (force_assignment) - { - DLIB_ASSERT(is_forced_assignment_problem(samples, labels), - "\t structural_svm_assignment_problem::structural_svm_assignment_problem()" - << "\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) - << "\n\t this: " << this - ); - } - else - { - DLIB_ASSERT(is_assignment_problem(samples, labels), - "\t structural_svm_assignment_problem::structural_svm_assignment_problem()" - << "\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) - << "\n\t this: " << this - ); - } -#endif - - } - - private: - virtual long get_num_dimensions ( - ) const - { - return fe.num_features()+1; // +1 for the bias term - } - - virtual long get_num_samples ( - ) const - { - return samples.size(); - } - - template <typename psi_type> - typename enable_if<is_matrix<psi_type> >::type get_joint_feature_vector ( - const sample_type& sample, - const label_type& label, - psi_type& psi - ) const - { - typename feature_extractor::feature_vector_type feats; - psi.set_size(get_num_dimensions()); - psi = 0; - for (unsigned long i = 0; i < sample.first.size(); ++i) - { - if (label[i] != -1) - { - fe.get_features(sample.first[i], sample.second[label[i]], feats); - set_rowm(psi,range(0,feats.size()-1)) += feats; - psi(get_num_dimensions()-1) += 1; - } - } - } - - template <typename T> - void append_to_sparse_vect ( - T& psi, - const T& vect - ) const - { - std::copy(vect.begin(), vect.end(), std::back_inserter(psi)); - } - - template <typename psi_type> - typename disable_if<is_matrix<psi_type> >::type get_joint_feature_vector ( - const sample_type& sample, - const label_type& label, - psi_type& psi - ) const - { - psi.clear(); - feature_vector_type feats; - int num_assignments = 0; - for (unsigned long i = 0; i < sample.first.size(); ++i) - { - if (label[i] != -1) - { - fe.get_features(sample.first[i], sample.second[label[i]], feats); - append_to_sparse_vect(psi, feats); - ++num_assignments; - } - } - psi.push_back(std::make_pair(get_num_dimensions()-1,num_assignments)); - } - - virtual void get_truth_joint_feature_vector ( - long idx, - feature_vector_type& psi - ) const - { - get_joint_feature_vector(samples[idx], labels[idx], psi); - } - - virtual void separation_oracle ( - const long idx, - const matrix_type& current_solution, - double& loss, - feature_vector_type& psi - ) const - { - matrix<double> cost; - unsigned long size; - if (force_assignment) - { - unsigned long lhs_size = samples[idx].first.size(); - unsigned long rhs_size = samples[idx].second.size(); - size = std::max(lhs_size, rhs_size); - } - else - { - unsigned long rhs_size = samples[idx].second.size() + samples[idx].first.size(); - size = rhs_size; - } - cost.set_size(size, size); - - typename feature_extractor::feature_vector_type feats; - - // now fill out the cost assignment matrix - for (long r = 0; r < cost.nr(); ++r) - { - for (long c = 0; c < cost.nc(); ++c) - { - if (r < (long)samples[idx].first.size()) - { - if (c < (long)samples[idx].second.size()) - { - fe.get_features(samples[idx].first[r], samples[idx].second[c], feats); - const double bias = current_solution(current_solution.size()-1); - cost(r,c) = dot(colm(current_solution,0,current_solution.size()-1), feats) + bias; - - // add in the loss since this corresponds to an incorrect prediction. - if (c != labels[idx][r]) - { - cost(r,c) += loss_per_false_association; - } - } - else - { - if (labels[idx][r] == -1) - cost(r,c) = 0; - else - cost(r,c) = loss_per_missed_association; - } - - } - else - { - cost(r,c) = 0; - } - } - } - - std::vector<long> assignment; - - if (cost.size() != 0) - { - // max_cost_assignment() only works with integer matrices, so convert from - // double to integer. - const double scale = (std::numeric_limits<dlib::int64>::max()/1000)/max(abs(cost)); - matrix<dlib::int64> int_cost = matrix_cast<dlib::int64>(round(cost*scale)); - assignment = max_cost_assignment(int_cost); - assignment.resize(samples[idx].first.size()); - } - - loss = 0; - // adjust assignment so that non-assignments have a value of -1. Also compute loss. - for (unsigned long i = 0; i < assignment.size(); ++i) - { - if (assignment[i] >= (long)samples[idx].second.size()) - assignment[i] = -1; - - if (assignment[i] != labels[idx][i]) - { - if (assignment[i] == -1) - loss += loss_per_missed_association; - else - loss += loss_per_false_association; - } - } - - get_joint_feature_vector(samples[idx], assignment, psi); - } - - const std::vector<sample_type>& samples; - const std::vector<label_type>& labels; - const feature_extractor& fe; - bool force_assignment; - const double loss_per_false_association; - const double loss_per_missed_association; - }; - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_Hh_ - |