// 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_