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// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_STRUCTURAL_SVM_SEQUENCE_LaBELING_PROBLEM_Hh_
#define DLIB_STRUCTURAL_SVM_SEQUENCE_LaBELING_PROBLEM_Hh_
#include "structural_svm_sequence_labeling_problem_abstract.h"
#include "../matrix.h"
#include "sequence_labeler.h"
#include <vector>
#include "structural_svm_problem_threaded.h"
// ----------------------------------------------------------------------------------------
namespace dlib
{
namespace fe_helpers
{
// ----------------------------------------------------------------------------------------
struct get_feats_functor
{
get_feats_functor(std::vector<std::pair<unsigned long, double> >& feats_) : feats(feats_) {}
inline void operator() (
unsigned long feat_index,
double feat_value
)
{
feats.push_back(std::make_pair(feat_index, feat_value));
}
inline void operator() (
unsigned long feat_index
)
{
feats.push_back(std::make_pair(feat_index, 1));
}
std::vector<std::pair<unsigned long, double> >& feats;
};
// ----------------------------------------------------------------------------------------
template <typename feature_extractor, typename sequence_type, typename EXP2>
void get_feature_vector(
std::vector<std::pair<unsigned long, double> >& feats,
const feature_extractor& fe,
const sequence_type& sequence,
const matrix_exp<EXP2>& candidate_labeling,
unsigned long position
)
{
get_feats_functor funct(feats);
fe.get_features(funct, sequence,candidate_labeling, position);
}
}
// ----------------------------------------------------------------------------------------
template <
typename feature_extractor
>
class structural_svm_sequence_labeling_problem : noncopyable,
public structural_svm_problem_threaded<matrix<double,0,1>, std::vector<std::pair<unsigned long,double> > >
{
public:
typedef matrix<double,0,1> matrix_type;
typedef std::vector<std::pair<unsigned long, double> > feature_vector_type;
typedef typename feature_extractor::sequence_type sequence_type;
structural_svm_sequence_labeling_problem(
const std::vector<sequence_type>& samples_,
const std::vector<std::vector<unsigned long> >& labels_,
const feature_extractor& fe_,
unsigned long num_threads = 2
) :
structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
samples(samples_),
labels(labels_),
fe(fe_)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_sequence_labeling_problem(samples,labels) == true &&
contains_invalid_labeling(fe, samples, labels) == false,
"\t structural_svm_sequence_labeling_problem::structural_svm_sequence_labeling_problem()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t is_sequence_labeling_problem(samples,labels): " << is_sequence_labeling_problem(samples,labels)
<< "\n\t contains_invalid_labeling(fe,samples,labels): " << contains_invalid_labeling(fe,samples,labels)
<< "\n\t this: " << this
);
#ifdef ENABLE_ASSERTS
for (unsigned long i = 0; i < labels.size(); ++i)
{
for (unsigned long j = 0; j < labels[i].size(); ++j)
{
// make sure requires clause is not broken
DLIB_ASSERT(labels[i][j] < fe.num_labels(),
"\t structural_svm_sequence_labeling_problem::structural_svm_sequence_labeling_problem()"
<< "\n\t The given labels in labels are invalid."
<< "\n\t labels[i][j]: " << labels[i][j]
<< "\n\t fe.num_labels(): " << fe.num_labels()
<< "\n\t i: " << i
<< "\n\t j: " << j
<< "\n\t this: " << this
);
}
}
#endif
loss_values.assign(num_labels(), 1);
}
unsigned long num_labels (
) const { return fe.num_labels(); }
double get_loss (
unsigned long label
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(label < num_labels(),
"\t void structural_svm_sequence_labeling_problem::get_loss()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t label: " << label
<< "\n\t num_labels(): " << num_labels()
<< "\n\t this: " << this
);
return loss_values[label];
}
void set_loss (
unsigned long label,
double value
)
{
// make sure requires clause is not broken
DLIB_ASSERT(label < num_labels() && value >= 0,
"\t void structural_svm_sequence_labeling_problem::set_loss()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t label: " << label
<< "\n\t num_labels(): " << num_labels()
<< "\n\t value: " << value
<< "\n\t this: " << this
);
loss_values[label] = value;
}
private:
virtual long get_num_dimensions (
) const
{
return fe.num_features();
}
virtual long get_num_samples (
) const
{
return samples.size();
}
void get_joint_feature_vector (
const sequence_type& sample,
const std::vector<unsigned long>& label,
feature_vector_type& psi
) const
{
psi.clear();
const int order = fe.order();
matrix<unsigned long,0,1> candidate_labeling;
for (unsigned long i = 0; i < sample.size(); ++i)
{
candidate_labeling = rowm(mat(label), range(i, std::max((int)i-order,0)));
fe_helpers::get_feature_vector(psi,fe,sample,candidate_labeling, i);
}
}
virtual void get_truth_joint_feature_vector (
long idx,
feature_vector_type& psi
) const
{
get_joint_feature_vector(samples[idx], labels[idx], psi);
}
class map_prob
{
public:
unsigned long order() const { return fe.order(); }
unsigned long num_states() const { return fe.num_labels(); }
map_prob(
const sequence_type& sequence_,
const std::vector<unsigned long>& label_,
const feature_extractor& fe_,
const matrix<double,0,1>& weights_,
const std::vector<double>& loss_values_
) :
sequence(sequence_),
label(label_),
fe(fe_),
weights(weights_),
loss_values(loss_values_)
{
}
unsigned long number_of_nodes(
) const
{
return sequence.size();
}
template <
typename EXP
>
double factor_value (
unsigned long node_id,
const matrix_exp<EXP>& node_states
) const
{
if (dlib::impl::call_reject_labeling_if_exists(fe, sequence, node_states, node_id))
return -std::numeric_limits<double>::infinity();
double loss = 0;
if (node_states(0) != label[node_id])
loss = loss_values[label[node_id]];
return fe_helpers::dot(weights, fe, sequence, node_states, node_id) + loss;
}
const sequence_type& sequence;
const std::vector<unsigned long>& label;
const feature_extractor& fe;
const matrix<double,0,1>& weights;
const std::vector<double>& loss_values;
};
virtual void separation_oracle (
const long idx,
const matrix_type& current_solution,
scalar_type& loss,
feature_vector_type& psi
) const
{
std::vector<unsigned long> y;
find_max_factor_graph_viterbi(map_prob(samples[idx],labels[idx],fe,current_solution,loss_values), y);
loss = 0;
for (unsigned long i = 0; i < y.size(); ++i)
{
if (y[i] != labels[idx][i])
loss += loss_values[labels[idx][i]];
}
get_joint_feature_vector(samples[idx], y, psi);
}
const std::vector<sequence_type>& samples;
const std::vector<std::vector<unsigned long> >& labels;
const feature_extractor& fe;
std::vector<double> loss_values;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_SVM_SEQUENCE_LaBELING_PROBLEM_Hh_
|
