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// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_Hh_
#define DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_Hh_
#include "../array.h"
#include "../graph_cuts/min_cut.h"
#include "svm.h"
#include "cross_validate_graph_labeling_trainer_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename graph_labeler,
typename graph_type
>
matrix<double,1,2> test_graph_labeling_function (
const graph_labeler& labeler,
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels,
const std::vector<std::vector<double> >& losses
)
{
#ifdef ENABLE_ASSERTS
std::string reason_for_failure;
DLIB_ASSERT(is_graph_labeling_problem(samples, labels, reason_for_failure) ,
"\t matrix test_graph_labeling_function()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t reason_for_failure: " << reason_for_failure
);
DLIB_ASSERT((losses.size() == 0 || sizes_match(labels, losses) == true) &&
all_values_are_nonnegative(losses) == true,
"\t matrix test_graph_labeling_function()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t labels.size(): " << labels.size()
<< "\n\t losses.size(): " << losses.size()
<< "\n\t sizes_match(labels,losses): " << sizes_match(labels,losses)
<< "\n\t all_values_are_nonnegative(losses): " << all_values_are_nonnegative(losses)
);
#endif
std::vector<bool> temp;
double num_pos_correct = 0;
double num_pos = 0;
double num_neg_correct = 0;
double num_neg = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
labeler(samples[i], temp);
for (unsigned long j = 0; j < labels[i].size(); ++j)
{
// What is the loss for this example? It's just 1 unless we have a
// per example loss vector.
const double loss = (losses.size() == 0) ? 1.0 : losses[i][j];
if (labels[i][j])
{
num_pos += loss;
if (temp[j])
num_pos_correct += loss;
}
else
{
num_neg += loss;
if (!temp[j])
num_neg_correct += loss;
}
}
}
matrix<double, 1, 2> res;
if (num_pos != 0)
res(0) = num_pos_correct/num_pos;
else
res(0) = 1;
if (num_neg != 0)
res(1) = num_neg_correct/num_neg;
else
res(1) = 1;
return res;
}
template <
typename graph_labeler,
typename graph_type
>
matrix<double,1,2> test_graph_labeling_function (
const graph_labeler& labeler,
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels
)
{
std::vector<std::vector<double> > losses;
return test_graph_labeling_function(labeler, samples, labels, losses);
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type,
typename graph_type
>
matrix<double,1,2> cross_validate_graph_labeling_trainer (
const trainer_type& trainer,
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels,
const std::vector<std::vector<double> >& losses,
const long folds
)
{
#ifdef ENABLE_ASSERTS
std::string reason_for_failure;
DLIB_ASSERT(is_graph_labeling_problem(samples, labels, reason_for_failure),
"\t matrix cross_validate_graph_labeling_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t reason_for_failure: " << reason_for_failure
);
DLIB_ASSERT( 1 < folds && folds <= static_cast<long>(samples.size()),
"\t matrix cross_validate_graph_labeling_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t folds: " << folds
);
DLIB_ASSERT((losses.size() == 0 || sizes_match(labels, losses) == true) &&
all_values_are_nonnegative(losses) == true,
"\t matrix cross_validate_graph_labeling_trainer()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t labels.size(): " << labels.size()
<< "\n\t losses.size(): " << losses.size()
<< "\n\t sizes_match(labels,losses): " << sizes_match(labels,losses)
<< "\n\t all_values_are_nonnegative(losses): " << all_values_are_nonnegative(losses)
);
#endif
typedef std::vector<bool> label_type;
const long num_in_test = samples.size()/folds;
const long num_in_train = samples.size() - num_in_test;
dlib::array<graph_type> samples_test, samples_train;
std::vector<label_type> labels_test, labels_train;
std::vector<std::vector<double> > losses_test, losses_train;
long next_test_idx = 0;
std::vector<bool> temp;
double num_pos_correct = 0;
double num_pos = 0;
double num_neg_correct = 0;
double num_neg = 0;
graph_type gtemp;
for (long i = 0; i < folds; ++i)
{
samples_test.clear();
labels_test.clear();
losses_test.clear();
samples_train.clear();
labels_train.clear();
losses_train.clear();
// load up the test samples
for (long cnt = 0; cnt < num_in_test; ++cnt)
{
copy_graph(samples[next_test_idx], gtemp);
samples_test.push_back(gtemp);
labels_test.push_back(labels[next_test_idx]);
if (losses.size() != 0)
losses_test.push_back(losses[next_test_idx]);
next_test_idx = (next_test_idx + 1)%samples.size();
}
// load up the training samples
long next = next_test_idx;
for (long cnt = 0; cnt < num_in_train; ++cnt)
{
copy_graph(samples[next], gtemp);
samples_train.push_back(gtemp);
labels_train.push_back(labels[next]);
if (losses.size() != 0)
losses_train.push_back(losses[next]);
next = (next + 1)%samples.size();
}
const typename trainer_type::trained_function_type& labeler = trainer.train(samples_train,labels_train,losses_train);
// check how good labeler is on the test data
for (unsigned long i = 0; i < samples_test.size(); ++i)
{
labeler(samples_test[i], temp);
for (unsigned long j = 0; j < labels_test[i].size(); ++j)
{
// What is the loss for this example? It's just 1 unless we have a
// per example loss vector.
const double loss = (losses_test.size() == 0) ? 1.0 : losses_test[i][j];
if (labels_test[i][j])
{
num_pos += loss;
if (temp[j])
num_pos_correct += loss;
}
else
{
num_neg += loss;
if (!temp[j])
num_neg_correct += loss;
}
}
}
} // for (long i = 0; i < folds; ++i)
matrix<double, 1, 2> res;
if (num_pos != 0)
res(0) = num_pos_correct/num_pos;
else
res(0) = 1;
if (num_neg != 0)
res(1) = num_neg_correct/num_neg;
else
res(1) = 1;
return res;
}
template <
typename trainer_type,
typename graph_type
>
matrix<double,1,2> cross_validate_graph_labeling_trainer (
const trainer_type& trainer,
const dlib::array<graph_type>& samples,
const std::vector<std::vector<bool> >& labels,
const long folds
)
{
std::vector<std::vector<double> > losses;
return cross_validate_graph_labeling_trainer(trainer, samples, labels, losses, folds);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_Hh_
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