// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_Hh_
#define DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_Hh_
#include "cross_validate_sequence_labeler_abstract.h"
#include <vector>
#include "../matrix.h"
#include "svm.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename sequence_labeler_type,
typename sequence_type
>
const matrix<double> test_sequence_labeler (
const sequence_labeler_type& labeler,
const std::vector<sequence_type>& samples,
const std::vector<std::vector<unsigned long> >& labels
)
{
// make sure requires clause is not broken
DLIB_ASSERT( is_sequence_labeling_problem(samples, labels) == true,
"\tmatrix test_sequence_labeler()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_sequence_labeling_problem(samples, labels): "
<< is_sequence_labeling_problem(samples, labels));
matrix<double> res(labeler.num_labels(), labeler.num_labels());
res = 0;
std::vector<unsigned long> pred;
for (unsigned long i = 0; i < samples.size(); ++i)
{
labeler.label_sequence(samples[i], pred);
for (unsigned long j = 0; j < pred.size(); ++j)
{
const unsigned long truth = labels[i][j];
if (truth >= static_cast<unsigned long>(res.nr()))
{
// ignore labels the labeler doesn't know about.
continue;
}
res(truth, pred[j]) += 1;
}
}
return res;
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type,
typename sequence_type
>
const matrix<double> cross_validate_sequence_labeler (
const trainer_type& trainer,
const std::vector<sequence_type>& samples,
const std::vector<std::vector<unsigned long> >& labels,
const long folds
)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_sequence_labeling_problem(samples,labels) == true &&
1 < folds && folds <= static_cast<long>(samples.size()),
"\tmatrix cross_validate_sequence_labeler()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t folds: " << folds
<< "\n\t is_sequence_labeling_problem(samples,labels): " << is_sequence_labeling_problem(samples,labels)
);
#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] < trainer.num_labels(),
"\t matrix cross_validate_sequence_labeler()"
<< "\n\t The labels are invalid."
<< "\n\t labels[i][j]: " << labels[i][j]
<< "\n\t trainer.num_labels(): " << trainer.num_labels()
<< "\n\t i: " << i
<< "\n\t j: " << j
);
}
}
#endif
const long num_in_test = samples.size()/folds;
const long num_in_train = samples.size() - num_in_test;
std::vector<sequence_type> x_test, x_train;
std::vector<std::vector<unsigned long> > y_test, y_train;
long next_test_idx = 0;
matrix<double> res;
for (long i = 0; i < folds; ++i)
{
x_test.clear();
y_test.clear();
x_train.clear();
y_train.clear();
// load up the test samples
for (long cnt = 0; cnt < num_in_test; ++cnt)
{
x_test.push_back(samples[next_test_idx]);
y_test.push_back(labels[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)
{
x_train.push_back(samples[next]);
y_train.push_back(labels[next]);
next = (next + 1)%samples.size();
}
res += test_sequence_labeler(trainer.train(x_train,y_train), x_test, y_test);
} // for (long i = 0; i < folds; ++i)
return res;
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_CROSS_VALIDATE_SEQUENCE_LABeLER_Hh_