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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_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_Hh_
#define DLIB_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_Hh_
#include "cross_validate_assignment_trainer_abstract.h"
#include <vector>
#include "../matrix.h"
#include "svm.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename assignment_function
>
double test_assignment_function (
const assignment_function& assigner,
const std::vector<typename assignment_function::sample_type>& samples,
const std::vector<typename assignment_function::label_type>& labels
)
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
if (assigner.forces_assignment())
{
DLIB_ASSERT(is_forced_assignment_problem(samples, labels),
"\t double test_assignment_function()"
<< "\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)
);
}
else
{
DLIB_ASSERT(is_assignment_problem(samples, labels),
"\t double test_assignment_function()"
<< "\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)
);
}
#endif
double total_right = 0;
double total = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
const std::vector<long>& out = assigner(samples[i]);
for (unsigned long j = 0; j < out.size(); ++j)
{
if (out[j] == labels[i][j])
++total_right;
++total;
}
}
if (total != 0)
return total_right/total;
else
return 1;
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type
>
double cross_validate_assignment_trainer (
const trainer_type& trainer,
const std::vector<typename trainer_type::sample_type>& samples,
const std::vector<typename trainer_type::label_type>& labels,
const long folds
)
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
if (trainer.forces_assignment())
{
DLIB_ASSERT(is_forced_assignment_problem(samples, labels) &&
1 < folds && folds <= static_cast<long>(samples.size()),
"\t double cross_validate_assignment_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t folds: " << folds
<< "\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)
);
}
else
{
DLIB_ASSERT(is_assignment_problem(samples, labels) &&
1 < folds && folds <= static_cast<long>(samples.size()),
"\t double cross_validate_assignment_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t samples.size(): " << samples.size()
<< "\n\t folds: " << folds
<< "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
<< "\n\t is_learning_problem(samples,labels): " << is_learning_problem(samples,labels)
);
}
#endif
typedef typename trainer_type::sample_type sample_type;
typedef typename trainer_type::label_type label_type;
const long num_in_test = samples.size()/folds;
const long num_in_train = samples.size() - num_in_test;
std::vector<sample_type> samples_test, samples_train;
std::vector<label_type> labels_test, labels_train;
long next_test_idx = 0;
double total_right = 0;
double total = 0;
for (long i = 0; i < folds; ++i)
{
samples_test.clear();
labels_test.clear();
samples_train.clear();
labels_train.clear();
// load up the test samples
for (long cnt = 0; cnt < num_in_test; ++cnt)
{
samples_test.push_back(samples[next_test_idx]);
labels_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)
{
samples_train.push_back(samples[next]);
labels_train.push_back(labels[next]);
next = (next + 1)%samples.size();
}
const typename trainer_type::trained_function_type& df = trainer.train(samples_train,labels_train);
// check how good df is on the test data
for (unsigned long i = 0; i < samples_test.size(); ++i)
{
const std::vector<long>& out = df(samples_test[i]);
for (unsigned long j = 0; j < out.size(); ++j)
{
if (out[j] == labels_test[i][j])
++total_right;
++total;
}
}
} // for (long i = 0; i < folds; ++i)
if (total != 0)
return total_right/total;
else
return 1;
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_Hh_
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