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// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_ONE_VS_ALL_TRAiNER_H__
#define DLIB_ONE_VS_ALL_TRAiNER_H__
#include "one_vs_all_trainer_abstract.h"
#include "one_vs_all_decision_function.h"
#include <vector>
#include "multiclass_tools.h"
#include <sstream>
#include <iostream>
#include "../any.h"
#include <map>
#include <set>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename any_trainer,
typename label_type_ = double
>
class one_vs_all_trainer
{
public:
typedef label_type_ label_type;
typedef typename any_trainer::sample_type sample_type;
typedef typename any_trainer::scalar_type scalar_type;
typedef typename any_trainer::mem_manager_type mem_manager_type;
typedef one_vs_all_decision_function<one_vs_all_trainer> trained_function_type;
one_vs_all_trainer (
) :
verbose(false)
{}
void set_trainer (
const any_trainer& trainer
)
{
default_trainer = trainer;
trainers.clear();
}
void set_trainer (
const any_trainer& trainer,
const label_type& l
)
{
trainers[l] = trainer;
}
void be_verbose (
)
{
verbose = true;
}
void be_quiet (
)
{
verbose = false;
}
struct invalid_label : public dlib::error
{
invalid_label(const std::string& msg, const label_type& l_
) : dlib::error(msg), l(l_) {};
virtual ~invalid_label(
) throw() {}
label_type l;
};
trained_function_type train (
const std::vector<sample_type>& all_samples,
const std::vector<label_type>& all_labels
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
"\t trained_function_type one_vs_all_trainer::train(all_samples,all_labels)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t all_samples.size(): " << all_samples.size()
<< "\n\t all_labels.size(): " << all_labels.size()
);
const std::vector<label_type> distinct_labels = select_all_distinct_labels(all_labels);
std::vector<scalar_type> labels;
typename trained_function_type::binary_function_table dfs;
for (unsigned long i = 0; i < distinct_labels.size(); ++i)
{
labels.clear();
const label_type l = distinct_labels[i];
// setup one of the one vs all training sets
for (unsigned long k = 0; k < all_samples.size(); ++k)
{
if (all_labels[k] == l)
labels.push_back(+1);
else
labels.push_back(-1);
}
if (verbose)
{
std::cout << "Training classifier for " << l << " vs. all" << std::endl;
}
// now train a binary classifier using the samples we selected
const typename binary_function_table::const_iterator itr = trainers.find(l);
if (itr != trainers.end())
{
dfs[l] = itr->second.train(all_samples, labels);
}
else if (default_trainer.is_empty() == false)
{
dfs[l] = default_trainer.train(all_samples, labels);
}
else
{
std::ostringstream sout;
sout << "In one_vs_all_trainer, no trainer registered for the " << l << " label.";
throw invalid_label(sout.str(), l);
}
}
return trained_function_type(dfs);
}
private:
any_trainer default_trainer;
typedef std::map<label_type, any_trainer> binary_function_table;
binary_function_table trainers;
bool verbose;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_ONE_VS_ALL_TRAiNER_H__
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