/usr/include/shogun/multiclass/MulticlassOneVsRestStrategy.h is in libshogun-dev 3.2.0-7.3build4.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2012 Chiyuan Zhang
* Written (W) 2013 Shell Hu and Heiko Strathmann
* Copyright (C) 2012 Chiyuan Zhang
*/
#ifndef MULTICLASSONEVSRESTSTRATEGY_H__
#define MULTICLASSONEVSRESTSTRATEGY_H__
#include <shogun/multiclass/MulticlassStrategy.h>
namespace shogun
{
/** @brief multiclass one vs rest strategy
* used to train generic multiclass machines
* for K-class problems with building
* ensemble of K binary classifiers
*
* multiclass probabilistic outputs can be
* obtained by using the heuristics described in [1]
*
* [1] J. Milgram, M. Cheriet, R.Sabourin, "One Against One" or "One Against One":
* Which One is Better for Handwriting Recognition with SVMs?
*/
class CMulticlassOneVsRestStrategy: public CMulticlassStrategy
{
public:
/** constructor */
CMulticlassOneVsRestStrategy();
/** constructor
* @param prob_heuris probability estimation heuristic
*/
CMulticlassOneVsRestStrategy(EProbHeuristicType prob_heuris);
/** destructor */
virtual ~CMulticlassOneVsRestStrategy() {}
/** start training */
virtual void train_start(CMulticlassLabels *orig_labels, CBinaryLabels *train_labels)
{
CMulticlassStrategy::train_start(orig_labels, train_labels);
}
/** has more training phase */
virtual bool train_has_more()
{
return m_train_iter < m_num_classes;
}
/** prepare for the next training phase.
* @return NULL, since no subset is needed in one-vs-rest strategy
*/
virtual SGVector<int32_t> train_prepare_next();
/** decide the final label.
* @param outputs a vector of output from each machine (in that order)
*/
virtual int32_t decide_label(SGVector<float64_t> outputs);
/** decide the final label.
* @param outputs a vector of output from each machine (in that order)
* @param n_outputs number of outputs
*/
virtual SGVector<index_t> decide_label_multiple_output(SGVector<float64_t> outputs, int32_t n_outputs);
/** get number of machines used in this strategy.
*/
virtual int32_t get_num_machines()
{
return m_num_classes;
}
/** get name */
virtual const char* get_name() const
{
return "MulticlassOneVsRestStrategy";
};
/** rescale multiclass outputs according to the selected heuristic
* @param outputs a vector of output from each machine (in that order)
*/
virtual void rescale_outputs(SGVector<float64_t> outputs);
/** rescale multiclass outputs according to the selected heuristic
* this function only being called with OVA_SOFTMAX heuristic
* @param outputs a vector of output from each machine (in that order)
* @param As fitted sigmoid parameters a one for each machine
* @param Bs fitted sigmoid parameters b one for each machine
*/
virtual void rescale_outputs(SGVector<float64_t> outputs,
const SGVector<float64_t> As, const SGVector<float64_t> Bs);
protected:
/** OVA normalization heuristic
* @param outputs a vector of output from each machine (in that order)
*/
void rescale_heuris_norm(SGVector<float64_t> outputs);
/** OVA softmax heuristic
* @param outputs a vector of output from each machine (in that order)
* @param As fitted sigmoid parameters a one for each machine
* @param Bs fitted sigmoid parameters b one for each machine
*/
void rescale_heuris_softmax(SGVector<float64_t> outputs,
const SGVector<float64_t> As, const SGVector<float64_t> Bs);
};
} // namespace shogun
#endif /* end of include guard: MULTICLASSONEVSRESTSTRATEGY_H__ */
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