/*!
 * 
 *
 * \brief       Stopping Criterion which stops, when the trainign error seems to converge
 * 
 * 
 *
 * \author      O. Krause
 * \date        2010
 *
 *
 * \par Copyright 1995-2015 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://image.diku.dk/shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */

#ifndef SHARK_TRAINERS_STOPPINGCRITERA_TRAININGERROR_H
#define SHARK_TRAINERS_STOPPINGCRITERA_TRAININGERROR_H

#include "AbstractStoppingCriterion.h"
#include <shark/Core/ResultSets.h>
#include <queue>
#include <numeric>
namespace shark{

///  \brief This stopping criterion tracks the improvement of the error function of the training error over an interval of iterations.
///
/// If at one point, the difference between the error values of the beginning and the end of the interval are smaller
/// than a certain value, this stopping criterion assumes convergence and stops.
/// Of course, this may be misleading, when the algorithm temporarily gets stuck at a saddle point of the error surface.
/// The functions assumes that the algorithm is minimizing. For details, see:
///
/// Lutz Prechelt. Early Stopping - but when? In Genevieve B. Orr and
/// Klaus-Robert Müller: Neural Networks: Tricks of the Trade, volume
/// 1524 of LNCS, Springer, 1997.
///
template<class PointType = RealVector>
class TrainingError: public AbstractStoppingCriterion< SingleObjectiveResultSet<PointType> >{
public:
	/// constructs the TrainingError generalization loss
	/// @param intervalSize size of the interval over which the progress is monitored
	/// @param minDifference minimum difference between start and end of the interval allowed before training stops
	TrainingError(size_t intervalSize, double minDifference){
		m_minDifference = minDifference;
		m_intervalSize = intervalSize;
		reset();
	}
	/// returns true if training should stop
	bool stop(const SingleObjectiveResultSet<PointType>& set){

		m_interval.pop();
		m_interval.push(set.value);
		return (m_interval.front()-set.value) >= 0
		    && (m_interval.front()-set.value) < m_minDifference;
	}
	/// resets the internal state
	void reset(){
		m_interval = std::queue<double>();
		for(size_t i = 0; i != m_intervalSize;++i) {
			m_interval.push(std::numeric_limits<double>::max());
		}
	}
protected:
	/// monitored training interval
	std::queue<double> m_interval;
	/// minmum difference allowed
	double m_minDifference;
	/// size of the interval
	size_t m_intervalSize;
};
}


#endif

