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/****************************************************************************
 **
 ** Copyright C 2002-2012 Laboratory for Active and Attentive Vision (LAAV), Department of Computer Science and Engineering, York University, Toronto, ON, Canada.
 ** All rights reserved.
 **
 ** This file is part of the TarzaNN Neural Network Simulator.
 **
 ** This file may be distributed and/or modified under the terms of the
 ** GNU General Public License version 2 as published by the Free Software
 ** Foundation and appearing in the file LICENSE.GPL included in the
 ** packaging of this file.
 **
 ** See http://www.tarzann.org/gpl/ for GPL licensing information.
 **
 ** Contact info@tarzann.org if any conditions of this licensing are
 ** not clear to you.
 **
 ** This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
 ** WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
 **
 ****************************************************************************/

#ifndef LearningFeaturePlane_H_INCLUDED
#define LearningFeaturePlane_H_INCLUDED

#include  <qimage.h>

#include "nowarn.h"
#include "common.h"
#include <stdlib.h>

#include "OutputImage.h"
#include "FeaturePlane.h"
#include "FilterBank.h"
#include "PlaneInputWindowSet.h"

//class LearningFeaturePlane : public FeaturePlaneAbstract {
class LearningFeaturePlane : public FeaturePlane {
        friend class GatingUnits;
        friend class FeaturePlaneFactory;
public:
    ~LearningFeaturePlane();
        virtual void read(QDataStream* inStream, QProgressDialog* progress, int* index)=0;
        virtual void save(QDataStream* outStream, QProgressDialog* progress, int* index)=0;

protected:
        
        LearningFeaturePlane( Network* net, int l, QString* name, int w, int h, int angles, int speeds, int alpha, int speed, bool isWTA, bool visible, Neuron* neuron, NotifyStrategyAbstract* notify, bool learning);
        FeaturePlaneInput* addFilter(Filter* f, FeaturePlaneAbstract*fp, int type, int fpOutputIndex, bool wta);
        float computeSkip(int input, int output, int filter);
        
        void setWindowRange(int input_width, int input_height, int filter_width, int filter_height);
        void computeGatingUnits();

        PlaneInputWindowSet *windows;
        Matrix* activations;
        Matrix* desiredOutcome;
        Matrix* errorMatrix;
        vector<Matrix*> backErrorMatrix;
                
        bool learning;
        vector<FilterBank*> inputFilters;
        float ita;
};
#endif //LearningFeaturePlane_H_INCLUDED