C:/Users/albertlr/projects/TarzaNN/TarzaNN/Neuron.h

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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 SIMPLE_NEURON_H_INCLUDED
#define SIMPLE_NEURON_H_INCLUDED

#include "FeaturePlaneAbstract.h"

#define SETUP_NEURON \
    switch(neuronType){ \
    case NEURON_TYPE_WILSON_COWAN: \
    neuron= new WilsonCowanNeuron(); \
    break; \
    case NEURON_TYPE_ST: \
    neuron= new STNeuron(); \
    break; \
    case NEURON_TYPE_SPIKING: \
    neuron =  new SpikingNeuron(); \
    break; \
    case NEURON_TYPE_SIGMOID: \
    neuron =  new SigmoidNeuron(max_activation, shift, slope); \
    break; \
    case NEURON_TYPE_TANH: \
    neuron =  new TanHNeuron(); \
    break; \
    case NEURON_TYPE_LINEAR_SIGMOID: \
    neuron =  new LinearSigmoidNeuron(max_activation, rangeLow, rangeHigh); \
    break; \
    case NEURON_TYPE_LINEAR: \
    neuron =  new LinearNeuron(); \
    break; \
    case NEURON_TYPE_GENERIC: \
    neuron =  new Neuron(); \
    break; \
    default: \
    qDebug("invalid neuron type for FP=%s (%d)\n",name->ascii(), neuronType); \
    exit(-ERROR_INVALID_NEURON_TYPE); \
    }

class Neuron{
public:
        Neuron();
        virtual void nonlinearity(Matrix* totalActivation, Matrix* workOutput, Matrix* spatialBias, float featureBias);
        virtual double nonlinearityDerivative(double S);

        virtual void setFeaturePlane(FeaturePlaneAbstract* fp){
        theFeaturePlane=fp;
    }
        bool addActivation(int count);
        static int getType(const QString & name);
        static QString getType(int value);
        float getMaxActivation();

    static void setNeuronParams( paramMap& params,  int neuronType, float nParam1, float nParam2);

protected:
        void processBiases(Matrix* workOutput, Matrix* spatialBias, float featureBias);

        FeaturePlaneAbstract* theFeaturePlane;
        float max_activation;
        float range;
};
#endif /*SIMPLE_NEURON_H_INCLUDED*/