feedforwardlayer.h

amygdata的神经网络算法源代码

/***************************************************************************
                          feedforwardlayer.h  -  description
                             -------------------
    copyright            : (C) 2005 by Matt Grover
    email                : [e-mail]
 ***************************************************************************/

/***************************************************************************
 *                                                                         *
 *   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 2 of the License, or     *
 *   (at your option) any later version.                                   *
 *                                                                         *
 ***************************************************************************/


#ifndef FEEDFORWARDLAYER_H
#define FEEDFORWARDLAYER_H


#include <amygdala/topology.h>
#include <amygdala/factory.h>
#include <string>


namespace Amygdala {

class SynapseProperties;
class NConncetor;

/** @class FeedForwardLayer feedforwardlayer.h amygdala/feedforwardlayer.h
 * @brief Topology with convenience functions to build feedforward connections
 *
 * FeedForwardLayer is a topology that is meant to be used in layered networks
 * in which all of the neurons of one layer will form connections to one or more
 * layers elsewhere in the network.
 * @author Matt Grover <[e-mail]>
 * @see Topology
 */
class FeedForwardLayer: public Topology {
public:
    typedef TopologyFactory<FeedForwardLayer> Factory;
    virtual ~FeedForwardLayer() {}

    /** Form random connections to another FeedForwardLayer with weights evenly distributed
     * between a maximum and minimum value.  Note that this will form connections with StaticSynapses only.
     * @param outLayer The output Topology 
     * @param nc The connector used to connect the synapses
     * @param sProps The synapses properties of the connections
     * @param percentConnect The percentage of neurons in this layer that should form connections to outLayer */
    void ConnectUniform(Topology * outLayer,
                        NConnector & nc,
                        SynapseProperties & sProps,
                        const float percentConnect = 100);

/*    /** Form random connections to another FeedForwardLayer with weights distributed according to a random
     * Gaussian distribution.  Note that this will form connections with StaticSynapses only.
     * @param outLayer The output FeedForwardLayer
     * @param percentConnect The percentage of neurons in this layer that should form connections to outLayer
     * @param meanWeight The mean weight (absolute value) for the Gaussian distribution
     * @param stdDev The standard deviation used by the Gaussian
     * @param delay The synaptic delay between the layers
    void ConnectGaussian(FeedForwardLayer* outLayer,
                        float percentConnect,
                        float meanWeight,
                        float stdDev,
                        float delay);*/
protected:
    FeedForwardLayer(std::string& name):Topology(name) {};
    /** Do any class-specific initialization after one of the MakeNeuron() functions has been called. */
    virtual void InitNewNeuron(Neuron* nrn);
private:
    friend class Network;
    template<class topology>
    friend class TopologyFactory;
};
namespace Factory {
    static FeedForwardLayer::Factory MakeFeedForwardLayer;
}
}   // namespace Amygdala

#endif