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/////////////////////////////////////////////////////////////////////////////
//
// class Neuron
//
// this is an implementation of one neuron of a Neural Network
// so this neuron has a Weight-Matrix IW and a bias vector b1
// this neuron can have n input values, but only one output value
// (see NeuralNet documentations for more information)
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
//
// 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 _INCLUDE_NEURON_NET__
#define _INCLUDE_NEURON_NET__
#include <stdlib.h>
#include <stdexcept>
#include "NNException.h"
#include "m_pd.h" //debug
namespace TheBrain
{
//------------------------------------------------------
/* class of one neuron
*/
class Neuron
{
protected:
/* this is the number of input values, which is
* automatically the input and the size of IW
*/
int inputs_;
/* the input weight matrix IW
* (size: inputs )
*/
float *IW_;
/* the bias vector b1
*/
float b1_;
/* the learning rate of the net
*/
float learn_rate_;
/* the range of the input values should be from 0
* to range_
* outputvalues are from -1 to 1
*/
float range_;
public:
/* Constructor
*/
Neuron(int inputs, int dummy=0);
/* Destructor
*/
virtual ~Neuron();
//-----------------------------------------------------
/* Set/Get learning rate
*/
virtual void setLearningRate(float learn_rate)
{ learn_rate_=learn_rate; }
virtual float getLearningRate() const
{ return learn_rate_; }
/* Set/Get range
*/
virtual void setRange(float range)
{ range_=range; }
virtual float getRange() const
{ return range_; }
/* some more get/set methods
*/
virtual int getInputs() const
{ return inputs_; }
virtual float *getIW() const
{ return IW_; }
virtual float getIW(int index) const
{ return IW_[index]; }
virtual void setIW(const float *IW)
{ for(int i=0; i<inputs_; i++) IW_[i] = IW[i]; }
virtual void setIW(int index, float value)
{ IW_[index] = value; }
virtual float getb1() const
{ return b1_; }
virtual void setb1(float b1)
{ b1_ = b1; }
/* dummies
*/
virtual int getMemory() const
{ return 0; }
virtual float *getLW() const
{ return NULL; }
virtual float getLW(int index) const
{ return 0; }
virtual void setLW(const float *LW)
{ }
virtual void setLW(int index, float value)
{ }
//-----------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
virtual void create()
throw(NNExcept);
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
virtual void initRand(const int &min, const int &max)
throw(NNExcept);
/* inits the net with a given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs !!!
*/
virtual void init(const float *IW, float b1)
throw(NNExcept);
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
virtual float calculate(float *input_data);
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data (output_data is not
* calculated in that method !), target_output is the desired
* output data
* (this is the LMS-algorithm to train linear neural networks)
* ATTENTION: the array input_data must be in the same
* size as inputs_
* returns the calculated value
*/
/* virtual float trainLMS(const float *input_data, */
/* const float &target_output); */
//-----------------------------------------------------
private:
/* Copy Construction is not allowed
*/
Neuron(const Neuron &src)
{ }
/* assignement operator is not allowed
*/
const Neuron& operator= (const Neuron& src)
{ return *this; }
};
} // end of namespace
#endif //_INCLUDE_NEURON_NET__
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