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/////////////////////////////////////////////////////////////////////////////
//
// class LinNeuralNet
//
// this is an implementation of a simple linear neural net with one neuron
// so this net has a Weight-Matrix IW and a bias vector b1
// this net can have n input values, but only one output value
// (see NeuralNet documentations for more information)
//
// header file
//
// Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
//
// For information on usage and redistribution, and for a DISCLAIMER OF ALL
// WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_LIN_NEURAL_NET__
#define _INCLUDE_LIN_NEURAL_NET__
#include <stdlib.h>
#include <ctime>
//#include "m_pd.h" // for debug
class LinNeuralNet
{
protected:
/* this is the number of input values, which is
* automatically the netsize and the size of IW
*/
int netsize_;
/* the input weight matrix IW
* (size: netsize )
*/
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
*/
LinNeuralNet(int netsize);
/* Destructor
*/
virtual ~LinNeuralNet();
//-----------------------------------------------------
/* 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 getNetsize() const
{ return netsize_; }
virtual float *getIW() const
{ return IW_; }
virtual void setIW(const float *IW)
{ for(int i=0; i<netsize_; i++) IW_[i] = IW[i]; }
virtual float getb1() const
{ return b1_; }
virtual void setb1(float b1)
{ b1_ = b1; }
//-----------------------------------------------------
/* creates a new IW-matrix (size: netsize_) and
* b1-vector
* returns false if there's a failure
* ATTENTION: if they exist they'll be deleted
*/
virtual bool createNeurons();
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
* returns false if there's a failure
*/
virtual bool initNetworkRand(const int &min, const int &max);
/* 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 netsize !!!
* returns false if there's a failure
*/
virtual bool initNetwork(const float *IW, float b1);
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as netsize_
*/
virtual float calculateNet(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)
* returns false if there's a failure
* ATTENTION: the array input_data must be in the same
* size as netsize_
*/
virtual bool trainNet(float *input_data, const float &output_data,
const float &target_output);
private:
/* Copy Construction is not allowed
*/
LinNeuralNet(const LinNeuralNet &src)
{ }
/* assignement operator is not allowed
*/
const LinNeuralNet& operator= (const LinNeuralNet& src)
{ return *this; }
};
#endif //_INCLUDE_LIN_NEURAL_NET__
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