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/////////////////////////////////////////////////////////////////////////////
//
// class Neuron
//
// source 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.
//
/////////////////////////////////////////////////////////////////////////////
#include "Neuron.h"
namespace TheBrain
{
//--------------------------------------------------
/* Constructor
*/
Neuron::Neuron(int inputs, int dummy)
: learn_rate_(0), range_(1), IW_(NULL), b1_(0)
{
inputs_ = (inputs<1) ? 1 : inputs;
}
//--------------------------------------------------
/* Destructor
*/
Neuron::~Neuron()
{
if(IW_)
delete[] IW_;
}
//--------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
void Neuron::create()
throw(NNExcept)
{
// delete if they exist
if(IW_)
delete[] IW_;
IW_ = new float[inputs_];
if(!IW_)
throw NNExcept("No memory for Neurons!");
}
//--------------------------------------------------
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
void Neuron::initRand(const int &min, const int &max)
throw(NNExcept)
{
if(!IW_)
throw NNExcept("You must first create the Net!");
// make randomvalue between 0 and 1
// then map it to the bounds
b1_ = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
for(int i=0; i<inputs_; i++)
{
IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
}
//post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
}
//--------------------------------------------------
/* 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 !!!
*/
void Neuron::init(const float *IW, float b1)
throw(NNExcept)
{
if(!IW_)
throw NNExcept("You must first create the Net!");
b1_ = b1;
for(int i=0; i<inputs_; i++)
IW_[i] = IW[i];
}
//--------------------------------------------------
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
float Neuron::calculate(float *input_data)
{
float output = 0;
// multiply the inputs with the weight matrix IW
// and add the bias vector b1
for(int i=0; i<inputs_; i++)
{
output += input_data[i] * IW_[i];
}
// map input values to the range
output /= range_;
//post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
//post("range: %f, in: %f %f %f, out: %f",range_,input_data[0],
// input_data[1], input_data[2], output+b1_);
return (output+b1_);
}
//--------------------------------------------------
/* 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
*/
// float Neuron::trainLMS(const float *input_data,
// const float &target_output)
// {
// float output = 0;
// // multiply the inputs with the weight matrix IW
// // and add the bias vector b1
// for(int i=0; i<inputs_; i++)
// {
// output += input_data[i] * IW_[i];
// }
// // map input values to the range
// output /= range_;
// output += b1_;
// //------------
// // this is the LMS-algorithm to train linear
// // neural networks
// // calculate the error signal:
// float error = (target_output - output);
// // now change the weights the bias
// for(int i=0; i<inputs_; i++)
// IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
// b1_ += 2 * learn_rate_ * error;
// //------------
// return (output);
// }
} // end of namespace
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