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/////////////////////////////////////////////////////////////////////////////
//
// class RecurrentNeuron
//
// 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 "RecurrentNeuron.h"
namespace TheBrain
{
//--------------------------------------------------
/* Constructor
*/
RecurrentNeuron::RecurrentNeuron(int inputs, int memory)
: Neuron(inputs), LW_(NULL), mem_data_(NULL)
{
memory_ = (memory<0) ? 1 : memory+1;
}
//--------------------------------------------------
/* Destructor
*/
RecurrentNeuron::~RecurrentNeuron()
{
if(LW_)
delete[] LW_;
if(mem_data_)
delete[] mem_data_;
}
//--------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
void RecurrentNeuron::create()
throw(NNExcept)
{
// delete if they exist
if(IW_)
delete[] IW_;
if(LW_)
delete[] LW_;
if(mem_data_)
delete[] mem_data_;
IW_ = new float[inputs_];
LW_ = new float[memory_];
mem_data_ = new float[memory_];
if(!IW_ || !LW_ || !mem_data_)
throw NNExcept("No memory for Neurons!");
index_=0;
}
//--------------------------------------------------
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
void RecurrentNeuron::initRand(const int &min, const int &max)
throw(NNExcept)
{
if(!IW_ || !LW_)
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;
}
for(int i=0; i<memory_; i++)
{
//LW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
LW_[i] = ((float)rand()/(float)RAND_MAX)*(min);
}
}
//--------------------------------------------------
/* inits the net with given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs (also for LW) !!!
*/
void RecurrentNeuron::init(const float *IW, const float *LW, float b1)
throw(NNExcept)
{
if(!IW_ || !LW_)
throw NNExcept("You must first create the Net!");
b1_ = b1;
for(int i=0; i<inputs_; i++)
IW_[i] = IW[i];
for(int i=0; i<memory_; i++)
LW_[i] = LW[i];
}
//--------------------------------------------------
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
float RecurrentNeuron::calculate(float *input_data)
{
float output = 0;
// multiply the inputs with the weight matrix IW
for(int i=0; i<inputs_; i++)
{
output += input_data[i] * IW_[i];
}
// map input values to the range
output /= range_;
// multiply memory with weight matrix LW
// the index is used to make something
// like a simple list or ringbuffer
for(int i=0; i<memory_; i++)
{
output += mem_data_[index_] * LW_[i];
index_ = (index_+1) % memory_;
}
// now add bias
output += b1_;
// finally save the new output in memory
mem_data_[index_] = output;
index_ = (index_+1) % memory_;
//post("input: %f %f %f, IW: %f %f %f, b: %f",
// input_data[0], input_data[1], input_data[2],
// IW_[0], IW_[1], IW_[2], b1_);
//post("output: %f",output);
return (output);
}
//--------------------------------------------------
/* 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 output
*/
// float RecurrentNeuron::trainLMS(const float *input_data,
// const float &target_output)
// {
// // calculate output value:
// float output = 0;
// // multiply the inputs with the weight matrix IW
// for(int i=0; i<inputs_; i++)
// {
// output += input_data[i] * IW_[i];
// }
// // map input values to the range
// output /= range_;
// // multiply memory with weight matrix LW
// // the index is used to make something
// // like a simple list or ringbuffer
// for(int i=0; i<memory_; i++)
// {
// output += mem_data_[index_] * LW_[i];
// index_ = (index_+1) % memory_;
// }
// // now add bias
// output += b1_;
// //----------------
// // this is the LMS-algorithm to train linear
// // neural networks
// // calculate the error signal:
// float error = (target_output - output);
// // now change IW
// for(int i=0; i<inputs_; i++)
// IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
// // change LW
// for(int i=0; i<memory_; i++)
// {
// LW_[i] += 2 * learn_rate_ * error * mem_data_[index_];
// index_ = (index_+1) % memory_;
// }
// // and the bias
// b1_ += 2 * learn_rate_ * error;
// //-----------------
// // finally save the new output in memory
// mem_data_[index_] = output;
// index_ = (index_+1) % memory_;
// return (output);
// }
} // end of namespace
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