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Diffstat (limited to 'pix_recNN/RecurrentNeuron.cpp')
| -rwxr-xr-x | pix_recNN/RecurrentNeuron.cpp | 226 |
1 files changed, 226 insertions, 0 deletions
diff --git a/pix_recNN/RecurrentNeuron.cpp b/pix_recNN/RecurrentNeuron.cpp new file mode 100755 index 0000000..1b322c1 --- /dev/null +++ b/pix_recNN/RecurrentNeuron.cpp @@ -0,0 +1,226 @@ +///////////////////////////////////////////////////////////////////////////// +// +// 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 |