diff options
Diffstat (limited to 'pix_recNN/Neuron.h')
| -rwxr-xr-x | pix_recNN/Neuron.h | 191 |
1 files changed, 191 insertions, 0 deletions
diff --git a/pix_recNN/Neuron.h b/pix_recNN/Neuron.h new file mode 100755 index 0000000..f10d993 --- /dev/null +++ b/pix_recNN/Neuron.h @@ -0,0 +1,191 @@ +///////////////////////////////////////////////////////////////////////////// +// +// 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__ |