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67 lines
1.7 KiB
C
67 lines
1.7 KiB
C
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/*******************************************************************
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* addapted by Trevor Standley for use as a function approximator
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* Addapted from:
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* Basic Feed Forward Neural Network Class
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* ------------------------------------------------------------------
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* Bobby Anguelov - takinginitiative.wordpress.com (2008)
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* MSN & email: banguelov@cs.up.ac.za
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********************************************************************/
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#ifndef NEURAL_NETWORK_H
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#define NEURAL_NETWORK_H
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//standard includes
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#include <iostream>
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#include <vector>
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#include <fstream>
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#include <cmath>
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#include <limits>
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class neuralNetwork
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{
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private:
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//number of neurons
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int nInput, nHidden1, nHidden2, nOutput;
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//neurons
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double* inputNeurons;
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double* hiddenNeurons1;
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double* hiddenNeurons2;
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double* outputNeurons;
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//weights
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double** wInputHidden;
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double** wHidden2Hidden;
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double** wHiddenOutput;
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public:
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//constructor & destructor
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neuralNetwork(int numInput, int numHidden1, int numHidden2, int numOutput);
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neuralNetwork(const neuralNetwork&);
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neuralNetwork();
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void operator = (const neuralNetwork&);
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~neuralNetwork();
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//weight operations
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double* feedForwardPattern( double* pattern );
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void backpropigate(double* pattern, double OLR, double H2LR, double H1LR );
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void tweakWeights(double howMuch);
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void mate(const neuralNetwork&n1,const neuralNetwork&n2);
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private:
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void initializeWeights();
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inline double activationFunction( double x );
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void feedForward( double* pattern );
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};
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std::istream & operator >> (std::istream &, neuralNetwork & ann);
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std::ostream & operator << (std::ostream &, const neuralNetwork & ann);
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#endif
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