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https://github.com/vcmi/vcmi.git
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416 lines
12 KiB
C++
416 lines
12 KiB
C++
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#include "neuralNetwork.h"
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//using namespace std;
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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float norm(void)//add desired mean, multiply to get desired SD
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{
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static float kept = 0;
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static bool in = 0;
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if(!in)
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{
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float x = (rand()+1)/float(RAND_MAX+1);
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float f = sqrtf( - 2.0f * log(x) );
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x = (rand()+1)/float(RAND_MAX+1);
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kept = f * cosf( 2.0f * M_PI * x );
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in = true;
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return f * sinf( 2.0f * M_PI * x );
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}
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else
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{
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in = false;
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return kept;
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}
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}
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/*******************************************************************
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* Constructors
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********************************************************************/
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neuralNetwork::neuralNetwork() : nInput(0), nHidden1(0), nHidden2(0), nOutput(0)
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{
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inputNeurons = new double[1] ;
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hiddenNeurons1 = new double[1] ;
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hiddenNeurons2 = new double[1] ;
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outputNeurons = new double[1] ;
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wInputHidden = new double*[1] ;
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wInputHidden[0] = new double[1];
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wHidden2Hidden = new double*[1] ;
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wHidden2Hidden[0] = new (double[1]);
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wHiddenOutput = new double*[1] ;
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wHiddenOutput[0] = new double[1];
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}
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neuralNetwork::neuralNetwork(const neuralNetwork& other): nInput(0), nHidden1(0), nHidden2(0), nOutput(0)
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{
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inputNeurons = new double[1] ;
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hiddenNeurons1 = new double[1] ;
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hiddenNeurons2 = new double[1] ;
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outputNeurons = new double[1] ;
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wInputHidden = new double*[1] ;
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wInputHidden[0] = new double[1];
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wHidden2Hidden = new double*[1] ;
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wHidden2Hidden[0] = new (double[1]);
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wHiddenOutput = new double*[1] ;
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wHiddenOutput[0] = new double[1];
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*this = other;
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}
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neuralNetwork::neuralNetwork(int nI, int nH1, int nH2, int nO) : nInput(nI), nHidden1(nH1), nHidden2(nH2), nOutput(nO)
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{
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//create neuron lists
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//--------------------------------------------------------------------------------------------------------
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inputNeurons = new double[nInput + 1] ;
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for ( int i=0; i < nInput; i++ ) inputNeurons[i] = 0;
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//create input bias neuron
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inputNeurons[nInput] = -1;
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hiddenNeurons1 = new double[nHidden1 + 1] ;
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for ( int i=0; i < nHidden1; i++ ) hiddenNeurons1[i] = 0;
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//create hidden bias neuron
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hiddenNeurons1[nHidden1] = -1;
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hiddenNeurons2 = new double[nHidden2 + 1] ;
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for ( int i=0; i < nHidden2; i++ ) hiddenNeurons2[i] = 0;
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//create hidden bias neuron
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hiddenNeurons2[nHidden2] = -1;
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outputNeurons = new double[nOutput] ;
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for ( int i=0; i < nOutput; i++ ) outputNeurons[i] = 0;
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//create weight lists (include bias neuron weights)
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//--------------------------------------------------------------------------------------------------------
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wInputHidden = new double*[nInput + 1] ;
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for ( int i=0; i <= nInput; i++ )
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{
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wInputHidden[i] = new double[nHidden1];
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for ( int j=0; j < nHidden1; j++ ) wInputHidden[i][j] = 0;
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}
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wHidden2Hidden = new double*[nHidden1 + 1] ;
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for ( int i=0; i <= nHidden1; i++ )
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{
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wHidden2Hidden[i] = new (double[nHidden2]);
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for ( int j=0; j < nHidden2; j++ ) wHidden2Hidden[i][j] = 0;
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}
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wHiddenOutput = new double*[nHidden2 + 1] ;
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for ( int i=0; i <= nHidden2; i++ )
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{
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wHiddenOutput[i] = new double[nOutput];
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for ( int j=0; j < nOutput; j++ ) wHiddenOutput[i][j] = 0;
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}
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//initialize weights
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//--------------------------------------------------------------------------------------------------------
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initializeWeights();
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}
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void neuralNetwork::operator = (const neuralNetwork&cpy)//assumes same structure
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{
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if( nInput != cpy.nInput || nHidden1 != cpy.nHidden1 || nHidden2 != cpy.nHidden2 || nOutput != cpy.nOutput)
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{
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delete[] inputNeurons;
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delete[] hiddenNeurons1;
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delete[] hiddenNeurons2;
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delete[] outputNeurons;
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//delete weight storage
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for (int i=0; i <= nInput; i++) delete[] wInputHidden[i];
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delete[] wInputHidden;
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for (int j=0; j <= nHidden2; j++) delete[] wHiddenOutput[j];
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delete[] wHiddenOutput;
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for (int j=0; j <= nHidden1; j++) delete[] wHidden2Hidden[j];
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delete[] wHidden2Hidden;
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nInput = cpy.nInput;
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nHidden1 = cpy.nHidden1;
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nHidden2 = cpy.nHidden2;
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nOutput = cpy.nOutput;
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inputNeurons = new double[nInput + 1] ;
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inputNeurons[nInput] = -1;
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hiddenNeurons1 = new double[nHidden1 + 1] ;
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hiddenNeurons1[nHidden1] = -1;
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hiddenNeurons2 = new double[nHidden2 + 1] ;
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hiddenNeurons2[nHidden2] = -1;
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outputNeurons = new double[nOutput] ;
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//create weight lists (include bias neuron weights)
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//--------------------------------------------------------------------------------------------------------
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wInputHidden = new double*[nInput + 1] ;
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for ( int i=0; i <= nInput; i++ )
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wInputHidden[i] = new double[nHidden1];
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wHidden2Hidden = new double*[nHidden1 + 1] ;
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for ( int i=0; i <= nHidden1; i++ )
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wHidden2Hidden[i] = new (double[nHidden2]);
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wHiddenOutput = new double*[nHidden2 + 1] ;
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for ( int i=0; i <= nHidden2; i++ )
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wHiddenOutput[i] = new double[nOutput];
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}
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for ( int i=0; i < nInput; i++ ) inputNeurons[i] = cpy.inputNeurons[i];
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for ( int i=0; i < nHidden1; i++ ) hiddenNeurons1[i] = cpy.hiddenNeurons1[i];
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for ( int i=0; i < nHidden2; i++ ) hiddenNeurons2[i] = cpy.hiddenNeurons2[i];
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for ( int i=0; i < nOutput; i++ ) outputNeurons[i] = cpy.outputNeurons[i];
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for ( int i=0; i <= nInput; i++ )
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for ( int j=0; j < nHidden1; j++ )
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wInputHidden[i][j] = cpy.wInputHidden[i][j];
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for ( int i=0; i <= nHidden1; i++ )
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for ( int j=0; j < nHidden2; j++ )
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wHidden2Hidden[i][j] = cpy.wHidden2Hidden[i][j];
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for ( int i=0; i <= nHidden2; i++ )
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for ( int j=0; j < nOutput; j++ )
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wHiddenOutput[i][j] = cpy.wHiddenOutput[i][j];
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}
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/*******************************************************************
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* Destructor
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********************************************************************/
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neuralNetwork::~neuralNetwork()
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{
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//delete neurons
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delete[] inputNeurons;
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delete[] hiddenNeurons1;
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delete[] hiddenNeurons2;
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delete[] outputNeurons;
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//delete weight storage
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for (int i=0; i <= nInput; i++) delete[] wInputHidden[i];
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delete[] wInputHidden;
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for (int j=0; j <= nHidden2; j++) delete[] wHiddenOutput[j];
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delete[] wHiddenOutput;
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for (int j=0; j <= nHidden1; j++) delete[] wHidden2Hidden[j];
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delete[] wHidden2Hidden;
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}
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double* neuralNetwork::feedForwardPattern(double *pattern)
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{
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feedForward(pattern);
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return outputNeurons;
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}
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void neuralNetwork::mate(const neuralNetwork&n1,const neuralNetwork&n2)
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{
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for(int i = 0; i <= nInput; i++)
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{
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for(int j = 0; j < nHidden1; j++)
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{
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if(rand()%2==0)
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wInputHidden[i][j] = n1.wInputHidden[i][j];
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else
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wInputHidden[i][j] = n2.wInputHidden[i][j];
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}
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}
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for(int i = 0; i <= nHidden1; i++)
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{
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for(int j = 0; j < nHidden2; j++)
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{
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if(rand()%2==0)
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wHidden2Hidden[i][j] =n1.wHidden2Hidden[i][j];
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else
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wHidden2Hidden[i][j] =n2.wHidden2Hidden[i][j];
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}
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}
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for(int i = 0; i <= nHidden2; i++)
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{
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for(int j = 0; j < nOutput; j++)
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{
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if(rand()%2==0)
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wHiddenOutput[i][j] =n1.wHiddenOutput[i][j];
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else
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wHiddenOutput[i][j] =n2.wHiddenOutput[i][j];
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}
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}
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}
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void neuralNetwork::tweakWeights(double howMuch)
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{
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//set range
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double rH = 1/sqrt( (double) nInput);
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double rO = 1/sqrt( (double) nHidden1);
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for(int i = 0; i <= nInput; i++)
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{
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for(int j = 0; j < nHidden1; j++)
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{
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wInputHidden[i][j] += howMuch*norm();
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}
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}
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for(int i = 0; i <= nHidden1; i++)
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{
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for(int j = 0; j < nHidden2; j++)
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{
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wHidden2Hidden[i][j] += howMuch*norm();
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}
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}
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for(int i = 0; i <= nHidden2; i++)
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{
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for(int j = 0; j < nOutput; j++)
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{
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wHiddenOutput[i][j] += howMuch* norm();
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}
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}
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//initializeWeights();
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}
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void neuralNetwork::initializeWeights()
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{
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//set range
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double rH = 2.0/sqrt( (double) nInput);
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double rO = 2.0/sqrt( (double) nHidden1);
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//set weights between input and hidden
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//--------------------------------------------------------------------------------------------------------
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for(int i = 0; i <= nInput; i++)
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{
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for(int j = 0; j < nHidden1; j++)
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{
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//set weights to random values
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wInputHidden[i][j] = norm()* rH;
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}
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}
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for(int i = 0; i <= nHidden1; i++)
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{
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for(int j = 0; j < nHidden2; j++)
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{
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//set weights to random values
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wHidden2Hidden[i][j] = norm()* rO;
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}
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}
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//set weights between hidden and output
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//--------------------------------------------------------------------------------------------------------
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for(int i = 0; i <= nHidden2; i++)
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{
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for(int j = 0; j < nOutput; j++)
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{
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//set weights to random values
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wHiddenOutput[i][j] = norm()* rO;
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}
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}
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}
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/*******************************************************************
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* Activation Function
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********************************************************************/
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inline double neuralNetwork::activationFunction( double x )
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{
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//sigmoid function
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return 1/(1+exp(-x));
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}
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/*******************************************************************
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* Feed Forward Operation
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********************************************************************/
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void neuralNetwork::feedForward(double* pattern)
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{
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//set input neurons to input values
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for(int i = 0; i < nInput; i++) inputNeurons[i] = pattern[i];
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//Calculate Hidden Layer values - include bias neuron
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//--------------------------------------------------------------------------------------------------------
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for(int j=0; j < nHidden1; j++)
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{
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//clear value
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hiddenNeurons1[j] = 0;
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//get weighted sum of pattern and bias neuron
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for( int i=0; i <= nInput; i++ ) hiddenNeurons1[j] += inputNeurons[i] * wInputHidden[i][j];
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//set to result of sigmoid
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hiddenNeurons1[j] = activationFunction( hiddenNeurons1[j] );
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}
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for(int j=0; j < nHidden2; j++)
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{
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//clear value
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hiddenNeurons2[j] = 0;
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//get weighted sum of pattern and bias neuron
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for( int i=0; i <= nHidden1; i++ ) hiddenNeurons2[j] += hiddenNeurons1[i] * wHidden2Hidden[i][j];
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//set to result of sigmoid
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hiddenNeurons2[j] = activationFunction( hiddenNeurons2[j] );
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}
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//Calculating Output Layer values - include bias neuron
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//--------------------------------------------------------------------------------------------------------
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for(int k=0; k < nOutput; k++)
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{
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//clear value
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outputNeurons[k] = 0;
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//get weighted sum of pattern and bias neuron
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for( int j=0; j <= nHidden2; j++ ) outputNeurons[k] += hiddenNeurons2[j] * wHiddenOutput[j][k];
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//set to result of sigmoid
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//outputNeurons[k] = activationFunction( outputNeurons[k] );
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}
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}
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void neuralNetwork::backpropigate(double* pattern, double OLR, double H2LR, double H1LR )
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{
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//inputError = new double[nInput + 1] ;
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double * hiddenError1 = new double[nHidden1 + 1] ;
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double * hiddenError2 = new double[nHidden2 + 1] ;
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double * outputError = new double[nOutput] ;
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memset(hiddenError1,0,sizeof(double)*nHidden1);
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memset(hiddenError2,0,sizeof(double)*nHidden2);
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for(int i = 0; i < nOutput; i++)
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{
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outputError[i] = (pattern[i]-outputNeurons[i]);//*(outputNeurons[i]*(1-outputNeurons[i]));
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for(int ii = 0; ii <= nHidden2;ii++)
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hiddenError2[ii]+=outputError[i]*wHiddenOutput[ii][i];
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for(int ii = 0; ii <= nHidden2;ii++)
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wHiddenOutput[ii][i]+=OLR*hiddenNeurons2[ii]*outputError[i];
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}
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for(int i = 0; i < nHidden2; i++)
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{
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hiddenError2[i] *= (hiddenNeurons2[i]*(1-hiddenNeurons2[i]));
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for(int ii = 0; ii <= nHidden1;ii++)
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hiddenError1[ii]+=hiddenError2[i]*wHidden2Hidden[ii][i];
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for(int ii = 0; ii <= nHidden1;ii++)
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wHidden2Hidden[ii][i]+=H2LR*hiddenNeurons1[ii]*hiddenError2[i];
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}
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for(int i = 0; i < nHidden1; i++)
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{
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hiddenError1[i] *= (hiddenNeurons1[i]*(1-hiddenNeurons1[i]));
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for(int ii = 0; ii <= nInput;ii++)
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wInputHidden[ii][i]+=H1LR*inputNeurons[ii]*hiddenError1[i];
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}
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delete [] hiddenError1;
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delete [] hiddenError2;
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delete [] outputError;
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} |