// Factory method
public static NeuronLayer copyLayer(int nbNeurons, int nbInputs, NeuronLayer original)
{
double[][] weights = new double[nbNeurons][];
Random geneticRandomizer = new Random((++someInt + Environment.TickCount) * 2);
Random weightRandomizer = new Random((++someInt + Environment.TickCount) * 2);
for (int i = 0; i < nbNeurons; i++)
{
weights[i] = new double[nbInputs + 1]; // +1 for the bias
for (int j = 0; j < nbInputs + 1; j++)
{
if (geneticRandomizer.Next(0, Params.MUTATION_FREQUENCY) % Params.MUTATION_FREQUENCY == 0) // Mutation
{
weights[i][j] = Params.getRandomWeight(weightRandomizer);
}
else // no mutation
{
weights[i][j] = original.InputWeights[i][j];
}
}
}
return(new NeuronLayer(nbNeurons, weights));
}
/**
* Build's the neural net with one input layer and nbHiddenLayers hidden layers
**/
public NeuralNet(int nbNeuronsPerLayer, int nbHiddenLayers)
{
this.nbNeuronsPerLayer = nbNeuronsPerLayer;
this.nbHiddenLayers = nbHiddenLayers;
// Build input layer
this.inputLayer = NeuronLayer.buildNewLayer(nbNeuronsPerLayer, Program.Params.NB_INPUT_COLUMNS);
// Build hidden layers
this.hiddenLayers = new NeuronLayer[nbHiddenLayers];
for (int i = 0; i < nbHiddenLayers; i++)
{
this.hiddenLayers[i] = NeuronLayer.buildNewLayer(nbNeuronsPerLayer, nbNeuronsPerLayer);
}
this.outputLayer = NeuronLayer.buildNewLayer(1, nbNeuronsPerLayer);
}
} // private constructor for cloning
/**
* Copy's the neural net and returns a new one with genetic mutations
**/
public static NeuralNet getGeneticClone(NeuralNet original)
{
NeuralNet clone = new NeuralNet();
clone.nbNeuronsPerLayer = original.nbNeuronsPerLayer;
clone.nbHiddenLayers = original.nbHiddenLayers;
clone.inputLayer = NeuronLayer.copyLayer(original.nbNeuronsPerLayer, Program.Params.NB_INPUT_COLUMNS, original.inputLayer);
clone.hiddenLayers = new NeuronLayer[original.nbHiddenLayers];
for (int i = 0; i < original.nbHiddenLayers; i++)
{
clone.hiddenLayers[i] = NeuronLayer.copyLayer(original.nbNeuronsPerLayer, original.nbNeuronsPerLayer, original.hiddenLayers[i]);
}
clone.outputLayer = NeuronLayer.copyLayer(1, original.nbNeuronsPerLayer, original.outputLayer);
return(clone);
}