/// <summary>
/// Initializes a new instance of the <see cref="DoubleArrayChromosome"/> class.
/// </summary>
///
/// <param name="source">Source chromosome to copy.</param>
///
/// <remarks><para>This is a copy constructor, which creates the exact copy
/// of specified chromosome.</para></remarks>
///
public DoubleArrayChromosome( DoubleArrayChromosome source )
{
this.chromosomeGenerator = source.chromosomeGenerator;
this.mutationMultiplierGenerator = source.mutationMultiplierGenerator;
this.mutationAdditionGenerator = source.mutationAdditionGenerator;
this.length = source.length;
this.fitness = source.fitness;
this.mutationBalancer = source.mutationBalancer;
this.crossoverBalancer = source.crossoverBalancer;
// copy genes
val = (double[]) source.val.Clone( );
}
/// <summary>
/// Runs learning epoch.
/// </summary>
///
/// <param name="input">Array of input vectors.</param>
/// <param name="output">Array of output vectors.</param>
///
/// <returns>Returns summary squared learning error for the entire epoch.</returns>
///
/// <remarks><para><note>While running the neural network's learning process, it is required to
/// pass the same <paramref name="input"/> and <paramref name="output"/> values for each
/// epoch. On the very first run of the method it will initialize evolutionary fitness
/// function with the given input/output. So, changing input/output in middle of the learning
/// process, will break it.</note></para></remarks>
///
public double RunEpoch( double[][] input, double[][] output )
{
//Debug.Assert( input.Length > 0 );
//Debug.Assert( output.Length > 0 );
// Debug.Assert( input.Length == output.Length );
// Debug.Assert( network.InputsCount == input.Length );
// check if it is a first run and create population if so
if ( population == null )
{
// sample chromosome
DoubleArrayChromosome chromosomeExample = new DoubleArrayChromosome(
chromosomeGenerator, mutationMultiplierGenerator, mutationAdditionGenerator,
numberOfNetworksWeights );
// create population ...
population = new Population( populationSize, chromosomeExample,
new EvolutionaryFitness(network, input, output), selectionMethod);
// ... and configure it
population.CrossoverRate = crossOverRate;
population.MutationRate = mutationRate;
population.RandomSelectionPortion = randomSelectionRate;
}
// run genetic epoch
population.RunEpoch( );
// get best chromosome of the population
DoubleArrayChromosome chromosome = (DoubleArrayChromosome) population.BestChromosome;
double[] chromosomeGenes = chromosome.Value;
// put best chromosome's value into neural network's weights
int v = 0;
for ( int i = 0; i < network.Layers.Length; i++ )
{
Layer layer = network.Layers[i];
for ( int j = 0; j < layer.Neurons.Length; j++ )
{
ActivationNeuron neuron = layer.Neurons[j] as ActivationNeuron;
for ( int k = 0; k < neuron.Weights.Length; k++ )
{
neuron.Weights[k] = chromosomeGenes[v++];
}
neuron.Threshold = chromosomeGenes[v++];
}
}
//Debug.Assert( v == numberOfNetworksWeights );
return 1.0 / chromosome.Fitness;
}