/// <summary>
/// Trains and evaluates a genetic algorithm with the specified parameters.
/// </summary>
/// <param name="data">The data to be used for training.</param>
/// <param name="solution">The reference to where the solution will be stored.</param>
/// <param name="bestChromosome">The best chromosome.</param>
/// <param name="error">The reference where error rate will be stored.</param>
/// <param name="predictions">The reference to where the predictions will be stored.</param>
/// <param name="iterations">The number of iterations to perform.</param>
/// <param name="population">The size of the population.</param>
/// <param name="inputCount">The number of inputs.</param>
/// <param name="shuffle">Value indicating whether to shuffle the chromosomes on each epoch.</param>
/// <param name="constants">The constant inputs.</param>
/// <param name="geneType">Type of the gene functions.</param>
/// <param name="chromosomeType">Type of the chromosome.</param>
/// <param name="selectionType">Type of the chromosome selection.</param>
/// <param name="cancelToken">The cancellation token for the async operation.</param>
/// <param name="progressCallback">The progress callback: current iteration.</param>
/// <returns>
/// <c>true</c> if the training and evaluation was successful, <c>false</c> otherwise.</returns>
/// <exception cref="ArgumentException">Array should be size of data minus number of inputs.</exception>
public static bool TrainAndEval(double[] data, ref double[] solution, ref string bestChromosome, ref double error, ref double[] predictions, int iterations, int population, int inputCount, bool shuffle, double[] constants, GeneFunctions geneType, Chromosomes chromosomeType, Selections selectionType, CancellationToken cancelToken, Action <int> progressCallback = null)
{
IGPGene gene;
switch (geneType)
{
case GeneFunctions.Simple:
gene = new SimpleGeneFunction(inputCount + constants.Length);
break;
case GeneFunctions.Extended:
gene = new ExtendedGeneFunction(inputCount + constants.Length);
break;
default: return(false);
}
IChromosome chromosome;
switch (chromosomeType)
{
case Chromosomes.GPT:
chromosome = new GPTreeChromosome(gene);
break;
case Chromosomes.GEP:
chromosome = new GEPChromosome(gene, 20);
break;
default: return(false);
}
ISelectionMethod selection;
switch (selectionType)
{
case Selections.Elite:
selection = new EliteSelection();
break;
case Selections.Rank:
selection = new RankSelection();
break;
case Selections.Roulette:
selection = new RouletteWheelSelection();
break;
default: return(false);
}
cancelToken.ThrowIfCancellationRequested();
var ga = new Population(
population,
chromosome,
new TimeSeriesPredictionFitness(data, inputCount, 0, constants),
selection
)
{
AutoShuffling = shuffle
};
if (solution.Length != data.Length - inputCount)
{
throw new ArgumentException("Array should be the size of data minus number of inputs.", nameof(solution));
}
var input = new double[inputCount + constants.Length];
for (var j = 0; j < data.Length - inputCount; j++)
{
solution[j] = j + inputCount;
}
Array.Copy(constants, 0, input, inputCount, constants.Length);
for (var i = 0; i < iterations; i++)
{
ga.RunEpoch();
progressCallback?.Invoke(i);
cancelToken.ThrowIfCancellationRequested();
}
error = 0.0;
bestChromosome = ga.BestChromosome.ToString();
for (int j = 0, n = data.Length - inputCount; j < n; j++)
{
for (int k = 0, b = j + inputCount - 1; k < inputCount; k++)
{
input[k] = data[b - k];
}
solution[j] = PolishExpression.Evaluate(bestChromosome, input);
error += Math.Abs((solution[j] - data[inputCount + j]) / data[inputCount + j]);
cancelToken.ThrowIfCancellationRequested();
}
error = error / (data.Length - inputCount) * 100;
if (predictions.Length != 0)
{
Array.Copy(solution, solution.Length - inputCount, predictions, 0, inputCount);
for (var i = inputCount; i < predictions.Length; i++)
{
for (int j = 0; j < inputCount; j++)
{
input[j] = predictions[(i - inputCount) + j];
}
predictions[i] = PolishExpression.Evaluate(bestChromosome, input);
cancelToken.ThrowIfCancellationRequested();
}
}
return(true);
}
/// <summary>
/// One-point recombination (crossover).
/// </summary>
///
/// <param name="pair">Pair chromosome to crossover with.</param>
///
public void RecombinationOnePoint( GEPChromosome pair )
{
// check for correct pair
if ( ( pair.length == length ) )
{
// crossover point
int crossOverPoint = rand.Next( length - 1 ) + 1;
// length of chromosome to be crossed
int crossOverLength = length - crossOverPoint;
// swap parts of chromosomes
Recombine( genes, pair.genes, crossOverPoint, crossOverLength );
}
}
/// <summary>
/// Two point recombination (crossover).
/// </summary>
///
/// <param name="pair">Pair chromosome to crossover with.</param>
///
public void RecombinationTwoPoint( GEPChromosome pair )
{
// check for correct pair
if ( ( pair.length == length ) )
{
// crossover point
int crossOverPoint = rand.Next( length - 1 ) + 1;
// length of chromosome to be crossed
int crossOverLength = length - crossOverPoint;
// if crossover length already equals to 1, then it becomes
// usual one point crossover. otherwise crossover length
// also randomly chosen
if ( crossOverLength != 1 )
{
crossOverLength = rand.Next( crossOverLength - 1 ) + 1;
}
// swap parts of chromosomes
Recombine( genes, pair.genes, crossOverPoint, crossOverLength );
}
}
/// <summary>
/// Initializes a new instance of the <see cref="GEPChromosome"/> class.
/// </summary>
///
/// <param name="source">Source GEP chromosome to clone from.</param>
///
protected GEPChromosome( GEPChromosome source )
{
headLength = source.headLength;
length = source.length;
fitness = source.fitness;
// allocate genes array
genes = new IGPGene[length];
// copy genes
for ( int i = 0; i < length; i++ )
genes[i] = source.genes[i].Clone( );
}
