/// <summary>
/// This method only selects the newest tournament results as training data.
/// </summary>
/// <param name="data">All historical training data.</param>
/// <returns>Only the training data where <see cref="TrainingDataWrapper.CurrentGeneration"/> == <see cref="GenomeTournamentResult.Generation"/>.</returns>
public AggregatedTrainingDataWrapper AggregateTargets(TrainingDataWrapper data)
{
// we just want to train on the newest results
// assumption: list of known results for genome g is ordered
// we need to be able to handle duplicates, since genomes are not neccessarily unique
var filteredObservations = data.Genomes.Select(g => data.TournamentResults[g].Where(t => t.Generation == data.CurrentGeneration).ToList())
.ToArray();
var relevantCount = filteredObservations.Sum(r => r.Count);
var relevantIndices = new int[relevantCount];
var relevantTargets = new double[relevantCount];
var currentRelevantIndex = 0;
for (var i = 0; i < filteredObservations.Length; i++)
{
// filteredObservations[i].Count can be 0.
for (var repeat = 0; repeat < filteredObservations[i].Count; repeat++)
{
relevantIndices[currentRelevantIndex] = i;
relevantTargets[currentRelevantIndex] = filteredObservations[i][repeat].TournamentRank;
currentRelevantIndex++;
}
}
// this should match
Debug.Assert(currentRelevantIndex == relevantCount, "Each feature column should have been handled.");
var relevantConvertedGenomes = data.ConvertedGenomes.Rows(relevantIndices);
var result = new AggregatedTrainingDataWrapper()
{
RelevantConvertedGenomes = relevantConvertedGenomes,
RelevantTargets = relevantTargets,
};
return result;
}
/// <summary>
/// Computes the average <see cref="GenomeTournamentResult.TournamentRank"/> over all <see cref="GenomeTournamentResult"/> for each <see cref="Genome"/> in <see cref="TrainingDataWrapper.Genomes"/>.
/// </summary>
/// <param name="data">
/// The training data.
/// </param>
/// <returns>
/// The aggregated training data.
/// </returns>
public AggregatedTrainingDataWrapper AggregateTargets(TrainingDataWrapper data)
{
var targets = data.Genomes.Select(g => data.TournamentResults[g].Average(r => r.TournamentRank)).ToArray();
var result = new AggregatedTrainingDataWrapper()
{
RelevantConvertedGenomes = data.ConvertedGenomes, RelevantTargets = targets
};
return(result);
}