/// <summary>
/// In order to also make use of historical data, <see cref="ReuseOldTreesStrategy"/> selects <see cref="KeepOldTreeRatio"/> * nTrees many <see cref="IEnsemblePredictor{TWeakPredictor}.InternalModels"/> (at random) and replaces them with (randomly chosen) models from the previous generation's <see cref="IEnsemblePredictor{TWeakPredictor}"/>.
/// In the first iteration, this method only sets <see cref="OldEnsemble"/> to <paramref name="newModel"/>.
/// </summary>
/// <param name="newModel">The freshly trained predictor model, using data from <see cref="AggregateTargets"/>.</param>
public void PostProcessModel(IEnsemblePredictor <IGenomePredictor> newModel)
{
// no old weak learners to reuse in 1st iteration
if (this.OldEnsemble == null)
{
this.OldEnsemble = newModel;
return;
}
// our RF stores the internal models as list. c# will not treat this as enumerable, but internally cast instead
var numberToReuse = (int)Math.Ceiling(this.OldEnsemble.InternalModels.Length * this.KeepOldTreeRatio);
var reusedTrees = Randomizer.Instance.ChooseRandomSubset(this.OldEnsemble.InternalModels, numberToReuse).ToArray();
var treeIndexToReplace = Randomizer.Instance.ChooseRandomSubset(Enumerable.Range(0, newModel.InternalModels.Length), numberToReuse)
.ToArray();
// replace trees
for (var replaceIndex = 0; replaceIndex < numberToReuse; replaceIndex++)
{
newModel.InternalModels[treeIndexToReplace[replaceIndex]] = reusedTrees[replaceIndex];
}
// update previous ensemble
this.OldEnsemble = newModel;
}
/// <summary>
/// Model is not altered.
/// </summary>
/// <param name="newModel">
/// Parameter is ignored.
/// </param>
public void PostProcessModel(IEnsemblePredictor <IGenomePredictor> newModel)
{
return;
}
