private GradientBoostedTreesModel(GradientBoostedTreesModel original, Cloner cloner)
: base(original, cloner)
{
this.weights = new List <double>(original.weights);
this.models = new List <IRegressionModel>(original.models.Select(m => cloner.Clone(m)));
this.isCompatibilityLoaded = original.isCompatibilityLoaded;
}
public IRegressionModel GetModel()
{
#pragma warning disable 618
var model = new GradientBoostedTreesModel(models, weights);
#pragma warning restore 618
// we don't know the number of iterations here but the number of weights is equal
// to the number of iterations + 1 (for the constant model)
// wrap the actual model in a surrogate that enables persistence and lazy recalculation of the model if necessary
return(new GradientBoostedTreesModelSurrogate(problemData, randSeed, lossFunction, weights.Count - 1, maxSize, r, m, nu, model));
}
public IRegressionModel GetModel() {
#pragma warning disable 618
var model = new GradientBoostedTreesModel(models, weights);
#pragma warning restore 618
// we don't know the number of iterations here but the number of weights is equal
// to the number of iterations + 1 (for the constant model)
// wrap the actual model in a surrogate that enables persistence and lazy recalculation of the model if necessary
return new GradientBoostedTreesModelSurrogate(problemData, randSeed, lossFunction, weights.Count - 1, maxSize, r, m, nu, model);
}
private GradientBoostedTreesModel(GradientBoostedTreesModel original, Cloner cloner)
: base(original, cloner) {
this.weights = new List<double>(original.weights);
this.models = new List<IRegressionModel>(original.models.Select(m => cloner.Clone(m)));
this.isCompatibilityLoaded = original.isCompatibilityLoaded;
}
