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; }