source: branches/GBT/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModel.cs @ 12332

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace GradientBoostedTrees {
  [Item("GradientBoostedTreesSolution", "")]
  [StorableClass]
  public sealed class GradientBoostedTreesModel : NamedItem, IRegressionModel {

    [Storable]
    private readonly IList<IRegressionModel> models;
    [Storable]
    private readonly IList<double> weights;

    [StorableConstructor]
    private GradientBoostedTreesModel(bool deserializing) : base(deserializing) { }
    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)));
    }
    public GradientBoostedTreesModel(IEnumerable<IRegressionModel> models, IEnumerable<double> weights)
      : base() {
      this.models = new List<IRegressionModel>(models);
      this.weights = new List<double>(weights);

      if (this.models.Count != this.weights.Count) throw new ArgumentException();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new GradientBoostedTreesModel(this, cloner);
    }

    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
      var tuples = (from idx in Enumerable.Range(0, models.Count)
                    let model = models[idx]
                    let weight = weights[idx]
                    select new { weight, enumerator = model.GetEstimatedValues(dataset, rows).GetEnumerator() }).ToArray();


      while (tuples.All(t => t.enumerator.MoveNext())) {
        yield return tuples.Sum(t => t.weight * t.enumerator.Current);
      }
    }

    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone());
    }
  }
}