source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicVectorRegressionEvaluator.cs @ 7624

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.Regression;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
using HeuristicLab.Common;

namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
  [StorableClass]
  public abstract class MultiObjectiveSymbolicVectorRegressionEvaluator : SymbolicVectorRegressionEvaluator, IMultiObjectiveSymbolicVectorRegressionEvaluator {
    private const string QualitiesParameterName = "Qualities";
    #region parameter properties
    public ILookupParameter<DoubleArray> QualitiesParameter {
      get { return (ILookupParameter<DoubleArray>)Parameters[QualitiesParameterName]; }
    }
    #endregion
    [StorableConstructor]
    protected MultiObjectiveSymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
    protected MultiObjectiveSymbolicVectorRegressionEvaluator(MultiObjectiveSymbolicVectorRegressionEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public MultiObjectiveSymbolicVectorRegressionEvaluator()
      : base() {
      Parameters.Add(new LookupParameter<DoubleValue>(QualitiesParameterName, "The qualities of the symbolic vector regression solution."));
    }


    public override IOperation Apply() {
      var interpreter = SymbolicExpressionTreeInterpreter;
      var tree = SymbolicExpressionTree;
      var problemData = MultiVariateDataAnalysisProblemData;

      IEnumerable<string> selectedTargetVariables =
        problemData.TargetVariables.CheckedItems
        .Select(x => x.Value.Value);

      // check if there is a vector component for each target variable
      if (selectedTargetVariables.Count() != tree.Root.SubTrees[0].SubTrees.Count)
        throw new ArgumentException("The dimension of the output-vector of the tree doesn't match the number of selected target variables.");
      int start = SamplesStart.Value;
      int end = SamplesEnd.Value;

      IEnumerable<int> rows = GenerateRowsToEvaluate(Random.Next(), RelativeNumberOfEvaluatedSamples.Value, start, end);

      QualitiesParameter.ActualValue = new DoubleArray(Evaluate(tree, interpreter, problemData, selectedTargetVariables, rows, LowerEstimationLimit, UpperEstimationLimit));

      return base.Apply();
    }

    public abstract double[] Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> selectedTargetVariables, IEnumerable<int> rows, DoubleArray LowerEstimationLimit, DoubleArray UpperEstimationLimit);
  }
}