source: branches/HeuristicLab.Hive_Milestone3/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/SimpleEvaluator.cs @ 4857

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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 System.Text;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.DataAnalysis;

namespace HeuristicLab.GP.StructureIdentification {
  public class SimpleEvaluator : GPEvaluatorBase {
    public SimpleEvaluator()
      : base() {
      AddVariableInfo(new VariableInfo("Values", "Target vs. predicted values", typeof(DoubleMatrixData), VariableKind.New | VariableKind.Out));
    }

    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
      DoubleMatrixData values = GetVariableValue<DoubleMatrixData>("Values", scope, false, false);
      if (values == null) {
        values = new DoubleMatrixData();
        IVariableInfo info = GetVariableInfo("Values");
        if (info.Local)
          AddVariable(new HeuristicLab.Core.Variable(info.ActualName, values));
        else
          scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName(info.FormalName), values));
      }

      double[,] v = new double[end - start, 2];

      for (int sample = start; sample < end; sample++) {
        double estimated = evaluator.Evaluate(sample);
        double original = dataset.GetValue(sample, targetVariable);
        if (updateTargetValues) {
          dataset.SetValue(sample, targetVariable, estimated);
        }
        v[sample - start, 0] = original;
        v[sample - start, 1] = estimated;
      }
      values.Data = v;
    }
  }
}