source: branches/3.2/sources/HeuristicLab.GP.StructureIdentification.ConditionalEvaluation/3.3/ConditionalVarianceAccountedForEvaluator.cs @ 8853

#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.DataAnalysis;
using HeuristicLab.Modeling;

namespace HeuristicLab.GP.StructureIdentification.ConditionalEvaluation {
  /// <summary>
  /// The Variance Accounted For (VAF) function calculates is computed as
  /// VAF(y,y') = ( 1 - var(y-y')/var(y) )
  /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
  /// </summary>
  public class ConditionalVarianceAccountedForEvaluator : ConditionalEvaluatorBase {
    public override string OutputVariableName {
      get {
        return "VAF";
      }
    }
    public override string Description {
      get {
        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates 
the variance-accounted-for quality measure for the estimated values vs. the real values of 'TargetVariable'.

The Variance Accounted For (VAF) function is computed as
VAF(y,y') = ( 1 - var(y-y')/var(y) )
where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.";
      }
    }

    public override double Evaluate(double[,] values) {
      try { return SimpleVarianceAccountedForEvaluator.Calculate(values); }
      catch (ArgumentException) {
        return double.NegativeInfinity;
      }
    }
  }
}