#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 .
*/
#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 VarianceAccountedForEvaluator : GPEvaluatorBase {
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.";
}
}
///
/// 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.
///
public VarianceAccountedForEvaluator()
: base() {
AddVariableInfo(new VariableInfo("VAF", "The variance-accounted-for quality of the model", typeof(DoubleData), VariableKind.New));
}
public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
int nSamples = end - start;
double[] errors = new double[nSamples];
double[] originalTargetVariableValues = new double[nSamples];
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);
}
if (!double.IsNaN(original) && !double.IsInfinity(original)) {
errors[sample - start] = original - estimated;
originalTargetVariableValues[sample - start] = original;
}
}
double errorsVariance = Statistics.Variance(errors);
double originalsVariance = Statistics.Variance(originalTargetVariableValues);
double quality = 1 - errorsVariance / originalsVariance;
if (double.IsNaN(quality) || double.IsInfinity(quality)) {
quality = double.MaxValue;
}
DoubleData vaf = GetVariableValue("VAF", scope, false, false);
if (vaf == null) {
vaf = new DoubleData();
scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("VAF"), vaf));
}
vaf.Data = quality;
}
}
}