#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 MeanSquaredErrorEvaluator : GPEvaluatorBase {
public override string Description {
get {
return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
for the estimated values vs. the real values of 'TargetVariable'.";
}
}
public MeanSquaredErrorEvaluator()
: base() {
AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
}
public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
double errorsSquaredSum = 0;
int n = 0;
for (int sample = start; sample < end; sample++) {
double original = dataset.GetValue(sample, targetVariable);
double estimated = evaluator.Evaluate(tree, sample);
if (updateTargetValues) {
dataset.SetValue(sample, targetVariable, estimated);
}
if (!double.IsNaN(original) && !double.IsInfinity(original)) {
double error = estimated - original;
errorsSquaredSum += error * error;
n++;
}
}
errorsSquaredSum /= n;
if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
errorsSquaredSum = double.MaxValue;
}
DoubleData mse = GetVariableValue("MSE", scope, false, false);
if (mse == null) {
mse = new DoubleData();
scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
}
mse.Data = errorsSquaredSum;
}
}
}