#region License Information
/* HeuristicLab
* Copyright (C) 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.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HEAL.Attic;
namespace HeuristicLab.Problems.DataAnalysis {
[StorableType("CB105C71-FA47-4FFC-BF56-57B6BB2E0BC7")]
[Item("RegressionEnsembleProblemData", "Represents an item containing all data defining a regression problem.")]
public sealed class RegressionEnsembleProblemData : RegressionProblemData {
public override bool IsTrainingSample(int index) {
return index >= 0 && index < Dataset.Rows &&
TrainingPartition.Start <= index && index < TrainingPartition.End;
}
public override bool IsTestSample(int index) {
return index >= 0 && index < Dataset.Rows &&
TestPartition.Start <= index && index < TestPartition.End;
}
private static readonly RegressionEnsembleProblemData emptyProblemData;
public new static RegressionEnsembleProblemData EmptyProblemData {
get { return emptyProblemData; }
}
static RegressionEnsembleProblemData() {
var problemData = new RegressionEnsembleProblemData();
problemData.Parameters.Clear();
problemData.Name = "Empty Regression ProblemData";
problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
problemData.isEmpty = true;
problemData.Parameters.Add(new FixedValueParameter(DatasetParameterName, "", new Dataset()));
problemData.Parameters.Add(new FixedValueParameter>(InputVariablesParameterName, ""));
problemData.Parameters.Add(new FixedValueParameter(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
problemData.Parameters.Add(new FixedValueParameter(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
problemData.Parameters.Add(new ConstrainedValueParameter(TargetVariableParameterName, new ItemSet()));
emptyProblemData = problemData;
}
[StorableConstructor]
private RegressionEnsembleProblemData(StorableConstructorFlag _) : base(_) { }
private RegressionEnsembleProblemData(RegressionEnsembleProblemData original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) {
if (this == emptyProblemData) return emptyProblemData;
return new RegressionEnsembleProblemData(this, cloner);
}
public RegressionEnsembleProblemData() : base() { }
public RegressionEnsembleProblemData(IRegressionProblemData regressionProblemData)
: base(regressionProblemData.Dataset, regressionProblemData.AllowedInputVariables, regressionProblemData.TargetVariable) {
TrainingPartition.Start = regressionProblemData.TrainingPartition.Start;
TrainingPartition.End = regressionProblemData.TrainingPartition.End;
TestPartition.Start = regressionProblemData.TestPartition.Start;
TestPartition.End = regressionProblemData.TestPartition.End;
}
public RegressionEnsembleProblemData(Dataset dataset, IEnumerable allowedInputVariables, string targetVariable)
: base(dataset, allowedInputVariables, targetVariable) {
}
}
}