#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 System.Linq;
using HeuristicLab.Common;
using HEAL.Attic;
namespace HeuristicLab.Problems.DataAnalysis {
///
/// Represents a regression data analysis solution that supports confidence estimates
///
[StorableType("C2D0DE07-E8F0-4850-AAF3-E2885EC1DDB6")]
public class ConfidenceRegressionSolution : RegressionSolution, IConfidenceRegressionSolution {
protected readonly Dictionary varianceEvaluationCache;
public new IConfidenceRegressionModel Model {
get { return (IConfidenceRegressionModel)base.Model; }
set { base.Model = value; }
}
[StorableConstructor]
protected ConfidenceRegressionSolution(StorableConstructorFlag _) : base(_) {
varianceEvaluationCache = new Dictionary();
}
protected ConfidenceRegressionSolution(ConfidenceRegressionSolution original, Cloner cloner)
: base(original, cloner) {
varianceEvaluationCache = new Dictionary(original.varianceEvaluationCache);
}
public ConfidenceRegressionSolution(IConfidenceRegressionModel model, IRegressionProblemData problemData)
: base(model, problemData) {
varianceEvaluationCache = new Dictionary(problemData.Dataset.Rows);
}
public override IDeepCloneable Clone(Cloner cloner) {
return new ConfidenceRegressionSolution(this, cloner);
}
public IEnumerable EstimatedVariances {
get { return GetEstimatedVariances(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
}
public IEnumerable EstimatedTrainingVariances {
get { return GetEstimatedVariances(ProblemData.TrainingIndices); }
}
public IEnumerable EstimatedTestVariances {
get { return GetEstimatedVariances(ProblemData.TestIndices); }
}
public IEnumerable GetEstimatedVariances(IEnumerable rows) {
var rowsToEvaluate = rows.Except(varianceEvaluationCache.Keys);
var rowsEnumerator = rowsToEvaluate.GetEnumerator();
var valuesEnumerator = Model.GetEstimatedVariances(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
varianceEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
}
return rows.Select(row => varianceEvaluationCache[row]);
}
protected override void OnProblemDataChanged() {
varianceEvaluationCache.Clear();
base.OnProblemDataChanged();
}
protected override void OnModelChanged() {
varianceEvaluationCache.Clear();
base.OnModelChanged();
}
}
}