#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(); } } }