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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionSolution.cs @ 14490

Last change on this file since 14490 was 14185, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 1.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Collections.Generic;
23namespace HeuristicLab.Problems.DataAnalysis {
24  public interface IRegressionSolution : IDataAnalysisSolution {
25    new IRegressionModel Model { get; }
26    new IRegressionProblemData ProblemData { get; set; }
27
28    IEnumerable<double> EstimatedValues { get; }
29    IEnumerable<double> EstimatedTrainingValues { get; }
30    IEnumerable<double> EstimatedTestValues { get; }
31    IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
32
33    double TrainingMeanSquaredError { get; }
34    double TestMeanSquaredError { get; }
35    double TrainingMeanAbsoluteError { get; }
36    double TestMeanAbsoluteError { get; }
37    double TrainingRSquared { get; }
38    double TestRSquared { get; }
39    double TrainingRelativeError { get; }
40    double TestRelativeError { get; }
41    double TrainingNormalizedMeanSquaredError { get; }
42    double TestNormalizedMeanSquaredError { get; }
43    double TrainingRootMeanSquaredError { get; }
44    double TestRootMeanSquaredError { get; }
45  }
46}
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