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source: branches/gp-algorithms-refactoring-#720/sources/HeuristicLab.Modeling/3.2/IAnalyzerModel.cs @ 2340

Last change on this file since 2340 was 2285, checked in by gkronber, 15 years ago

Worked on #722 (IModel should provide a Predict() method to get predicted values for an input vector).
At the same time removed parameter PunishmentFactor from GP algorithms (this parameter is internal to TreeEvaluators now).

File size: 2.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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;
23using System.Collections.Generic;
24using System.Text;
25using HeuristicLab.Core;
26using HeuristicLab.DataAnalysis;
27
28namespace HeuristicLab.Modeling {
29   public interface IAnalyzerModel {
30    Dataset Dataset { get; set; }
31    string TargetVariable { get; set; }
32    IEnumerable<string> InputVariables { get; }
33    int TrainingSamplesStart { get; set; }
34    int TrainingSamplesEnd { get; set; }
35    int ValidationSamplesStart { get; set; }
36    int ValidationSamplesEnd { get; set; }
37    int TestSamplesStart { get; set; }
38    int TestSamplesEnd { get; set; }
39    double TrainingMeanSquaredError { get; set; }
40    double ValidationMeanSquaredError { get; set; }
41    double TestMeanSquaredError { get; set; }
42    double TrainingMeanAbsolutePercentageError { get; set; }
43    double ValidationMeanAbsolutePercentageError { get; set; }
44    double TestMeanAbsolutePercentageError { get; set; }
45    double TrainingMeanAbsolutePercentageOfRangeError { get; set; }
46    double ValidationMeanAbsolutePercentageOfRangeError { get; set; }
47    double TestMeanAbsolutePercentageOfRangeError { get; set; }
48    double TrainingCoefficientOfDetermination { get; set; }
49    double ValidationCoefficientOfDetermination { get; set; }
50    double TestCoefficientOfDetermination { get; set; }
51    double TrainingVarianceAccountedFor { get; set; }
52    double ValidationVarianceAccountedFor { get; set; }
53    double TestVarianceAccountedFor { get; set; }
54    double GetVariableEvaluationImpact(string variableName);
55    double GetVariableQualityImpact(string variableName);
56    void AddInputVariable(string variableName);
57    void SetVariableEvaluationImpact(string variableName, double impact);
58    void SetVariableQualityImpact(string variableName, double impact);
59    IPredictor Predictor { get; set; }
60  }
61}
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