1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 | using System;
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22 | using HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HEAL.Attic;
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25 |
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26 | namespace HeuristicLab.Algorithms.DataAnalysis {
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27 | [StorableType("7B76ECDD-A7B1-450F-B542-D25E19480FC5")]
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28 | [Item(Name = "MeanZero", Description = "Constant zero mean function for Gaussian processes.")]
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29 | public sealed class MeanZero : Item, IMeanFunction {
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30 | [StorableConstructor]
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31 | private MeanZero(StorableConstructorFlag _) : base(_) { }
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32 | private MeanZero(MeanZero original, Cloner cloner)
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33 | : base(original, cloner) {
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34 | }
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35 | public MeanZero() {
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36 | }
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37 |
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38 | public override IDeepCloneable Clone(Cloner cloner) {
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39 | return new MeanZero(this, cloner);
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40 | }
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41 |
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42 | public int GetNumberOfParameters(int numberOfVariables) {
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43 | return 0;
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44 | }
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45 |
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46 | public void SetParameter(double[] p) {
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47 | if (p.Length > 0) throw new ArgumentException("No parameters allowed for zero mean function.", "p");
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48 | }
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49 |
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50 | public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, int[] columnIndices) {
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51 | if (p.Length > 0) throw new ArgumentException("No parameters allowed for zero mean function.", "p");
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52 | var mf = new ParameterizedMeanFunction();
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53 | mf.Mean = (x, i) => 0.0;
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54 | mf.Gradient = (x, i, k) => {
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55 | if (k > 0)
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56 | throw new ArgumentException();
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57 | return 0.0;
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58 | };
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59 | return mf;
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60 | }
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61 | }
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62 | }
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