[8401] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14186] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8401] | 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 |
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[8323] | 22 | using System;
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[8982] | 23 | using System.Collections.Generic;
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[8323] | 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[8612] | 27 | using HeuristicLab.Data;
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[8982] | 28 | using HeuristicLab.Parameters;
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[8323] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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[8371] | 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[8323] | 32 | [StorableClass]
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| 33 | [Item(Name = "MeanConst", Description = "Constant mean function for Gaussian processes.")]
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[8612] | 34 | public sealed class MeanConst : ParameterizedNamedItem, IMeanFunction {
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[8982] | 35 | public IValueParameter<DoubleValue> ValueParameter {
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| 36 | get { return (IValueParameter<DoubleValue>)Parameters["Value"]; }
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| 37 | }
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[8473] | 38 |
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[8323] | 39 | [StorableConstructor]
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[8612] | 40 | private MeanConst(bool deserializing) : base(deserializing) { }
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| 41 | private MeanConst(MeanConst original, Cloner cloner)
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[8323] | 42 | : base(original, cloner) {
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| 43 | }
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| 44 | public MeanConst()
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| 45 | : base() {
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[8612] | 46 | this.name = ItemName;
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| 47 | this.description = ItemDescription;
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| 48 |
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[8982] | 49 | Parameters.Add(new OptionalValueParameter<DoubleValue>("Value", "The constant value for the constant mean function."));
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[8323] | 50 | }
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| 51 |
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[8612] | 52 | public override IDeepCloneable Clone(Cloner cloner) {
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| 53 | return new MeanConst(this, cloner);
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[8323] | 54 | }
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[8612] | 55 |
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| 56 | public int GetNumberOfParameters(int numberOfVariables) {
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[8982] | 57 | return ValueParameter.Value != null ? 0 : 1;
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[8612] | 58 | }
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| 59 |
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[8982] | 60 | public void SetParameter(double[] p) {
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| 61 | double c;
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| 62 | GetParameters(p, out c);
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| 63 | ValueParameter.Value = new DoubleValue(c);
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[8612] | 64 | }
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| 65 |
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[8982] | 66 | private void GetParameters(double[] p, out double c) {
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| 67 | if (ValueParameter.Value == null) {
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| 68 | c = p[0];
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| 69 | } else {
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| 70 | if (p.Length > 0)
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| 71 | throw new ArgumentException(
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| 72 | "The length of the parameter vector does not match the number of free parameters for the constant mean function.",
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| 73 | "p");
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| 74 | c = ValueParameter.Value.Value;
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| 75 | }
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[8323] | 76 | }
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| 77 |
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[13981] | 78 | public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, int[] columnIndices) {
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[8982] | 79 | double c;
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| 80 | GetParameters(p, out c);
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| 81 | var mf = new ParameterizedMeanFunction();
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| 82 | mf.Mean = (x, i) => c;
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| 83 | mf.Gradient = (x, i, k) => {
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| 84 | if (k > 0) throw new ArgumentException();
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| 85 | return 1.0;
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| 86 | };
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| 87 | return mf;
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[8323] | 88 | }
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| 89 | }
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| 90 | }
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