[14386] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[14386] | 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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| 22 | using System;
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| 23 | using System.Collections.Generic;
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[14887] | 24 | using System.Linq;
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[14386] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Parameters;
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[17097] | 28 | using HEAL.Attic;
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[14386] | 29 |
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[15249] | 30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[17097] | 31 | [StorableType("3449B830-E1E5-4176-B56D-AA32235F061B")]
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[14887] | 32 | public abstract class KernelBase : ParameterizedNamedItem, IKernel {
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[14386] | 33 |
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| 34 | private const string DistanceParameterName = "Distance";
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[15249] | 35 |
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| 36 | public IValueParameter<IDistance> DistanceParameter {
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| 37 | get { return (IValueParameter<IDistance>)Parameters[DistanceParameterName]; }
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[14386] | 38 | }
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| 39 |
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[14887] | 40 | [Storable]
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[15249] | 41 | private double? beta;
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| 42 | public double? Beta {
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| 43 | get { return beta; }
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| 44 | set {
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| 45 | if (value != beta) {
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| 46 | beta = value;
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| 47 | RaiseBetaChanged();
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| 48 | }
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| 49 | }
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| 50 | }
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| 51 |
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[14872] | 52 | public IDistance Distance {
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[14386] | 53 | get { return DistanceParameter.Value; }
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[15249] | 54 | set {
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| 55 | if (DistanceParameter.Value != value) {
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| 56 | DistanceParameter.Value = value;
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| 57 | }
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| 58 | }
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[14386] | 59 | }
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| 60 |
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| 61 | [StorableConstructor]
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[17097] | 62 | protected KernelBase(StorableConstructorFlag _) : base(_) { }
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[14386] | 63 |
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[14872] | 64 | protected KernelBase(KernelBase original, Cloner cloner)
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[14887] | 65 | : base(original, cloner) {
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[15249] | 66 | beta = original.beta;
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| 67 | RegisterEvents();
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[14887] | 68 | }
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[14386] | 69 |
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| 70 | protected KernelBase() {
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[14872] | 71 | Parameters.Add(new ValueParameter<IDistance>(DistanceParameterName, "The distance function used for kernel calculation"));
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| 72 | DistanceParameter.Value = new EuclideanDistance();
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[15249] | 73 | RegisterEvents();
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[14386] | 74 | }
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| 75 |
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[15249] | 76 | [StorableHook(HookType.AfterDeserialization)]
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| 77 | private void AfterDeserialization() {
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| 78 | RegisterEvents();
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| 79 | }
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| 80 |
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| 81 | private void RegisterEvents() {
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| 82 | DistanceParameter.ValueChanged += (sender, args) => RaiseDistanceChanged();
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| 83 | }
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| 84 |
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[14872] | 85 | public double Get(object a, object b) {
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[14386] | 86 | return Get(Distance.Get(a, b));
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| 87 | }
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| 88 |
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| 89 | protected abstract double Get(double norm);
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| 90 |
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| 91 | public int GetNumberOfParameters(int numberOfVariables) {
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[14887] | 92 | return Beta.HasValue ? 0 : 1;
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[14386] | 93 | }
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| 94 |
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| 95 | public void SetParameter(double[] p) {
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[14887] | 96 | if (p != null && p.Length == 1) Beta = new double?(p[0]);
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[14386] | 97 | }
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| 98 |
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| 99 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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[14887] | 100 | if (p.Length != GetNumberOfParameters(columnIndices.Length)) throw new ArgumentException("Illegal parametrization");
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[15249] | 101 | var myClone = (KernelBase)Clone();
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[14887] | 102 | myClone.SetParameter(p);
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[14386] | 103 | var cov = new ParameterizedCovarianceFunction {
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| 104 | Covariance = (x, i, j) => myClone.Get(GetNorm(x, x, i, j, columnIndices)),
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| 105 | CrossCovariance = (x, xt, i, j) => myClone.Get(GetNorm(x, xt, i, j, columnIndices)),
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| 106 | CovarianceGradient = (x, i, j) => new List<double> { myClone.GetGradient(GetNorm(x, x, i, j, columnIndices)) }
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| 107 | };
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| 108 | return cov;
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| 109 | }
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| 110 |
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| 111 | protected abstract double GetGradient(double norm);
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| 112 |
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| 113 | protected double GetNorm(double[,] x, double[,] xt, int i, int j, int[] columnIndices) {
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| 114 | var dist = Distance as IDistance<IEnumerable<double>>;
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[14872] | 115 | if (dist == null) throw new ArgumentException("The distance needs to apply to double vectors");
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[14887] | 116 | var r1 = columnIndices.Select(c => x[i, c]);
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| 117 | var r2 = columnIndices.Select(c => xt[j, c]);
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[14386] | 118 | return dist.Get(r1, r2);
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| 119 | }
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[15249] | 120 |
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| 121 | #region events
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| 122 | public event EventHandler BetaChanged;
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| 123 | public event EventHandler DistanceChanged;
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| 124 |
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| 125 | protected void RaiseBetaChanged() {
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| 126 | var handler = BetaChanged;
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| 127 | if (handler != null) handler(this, EventArgs.Empty);
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| 128 | }
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| 129 |
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| 130 | protected void RaiseDistanceChanged() {
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| 131 | var handler = DistanceChanged;
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| 132 | if (handler != null) handler(this, EventArgs.Empty);
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| 133 | }
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| 134 | #endregion
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[14386] | 135 | }
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| 136 | } |
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