[9129] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11576] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[9129] | 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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[9302] | 22 | using System;
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| 23 | using System.Linq;
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[9129] | 24 | using HeuristicLab.Analysis;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[9302] | 27 | using HeuristicLab.Data;
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[9129] | 28 | using HeuristicLab.Encodings.RealVectorEncoding;
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| 29 | using HeuristicLab.Operators;
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| 30 | using HeuristicLab.Optimization;
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| 31 | using HeuristicLab.Parameters;
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| 32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 33 |
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| 34 | namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
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| 35 | [Item("CMAAnalyzer", "Analyzes the development of strategy parameters and visualizes the performance of CMA-ES.")]
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| 36 | [StorableClass]
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| 37 | public sealed class CMAAnalyzer : SingleSuccessorOperator, IAnalyzer {
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| 38 |
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| 39 | public bool EnabledByDefault {
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| 40 | get { return false; }
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| 41 | }
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| 42 |
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| 43 | #region Parameter Properties
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| 44 | public ILookupParameter<CMAParameters> StrategyParametersParameter {
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| 45 | get { return (ILookupParameter<CMAParameters>)Parameters["StrategyParameters"]; }
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| 46 | }
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| 47 |
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[9132] | 48 | public ILookupParameter<RealVector> MeanParameter {
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| 49 | get { return (ILookupParameter<RealVector>)Parameters["Mean"]; }
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[9129] | 50 | }
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| 51 |
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[9302] | 52 | public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 53 | get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 54 | }
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| 55 |
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[9129] | 56 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 57 | get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
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| 58 | }
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| 59 | #endregion
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| 60 |
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| 61 | [StorableConstructor]
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| 62 | private CMAAnalyzer(bool deserializing) : base(deserializing) { }
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| 63 | private CMAAnalyzer(CMAAnalyzer original, Cloner cloner) : base(original, cloner) { }
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| 64 | public CMAAnalyzer()
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| 65 | : base() {
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| 66 | Parameters.Add(new LookupParameter<CMAParameters>("StrategyParameters", "The CMA strategy parameters to be analyzed."));
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[9132] | 67 | Parameters.Add(new LookupParameter<RealVector>("Mean", "The mean real vector that is being optimized."));
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[9302] | 68 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions."));
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[9129] | 69 | Parameters.Add(new LookupParameter<ResultCollection>("Results", "The collection to store the results in."));
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| 70 | }
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| 71 |
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| 72 | public override IDeepCloneable Clone(Cloner cloner) {
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| 73 | return new CMAAnalyzer(this, cloner);
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| 74 | }
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| 75 |
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| 76 | public override IOperation Apply() {
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| 77 | var sp = StrategyParametersParameter.ActualValue;
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[9132] | 78 | var vector = MeanParameter.ActualValue;
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[9129] | 79 | var results = ResultsParameter.ActualValue;
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[9302] | 80 | var qualities = QualityParameter.ActualValue;
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| 81 | double min = qualities[0].Value, max = qualities[0].Value, avg = qualities[0].Value;
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| 82 | for (int i = 1; i < qualities.Length; i++) {
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| 83 | if (qualities[i].Value < min) min = qualities[i].Value;
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| 84 | if (qualities[i].Value > max) max = qualities[i].Value;
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| 85 | avg += qualities[i].Value;
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[9129] | 86 | }
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[9302] | 87 | avg /= qualities.Length;
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[9129] | 88 |
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[9302] | 89 | DataTable progress;
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| 90 | if (results.ContainsKey("Progress")) {
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| 91 | progress = (DataTable)results["Progress"].Value;
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[9129] | 92 | } else {
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[9302] | 93 | progress = new DataTable("Progress");
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| 94 | progress.Rows.Add(new DataRow("AxisRatio"));
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| 95 | progress.Rows.Add(new DataRow("Sigma"));
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| 96 | progress.Rows.Add(new DataRow("Min Quality"));
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| 97 | progress.Rows.Add(new DataRow("Max Quality"));
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| 98 | progress.Rows.Add(new DataRow("Avg Quality"));
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| 99 | progress.VisualProperties.YAxisLogScale = true;
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| 100 | results.Add(new Result("Progress", progress));
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[9129] | 101 | }
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[9302] | 102 | progress.Rows["AxisRatio"].Values.Add(sp.AxisRatio);
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| 103 | progress.Rows["Sigma"].Values.Add(sp.Sigma);
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| 104 | progress.Rows["Min Quality"].Values.Add(min);
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| 105 | progress.Rows["Max Quality"].Values.Add(max);
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| 106 | progress.Rows["Avg Quality"].Values.Add(avg);
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[9129] | 107 |
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| 108 | DataTable scaling;
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| 109 | if (results.ContainsKey("Scaling")) {
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| 110 | scaling = (DataTable)results["Scaling"].Value;
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| 111 | } else {
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| 112 | scaling = new DataTable("Scaling");
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[9141] | 113 | scaling.VisualProperties.YAxisLogScale = true;
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[9297] | 114 | for (int i = 0; i < sp.C.GetLength(0); i++)
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[9129] | 115 | scaling.Rows.Add(new DataRow("Axis" + i.ToString()));
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| 116 | results.Add(new Result("Scaling", scaling));
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| 117 | }
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[9297] | 118 | for (int i = 0; i < sp.C.GetLength(0); i++)
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[9129] | 119 | scaling.Rows["Axis" + i.ToString()].Values.Add(sp.D[i]);
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| 120 |
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| 121 | DataTable realVector;
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[9302] | 122 | if (results.ContainsKey("Object Variables")) {
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| 123 | realVector = (DataTable)results["Object Variables"].Value;
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[9129] | 124 | } else {
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[9302] | 125 | realVector = new DataTable("Object Variables");
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[9129] | 126 | for (int i = 0; i < vector.Length; i++)
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| 127 | realVector.Rows.Add(new DataRow("Axis" + i.ToString()));
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[9302] | 128 | results.Add(new Result("Object Variables", realVector));
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[9129] | 129 | }
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| 130 | for (int i = 0; i < vector.Length; i++)
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| 131 | realVector.Rows["Axis" + i.ToString()].Values.Add(vector[i]);
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| 132 |
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[9140] | 133 | DataTable stdDevs;
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[9302] | 134 | if (results.ContainsKey("Standard Deviations")) {
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| 135 | stdDevs = (DataTable)results["Standard Deviations"].Value;
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[9140] | 136 | } else {
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[9302] | 137 | stdDevs = new DataTable("Standard Deviations");
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[9141] | 138 | stdDevs.VisualProperties.YAxisLogScale = true;
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[9302] | 139 | stdDevs.Rows.Add(new DataRow("MinStdDev"));
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| 140 | stdDevs.Rows.Add(new DataRow("MaxStdDev"));
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[9140] | 141 | for (int i = 0; i < vector.Length; i++)
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| 142 | stdDevs.Rows.Add(new DataRow("Axis" + i.ToString()));
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[9302] | 143 | results.Add(new Result("Standard Deviations", stdDevs));
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[9140] | 144 | }
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| 145 | for (int i = 0; i < vector.Length; i++)
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[9148] | 146 | stdDevs.Rows["Axis" + i.ToString()].Values.Add(Math.Sqrt(sp.C[i, i]));
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[9302] | 147 | stdDevs.Rows["MinStdDev"].Values.Add(sp.D.Min() * sp.Sigma);
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| 148 | stdDevs.Rows["MaxStdDev"].Values.Add(sp.D.Max() * sp.Sigma);
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[9140] | 149 |
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[9129] | 150 | return base.Apply();
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| 151 | }
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| 152 | }
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| 153 | } |
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