#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
[Item("CMAAnalyzer", "Analyzes the development of strategy parameters and visualizes the performance of CMA-ES.")]
[StorableClass]
public sealed class CMAAnalyzer : SingleSuccessorOperator, IAnalyzer, ISingleObjectiveOperator {
public bool EnabledByDefault {
get { return false; }
}
#region Parameter Properties
public ILookupParameter StrategyParametersParameter {
get { return (ILookupParameter)Parameters["StrategyParameters"]; }
}
public ILookupParameter MeanParameter {
get { return (ILookupParameter)Parameters["Mean"]; }
}
public IScopeTreeLookupParameter QualityParameter {
get { return (IScopeTreeLookupParameter)Parameters["Quality"]; }
}
public ILookupParameter ResultsParameter {
get { return (ILookupParameter)Parameters["Results"]; }
}
#endregion
[StorableConstructor]
private CMAAnalyzer(bool deserializing) : base(deserializing) { }
private CMAAnalyzer(CMAAnalyzer original, Cloner cloner) : base(original, cloner) { }
public CMAAnalyzer()
: base() {
Parameters.Add(new LookupParameter("StrategyParameters", "The CMA strategy parameters to be analyzed."));
Parameters.Add(new LookupParameter("Mean", "The mean real vector that is being optimized."));
Parameters.Add(new ScopeTreeLookupParameter("Quality", "The qualities of the solutions."));
Parameters.Add(new LookupParameter("Results", "The collection to store the results in."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CMAAnalyzer(this, cloner);
}
public override IOperation Apply() {
var sp = StrategyParametersParameter.ActualValue;
var vector = MeanParameter.ActualValue;
var results = ResultsParameter.ActualValue;
var qualities = QualityParameter.ActualValue;
double min = qualities[0].Value, max = qualities[0].Value, avg = qualities[0].Value;
for (int i = 1; i < qualities.Length; i++) {
if (qualities[i].Value < min) min = qualities[i].Value;
if (qualities[i].Value > max) max = qualities[i].Value;
avg += qualities[i].Value;
}
avg /= qualities.Length;
DataTable progress;
if (results.ContainsKey("Progress")) {
progress = (DataTable)results["Progress"].Value;
} else {
progress = new DataTable("Progress");
progress.Rows.Add(new DataRow("AxisRatio"));
progress.Rows.Add(new DataRow("Sigma"));
progress.Rows.Add(new DataRow("Min Quality"));
progress.Rows.Add(new DataRow("Max Quality"));
progress.Rows.Add(new DataRow("Avg Quality"));
progress.VisualProperties.YAxisLogScale = true;
results.Add(new Result("Progress", progress));
}
progress.Rows["AxisRatio"].Values.Add(sp.AxisRatio);
progress.Rows["Sigma"].Values.Add(sp.Sigma);
progress.Rows["Min Quality"].Values.Add(min);
progress.Rows["Max Quality"].Values.Add(max);
progress.Rows["Avg Quality"].Values.Add(avg);
DataTable scaling;
if (results.ContainsKey("Scaling")) {
scaling = (DataTable)results["Scaling"].Value;
} else {
scaling = new DataTable("Scaling");
scaling.VisualProperties.YAxisLogScale = true;
for (int i = 0; i < sp.C.GetLength(0); i++)
scaling.Rows.Add(new DataRow("Axis" + i.ToString()));
results.Add(new Result("Scaling", scaling));
}
for (int i = 0; i < sp.C.GetLength(0); i++)
scaling.Rows["Axis" + i.ToString()].Values.Add(sp.D[i]);
DataTable realVector;
if (results.ContainsKey("Object Variables")) {
realVector = (DataTable)results["Object Variables"].Value;
} else {
realVector = new DataTable("Object Variables");
for (int i = 0; i < vector.Length; i++)
realVector.Rows.Add(new DataRow("Axis" + i.ToString()));
results.Add(new Result("Object Variables", realVector));
}
for (int i = 0; i < vector.Length; i++)
realVector.Rows["Axis" + i.ToString()].Values.Add(vector[i]);
DataTable stdDevs;
if (results.ContainsKey("Standard Deviations")) {
stdDevs = (DataTable)results["Standard Deviations"].Value;
} else {
stdDevs = new DataTable("Standard Deviations");
stdDevs.VisualProperties.YAxisLogScale = true;
stdDevs.Rows.Add(new DataRow("MinStdDev"));
stdDevs.Rows.Add(new DataRow("MaxStdDev"));
for (int i = 0; i < vector.Length; i++)
stdDevs.Rows.Add(new DataRow("Axis" + i.ToString()));
results.Add(new Result("Standard Deviations", stdDevs));
}
for (int i = 0; i < vector.Length; i++)
stdDevs.Rows["Axis" + i.ToString()].Values.Add(Math.Sqrt(sp.C[i, i]));
stdDevs.Rows["MinStdDev"].Values.Add(sp.D.Min() * sp.Sigma);
stdDevs.Rows["MaxStdDev"].Values.Add(sp.D.Max() * sp.Sigma);
return base.Apply();
}
}
}