#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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.Knapsack {
///
/// An operator for analyzing the best solution for a Knapsack problem.
///
[Item("BestKnapsackSolutionAnalyzer", "An operator for analyzing the best solution for a Knapsack problem.")]
[StorableClass]
public class BestKnapsackSolutionAnalyzer : SingleSuccessorOperator, IAnalyzer, ISingleObjectiveOperator {
public virtual bool EnabledByDefault {
get { return true; }
}
public LookupParameter MaximizationParameter {
get { return (LookupParameter)Parameters["Maximization"]; }
}
public ScopeTreeLookupParameter BinaryVectorParameter {
get { return (ScopeTreeLookupParameter)Parameters["BinaryVector"]; }
}
public LookupParameter KnapsackCapacityParameter {
get { return (LookupParameter)Parameters["KnapsackCapacity"]; }
}
public LookupParameter WeightsParameter {
get { return (LookupParameter)Parameters["Weights"]; }
}
public LookupParameter ValuesParameter {
get { return (LookupParameter)Parameters["Values"]; }
}
public ScopeTreeLookupParameter QualityParameter {
get { return (ScopeTreeLookupParameter)Parameters["Quality"]; }
}
public LookupParameter BestSolutionParameter {
get { return (LookupParameter)Parameters["BestSolution"]; }
}
public ValueLookupParameter ResultsParameter {
get { return (ValueLookupParameter)Parameters["Results"]; }
}
public LookupParameter BestKnownQualityParameter {
get { return (LookupParameter)Parameters["BestKnownQuality"]; }
}
public LookupParameter BestKnownSolutionParameter {
get { return (LookupParameter)Parameters["BestKnownSolution"]; }
}
[StorableConstructor]
protected BestKnapsackSolutionAnalyzer(bool deserializing) : base(deserializing) { }
protected BestKnapsackSolutionAnalyzer(BestKnapsackSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
public BestKnapsackSolutionAnalyzer()
: base() {
Parameters.Add(new LookupParameter("Maximization", "True if the problem is a maximization problem."));
Parameters.Add(new ScopeTreeLookupParameter("BinaryVector", "The Knapsack solutions from which the best solution should be visualized."));
Parameters.Add(new LookupParameter("KnapsackCapacity", "Capacity of the Knapsack."));
Parameters.Add(new LookupParameter("Weights", "The weights of the items."));
Parameters.Add(new LookupParameter("Values", "The values of the items."));
Parameters.Add(new ScopeTreeLookupParameter("Quality", "The qualities of the Knapsack solutions which should be visualized."));
Parameters.Add(new LookupParameter("BestSolution", "The best Knapsack solution."));
Parameters.Add(new ValueLookupParameter("Results", "The result collection where the knapsack solution should be stored."));
Parameters.Add(new LookupParameter("BestKnownQuality", "The quality of the best known solution."));
Parameters.Add(new LookupParameter("BestKnownSolution", "The best known solution."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new BestKnapsackSolutionAnalyzer(this, cloner);
}
public override IOperation Apply() {
ItemArray binaryVectors = BinaryVectorParameter.ActualValue;
ItemArray qualities = QualityParameter.ActualValue;
ResultCollection results = ResultsParameter.ActualValue;
bool max = MaximizationParameter.ActualValue.Value;
DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
int i = -1;
if (!max)
i = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
else i = qualities.Select((x, index) => new { index, x.Value }).OrderByDescending(x => x.Value).First().index;
if (bestKnownQuality == null ||
max && qualities[i].Value > bestKnownQuality.Value ||
!max && qualities[i].Value < bestKnownQuality.Value) {
BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value);
BestKnownSolutionParameter.ActualValue = (BinaryVector)binaryVectors[i].Clone();
}
KnapsackSolution solution = BestSolutionParameter.ActualValue;
if (solution == null) {
solution = new KnapsackSolution((BinaryVector)binaryVectors[i].Clone(), new DoubleValue(qualities[i].Value),
KnapsackCapacityParameter.ActualValue, WeightsParameter.ActualValue, ValuesParameter.ActualValue);
BestSolutionParameter.ActualValue = solution;
results.Add(new Result("Best Knapsack Solution", solution));
} else {
if (max && qualities[i].Value > solution.Quality.Value ||
!max && qualities[i].Value < solution.Quality.Value) {
solution.BinaryVector = (BinaryVector)binaryVectors[i].Clone();
solution.Quality = new DoubleValue(qualities[i].Value);
solution.Capacity = KnapsackCapacityParameter.ActualValue;
solution.Weights = WeightsParameter.ActualValue;
solution.Values = ValuesParameter.ActualValue;
}
}
return base.Apply();
}
}
}