#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 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.PermutationEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.LinearAssignment {
[Item("BestLAPSolutionAnalyzer", "Analyzes the best solution found.")]
[StorableClass]
public class BestLAPSolutionAnalyzer : SingleSuccessorOperator, IAnalyzer {
public bool EnabledByDefault { get { return true; } }
public ILookupParameter MaximizationParameter {
get { return (ILookupParameter)Parameters["Maximization"]; }
}
public ILookupParameter CostsParameter {
get { return (ILookupParameter)Parameters["Costs"]; }
}
public IValueLookupParameter RowNamesParameter {
get { return (IValueLookupParameter)Parameters["RowNames"]; }
}
public IValueLookupParameter ColumnNamesParameter {
get { return (IValueLookupParameter)Parameters["ColumnNames"]; }
}
public IScopeTreeLookupParameter AssignmentParameter {
get { return (IScopeTreeLookupParameter)Parameters["Assignment"]; }
}
public IScopeTreeLookupParameter QualityParameter {
get { return (IScopeTreeLookupParameter)Parameters["Quality"]; }
}
public ILookupParameter BestSolutionParameter {
get { return (ILookupParameter)Parameters["BestSolution"]; }
}
public ILookupParameter BestKnownQualityParameter {
get { return (ILookupParameter)Parameters["BestKnownQuality"]; }
}
public ILookupParameter> BestKnownSolutionsParameter {
get { return (ILookupParameter>)Parameters["BestKnownSolutions"]; }
}
public ILookupParameter BestKnownSolutionParameter {
get { return (ILookupParameter)Parameters["BestKnownSolution"]; }
}
public IValueLookupParameter ResultsParameter {
get { return (IValueLookupParameter)Parameters["Results"]; }
}
[StorableConstructor]
protected BestLAPSolutionAnalyzer(bool deserializing) : base(deserializing) { }
protected BestLAPSolutionAnalyzer(BestLAPSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
public BestLAPSolutionAnalyzer()
: base() {
Parameters.Add(new LookupParameter("Maximization", "True if the problem is a maximization problem."));
Parameters.Add(new LookupParameter("Costs", LinearAssignmentProblem.CostsDescription));
Parameters.Add(new ValueLookupParameter("RowNames", LinearAssignmentProblem.RowNamesDescription));
Parameters.Add(new ValueLookupParameter("ColumnNames", LinearAssignmentProblem.ColumnNamesDescription));
Parameters.Add(new ScopeTreeLookupParameter("Assignment", "The LAP solutions from which the best solution should be analyzed."));
Parameters.Add(new ScopeTreeLookupParameter("Quality", "The qualities of the LAP solutions which should be analyzed."));
Parameters.Add(new LookupParameter("BestSolution", "The best LAP solution."));
Parameters.Add(new ValueLookupParameter("Results", "The result collection where the best LAP solution should be stored."));
Parameters.Add(new LookupParameter("BestKnownQuality", "The quality of the best known solution of this LAP instance."));
Parameters.Add(new LookupParameter>("BestKnownSolutions", "The best known solutions (there may be multiple) of this LAP instance."));
Parameters.Add(new LookupParameter("BestKnownSolution", "The best known solution of this LAP instance."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new BestLAPSolutionAnalyzer(this, cloner);
}
public override IOperation Apply() {
var costs = CostsParameter.ActualValue;
var rowNames = RowNamesParameter.ActualValue;
var columnNames = ColumnNamesParameter.ActualValue;
var permutations = AssignmentParameter.ActualValue;
var qualities = QualityParameter.ActualValue;
var results = ResultsParameter.ActualValue;
bool max = MaximizationParameter.ActualValue.Value;
DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
var sorted = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).ToArray();
if (max) sorted = sorted.Reverse().ToArray();
int i = sorted.First().index;
if (bestKnownQuality == null
|| max && qualities[i].Value > bestKnownQuality.Value
|| !max && qualities[i].Value < bestKnownQuality.Value) {
// if there isn't a best-known quality or we improved the best-known quality we'll add the current solution as best-known
BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value);
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
BestKnownSolutionsParameter.ActualValue = new ItemSet(new PermutationEqualityComparer());
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[i].Clone());
} else if (bestKnownQuality.Value == qualities[i].Value) {
// if we matched the best-known quality we'll try to set the best-known solution if it isn't null
// and try to add it to the pool of best solutions if it is different
if (BestKnownSolutionParameter.ActualValue == null)
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
if (BestKnownSolutionsParameter.ActualValue == null)
BestKnownSolutionsParameter.ActualValue = new ItemSet(new PermutationEqualityComparer());
foreach (var k in sorted) { // for each solution that we found check if it is in the pool of best-knowns
if (!max && k.Value > qualities[i].Value
|| max && k.Value < qualities[i].Value) break; // stop when we reached a solution worse than the best-known quality
Permutation p = permutations[k.index];
if (!BestKnownSolutionsParameter.ActualValue.Contains(p))
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[k.index].Clone());
}
}
LAPAssignment assignment = BestSolutionParameter.ActualValue;
if (assignment == null) {
assignment = new LAPAssignment(costs, rowNames, columnNames, (Permutation)permutations[i].Clone(), new DoubleValue(qualities[i].Value));
BestSolutionParameter.ActualValue = assignment;
results.Add(new Result("Best LAP Solution", assignment));
} else {
if (max && assignment.Quality.Value < qualities[i].Value ||
!max && assignment.Quality.Value > qualities[i].Value) {
assignment.Costs = costs;
assignment.Assignment = (Permutation)permutations[i].Clone();
assignment.Quality.Value = qualities[i].Value;
if (rowNames != null)
assignment.RowNames = rowNames;
else assignment.RowNames = null;
if (columnNames != null)
assignment.ColumnNames = columnNames;
else assignment.ColumnNames = null;
}
}
return base.Apply();
}
}
}