[3641] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11203] | 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3641] | 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.Linq;
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[4722] | 23 | using HeuristicLab.Common;
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[4068] | 24 | using HeuristicLab.Core;
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[3641] | 25 | using HeuristicLab.Data;
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[4068] | 26 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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[3641] | 27 | using HeuristicLab.Operators;
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[4068] | 28 | using HeuristicLab.Optimization;
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[3641] | 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 |
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[3797] | 32 | namespace HeuristicLab.Problems.Knapsack {
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[3641] | 33 | /// <summary>
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[4513] | 34 | /// An operator for analyzing the best solution for a Knapsack problem.
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[3641] | 35 | /// </summary>
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[4513] | 36 | [Item("BestKnapsackSolutionAnalyzer", "An operator for analyzing the best solution for a Knapsack problem.")]
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[3641] | 37 | [StorableClass]
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[4722] | 38 | public class BestKnapsackSolutionAnalyzer : SingleSuccessorOperator, IAnalyzer {
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[7172] | 39 | public virtual bool EnabledByDefault {
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| 40 | get { return true; }
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| 41 | }
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| 42 |
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[3787] | 43 | public LookupParameter<BoolValue> MaximizationParameter {
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| 44 | get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
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| 45 | }
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[3667] | 46 | public ScopeTreeLookupParameter<BinaryVector> BinaryVectorParameter {
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| 47 | get { return (ScopeTreeLookupParameter<BinaryVector>)Parameters["BinaryVector"]; }
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[3641] | 48 | }
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[3667] | 49 | public LookupParameter<IntValue> KnapsackCapacityParameter {
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| 50 | get { return (LookupParameter<IntValue>)Parameters["KnapsackCapacity"]; }
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[3641] | 51 | }
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[3667] | 52 | public LookupParameter<IntArray> WeightsParameter {
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| 53 | get { return (LookupParameter<IntArray>)Parameters["Weights"]; }
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[3641] | 54 | }
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[3667] | 55 | public LookupParameter<IntArray> ValuesParameter {
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| 56 | get { return (LookupParameter<IntArray>)Parameters["Values"]; }
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[3641] | 57 | }
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[3667] | 58 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 59 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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[3641] | 60 | }
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[3667] | 61 | public LookupParameter<KnapsackSolution> BestSolutionParameter {
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| 62 | get { return (LookupParameter<KnapsackSolution>)Parameters["BestSolution"]; }
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[3641] | 63 | }
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[3667] | 64 | public ValueLookupParameter<ResultCollection> ResultsParameter {
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| 65 | get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
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[3641] | 66 | }
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[3787] | 67 | public LookupParameter<DoubleValue> BestKnownQualityParameter {
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| 68 | get { return (LookupParameter<DoubleValue>)Parameters["BestKnownQuality"]; }
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| 69 | }
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| 70 | public LookupParameter<BinaryVector> BestKnownSolutionParameter {
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| 71 | get { return (LookupParameter<BinaryVector>)Parameters["BestKnownSolution"]; }
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| 72 | }
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[3641] | 73 |
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[4722] | 74 | [StorableConstructor]
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| 75 | protected BestKnapsackSolutionAnalyzer(bool deserializing) : base(deserializing) { }
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| 76 | protected BestKnapsackSolutionAnalyzer(BestKnapsackSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
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[3641] | 77 | public BestKnapsackSolutionAnalyzer()
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| 78 | : base() {
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[3787] | 79 | Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
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[4513] | 80 | Parameters.Add(new ScopeTreeLookupParameter<BinaryVector>("BinaryVector", "The Knapsack solutions from which the best solution should be visualized."));
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[3641] | 81 | Parameters.Add(new LookupParameter<IntValue>("KnapsackCapacity", "Capacity of the Knapsack."));
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| 82 | Parameters.Add(new LookupParameter<IntArray>("Weights", "The weights of the items."));
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| 83 | Parameters.Add(new LookupParameter<IntArray>("Values", "The values of the items."));
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| 84 |
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[4513] | 85 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the Knapsack solutions which should be visualized."));
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| 86 | Parameters.Add(new LookupParameter<KnapsackSolution>("BestSolution", "The best Knapsack solution."));
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[3641] | 87 | Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the knapsack solution should be stored."));
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[3787] | 88 | Parameters.Add(new LookupParameter<DoubleValue>("BestKnownQuality", "The quality of the best known solution."));
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| 89 | Parameters.Add(new LookupParameter<BinaryVector>("BestKnownSolution", "The best known solution."));
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[3641] | 90 | }
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| 91 |
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[4722] | 92 | public override IDeepCloneable Clone(Cloner cloner) {
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| 93 | return new BestKnapsackSolutionAnalyzer(this, cloner);
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| 94 | }
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| 95 |
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[3641] | 96 | public override IOperation Apply() {
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[3667] | 97 | ItemArray<BinaryVector> binaryVectors = BinaryVectorParameter.ActualValue;
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| 98 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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[3641] | 99 | ResultCollection results = ResultsParameter.ActualValue;
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[3787] | 100 | bool max = MaximizationParameter.ActualValue.Value;
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| 101 | DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
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[3641] | 102 |
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[3787] | 103 | int i = -1;
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[4068] | 104 | if (!max)
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[3787] | 105 | i = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
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| 106 | else i = qualities.Select((x, index) => new { index, x.Value }).OrderByDescending(x => x.Value).First().index;
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[3667] | 107 |
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[3787] | 108 | if (bestKnownQuality == null ||
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| 109 | max && qualities[i].Value > bestKnownQuality.Value ||
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| 110 | !max && qualities[i].Value < bestKnownQuality.Value) {
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| 111 | BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value);
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| 112 | BestKnownSolutionParameter.ActualValue = (BinaryVector)binaryVectors[i].Clone();
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| 113 | }
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| 114 |
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[3641] | 115 | KnapsackSolution solution = BestSolutionParameter.ActualValue;
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| 116 | if (solution == null) {
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[3787] | 117 | solution = new KnapsackSolution((BinaryVector)binaryVectors[i].Clone(), new DoubleValue(qualities[i].Value),
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[3641] | 118 | KnapsackCapacityParameter.ActualValue, WeightsParameter.ActualValue, ValuesParameter.ActualValue);
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| 119 | BestSolutionParameter.ActualValue = solution;
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| 120 | results.Add(new Result("Best Knapsack Solution", solution));
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[4068] | 121 | } else {
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[3787] | 122 | if (max && qualities[i].Value > solution.Quality.Value ||
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| 123 | !max && qualities[i].Value < solution.Quality.Value) {
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| 124 | solution.BinaryVector = (BinaryVector)binaryVectors[i].Clone();
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| 125 | solution.Quality = new DoubleValue(qualities[i].Value);
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| 126 | solution.Capacity = KnapsackCapacityParameter.ActualValue;
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| 127 | solution.Weights = WeightsParameter.ActualValue;
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| 128 | solution.Values = ValuesParameter.ActualValue;
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| 129 | }
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[3641] | 130 | }
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| 131 |
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| 132 | return base.Apply();
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| 133 | }
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| 134 | }
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| 135 | }
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