source: branches/PerformanceComparison/HeuristicLab.Analysis.FitnessLandscape/3.3/Analysis/UpDownWalkAnalyzer.cs @ 13583

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using System.Linq;

namespace HeuristicLab.Analysis.FitnessLandscape {
  [Item("Up/Down Walk Analyzer", "Analyzes the quality trail produced from an up/down walk.")]
  [StorableClass]
  public class UpDownWalkAnalyzer : SingleSuccessorOperator, IQualityTrailAnalyzer {
    public bool EnabledByDefault {
      get { return false; }
    }

    #region Parameters
    public LookupParameter<DataTable> QualityTrailParameter {
      get { return (LookupParameter<DataTable>)Parameters["Quality Trail"]; }
    }
    public LookupParameter<ResultCollection> ResultsParameter {
      get { return (LookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    public LookupParameter<DoubleValue> UpWalkLengthParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["UpWalkLength"]; }
    }
    public LookupParameter<DoubleValue> DownWalkLengthParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["DownWalkLength"]; }
    }
    public LookupParameter<DoubleValue> UpWalkLenVarParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["UpWalkLenVar"]; }
    }
    public LookupParameter<DoubleValue> DownWalkLenVarParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["DownWalkLenVar"]; }
    }
    public LookupParameter<DoubleValue> UpperVarianceParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["UpperVariance"]; }
    }
    public LookupParameter<DoubleValue> LowerVarianceParameter {
      get { return (LookupParameter<DoubleValue>)Parameters["LowerVariance"]; }
    }
    #endregion

    [StorableConstructor]
    protected UpDownWalkAnalyzer(bool deserializing) : base(deserializing) { }
    protected UpDownWalkAnalyzer(UpDownWalkAnalyzer original, Cloner cloner) : base(original, cloner) { }
    public UpDownWalkAnalyzer() {
      Parameters.Add(new LookupParameter<DataTable>("Quality Trail", "The qualities of the solutions"));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The collection of all results of this algorithm"));
      Parameters.Add(new LookupParameter<DoubleValue>("DownWalkLength", "Average downward walk length."));
      Parameters.Add(new LookupParameter<DoubleValue>("UpWalkLength", "Average updward walk length."));
      Parameters.Add(new LookupParameter<DoubleValue>("UpWalkLenVar", "Upward walk length variance."));
      Parameters.Add(new LookupParameter<DoubleValue>("DownWalkLenVar", "Downward walk length variance."));
      Parameters.Add(new LookupParameter<DoubleValue>("LowerVariance", "Lower level variance."));
      Parameters.Add(new LookupParameter<DoubleValue>("UpperVariance", "Upper level variance."));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new UpDownWalkAnalyzer(this, cloner);
    }

    public override IOperation Apply() {
      var qualityTrail = QualityTrailParameter.ActualValue;
      if (qualityTrail != null && qualityTrail.Rows.Count > 0) {
        var qualities = qualityTrail.Rows.First().Values.ToList();
        if (qualities.Count > 2) {
          var results = ResultsParameter.ActualValue;
          var extremes = qualities
            .Delta((a, b) => new { a, b, diff = b - a })
            .Select((p, i) => new { p.a, p.b, p.diff, i = i + 1 })
            .Delta((s1, s2) => new {
              s1.i,
              value = s2.a,
              top = s1.diff >= 0 && s2.diff < 0,
              bottom = s1.diff <= 0 && s2.diff > 0
            })
            .Where(x => x.top || x.bottom)
            .GroupConsecutive(x => x.bottom)
            .Select(g => g.Count() == 1
                           ? g.First()
                           : (g.First().bottom
                                ? g.OrderBy(x => x.value).First()
                                : g.OrderByDescending(x => x.value).First())).ToList();
          var maxima = extremes.Where(x => x.top).ToList();
          var minima = extremes.Where(x => x.bottom).ToList();
          var tops = Enumerable.Repeat(new { length = 0, value = 0.0 }, 0).ToList();
          var bottoms = tops;
          if (maxima.Count > 0 && minima.Count > 0) {
            if (maxima.First().i < minima.First().i) {
              bottoms = maxima.Zip(minima, (t, b) => new { length = b.i - t.i, b.value }).ToList();
              minima.Insert(0, new { i = -1, value = 0.0, top = false, bottom = false });
              tops = maxima.Zip(minima, (t, b) => new { length = t.i - b.i, t.value }).ToList();
            } else {
              tops = maxima.Zip(minima, (t, b) => new { length = t.i - b.i, t.value }).ToList();
              maxima.Insert(0, new { i = -1, value = 0.0, top = false, bottom = false });
              bottoms = maxima.Zip(minima, (t, b) => new { length = b.i - t.i, b.value }).ToList();
            }
            if (tops.Count > 0) {
              var topLengths = tops.Select(t => (double)t.length).ToList();
              var topVals = tops.Select(t => t.value).ToList();
              var uv = new DoubleValue(topVals.Variance());
              UpperVarianceParameter.ActualValue = uv;
              AddOrUpdateResult(results, UpperVarianceParameter.Name, uv);
              var ul = new DoubleValue(topLengths.Average());
              UpWalkLengthParameter.ActualValue = ul;
              AddOrUpdateResult(results, UpWalkLengthParameter.Name, ul);
              var ulv = new DoubleValue(topLengths.Variance());
              UpWalkLenVarParameter.ActualValue = ulv;
              AddOrUpdateResult(results, UpWalkLenVarParameter.Name, ulv);
            }
            if (bottoms.Count > 0) {
              var bottomLengths = bottoms.Select(b => (double)b.length).ToList();
              var bottomVals = bottoms.Select(b => b.value).ToList();
              var lv = new DoubleValue(bottomVals.Variance());
              LowerVarianceParameter.ActualValue = lv;
              AddOrUpdateResult(results, LowerVarianceParameter.Name, lv);
              var dl = new DoubleValue(bottomLengths.Average());
              DownWalkLengthParameter.ActualValue = dl;
              AddOrUpdateResult(results, DownWalkLengthParameter.Name, dl);
              var dlv = new DoubleValue(bottomLengths.Variance());
              DownWalkLenVarParameter.ActualValue = dlv;
              AddOrUpdateResult(results, DownWalkLenVarParameter.Name, dlv);
            }
          }
        }
      }
      return base.Apply();
    }

    private static void AddOrUpdateResult(ResultCollection results, string name, IItem item, bool clone = false) {
      IResult r;
      if (!results.TryGetValue(name, out r)) {
        results.Add(new Result(name, clone ? (IItem)item.Clone() : item));
      } else r.Value = clone ? (IItem)item.Clone() : item;
    }
  }
}