source: branches/LearningClassifierSystems/HeuristicLab.Encodings.DecisionList/3.3/Variable/DoubleVariable.cs @ 9392

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Optimization.Operators.LCS;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Encodings.DecisionList {
  [StorableClass]
  [Item("DoubleVariable", "")]
  public class DoubleVariable : Variable<double> {

    [Storable]
    private IDiscretizer discretizer;

    [Storable]
    private IList<int> curIntervals;

    [Storable]
    private int maxIntervals;

    [StorableConstructor]
    protected DoubleVariable(bool deserializing) : base(deserializing) { }
    protected DoubleVariable(DoubleVariable original, Cloner cloner)
      : base(original, cloner) {
      if (original.discretizer != null) {
        discretizer = (IDiscretizer)original.discretizer.Clone();
        curIntervals = new List<int>(original.curIntervals);
      }
      maxIntervals = original.maxIntervals;
    }
    public DoubleVariable(string variableName, int maxIntervals)
      : base(variableName) {
      this.maxIntervals = maxIntervals;
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new DoubleVariable(this, cloner);
    }

    public override void Randomize(IRandom random, double onePercentage, IEnumerable<IDiscretizer> discretizers) {
      discretizer = discretizers.ElementAt(random.Next(0, discretizers.Count()));
      int microIntervals = discretizer.NumberOfMicroIntervals(variableName);
      int maxAllowedIntervals = Math.Min(maxIntervals, microIntervals);
      int numberOfIntervals = 2;
      if (maxAllowedIntervals > 2) {
        numberOfIntervals = random.Next(2, maxAllowedIntervals);
      }

      attributes = new List<bool>(numberOfIntervals);
      curIntervals = new List<int>(numberOfIntervals);

      for (int i = 0; i < numberOfIntervals - 1; i++) {
        int microInt = random.Next(1, microIntervals - (numberOfIntervals - i - 1));
        microIntervals -= microInt;
        curIntervals.Add(microInt);
        attributes.Add(random.NextDouble() < onePercentage);
      }
      // add last interval
      curIntervals.Add(microIntervals);
      attributes.Add(random.NextDouble() < onePercentage);
    }

    public override double ComputeTheoryLength() {
      int intervalCount = 0;
      bool curSet = attributes.First();
      // first interval started
      if (curSet) {
        intervalCount++;
      }
      foreach (var attribute in attributes) {
        if (curSet != attribute) {
          intervalCount++;
          curSet = attribute;
        }
      }
      // if the last last interval is not set
      if (!curSet) {
        intervalCount--;
      }
      return intervalCount;
    }

    public override bool Match(string input) {
      return Match(double.Parse(input));
    }

    public bool Match(double input) {
      var realCutpoints = GetValuesToCutPoints(discretizer.GetCutPoints(variableName), curIntervals);
      int pos = 0;
      while (input >= realCutpoints[pos]) {
        pos++;
      }
      return attributes[pos];
    }

    private IList<double> GetValuesToCutPoints(IEnumerable<double> cutpoints, IList<int> microIntervals) {
      var intervalValues = new List<double>();
      var cutpointList = cutpoints.ToList();
      int cur = -1;
      for (int i = 0; i < microIntervals.Count - 1; i++) {
        cur += microIntervals[i];
        intervalValues.Add(cutpointList[cur]);
      }
      intervalValues.Add(Double.PositiveInfinity);
      return intervalValues;
    }

    public override void Split(IRandom random) {
      // cannot create more than maximal allowed intervals
      if (curIntervals.Count >= maxIntervals) { return; }
      int selectedInterval = random.Next(0, curIntervals.Count);
      // a single microinterval cannot be split
      if (curIntervals[selectedInterval] <= 1) { return; }

      // split interval
      int splitPart1 = random.Next(1, curIntervals[selectedInterval]);
      int splitPart2 = curIntervals[selectedInterval] - splitPart1;

      // resize old interval
      curIntervals[selectedInterval] = splitPart1;
      // insert new interval at correct position
      curIntervals.Insert(selectedInterval + 1, splitPart2);

      // set new interval to the same bool value as old one
      attributes.Insert(selectedInterval + 1, attributes[selectedInterval]);
      return;
    }

    public override void Merge(IRandom random) {
      // cannot merge a single interval
      if (curIntervals.Count == 1) { return; }
      int selectedInterval = random.Next(0, curIntervals.Count);
      int neighbour;
      //if selected interval is the leftmost one, the neighbour on the right side is used
      if (selectedInterval == 0) {
        neighbour = selectedInterval + 1;
        //if the selected interval is the rightmost, the neighbour on the left side is used
      } else if (selectedInterval == curIntervals.Count - 1) {
        neighbour = selectedInterval - 1;
      } else {
        if (random.Next() < 0.5) {
          neighbour = selectedInterval - 1;
        } else {
          neighbour = selectedInterval + 1;
        }
      }

      bool value;
      if (curIntervals[selectedInterval] > curIntervals[neighbour]) {
        value = attributes[selectedInterval];
      } else if (curIntervals[selectedInterval] < curIntervals[neighbour]) {
        value = attributes[neighbour];
      } else {
        if (random.Next() < 0.5) {
          value = attributes[selectedInterval];
        } else {
          value = attributes[neighbour];
        }
      }

      curIntervals[selectedInterval] += curIntervals[neighbour];
      curIntervals.RemoveAt(neighbour);

      attributes[selectedInterval] = value;
      attributes.RemoveAt(neighbour);
      return;
    }

    public override void Reinitialize(IRandom random, double onePercentage, IEnumerable<IDiscretizer> descretizers) {
      Randomize(random, onePercentage, descretizers);
      return;
    }
  }
}