public static void Apply(IRandom random, LinearLinkage lle, int n) {
      var grouping = lle.GetGroups().ToList();
      var groupsLargerOne = grouping.Select((v, i) => Tuple.Create(i, v))
                                    .Where(x => x.Item2.Count > 1)
                                    .ToDictionary(x => x.Item1, x => x.Item2);
      if (groupsLargerOne.Count == 0) return;
      var toRemove = new List<int>();

      for (var i = 0; i < n; i++) {
        var g = groupsLargerOne.Keys.SampleRandom(random);
        var idx = random.Next(1, groupsLargerOne[g].Count);
        // shuffle here to avoid a potential bias of grouping smaller and larger numbers together
        var tmp = groupsLargerOne[g].Shuffle(random);
        var before = new List<int>();
        var after = new List<int>();
        foreach (var t in tmp) {
          if (idx > 0) before.Add(t);
          else after.Add(t);
          idx--;
        }
        if (before.Count > 1) groupsLargerOne[grouping.Count] = before;
        grouping.Add(before);
        if (after.Count > 1) groupsLargerOne[grouping.Count] = after;
        grouping.Add(after);
        toRemove.Add(g);
        groupsLargerOne.Remove(g);
        if (groupsLargerOne.Count == 0) break;
      }
      foreach (var r in toRemove.OrderByDescending(x => x))
        grouping.RemoveAt(r);

      lle.SetGroups(grouping);
    }
    public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {
      var len = parents[0].Length;

      var child = new LinearLinkage(len);
      var childGroup = new List<HashSet<int>>();
      var currentParent = random.Next(parents.Length);
      var groups = parents.Select(x => x.GetGroups().Select(y => new HashSet<int>(y)).ToList()).ToList();
      bool remaining;
      do {
        var maxGroup = groups[currentParent].Select((v, i) => Tuple.Create(i, v))
          .MaxItems(x => x.Item2.Count)
          .SampleRandom(random).Item1;
        var group = groups[currentParent][maxGroup];
        groups[currentParent].RemoveAt(maxGroup);
        childGroup.Add(group);

        remaining = false;
        for (var p = 0; p < groups.Count; p++) {
          for (var j = 0; j < groups[p].Count; j++) {
            foreach (var elem in group) groups[p][j].Remove(elem);
            if (!remaining && groups[p][j].Count > 0) remaining = true;
          }
        }

        currentParent = (currentParent + 1) % parents.Length;
      } while (remaining);

      child.SetGroups(childGroup);
      return child;
    }
 public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2) {
   var length = p1.Length;
   var child = new LinearLinkage(length);
   var endNodes = new HashSet<int>();
   for (var i = 0; i < length; i++) {
     if ((p1[i] == i && p2[i] == i)
       || ((p1[i] == i || p2[i] == i) && random.NextDouble() < 0.5)) {
       child[i] = i;
       endNodes.Add(i);
     }
   }
   for (var i = 0; i < length; i++) {
     if (endNodes.Contains(i)) continue;
     var p1End = endNodes.Contains(p1[i]);
     var p2End = endNodes.Contains(p2[i]);
     if ((p1End && p2End) || (!p1End && !p2End)) {
       child[i] = random.NextDouble() < 0.5 ? p1[i] : p2[i];
     } else if (p1End) {
       child[i] = p1[i];
     } else {
       child[i] = p2[i];
     }
   }
   child.LinearizeTreeStructures();
   return child;
 }
    public static void Apply(LinearLinkage lle, Swap2Move move) {
      var groups = lle.GetGroups().ToList();
      int g1 = -1, g2 = -1, g1Idx = -1, g2Idx = -1;
      for (var i = 0; i < groups.Count; i++) {
        if (g1 < 0) {
          g1Idx = groups[i].IndexOf(move.Item1);
          if (g1Idx >= 0) {
            g1 = i;
            continue;
          }
        }
        if (g2 < 0) {
          g2Idx = groups[i].IndexOf(move.Item2);
          if (g2Idx >= 0) g2 = i;
        }
      }

      // can happen if (for some reason) the items belong to the same group
      if (g1 < 0 || g2 < 0) throw new InvalidOperationException("Swap2MoveMaker: Cannot apply swap move, items are not found in different groups.");

      var h = groups[g1][g1Idx];
      groups[g1][g1Idx] = groups[g2][g2Idx];
      groups[g2][g2Idx] = h;
      lle.SetGroups(groups);
    }
 public static Swap2Move[] Apply(LinearLinkage lle, IRandom random, int sampleSize) {
   int length = lle.Length;
   Swap2Move[] moves = new Swap2Move[sampleSize];
   for (int i = 0; i < sampleSize; i++) {
     moves[i] = StochasticSwap2SingleMoveGenerator.Apply(lle, random);
   }
   return moves;
 }
 public static LinearLinkage Apply(IRandom random, int length, int maxGroups) {
   var solution = new LinearLinkage(length);
   var groups = Enumerable.Range(0, length).Select(x => Tuple.Create(x, random.Next(maxGroups)))
                                           .GroupBy(x => x.Item2)
                                           .Select(x => x.Select(y => y.Item1).ToList());
   solution.SetGroups(groups);
   return solution;
 }
 public static void Apply(IRandom random, LinearLinkage lle) {
   int tries = lle.Length;
   var index = random.Next(lle.Length);
   while (tries > 0 && lle[index] == index) {
     index = random.Next(lle.Length);
     tries--;
   }
   if (lle[index] != index) Apply(random, lle, index);
 }
 public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2) {
   var length = p1.Length;
   var child = new LinearLinkage(length);
   var bp = random.Next(length - 1);
   for (var i = 0; i <= bp; i++) child[i] = p1[i];
   for (var i = bp + 1; i < length; i++) child[i] = p2[i];
   child.LinearizeTreeStructures();
   return child;
 }
    public static LinearLinkage Apply(IRandom random, int length, int groups) {
      var solution = new LinearLinkage(length);
      var groupNumbers = Enumerable.Range(0, length).Select(x => x % groups).Shuffle(random);
      var grouping = Enumerable.Range(0, groups).Select(x => new List<int>()).ToList();
      var idx = 0;
      foreach (var g in groupNumbers)
        grouping[g].Add(idx++);

      solution.SetGroups(grouping);
      return solution;
    }
    public static IEnumerable<Swap2Move> Generate(LinearLinkage lle) {
      int length = lle.Length;
      if (length == 1) throw new ArgumentException("ExhaustiveSwap2MoveGenerator: There cannot be an Swap move given only one item.", "lle");

      var groups = lle.GetGroups().ToList();
      if (groups.Count == 1) throw new InvalidOperationException("ExhaustiveSwap2MoveGenerator: Swap moves cannot be applied when there is only one group.");

      for (int i = 0; i < groups.Count; i++)
        for (int j = i + 1; j < groups.Count; j++)
          for (var k = 0; k < groups[i].Count; k++)
            for (var m = 0; m < groups[j].Count; m++) {
              yield return new Swap2Move(groups[i][k], groups[j][m]);
            }
    }
    public static void Apply(IRandom random, LinearLinkage lle, int n) {
      var grouping = lle.GetGroups().ToList();
      if (grouping.Count == 1) return; // nothing to merge

      for (var i = 0; i < n; i++) {
        var g1 = random.Next(grouping.Count);
        var g2 = random.Next(grouping.Count);
        while (g1 == g2) g2 = random.Next(grouping.Count);
        grouping[g1].AddRange(grouping[g2]);
        grouping.RemoveAt(g2);
        if (grouping.Count == 1) break;
      }

      lle.SetGroups(grouping);
    }
    public static Swap2Move Apply(LinearLinkage lle, IRandom random) {
      int length = lle.Length;
      if (length < 2) throw new ArgumentException("StochasticSwap2SingleMoveGenerator: There cannot be a swap-2 move given only one item.", "lle");

      var groups = lle.GetGroups().ToList();
      if (groups.Count == 1) throw new InvalidOperationException("StochasticSwap2SingleMoveGenerator: Swap moves cannot be applied when there is only one group.");

      int index1 = random.Next(groups.Count), index2 = 0;
      do {
        index2 = random.Next(length);
      } while (index1 == index2);

      var item1 = random.Next(groups[index1].Count);
      var item2 = random.Next(groups[index2].Count);

      return new Swap2Move(groups[index1][item1], groups[index2][item2]);
    }
    public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {
      var len = parents[0].Length;
      var child = new LinearLinkage(len);
      var remaining = new SortedSet<int>(Enumerable.Range(0, len));
      do {
        var groups = parents.Select(x => x.GetGroupForward(remaining.Min).Where(y => remaining.Contains(y)).ToList()).ToList();
        var max = groups.Select((v, idx) => Tuple.Create(idx, v.Count)).MaxItems(x => x.Item2).SampleRandom(random).Item1;
        var i = groups[max][0];
        for (var k = 1; k < groups[max].Count; k++) {
          child[i] = groups[max][k];
          remaining.Remove(i);
          i = child[i];
        }
        child[i] = i;
        remaining.Remove(i);
      } while (remaining.Count > 0);

      return child;
    }
    public static void Apply(IRandom random, LinearLinkage lle, int index) {
      var groups = lle.Select((val, idx) => Tuple.Create(idx, val))
                      .Where(x => x.Item1 == x.Item2)
                      .Select(x => x.Item2).ToList();
      var z = groups.Count;

      if (random.NextDouble() < 0.5)
        lle[index] = index; // divide the cluster into two
      else {
        var c = random.Next(z);
        if (groups[c] > index)
          lle[index] = groups[c]; // combine the portion with another class
        else {
          // combine the other class here
          lle[groups[c]] = lle[index];
          lle[index] = index;
        }
        lle.LinearizeTreeStructures();
      }
    }
Example #15
0
    public static void Apply(IRandom random, LinearLinkage lle, int n) {
      var grouping = lle.GetGroups().Select(x => x.ToList()).ToList();
      if (grouping.Count == 1) return; // nothing can be changed

      var prevGroup = random.Next(grouping.Count);
      var prevItem = random.Next(grouping[prevGroup].Count);
      for (var i = 0; i < n; i++) {
        int nextGroup, nextItem;
        do {
          nextGroup = random.Next(grouping.Count);
          nextItem = random.Next(grouping[nextGroup].Count);
        } while (nextGroup == prevGroup);
        var h = grouping[nextGroup][nextItem];
        grouping[nextGroup][nextItem] = grouping[prevGroup][prevItem];
        grouping[prevGroup][prevItem] = h;
        prevGroup = nextGroup;
        prevItem = nextItem;
      }

      lle.SetGroups(grouping);
    }
    public static LinearLinkage Apply(IRandom random, ItemArray<LinearLinkage> parents) {
      var len = parents[0].Length;
      var p = random.Next(parents.Length);
      var child = new LinearLinkage(len);
      var remaining = new SortedSet<int>(Enumerable.Range(0, len));
      do {
        var i = remaining.Min;
        foreach (var g in parents[p].GetGroupForward(i)) {
          if (!remaining.Contains(g)) continue;
          child[i] = g;
          i = g;
          remaining.Remove(g);
        }
        child[i] = i;
        remaining.Remove(i);

        p = (p + 1) % parents.Length;
      } while (remaining.Count > 0);

      return child;
    }
    public static void Apply(IRandom random, LinearLinkage lle, int n) {
      var grouping = lle.GetGroups().ToList();
      grouping.Add(new List<int>()); // add an empty group

      for (var i = 0; i < n; i++) {
        var src = random.Next(grouping.Count - 1); // only select from non-empty groups
        var dest = random.Next(grouping.Count);
        while (src == dest || grouping[src].Count == 1 && dest == grouping.Count - 1) {
          src = random.Next(grouping.Count - 1);
          dest = random.Next(grouping.Count);
        }
        if (dest == grouping.Count - 1) grouping.Add(new List<int>());

        var e = random.Next(grouping[src].Count);
        grouping[dest].Add(grouping[src][e]);
        grouping[src].RemoveAt(e);
        if (grouping[src].Count == 0)
          grouping.RemoveAt(src);
      }

      lle.SetGroups(grouping.Where(x => x.Count > 0));
    }
 protected override Swap2Move[] GenerateMoves(LinearLinkage lle) {
   IRandom random = RandomParameter.ActualValue;
   return new[] { Apply(lle, random) };
 }
 protected override void Manipulate(IRandom random, LinearLinkage lle) {
   Apply(random, lle);
 }
 protected abstract void Manipulate(IRandom random, LinearLinkage lle);
Example #21
0
        protected override void Manipulate(IRandom random, LinearLinkage lle)
        {
            var N = NParameter.ActualValue.Value;

            Apply(random, lle, N);
        }
 protected override void Manipulate(IRandom random, LinearLinkage lle) {
   var N = NParameter.ActualValue.Value;
   Apply(random, lle, N);
 }
 protected abstract Swap2Move[] GenerateMoves(LinearLinkage lle);
 protected abstract void Manipulate(IRandom random, LinearLinkage lle);
 protected override void Manipulate(IRandom random, LinearLinkage lle)
 {
     Apply(random, lle);
 }
Example #26
0
 protected abstract Swap2Move[] GenerateMoves(LinearLinkage lle);
Example #27
0
        protected override Swap2Move[] GenerateMoves(LinearLinkage lle)
        {
            IRandom random = RandomParameter.ActualValue;

            return(Apply(lle, random, SampleSizeParameter.ActualValue.Value));
        }
Example #28
0
 protected abstract EMSSMove[] GenerateMoves(LinearLinkage lle);
 protected override Swap2Move[] GenerateMoves(LinearLinkage lle) {
   IRandom random = RandomParameter.ActualValue;
   return Apply(lle, random, SampleSizeParameter.ActualValue.Value);
 }
Example #30
0
 public Swap2Move(int item1, int item2, LinearLinkage lle)
   : base() {
   Item1 = item1;
   Item2 = item2;
   LLE = lle;
 }
Example #31
0
 private LinearLinkage(LinearLinkage original, Cloner cloner) : base(original, cloner) { }
 protected override Swap2Move[] GenerateMoves(LinearLinkage lle) {
   return Generate(lle).ToArray();
 }
 private LinearLinkage(LinearLinkage original, Cloner cloner) : base(original, cloner)
 {
 }