Changeset 16760

Timestamp:

04/04/19 14:33:12 (5 years ago)

Author:

msemenki

Message:

#2988: ADD:

1. new type of mutation that respect node type and do not change trigonometric node to "add" node;
2. variation of previous mutation type that do not change type of terminal node;
3. some interface adjusting for comfort work with algorithm;
4. second variant of map, that is not "island" map but provide "net" map.

Location:

branches/2988_ModelsOfModels2

Files:

• HeuristicLab.Algorithms.EMM/EMMAlgorithm.cs (modified) (11 diffs)
• HeuristicLab.Algorithms.EMM/EMMBaseAlgorithm.cs (modified) (8 diffs)
• HeuristicLab.Algorithms.EMM/EMMClustering.cs (modified) (8 diffs)
• HeuristicLab.Algorithms.EMM/EMMMapBase.cs (modified) (1 diff)
• HeuristicLab.Algorithms.EMM/EMMMapTreeModel.cs (modified) (2 diffs)
• HeuristicLab.Algorithms.EMM/EMMMultyPointsMutator.cs (added)
• HeuristicLab.Algorithms.EMM/EMMMultyPointsMutatorNodeTypeSaving.cs (added)
• HeuristicLab.Algorithms.EMM/EMMMutators.cs (modified) (2 diffs)
• HeuristicLab.Algorithms.EMM/HeuristicLab.Algorithms.EvolvmentModelsOfModels.csproj (modified) (1 diff)
• HeuristicLab.Algorithms.EMM/Plugin.cs (modified) (1 diff)
• HeuristicLab.Encodings.SymbolicExpressionTreeEncoding/3.4/HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4.csproj (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/TerminalNodesFrequencyAnalyzer.cs (added)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj (modified) (1 diff)

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMAlgorithm.cs

@@ -28 +28 @@
 using HeuristicLab.Random;
 using HeuristicLab.Selection;
+using System;
 using System.Collections.Generic;
 using System.IO;
@@ -61 +62 @@
          trees.First().Root.Grammar.ContainsSymbol((IVariable)a).
            = this.Problem.SymbolicExpressionTreeGrammar;*/
-      int flag = 1;
-      switch (flag) {
-        case 0: // for case when we want only create map, and do  not want made somting also.
-          Map = new EMMMapTreeModel(RandomParameter.Value, trees, ClusterNumbersParameter.Value.Value);
+      int neghboorNumber = 10;
+      switch (MapCreationType.Value) { // Configure Map type and it's parameters: IslandMap, FullMap and Percent, Number
+        case "IslandMap": break;
+        case "FullMap":
+          ClusterNumbersParameter.Value.Value = trees.Count();
+          switch (NegbourType.Value) {
+            case "Percent": neghboorNumber = Convert.ToInt32((Convert.ToDouble(ClusterNumbersParameter.Value.Value)) * (Convert.ToDouble(NegbourNumber.Value)) / 100.0); break;
+            case "Number": neghboorNumber = NegbourNumber.Value; break;
+            default: neghboorNumber = NegbourNumber.Value; break;
+          }
+          break;
+        default: MapCreationType.Value = "IslandMap"; break;
+      }
+      switch (AlgorithmImplemetationType.Value) { //Configure type of algorithm application
+        case "OnlyMapCreation": // for case when we want only create map, and do  not want made somting also. OnlyMapCreation, CrateMapAndGo, ReadMapAndGo
+          Map = new EMMMapTreeModel(RandomParameter.Value, trees, ClusterNumbersParameter.Value.Value, neghboorNumber);
           ClusterNumbersParameter.Value.Value = Map.K;
           File.WriteAllLines("Map.txt", Map.MapToString());
@@ -71 +84 @@
           executionContext = new ExecutionContext(null, this, globalScope);
           break;
-        case 1: // for case when we want read existed map and work with it;
+        case "ReadMapAndGo": // for case when we want read existed map and work with it;
           Map = new EMMMapTreeModel(RandomParameter.Value, trees);
           ClusterNumbersParameter.Value.Value = Map.K;
@@ -78 +91 @@
           }
           globalScope.Variables.Add(new Variable("TreeModelMap", Map));
-          EMMEvolutionaryAlgorithmRun(cancellationToken);
-          break;
-
-        default: //for case of usial from zero step starting
-          Map = new EMMMapTreeModel(RandomParameter.Value, trees, ClusterNumbersParameter.Value.Value);
+          EMMAlgorithmRun(cancellationToken);
+          break;
+        case "CreateMapAndGo":
+          Map = new EMMMapTreeModel(RandomParameter.Value, trees, ClusterNumbersParameter.Value.Value, neghboorNumber);
           ClusterNumbersParameter.Value.Value = Map.K;
           if (previousExecutionState != ExecutionState.Paused) {
@@ -88 +100 @@
           }
           globalScope.Variables.Add(new Variable("TreeModelMap", Map));
-          EMMEvolutionaryAlgorithmRun(cancellationToken);
-          break;
-      }
-
-    }
-    private void EMMEvolutionaryAlgorithmRun(CancellationToken cancellationToken) {
+          File.WriteAllLines("Map.txt", Map.MapToString());
+          File.WriteAllLines("MapToSee.txt", Map.MapToSee());
+          EMMAlgorithmRun(cancellationToken);
+          break;
+        default: //for case of usial from zero step starting
+          AlgorithmImplemetationType.Value = "CreateMapAndGo";
+          Map = new EMMMapTreeModel(RandomParameter.Value, trees, ClusterNumbersParameter.Value.Value, neghboorNumber);
+          ClusterNumbersParameter.Value.Value = Map.K;
+          File.WriteAllLines("Map.txt", Map.MapToString());
+          File.WriteAllLines("MapToSee.txt", Map.MapToSee());
+          if (previousExecutionState != ExecutionState.Paused) {
+            InitializeAlgorithm(cancellationToken);
+          }
+          globalScope.Variables.Add(new Variable("TreeModelMap", Map));
+          EMMAlgorithmRun(cancellationToken);
+          break;
+      }
+
+    }
+    private void EMMAlgorithmRun(CancellationToken cancellationToken) {
       var bestSelector = new BestSelector();
       bestSelector.CopySelected = new BoolValue(false);
@@ -116 +142 @@
       var innerToken = new CancellationToken();
 
-      while (evaluatedSolutions < maximumEvaluatedSolutions && !cancellationToken.IsCancellationRequested) {
+      while (EvaluatedSolutions < maximumEvaluatedSolutions && !cancellationToken.IsCancellationRequested) {
 
         var op4 = executionContext.CreateChildOperation(bestSelector, executionContext.Scope); // select elites
@@ -125 +151 @@
         var selected = executionContext.Scope.SubScopes.Single(x => x.Name == "Selected");
         executionContext.Scope.SubScopes.AddRange(selected.SubScopes);
-        population.Clear();
-        population.AddRange(selected.SubScopes.Select(x => new EMMSolution(x)));
+        Population.Clear();
+        Population.AddRange(selected.SubScopes.Select(x => new EMMSolution(x)));
         executionContext.Scope.SubScopes.Remove(remaining);
         executionContext.Scope.SubScopes.Remove(selected);
 
-        var op = executionContext.CreateChildOperation(selector, executionContext.Scope);// select the rest of the population
+        var op = executionContext.CreateChildOperation(selector, executionContext.Scope);// select the rest of the Population
         ExecuteOperation(executionContext, innerToken, op);
 
@@ -163 +189 @@
             // set child qualities
             childSolution.Qualities = qualities;
-            ++evaluatedSolutions;
-            population.Add(new EMMSolution(childScope));
+            ++EvaluatedSolutions;
+            Population.Add(new EMMSolution(childScope));
           } else {// no crossover or mutation were applied, a child was not produced, do nothing
-            population.Add(new EMMSolution(selected.SubScopes[i]));
-          }
-
-          if (evaluatedSolutions >= maximumEvaluatedSolutions) {
+            Population.Add(new EMMSolution(selected.SubScopes[i]));
+          }
+
+          if (EvaluatedSolutions >= maximumEvaluatedSolutions) {
             break;
           }
 
         }
-        globalScope.SubScopes.Replace(population.Select(x => (IScope)x.Individual));
+        globalScope.SubScopes.Replace(Population.Select(x => (IScope)x.Individual));
         // run analyzer
         var analyze = executionContext.CreateChildOperation(analyzer, globalScope);
         ExecuteOperation(executionContext, innerToken, analyze);
-        Results.AddOrUpdateResult("Evaluated Solutions", new IntValue(evaluatedSolutions));
+        Results.AddOrUpdateResult("Evaluated Solutions", new IntValue(EvaluatedSolutions));
       }
     }
@@ -211 +237 @@
       globalScope.Variables.Add(new Core.Variable("ClusterNumber", clusterNumber));
 
-      evaluatedSolutions = populationSize;
+      EvaluatedSolutions = populationSize;
       base.Initialize(cancellationToken);
     }
@@ -218 +244 @@
       var evaluator = Problem.Evaluator;
       var populationSize = PopulationSize.Value;
-      population = new List<IEMMSolution>(populationSize);
+      Population = new List<IEMMSolution>(populationSize);
 
       var parentScope = executionContext.Scope; //main scope for the next step work
@@ -245 +271 @@
 
         solution.Qualities = qualities;
-        population.Add(solution);  // push solution to population 
+        Population.Add(solution);  // push solution to Population 
       }
     }

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMBaseAlgorithm.cs

@@ -44 +44 @@
     #region data members
     [Storable]
-    protected IList<IEMMSolution> solutions;
-    [Storable]
-    protected List<IEMMSolution> population;
-    [Storable]
-    protected List<IEMMSolution> offspringPopulation;
-    [Storable]
-    protected List<IEMMSolution> jointPopulation;
-    [Storable]
-    protected int evaluatedSolutions;
+    protected IList<IEMMSolution> Solutions;
+    [Storable]
+    protected List<IEMMSolution> Population;
+    [Storable]
+    protected int EvaluatedSolutions;
     [Storable]
     protected ExecutionContext executionContext;
@@ -76 +72 @@
     private const string InputFileParameterName = "InputFile";
     private const string ClusterNumbersParameterName = "ClusterNumbers";
-
+    private const string AlgorithmImplementationTypeParameterName = "AlgorithmImplemetationType";
+    private const string MapCreationTypeParameterName = "MapCreationType";
+    private const string NegbourTypeParameterName = "NegbourType";
+    private const string NegbourNumberParameterName = "NegbourNumber";
     public IValueParameter<MultiAnalyzer> AnalyzerParameter {
       get { return (ValueParameter<MultiAnalyzer>)Parameters[AnalyzerParameterName]; }
@@ -85 +84 @@
     public IValueParameter<IntValue> ClusterNumbersParameter {
       get { return (IValueParameter<IntValue>)Parameters[ClusterNumbersParameterName]; }
+    }
+    public IFixedValueParameter<StringValue> AlgorithmImplementationTypeParameter {
+      get { return (IFixedValueParameter<StringValue>)Parameters[AlgorithmImplementationTypeParameterName]; }
+    }
+    public IFixedValueParameter<StringValue> MapCreationTypeParameter {
+      get { return (IFixedValueParameter<StringValue>)Parameters[MapCreationTypeParameterName]; }
+    }
+    public IFixedValueParameter<StringValue> NegbourTypeParameter {
+      get { return (IFixedValueParameter<StringValue>)Parameters[NegbourTypeParameterName]; }
+    }
+    public IFixedValueParameter<IntValue> NegbourNumberParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[NegbourNumberParameterName]; }
     }
     public IFixedValueParameter<StringValue> InputFileParameter {
@@ -133 +144 @@
       set { ClusterNumbersParameter.Value = value; }
     }
+    public StringValue AlgorithmImplemetationType {
+      get { return AlgorithmImplementationTypeParameter.Value; }
+      set { AlgorithmImplementationTypeParameter.Value.Value = value.Value; }
+    }
+    public StringValue MapCreationType {
+      get { return MapCreationTypeParameter.Value; }
+      set { MapCreationTypeParameter.Value.Value = value.Value; }
+    }
+    public StringValue NegbourType {
+      get { return NegbourTypeParameter.Value; }
+      set { NegbourTypeParameter.Value.Value = value.Value; }
+    }
+    public IntValue NegbourNumber {
+      get { return NegbourNumberParameter.Value; }
+      set { NegbourNumberParameter.Value.Value = value.Value; }
+    }
     public StringValue InputFile {
       get { return InputFileParameter.Value; }
@@ -186 +213 @@
 
       Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
-      Parameters.Add(new FixedValueParameter<StringValue>(InputFileParameterName, "The file with set of models that will be .", new StringValue("input.txt")));
+      Parameters.Add(new FixedValueParameter<StringValue>(InputFileParameterName, "The file with set of models that will be.", new StringValue("input.txt")));
+      Parameters.Add(new FixedValueParameter<StringValue>(AlgorithmImplementationTypeParameterName, "The Type of possible algorith, implemetation, choose one: OnlyMapCreation, CreateMapAndGo, ReadMapAndGo.", new StringValue("CreateMapAndGo")));
+      Parameters.Add(new FixedValueParameter<StringValue>(MapCreationTypeParameterName, "The type of map crearion algorithm. Use one from: IslandMap, FullMap.", new StringValue("IslandMap")));
+      Parameters.Add(new FixedValueParameter<IntValue>(NegbourNumberParameterName, "The parametr for FullMap type of map crearion algorithm. Use one from: 10, 20.", new IntValue(10)));
+      Parameters.Add(new FixedValueParameter<StringValue>(NegbourTypeParameterName, "The parametr for FullMap type of map crearion algorithm. Use one from: Percent, Number.", new StringValue("Number")));
       Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
-      Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
+      Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of Solutions.", new IntValue(100)));
       Parameters.Add(new ConstrainedValueParameter<ISelector>(SelectorParameterName, "The operator used to sellect parents."));
       Parameters.Add(new ValueParameter<PercentValue>(CrossoverProbabilityParameterName, "The probability that the crossover operator is applied.", new PercentValue(0.9)));
       Parameters.Add(new ValueParameter<IntValue>(GroupSizeParameterName, "The GoupSize that the Selector operator is applied.", new IntValue(3)));
-      Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross solutions."));
+      Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross Solutions."));
       Parameters.Add(new ValueParameter<PercentValue>(MutationProbabilityParameterName, "The probability that the mutation operator is applied on a solution.", new PercentValue(0.25)));
-      Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate solutions."));
+      Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate Solutions."));
       Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
-      Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated solutions (approximately).", new IntValue(100_000)));
+      Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated Solutions (approximately).", new IntValue(100_000)));
       Parameters.Add(new ValueParameter<IRandom>(RandomParameterName, new MersenneTwister()));
-      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
+      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite Solutions which are kept in each generation.", new IntValue(1)));
       Parameters.Add(new ValueParameter<IntValue>(ClusterNumbersParameterName, "The number of clusters for model Map.", new IntValue(100)));
       foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
@@ -218 +249 @@
 
     protected EvolvmentModelsOfModelsAlgorithmBase(EvolvmentModelsOfModelsAlgorithmBase original, Cloner cloner) : base(original, cloner) {
-      evaluatedSolutions = original.evaluatedSolutions;
+      EvaluatedSolutions = original.EvaluatedSolutions;
       previousExecutionState = original.previousExecutionState;
 
-      if (original.solutions != null) {
-        solutions = original.solutions.Select(cloner.Clone).ToArray();
-      }
-
-      if (original.population != null) {
-        population = original.population.Select(cloner.Clone).ToList();
-      }
-
-      if (original.offspringPopulation != null) {
-        offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToList();
-      }
-
-      if (original.jointPopulation != null) {
-        jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToList();
-      }
+      if (original.Solutions != null) {
+        Solutions = original.Solutions.Select(cloner.Clone).ToArray();
+      }
+
+      if (original.Population != null) {
+        Population = original.Population.Select(cloner.Clone).ToList();
+      }
+
+      //if (original.offspringPopulation != null) {
+      //  offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToList();
+      //}
+
+      //if (original.jointPopulation != null) {
+      //  jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToList();
+      //}
 
       if (original.executionContext != null) {
@@ -264 +295 @@
 
     public IList<IEMMSolution> GetResult(IRandom random) {
-      return population;
+      return Population;
     }
 
@@ -384 +415 @@
 
     protected override void OnStopped() {
-      if (solutions != null) {
-        solutions.Clear();
-      }
-      if (population != null) {
-        population.Clear();
-      }
-      if (offspringPopulation != null) {
-        offspringPopulation.Clear();
-      }
-      if (jointPopulation != null) {
-        jointPopulation.Clear();
-      }
+      if (Solutions != null) {
+        Solutions.Clear();
+      }
+      if (Population != null) {
+        Population.Clear();
+      }
+      //if (offspringPopulation != null) {
+      //  offspringPopulation.Clear();
+      //}
+      //if (jointPopulation != null) {
+      //  jointPopulation.Clear();
+      //}
       executionContext.Scope.SubScopes.Clear();
       base.OnStopped();

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMClustering.cs

@@ -36 +36 @@
     }
     public int Apply(IRandom random, double[,] distances, List<int> numberCluster) {
+
+      K = ApplyClusteringAlgorithm(random, distances, numberCluster, K);
+      return K;
+    }
+    public static void ApplyFullConectedMapCreationAlgorithm(IRandom random, double[,] distances, List<List<int>> map, int k, int neghboorNumber = 10) {
+      int mapSize = distances.GetLength(0);
+      List<double> currentList = new List<double>();
+      for (int i = 0; i < mapSize; i++) {
+        map.Add(new List<int>());
+        for (int j = 0; j < mapSize; j++) {
+          currentList.Add(distances[i, j]);
+        }
+        map[i].Add(ChooseMinElement(currentList));
+        while (map[i].Count < neghboorNumber) {
+          map[i].Add(ChooseMinElement(currentList, i, map[i]));
+        }
+        currentList.Clear();
+      }
+    }
+    private static bool CheckNumberIsInList(int number, List<int> priviousNumber) {
+      foreach (var pNum in priviousNumber) {
+        if (number == pNum)
+          return true;
+      }
+      return false;
+    }
+    public static int ApplyClusteringAlgorithm(IRandom random, double[,] distances, List<int> numberCluster, int k) {
       int mapSize = distances.GetLength(0);
       List<int> centroids = new List<int>(); // capacity is equal K
       List<double> averageClusterDistance = new List<double>();
       List<List<int>> clusters = new List<List<int>>(); // capacity is equal K
-      CentroidsRandomSetUp(random, centroids, mapSize);
+      CentroidsRandomSetUp(random, centroids, mapSize, k);
       if (numberCluster.Count == 0) {
         for (int i = 0; i < mapSize; i++) {
@@ -50 +77 @@
       while (flag) {
         clusters.Clear();
-        for (int i = 0; i < K; i++) {
+        for (int i = 0; i < k; i++) {
           clusters.Add(new List<int>());
 
         }
         for (int i = 0; i < mapSize; i++) {
-          numberCluster[i] = LookCloseCentroid(centroids, mapSize, distances, i);
+          numberCluster[i] = LookCloseCentroid(centroids, mapSize, distances, i, k);
           clusters[numberCluster[i]].Add(numberCluster[i]);
         }
-        NullSizeClusterDelete(centroids, clusters, mapSize, numberCluster);
+        k = NullSizeClusterDelete(centroids, clusters, mapSize, numberCluster, k);
         flag = false;
-        for (int i = 0; i < K; i++) {
+        for (int i = 0; i < k; i++) {
           AverageClusterDistanceCalculation(averageClusterDistance, distances, numberCluster, mapSize, i);
           var newCentroid = clusters[i][ChooseMinElement(averageClusterDistance)];
@@ -70 +97 @@
         }
       }
-      return K;
+      return k;
     }
-    private void CentroidsRandomSetUp(IRandom random, List<int> centroids, int size) {
-      for (int i = 0; i < K; i++) {
+    private static void CentroidsRandomSetUp(IRandom random, List<int> centroids, int size, int k) {
+      for (int i = 0; i < k; i++) {
         centroids.Add(random.Next(size));
       }
     }
-    private int LookCloseCentroid(List<int> centroids, int MapSize, double[,] distances, int currentNumber) {
+    private static int LookCloseCentroid(List<int> centroids, int MapSize, double[,] distances, int currentNumber, int k) {
       double minDistanse = distances[currentNumber, centroids[0]];
       int clusterNum = 0;
-      for (int i = 1; i < K; i++) {
+      for (int i = 1; i < k; i++) {
         if (minDistanse > distances[currentNumber, centroids[i]]) {
           minDistanse = distances[currentNumber, centroids[i]];
@@ -88 +115 @@
       return clusterNum;
     }
-    private void NullSizeClusterDelete(List<int> centroids, List<List<int>> clusters, int mapSize, List<int> numberCluster) {
+    private static int NullSizeClusterDelete(List<int> centroids, List<List<int>> clusters, int mapSize, List<int> numberCluster, int k) {
       int iter = 0;
-      for (int i = 0; i < K; i++) {
+      for (int i = 0; i < k; i++) {
         if (clusters[i - iter].Count == 0) {
           for (int j = 0; j < mapSize; j++) {
@@ -97 +124 @@
           }
 
-          for (int j = 0; j < K - iter; j++) {
+          for (int j = 0; j < k - iter; j++) {
             if (j != i - iter) {
               for (int m = 0; m < clusters[j].Count; m++)
@@ -109 +136 @@
         }
       }
-      K -= iter;
+      k -= iter;
+      return k;
     }
-    private void AverageClusterDistanceCalculation(List<double> averageClusterDistance, double[,] distances, List<int> numberCluster, int MapSize, int currentClusterNumber) {
+    private static void AverageClusterDistanceCalculation(List<double> averageClusterDistance, double[,] distances, List<int> numberCluster, int MapSize, int currentClusterNumber) {
       int m = 0;
       for (int i = 0; i < MapSize; i++) {
@@ -124 +152 @@
       }
     }
-    private int ChooseMinElement(List<double> averageClusterDistance) {
+    private static int ChooseMinElement(List<double> averageClusterDistance) {
       double minValue = averageClusterDistance[0];
       int minElementNumber = 0;
@@ -135 +163 @@
       return minElementNumber;
     }
+    private static int ChooseMinElement(List<double> distances, int currentElement, List<int> previousNumbers) {
+      double minValue = 100;
+      int minElementNumber = 0;
+      int temp = 0, i = 0;
+      while (temp == 0) {
+        if ((currentElement != i) && (!CheckNumberIsInList(i, previousNumbers))) {
+          minValue = distances[i];
+          minElementNumber = i;
+          temp = i;
+        }
+        i++;
+      }
+      for (i = 0; i < distances.Count(); i++) {
+        if ((distances[i] < minValue) && (currentElement != i) && (!CheckNumberIsInList(i, previousNumbers))) {
+          minValue = distances[i];
+          minElementNumber = i;
+        }
+      }
+      return minElementNumber;
+    }
   }
 }

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMMapBase.cs

@@ -34 +34 @@
 
     protected abstract void CalculateDistances();
-    protected abstract void CreateMap(IRandom random, int k);
+    protected abstract void CreateIslandMap(IRandom random, int k);
     public abstract T NewModelForInizializtion(IRandom random, out int cluster, out int treeNumber);
 

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMMapTreeModel.cs

@@ -58 +58 @@
       K = original.K;
     }
-    public EMMMapTreeModel(IRandom random, IEnumerable<ISymbolicExpressionTree> trees, int k) : this(k) {
+    public EMMMapTreeModel(IRandom random, IEnumerable<ISymbolicExpressionTree> trees, int k, int neghboorNumber) : this(k) {
       ModelSet = trees.ToList();
       CalculateDistances();
-      CreateMap(random, k);
+      if (k < ModelSet.Count)
+        CreateIslandMap(random, k);
+      else if (k == ModelSet.Count) {
+        CreateFullConnectedMap(random, k, neghboorNumber);
+      } else {
+        k -= ModelSet.Count;
+        CreateIslandMap(random, k);
+      }
     }
     public EMMMapTreeModel(IRandom random, IEnumerable<ISymbolicExpressionTree> trees) : this(1) {
@@ -91 +98 @@
       }
     }
-    override protected void CreateMap(IRandom random, int k) {
+    protected void CreateFullConnectedMap(IRandom random, int k, int neghboorNumber) {
+      EMModelsClusterizationAlgorithm.ApplyFullConectedMapCreationAlgorithm(random, Distances, Map, k, neghboorNumber);
       K = k;
+      for (int i = 0; i < Map.Count; i++) {
+        ClusterNumber.Add(i);
+      }
+    }
+    override protected void CreateIslandMap(IRandom random, int k) {
       //Clusterization
-      EMModelsClusterizationAlgorithm clusteringAlgorithm = new EMModelsClusterizationAlgorithm(K);
-      K = clusteringAlgorithm.Apply(random, Distances, ClusterNumber);
+      K = EMModelsClusterizationAlgorithm.ApplyClusteringAlgorithm(random, Distances, ClusterNumber, k);
       // Cheking a Map size
       if (Map != null) Map.Clear();

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMMutators.cs

@@ -71 +71 @@
 
     protected override void Manipulate(IRandom random, ISymbolicExpressionTree symbolicExpressionTree) {
-      //EMMMapTreeModel map= (EMMMapTreeModel)ExecutionContext.Scope.Variables["TreeModelMap"].Value;
       EMMMutatorsPart(random, symbolicExpressionTree, ModelSet, ClusterNumber, Map);
     }
@@ -121 +120 @@
 
         var newNode = newSymbol.CreateTreeNode();
-        if (newNode.HasLocalParameters)
-          if (newNode is TreeModelTreeNode treeNode) {
-            int p = random.Next(map.Count);
-            if (child is TreeModelTreeNode chNode) // in real life never bacame true. It need some cheking
-            { p = chNode.ClusterNumer; }
-            treeNode.TreeNumber = map[p].SampleRandom(random).Value;
-            treeNode.Tree = (ISymbolicExpressionTree)modelSet[treeNode.TreeNumber].Clone();
-            treeNode.SetLocalParameters(random, 0.5);
-          } else
-            newNode.ResetLocalParameters(random);
+
+        if (newNode is TreeModelTreeNode treeNode) {
+          int p = random.Next(map.Count);
+          if (child is TreeModelTreeNode chNode) { p = chNode.ClusterNumer; }
+          treeNode.TreeNumber = map[p].SampleRandom(random).Value;
+          treeNode.Tree = (ISymbolicExpressionTree)modelSet[treeNode.TreeNumber].Clone();
+          treeNode.ClusterNumer = p;
+          treeNode.SetLocalParameters(random, 0.5);
+        } else if (newNode.HasLocalParameters)
+          newNode.ResetLocalParameters(random);
         foreach (var subtree in child.Subtrees)
           newNode.AddSubtree(subtree);

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/HeuristicLab.Algorithms.EvolvmentModelsOfModels.csproj

@@ -109 +109 @@
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="EMMMultyPointsMutator.cs" />
     <Compile Include="EMMAlgorithm.cs" />
     <Compile Include="EMMClustering.cs" />
     <Compile Include="EMMMapBase.cs" />
     <Compile Include="EMMMapTreeModel.cs" />
+    <Compile Include="EMMMultyPointsMutatorNodeTypeSaving.cs" />
     <Compile Include="EMMMutators.cs" />
     <Compile Include="EMMSolution.cs" />

branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/Plugin.cs

@@ -26 +26 @@
   /// Plugin class for HeuristicLab.Algorithms.EMMAlgorithm plugin.
   /// </summary>
-  [Plugin("HeuristicLab.Algorithms.EMMAlgorithm", "3.3.15.16722")]
+  [Plugin("HeuristicLab.Algorithms.EMMAlgorithm", "3.3.15.16734")]
   [PluginFile("HeuristicLab.Algorithms.EMMAlgorithm-3.3.dll", PluginFileType.Assembly)]
   [PluginDependency("HeuristicLab.Analysis", "3.3")]

branches/2988_ModelsOfModels2/HeuristicLab.Encodings.SymbolicExpressionTreeEncoding/3.4/HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4.csproj

@@ -182 +182 @@
     <Compile Include="ArchitectureManipulators\SubroutineDuplicater.cs" />
     <Compile Include="ArchitectureManipulators\SymbolicExpressionTreeArchitectureManipulator.cs" />
-    <Compile Include="Grammars\EMMTreeModelGrammar.cs" />
     <Compile Include="Grammars\GrammarUtils.cs" />
     <Compile Include="SymbolicExpressionTreeProblem.cs" />

branches/2988_ModelsOfModels2/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj

@@ -235 +235 @@
     </Compile>
     <Compile Include="Analyzers\SymbolicDataAnalysisVariableFrequencyAnalyzer.cs" />
+    <Compile Include="Analyzers\TerminalNodesFrequencyAnalyzer.cs" />
     <Compile Include="Converters\LinearModelToTreeConverter.cs" />
     <Compile Include="Converters\TreeSimplifier.cs" />