[16722] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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 HEAL.Attic;
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[16899] | 23 | using HeuristicLab.Common;
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[16722] | 24 | using HeuristicLab.Core;
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[16899] | 25 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 26 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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[17002] | 27 | using HeuristicLab.Random;
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[16722] | 28 | using System.Collections.Generic;
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[16899] | 29 | using System.IO;
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| 30 | using System.Linq;
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[16722] | 31 |
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| 32 | namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
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[16734] | 33 | [StorableType("83CF9650-98FF-454B-9072-82EA4D39C752")]
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[16722] | 34 | public abstract class EMMMapBase<T> : Item where T : class {
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[17002] | 35 | #region data members
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[16899] | 36 | [Storable]
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[16722] | 37 | public List<T> ModelSet { get; set; }
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[16899] | 38 | [Storable]
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[16722] | 39 | public List<List<int>> Map { get; set; }
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[17002] | 40 | #endregion
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| 41 | #region constructors
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| 42 | [StorableConstructor]
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| 43 | protected EMMMapBase(StorableConstructorFlag _) : base(_) { }
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| 44 | public EMMMapBase() {
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| 45 | Map = new List<List<int>>();
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| 46 | }
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| 47 | public EMMMapBase(EMMMapBase<T> original, Cloner cloner) {
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| 48 | if (original.ModelSet != null) {
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| 49 | if (original.ModelSet is List<ISymbolicExpressionTree> originalSet && ModelSet is List<ISymbolicExpressionTree> set)
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| 50 | set = originalSet.Select(cloner.Clone).ToList();
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| 51 | else ModelSet = original.ModelSet.ToList(); /// check this if you want to use it
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| 52 | }
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| 53 | if (original.Map != null) {
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| 54 | Map = original.Map.Select(x => x.ToList()).ToList();
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| 55 | }
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| 56 | }
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| 57 | #endregion
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| 58 | #region map creation functions
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| 59 | protected double[,] CalculateDistances() {
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| 60 | double[,] distances;
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[16899] | 61 | if (ModelSet is List<ISymbolicExpressionTree> set) {
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[17002] | 62 | distances = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(set, simplify: false, strict: true);
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[16899] | 63 | } else { /// for future work
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[17002] | 64 | distances = new double[ModelSet.Count, ModelSet.Count];
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[16899] | 65 | for (int i = 0; i < ModelSet.Count - 1; i++) {
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| 66 | for (int j = 0; j <= i; j++) {
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[17002] | 67 | distances[i, j] = 0;
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[16899] | 68 | }
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| 69 | }
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| 70 | }
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| 71 | for (int i = 0; i < ModelSet.Count - 1; i++) {
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| 72 | for (int j = i + 1; j < ModelSet.Count; j++) {
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[17002] | 73 | distances[j, i] = distances[i, j] = 1 - distances[i, j];
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[16899] | 74 | }
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| 75 | }
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[17002] | 76 | return distances;
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[16899] | 77 | }
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| 78 | public abstract void CreateMap(IRandom random, int k);
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[17002] | 79 | public void MapCreationPrepare(IEnumerable<T> trees) {
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| 80 | ModelSet = trees.ToList();
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[16899] | 81 | }
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| 82 |
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[17002] | 83 | protected void MapSizeCheck(int k) {
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| 84 | if (Map != null) Map.Clear();
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| 85 | else Map = new List<List<int>>();
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| 86 | if (Map.Count != k) {
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| 87 | if (Map.Count != 0) {
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| 88 | Map.Clear();
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| 89 | }
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| 90 | for (int i = 0; i < k; i++) {
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| 91 | Map.Add(new List<int>());
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| 92 | }
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[16899] | 93 | }
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| 94 | }
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[17002] | 95 | #endregion
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| 96 | #region map and files
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[16899] | 97 | public void MapRead(IRandom random, IEnumerable<T> trees, string fileName = "Map.txt") {
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| 98 | ModelSet = trees.ToList();
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| 99 | MapFromFileRead(fileName);
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[17002] | 100 | if (this is EMMIslandMap island) { island.ClusterNumbersCalculate(); }
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[16899] | 101 | if (this is EMMNetworkMap one) { one.NeghboorNumber = Map[0].Count; }
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| 102 | }
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| 103 | public void WriteMapToTxtFile(IRandom random) {
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| 104 | string s = random.ToString();
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| 105 | string fileName = "Map";
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| 106 | fileName += s;
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| 107 | fileName += ".txt";
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| 108 | File.WriteAllLines(fileName, MapToString());
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| 109 | string fileName2 = "MapToSee";
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| 110 | fileName2 += s;
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| 111 | fileName2 += ".txt";
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[17002] | 112 | File.WriteAllLines(fileName2, MapToSee());
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[16899] | 113 | }
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| 114 | public string[] MapToString() { // Function that preapre Map to printing in .txt File: create a set of strings for future reading by computer
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| 115 | string[] s;
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[17002] | 116 | s = new string[Map.Count];
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| 117 | for (int i = 0; i < Map.Count; i++) {
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[16899] | 118 | s[i] = "";
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| 119 | for (int j = 0; j < Map[i].Count; j++) {
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| 120 | s[i] += Map[i][j].ToString();
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| 121 | s[i] += " ";
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| 122 | }
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| 123 | }
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| 124 | return s;
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| 125 | }
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| 126 | public string[] MapToSee() { // Function that prepare Map to printing in .txt File: create a set of strings in human readable view
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| 127 | var fmt = new InfixExpressionFormatter();
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| 128 | string[] s;
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| 129 | s = new string[(ModelSet.Count) + 1];
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| 130 | s[0] = "ClusterNumber" + "," + "ModelNumber" + "," + "Model";
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| 131 | for (int i = 1; i < ((ModelSet.Count) + 1); i++) {
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[17002] | 132 | s[i] = "";
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| 133 | if (this is EMMIslandMap island) {
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| 134 | s[i] += island.ClusterNumber[i - 1].ToString() + ",";
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| 135 | }
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| 136 | s[i] += (i - 1).ToString() + ",";
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[16899] | 137 | if (ModelSet[i - 1] is ISymbolicExpressionTree model) {
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| 138 | s[i] += fmt.Format(model);
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| 139 | } else { s[i] += ModelSet[i - 1].ToString(); }
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| 140 | }
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| 141 | return s;
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| 142 | }
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| 143 | public void MapFromFileRead(string fileName) {
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| 144 | string input = File.ReadAllText(fileName);
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| 145 | int i = 0;
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| 146 | foreach (var row in input.Split('\n')) {
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| 147 | Map.Add(new List<int>());
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| 148 | foreach (var col in row.Trim().Split(' ')) {
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| 149 | Map[i].Add(int.Parse(col.Trim()));
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| 150 | }
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| 151 | i++;
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| 152 | }
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| 153 | }
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[17002] | 154 | #endregion
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| 155 |
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| 156 | #region map use functions
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| 157 | public abstract T NewModelForInizializtionNotTree(IRandom random, out int treeNumber);
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| 158 | public ISymbolicExpressionTree NewModelForInizializtion(IRandom random, out int treeNumber) {
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| 159 | treeNumber = random.Next(ModelSet.Count);
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| 160 | if (ModelSet[treeNumber] is ISymbolicExpressionTree model)
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| 161 | return (ISymbolicExpressionTree)(model.Clone());
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| 162 | return new SymbolicExpressionTree();
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[16899] | 163 | }
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[17002] | 164 | public void NodeManipulationForInizializtion(IRandom random, TreeModelTreeNode node) {
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| 165 | node.Tree = NewModelForInizializtion(random, out int treeNumber);
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| 166 | node.SetLocalParameters(random, 0.5);
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| 167 | node.TreeNumber = treeNumber;
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| 168 | }
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| 169 | public abstract ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber);
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| 170 | public virtual void NodeForMutationChange(IRandom random, TreeModelTreeNode treeNode) {
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| 171 | int treeNumber = treeNode.TreeNumber;
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| 172 | int treeNumber2 = treeNode.TreeNumber;
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| 173 | treeNode.Tree = new SymbolicExpressionTree(NewModelForMutation(random, out treeNumber, treeNumber2).Root);
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| 174 | treeNode.TreeNumber = treeNumber;
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| 175 | SetLocalParametersForTree(random, 0.5, treeNode.Tree);
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| 176 | }
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| 177 | public void SetLocalParametersForTree(IRandom random, double shakingFactor, ISymbolicExpressionTree tree) {
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| 178 | foreach (var node in tree.IterateNodesPrefix().Where(x => x.HasLocalParameters)) {
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| 179 | if (node is VariableTreeNode variableTreeNode) {
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| 180 | var symbol = variableTreeNode.Symbol;
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| 181 | variableTreeNode.Weight = NormalDistributedRandom.NextDouble(random, symbol.WeightManipulatorMu, symbol.WeightManipulatorSigma);
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| 182 | } else {
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| 183 | node.ResetLocalParameters(random);
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[16899] | 184 | }
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| 185 | }
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| 186 | }
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[17002] | 187 | public virtual void MapUpDate(Dictionary<ISymbolicExpressionTree, double> population) { }
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| 188 | #endregion
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[16722] | 189 | }
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| 190 | }
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