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source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMBaseMap.cs @ 16899

Last change on this file since 16899 was 16899, checked in by msemenki, 5 years ago

#2988: New version of class structure.

File size: 7.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HEAL.Attic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Problems.DataAnalysis.Symbolic;
27using System.Collections.Generic;
28using System.IO;
29using System.Linq;
30
31namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
32  [StorableType("83CF9650-98FF-454B-9072-82EA4D39C752")]
33  public abstract class EMMMapBase<T> : Item where T : class {
34    [Storable]
35    public List<T> ModelSet { get; set; }
36    [Storable]
37    public List<int> ClusterNumber { get; set; }
38    [Storable]
39    public List<List<int>> Map { get; set; }
40    [Storable]
41    public double[,] Distances { get; set; }
42    [Storable]
43    public int K { get; protected set; }
44
45    protected void CalculateDistances() {
46      if (ModelSet is List<ISymbolicExpressionTree> set) {
47        Distances = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(set, simplify: false, strict: true);
48      } else { /// for future work
49        for (int i = 0; i < ModelSet.Count - 1; i++) {
50          for (int j = 0; j <= i; j++) {
51            Distances[i, j] = 0;
52          }
53        }
54      }
55      for (int i = 0; i < ModelSet.Count - 1; i++) {
56        for (int j = i + 1; j < ModelSet.Count; j++) {
57          Distances[j, i] = Distances[i, j] = 1 - Distances[i, j];
58        }
59      }
60
61    }
62    public abstract void CreateMap(IRandom random, int k);
63    public abstract T NewModelForInizializtionNotTree(IRandom random, out int cluster, out int treeNumber);
64    public ISymbolicExpressionTree NewModelForInizializtion(IRandom random, out int cluster, out int treeNumber) {
65      treeNumber = random.Next(ModelSet.Count);
66      cluster = ClusterNumber[treeNumber];
67      if (ModelSet[treeNumber] is ISymbolicExpressionTree model)
68        return (ISymbolicExpressionTree)(model.Clone());
69      return new SymbolicExpressionTree();
70    }
71
72    [StorableConstructor]
73    protected EMMMapBase(StorableConstructorFlag _) : base(_) { }
74    protected EMMMapBase() : this(1) { }
75    public EMMMapBase(int k) {
76      K = k;
77      ClusterNumber = new List<int>();
78      Map = new List<List<int>>();
79    }
80    public EMMMapBase(EMMMapBase<T> original, Cloner cloner) {
81      if (original.ModelSet != null) {
82        if (original.ModelSet is List<ISymbolicExpressionTree> originalSet && ModelSet is List<ISymbolicExpressionTree> set)
83          set = originalSet.Select(cloner.Clone).ToList();
84        else ModelSet = original.ModelSet.ToList(); /// check this if you want to use it
85      }
86      if (original.ClusterNumber != null) {
87        ClusterNumber = original.ClusterNumber.ToList();
88      }
89      if (original.Map != null) {
90        Map = original.Map.Select(x => x.ToList()).ToList();
91      }
92      K = original.K;
93    }
94    //public EMMMapBase(IRandom random, IEnumerable<T> trees, int k) : this(k) {
95    //  // constructor that should be used in case of creation of a new map from the start point
96    //  ModelSet = trees.ToList();
97    //  CalculateDistances();
98    //  CreateMap(random, k);
99    //}
100    //public EMMMapBase(IRandom random, IEnumerable<T> trees, string fileName = "Map.txt") : this(1) {
101    //  // constructor that shoud be used in case of using of "old" map, that was previously created and now shoud be readed from file
102    //  ModelSet = trees.ToList();
103    //  MapFromFileRead(fileName);
104    //  K = Map.Count;
105    //  MapPreparation();
106    //}
107    public void MapCreationPrepare(IRandom random, IEnumerable<T> trees, int k) {
108      ModelSet = trees.ToList();
109      CalculateDistances();
110      CreateMap(random, k);
111    }
112    public void MapRead(IRandom random, IEnumerable<T> trees, string fileName = "Map.txt") {
113      ModelSet = trees.ToList();
114      MapFromFileRead(fileName);
115      K = Map.Count;
116      MapPreparation();
117      if (this is EMMNetworkMap one) { one.NeghboorNumber = Map[0].Count; }
118    }
119    public void WriteMapToTxtFile(IRandom random) {
120      string s = random.ToString();
121      string fileName = "Map";
122      fileName += s;
123      fileName += ".txt";
124      File.WriteAllLines(fileName, MapToString());
125      string fileName2 = "MapToSee";
126      fileName2 += s;
127      fileName2 += ".txt";
128      File.WriteAllLines(fileName, MapToSee());
129    }
130    public string[] MapToString() { // Function that preapre Map to printing in .txt File: create a set of strings for future reading by computer
131      string[] s;
132      s = new string[K];
133      for (int i = 0; i < K; i++) {
134        s[i] = "";
135        for (int j = 0; j < Map[i].Count; j++) {
136          s[i] += Map[i][j].ToString();
137          s[i] += " ";
138        }
139      }
140      return s;
141    }
142    public string[] MapToSee() { // Function that prepare Map to printing in .txt File: create a set of strings in human readable view
143      var fmt = new InfixExpressionFormatter();
144      string[] s;
145      s = new string[(ModelSet.Count) + 1];
146      s[0] = "ClusterNumber" + "," + "ModelNumber" + "," + "Model";
147      for (int i = 1; i < ((ModelSet.Count) + 1); i++) {
148        s[i] = ClusterNumber[i - 1].ToString() + "," + (i - 1).ToString() + ",";
149        if (ModelSet[i - 1] is ISymbolicExpressionTree model) {
150          s[i] += fmt.Format(model);
151        } else { s[i] += ModelSet[i - 1].ToString(); }
152      }
153      return s;
154    }
155    public void MapFromFileRead(string fileName) {
156      string input = File.ReadAllText(fileName);
157      int i = 0;
158      foreach (var row in input.Split('\n')) {
159        Map.Add(new List<int>());
160        foreach (var col in row.Trim().Split(' ')) {
161          Map[i].Add(int.Parse(col.Trim()));
162        }
163        i++;
164      }
165    }
166    protected void MapSizeCheck() {
167      if (Map != null) Map.Clear();
168      else Map = new List<List<int>>();
169      if (Map.Count != K) {
170        if (Map.Count != 0) {
171          Map.Clear();
172        }
173        for (int i = 0; i < K; i++) {
174          Map.Add(new List<int>());
175        }
176      }
177    }
178    protected void MapPreparation() {
179      for (int i = 0; i < Map.Count; i++) {
180        for (int j = 0; j < Map[i].Count; j++) {
181          ClusterNumber.Add(0);
182        }
183      }
184      for (int i = 0; i < Map.Count; i++) {
185        for (int j = 0; j < Map[i].Count; j++) {
186          ClusterNumber[Map[i][j]] = i;
187        }
188      }
189    }
190  }
191}
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