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source: trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Variable.cs @ 1835

Last change on this file since 1835 was 1618, checked in by gkronber, 16 years ago

Implemented #594 (Manipulators and initialization operators for variables and constants should use more intuitive default values)
File size: 9.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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 System;
23using System.Collections.Generic;
24using System.Text;
25using HeuristicLab.Core;
26using System.Diagnostics;
27using HeuristicLab.Data;
28using HeuristicLab.Constraints;
29using HeuristicLab.DataAnalysis;
30using HeuristicLab.Random;
31using HeuristicLab.Operators;
32
33namespace HeuristicLab.GP.StructureIdentification {
34  public class Variable : FunctionBase {
35
36    public const string WEIGHT = "Weight";
37    public const string OFFSET = "SampleOffset";
38    public const string INDEX = "Variable";
39
40    private int minIndex;
41    private int maxIndex;
42    private int minOffset;
43    private int maxOffset;
44
45    public override string Description {
46      get {
47        return @"Variable reads a value from a dataset, multiplies that value with a given factor and returns the result.
48The variable 'SampleOffset' can be used to read a value from previous or following rows.
49The index of the row that is actually read is SampleIndex+SampleOffset).";
50      }
51    }
52
53    public Variable()
54      : base() {
55      AddVariableInfo(new VariableInfo(INDEX, "Index of the variable in the dataset representing this feature", typeof(ConstrainedIntData), VariableKind.None));
56      GetVariableInfo(INDEX).Local = true;
57      AddVariableInfo(new VariableInfo(WEIGHT, "Weight is multiplied to the feature value", typeof(ConstrainedDoubleData), VariableKind.None));
58      GetVariableInfo(WEIGHT).Local = true;
59      AddVariableInfo(new VariableInfo(OFFSET, "SampleOffset is added to the sample index", typeof(ConstrainedIntData), VariableKind.None));
60      GetVariableInfo(OFFSET).Local = true;
61      AddVariableInfo(new VariableInfo(INITIALIZATION, "Initialization operator for variables", typeof(CombinedOperator), VariableKind.None));
62      GetVariableInfo(INITIALIZATION).Local = false;
63      AddVariableInfo(new VariableInfo(MANIPULATION, "Manipulation operator for variables", typeof(CombinedOperator), VariableKind.None));
64      GetVariableInfo(MANIPULATION).Local = false;
65
66      ConstrainedDoubleData weight = new ConstrainedDoubleData();
67      // initialize a totally arbitrary range for the weight = [-1.0, 1.0]
68      weight.AddConstraint(new DoubleBoundedConstraint(-1.0, 1.0));
69      AddVariable(new HeuristicLab.Core.Variable(WEIGHT, weight));
70
71      ConstrainedIntData variable = new ConstrainedIntData();
72      AddVariable(new HeuristicLab.Core.Variable(INDEX, variable));
73      minIndex = 0; maxIndex = 1000;
74
75      ConstrainedIntData sampleOffset = new ConstrainedIntData();
76      AddVariable(new HeuristicLab.Core.Variable(OFFSET, sampleOffset));
77
78      SetupInitialization();
79      SetupManipulation();
80
81      // variable can't have suboperators
82      AddConstraint(new NumberOfSubOperatorsConstraint(0, 0));
83    }
84
85    private void SetupInitialization() {
86      CombinedOperator combinedOp = new CombinedOperator();
87      SequentialProcessor seq = new SequentialProcessor();
88      UniformRandomizer indexRandomizer = new UniformRandomizer();
89      indexRandomizer.Min = minIndex;
90      indexRandomizer.Max = maxIndex + 1; // uniform randomizer generates numbers in the range [min, max[
91      indexRandomizer.GetVariableInfo("Value").ActualName = INDEX;
92      indexRandomizer.Name = "Index Randomizer";
93      NormalRandomizer weightRandomizer = new NormalRandomizer();
94      weightRandomizer.Mu = 0.0;
95      weightRandomizer.Sigma = 1.0;
96      weightRandomizer.GetVariableInfo("Value").ActualName = WEIGHT;
97      weightRandomizer.Name = "Weight Randomizer";
98      UniformRandomizer offsetRandomizer = new UniformRandomizer();
99      offsetRandomizer.Min = minOffset;
100      offsetRandomizer.Max = maxOffset + 1;
101      offsetRandomizer.GetVariableInfo("Value").ActualName = OFFSET;
102      offsetRandomizer.Name = "Offset Randomizer";
103
104      combinedOp.OperatorGraph.AddOperator(seq);
105      combinedOp.OperatorGraph.AddOperator(indexRandomizer);
106      combinedOp.OperatorGraph.AddOperator(weightRandomizer);
107      combinedOp.OperatorGraph.AddOperator(offsetRandomizer);
108      combinedOp.OperatorGraph.InitialOperator = seq;
109      seq.AddSubOperator(indexRandomizer);
110      seq.AddSubOperator(weightRandomizer);
111      seq.AddSubOperator(offsetRandomizer);
112      HeuristicLab.Core.IVariable initOp = GetVariable(INITIALIZATION);
113      if(initOp == null) {
114        AddVariable(new HeuristicLab.Core.Variable(INITIALIZATION, combinedOp));
115      } else {
116        initOp.Value = combinedOp;
117      }
118    }
119
120    private void SetupManipulation() {
121      // manipulation operator
122      CombinedOperator combinedOp = new CombinedOperator();
123      SequentialProcessor seq = new SequentialProcessor();
124      UniformRandomizer indexRandomizer = new UniformRandomizer();
125      indexRandomizer.Min = minIndex;
126      indexRandomizer.Max = maxIndex + 1;
127      indexRandomizer.GetVariableInfo("Value").ActualName = INDEX;
128      indexRandomizer.Name = "Index Randomizer";
129      NormalRandomAdder weightRandomAdder = new NormalRandomAdder();
130      weightRandomAdder.Mu = 0.0;
131      weightRandomAdder.Sigma = 1.0;
132      weightRandomAdder.GetVariableInfo("Value").ActualName = WEIGHT;
133      weightRandomAdder.Name = "Weight Adder";
134      NormalRandomAdder offsetRandomAdder = new NormalRandomAdder();
135      offsetRandomAdder.Mu = 0.0;
136      offsetRandomAdder.Sigma = 1.0;
137      offsetRandomAdder.GetVariableInfo("Value").ActualName = OFFSET;
138      offsetRandomAdder.Name = "Offset Adder";
139
140      combinedOp.OperatorGraph.AddOperator(seq);
141      combinedOp.OperatorGraph.AddOperator(indexRandomizer);
142      combinedOp.OperatorGraph.AddOperator(weightRandomAdder);
143      combinedOp.OperatorGraph.AddOperator(offsetRandomAdder);
144      combinedOp.OperatorGraph.InitialOperator = seq;
145      seq.AddSubOperator(indexRandomizer);
146      seq.AddSubOperator(weightRandomAdder);
147      seq.AddSubOperator(offsetRandomAdder);
148      HeuristicLab.Core.IVariable manipulationOp = GetVariable(MANIPULATION);
149      if(manipulationOp == null) {
150        AddVariable(new HeuristicLab.Core.Variable(MANIPULATION, combinedOp));
151      } else {
152        manipulationOp.Value = combinedOp;
153      }
154    }
155
156    public void SetConstraints(int[] allowedIndexes, int minSampleOffset, int maxSampleOffset) {
157      ConstrainedIntData offset = GetVariableValue<ConstrainedIntData>(OFFSET, null, false);
158      IntBoundedConstraint rangeConstraint = new IntBoundedConstraint();
159      this.minOffset = minSampleOffset;
160      this.maxOffset = maxSampleOffset;
161      rangeConstraint.LowerBound = minSampleOffset;
162      rangeConstraint.LowerBoundEnabled = true;
163      rangeConstraint.LowerBoundIncluded = true;
164      rangeConstraint.UpperBound = maxSampleOffset;
165      rangeConstraint.UpperBoundEnabled = true;
166      rangeConstraint.UpperBoundIncluded = true;
167      offset.AddConstraint(rangeConstraint);
168
169      ConstrainedIntData index = GetVariableValue<ConstrainedIntData>(INDEX, null, false);
170      Array.Sort(allowedIndexes);
171      minIndex = allowedIndexes[0]; maxIndex = allowedIndexes[allowedIndexes.Length - 1];
172      List<IConstraint> constraints = new List<IConstraint>();
173      int start = allowedIndexes[0];
174      int prev = start;
175      for(int i = 1; i < allowedIndexes.Length; i++) {
176        if(allowedIndexes[i] != prev + 1) {
177          IntBoundedConstraint lastRange = new IntBoundedConstraint();
178          lastRange.LowerBound = start;
179          lastRange.LowerBoundEnabled = true;
180          lastRange.LowerBoundIncluded = true;
181          lastRange.UpperBound = prev;
182          lastRange.UpperBoundEnabled = true;
183          lastRange.UpperBoundIncluded = true;
184          constraints.Add(lastRange);
185          start = allowedIndexes[i];
186          prev = start;
187        }
188        prev = allowedIndexes[i];
189      }
190      IntBoundedConstraint range = new IntBoundedConstraint();
191      range.LowerBound = start;
192      range.LowerBoundEnabled = true;
193      range.LowerBoundIncluded = true;
194      range.UpperBound = prev;
195      range.UpperBoundEnabled = true;
196      range.UpperBoundIncluded = true;
197      constraints.Add(range);
198      if(constraints.Count > 1) {
199        OrConstraint or = new OrConstraint();
200        foreach(IConstraint c in constraints) or.Clauses.Add(c);
201        index.AddConstraint(or);
202      } else {
203        index.AddConstraint(constraints[0]);
204      }
205
206      SetupInitialization();
207      SetupManipulation();
208    }
209  }
210}
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