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source: branches/2839_HiveProjectManagement/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Transformations/TransformationToSymbolicTreeMapper.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 6 years ago

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
24
25namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
26  public class TransformationToSymbolicTreeMapper : ITransformationMapper<ISymbolicExpressionTreeNode> {
27    private ITransformation transformation;
28    private string column;
29
30    #region ITransformationMapper<ISymbolicExpressionTree> Members
31
32    public ISymbolicExpressionTreeNode GenerateModel(ITransformation transformation) {
33      InitComponents(transformation);
34
35      if (transformation is LinearTransformation) {
36        return GenerateModelForLinearTransformation();
37      } else if (transformation is ExponentialTransformation) {
38        return GenerateModelForExponentialTransformation();
39      } else if (transformation is LogarithmicTransformation) {
40        return GenerateModelForLogarithmicTransformation();
41      } else if (transformation is PowerTransformation) {
42        return GenerateModelForPowerTransformation();
43      } else if (transformation is ReciprocalTransformation) {
44        return GenerateModelForReciprocalTransformation();
45      } else if (transformation is ShiftStandardDistributionTransformation) {
46        return GenerateModelForShiftStandardDistributionTransformation();
47      } else if (transformation is CopyColumnTransformation) {
48        return GenerateTreeNodeForCopyColumnTransformation();
49      }
50      throw new NotImplementedException();
51    }
52
53    public ISymbolicExpressionTreeNode GenerateInverseModel(ITransformation transformation) {
54      InitComponents(transformation);
55
56      if (transformation is LinearTransformation) {
57        return GenerateInverseModelForLinearTransformation();
58      } else if (transformation is ExponentialTransformation) {
59        return GenerateInverseModelForExponentialTransformation();
60      } else if (transformation is LogarithmicTransformation) {
61        return GenerateInverseModelForLogarithmicTransformation();
62      } else if (transformation is PowerTransformation) {
63        return GenerateInverseModelForPowerTransformation();
64      } else if (transformation is ReciprocalTransformation) {
65        return GenerateInverseModelForReciprocalTransformation();
66      } else if (transformation is ShiftStandardDistributionTransformation) {
67        GenerateInverseModelForShiftStandardDistributionTransformation();
68      } else if (transformation is CopyColumnTransformation) {
69        return GenerateTreeNodeForCopyColumnTransformation();
70      }
71
72      throw new NotImplementedException();
73    }
74
75    #endregion
76
77    // helper
78
79    private ISymbolicExpressionTreeNode GenerateModelForLinearTransformation() {
80      var linearTransformation = (LinearTransformation)transformation;
81      var kValue = linearTransformation.Multiplier;
82      var dValue = linearTransformation.Addend;
83
84      // k * x
85      var multiplicationNode = new Multiplication().CreateTreeNode();
86      var kNode = CreateConstantTreeNode("k", kValue);
87      var xNode = CreateVariableTreeNode(column, "x");
88      multiplicationNode.AddSubtree(kNode);
89      multiplicationNode.AddSubtree(xNode);
90
91      // ( k * x ) + d
92      var additionNode = new Addition().CreateTreeNode();
93      var dNode = CreateConstantTreeNode("d", dValue);
94      additionNode.AddSubtree(multiplicationNode);
95      additionNode.AddSubtree(dNode);
96
97      return additionNode;
98    }
99
100    private ISymbolicExpressionTreeNode GenerateInverseModelForLinearTransformation() {
101      var linearTransformation = (LinearTransformation)transformation;
102      var kValue = linearTransformation.Multiplier;
103      var dValue = linearTransformation.Addend;
104
105      // x - d
106      var substractionNode = new Subtraction().CreateTreeNode();
107      var dNode = CreateConstantTreeNode("d", dValue);
108      var xNode = CreateVariableTreeNode(column, "x");
109      substractionNode.AddSubtree(xNode);
110      substractionNode.AddSubtree(dNode);
111
112      // ( x - d ) / k
113      var divisionNode = new Division().CreateTreeNode();
114      var kNode = CreateConstantTreeNode("k", kValue);
115      divisionNode.AddSubtree(substractionNode);
116      divisionNode.AddSubtree(kNode);
117
118      return divisionNode;
119    }
120
121
122    private ISymbolicExpressionTreeNode GenerateModelForExponentialTransformation() {
123      var exponentialTransformation = (ExponentialTransformation)transformation;
124      var bValue = exponentialTransformation.Base;
125
126      return GenTreePow_b_x(bValue);
127    }
128
129    private ISymbolicExpressionTreeNode GenerateInverseModelForExponentialTransformation() {
130      var exponentialTransformation = (ExponentialTransformation)transformation;
131      var bValue = exponentialTransformation.Base;
132
133      return GenTreeLog_x_b(bValue);
134    }
135
136
137    private ISymbolicExpressionTreeNode GenerateModelForLogarithmicTransformation() {
138      var logarithmicTransformation = (LogarithmicTransformation)transformation;
139      var bValue = logarithmicTransformation.Base;
140
141      return GenTreeLog_x_b(bValue);
142    }
143
144    private ISymbolicExpressionTreeNode GenerateInverseModelForLogarithmicTransformation() {
145      var logarithmicTransformation = (LogarithmicTransformation)transformation;
146      var bValue = logarithmicTransformation.Base;
147
148      return GenTreePow_b_x(bValue);
149    }
150
151
152    private ISymbolicExpressionTreeNode GenerateModelForPowerTransformation() {
153      var powerTransformation = (PowerTransformation)transformation;
154      var expValue = powerTransformation.Exponent;
155
156      // x ^ exp
157      var powerNode = new Power().CreateTreeNode();
158      var xNode = CreateVariableTreeNode(column, "x");
159      var expNode = CreateConstantTreeNode("exp", expValue);
160      powerNode.AddSubtree(xNode);
161      powerNode.AddSubtree(expNode);
162
163      return powerNode;
164    }
165
166    private ISymbolicExpressionTreeNode GenerateInverseModelForPowerTransformation() {
167      var powerTransformation = (PowerTransformation)transformation;
168      var expValue = powerTransformation.Exponent;
169
170      // rt(x, b)
171      var rootNode = new Root().CreateTreeNode();
172      var xNode = CreateVariableTreeNode(column, "x");
173      var bNode = CreateConstantTreeNode("b", expValue);
174      rootNode.AddSubtree(xNode);
175      rootNode.AddSubtree(bNode);
176
177      return rootNode;
178    }
179
180
181    private ISymbolicExpressionTreeNode GenerateModelForReciprocalTransformation() {
182      return GenTreeDiv_1_x();
183    }
184
185    private ISymbolicExpressionTreeNode GenerateInverseModelForReciprocalTransformation() {
186      return GenTreeDiv_1_x();
187    }
188
189
190    private ISymbolicExpressionTreeNode GenerateModelForShiftStandardDistributionTransformation() {
191      var shiftStandardDistributionTransformation = (ShiftStandardDistributionTransformation)transformation;
192      var m_orgValue = shiftStandardDistributionTransformation.OriginalMean;
193      var s_orgValue = shiftStandardDistributionTransformation.OriginalStandardDeviation;
194      var m_tarValue = shiftStandardDistributionTransformation.Mean;
195      var s_tarValue = shiftStandardDistributionTransformation.StandardDeviation;
196
197      return GenTreeShiftStdDist(column, m_orgValue, s_orgValue, m_tarValue, s_tarValue);
198    }
199
200    private ISymbolicExpressionTreeNode GenerateInverseModelForShiftStandardDistributionTransformation() {
201      var shiftStandardDistributionTransformation = (ShiftStandardDistributionTransformation)transformation;
202      var m_orgValue = shiftStandardDistributionTransformation.OriginalMean;
203      var s_orgValue = shiftStandardDistributionTransformation.OriginalStandardDeviation;
204      var m_tarValue = shiftStandardDistributionTransformation.Mean;
205      var s_tarValue = shiftStandardDistributionTransformation.StandardDeviation;
206
207      return GenTreeShiftStdDist(column, m_tarValue, s_tarValue, m_orgValue, s_orgValue);
208    }
209
210    private ISymbolicExpressionTreeNode GenerateTreeNodeForCopyColumnTransformation() {
211      var copyColumnTransformation = (CopyColumnTransformation)transformation;
212      var copiedColumnName = copyColumnTransformation.CopiedColumnName;
213
214      return CreateVariableTreeNode(copiedColumnName, copiedColumnName + "(original)");
215    }
216
217    // helper's helper:
218
219    private ISymbolicExpressionTreeNode GenTreeLog_x_b(double b) {
220      // log(x, b)
221      var logNode = new Logarithm().CreateTreeNode();
222      var bNode = CreateConstantTreeNode("b", b);
223      var xNode = CreateVariableTreeNode(column, "x");
224      logNode.AddSubtree(xNode);
225      logNode.AddSubtree(bNode);
226
227      return logNode;
228    }
229
230    private ISymbolicExpressionTreeNode GenTreePow_b_x(double b) {
231      // b ^ x
232      var powerNode = new Power().CreateTreeNode();
233      var bNode = CreateConstantTreeNode("b", b);
234      var xNode = CreateVariableTreeNode(column, "x");
235      powerNode.AddSubtree(bNode);
236      powerNode.AddSubtree(xNode);
237
238      return powerNode;
239    }
240
241    private ISymbolicExpressionTreeNode GenTreeDiv_1_x() {
242      // 1 / x
243      var divNode = new Division().CreateTreeNode();
244      var oneNode = CreateConstantTreeNode("1", 1.0);
245      var xNode = CreateVariableTreeNode(column, "x");
246      divNode.AddSubtree(oneNode);
247      divNode.AddSubtree(xNode);
248
249      return divNode;
250    }
251
252    private ISymbolicExpressionTreeNode GenTreeShiftStdDist(string variable, double m_org, double s_org, double m_tar, double s_tar) {
253      // x - m_org
254      var substractionNode = new Subtraction().CreateTreeNode();
255      var xNode = CreateVariableTreeNode(variable, "x");
256      var m_orgNode = CreateConstantTreeNode("m_org", m_org);
257      substractionNode.AddSubtree(xNode);
258      substractionNode.AddSubtree(m_orgNode);
259
260      // (x - m_org) / s_org
261      var divisionNode = new Division().CreateTreeNode();
262      var s_orgNode = CreateConstantTreeNode("s_org", s_org);
263      divisionNode.AddSubtree(substractionNode);
264      divisionNode.AddSubtree(s_orgNode);
265
266      // ((x - m_org) / s_org ) * s_tar
267      var multiplicationNode = new Multiplication().CreateTreeNode();
268      var s_tarNode = CreateConstantTreeNode("s_tar", s_tar);
269      multiplicationNode.AddSubtree(divisionNode);
270      multiplicationNode.AddSubtree(s_tarNode);
271
272      // ((x - m_org) / s_org ) * s_tar + m_tar
273      var additionNode = new Addition().CreateTreeNode();
274      var m_tarNode = CreateConstantTreeNode("m_tar", m_tar);
275      additionNode.AddSubtree(multiplicationNode);
276      additionNode.AddSubtree(m_tarNode);
277
278      return additionNode;
279    }
280
281    private ConstantTreeNode CreateConstantTreeNode(string description, double value) {
282      return new ConstantTreeNode(new Constant()) { Value = value };
283    }
284
285    private VariableTreeNode CreateVariableTreeNode(string name, string description) {
286      return new VariableTreeNode(new Variable(name, description)) { VariableName = name, Weight = 1.0 };
287    }
288
289    private void InitComponents(ITransformation transformation) {
290      this.transformation = transformation;
291      column = transformation.Column;
292    }
293  }
294}
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