[10845] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2013 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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[10846] | 22 | using System;
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[10845] | 23 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 24 |
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| 25 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[10872] | 26 | public class TransformationToSymbolicTreeMapper : ITransformationMapper<ISymbolicExpressionTreeNode> {
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[10846] | 27 | private ITransformation transformation;
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| 28 | private string column;
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| 29 |
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[10845] | 30 | #region ITransformationMapper<ISymbolicExpressionTree> Members
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| 31 |
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[10872] | 32 | public ISymbolicExpressionTreeNode GenerateModel(ITransformation transformation) {
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[10846] | 33 | InitComponents(transformation);
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[10845] | 34 |
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| 35 | if (transformation is LinearTransformation) {
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[10846] | 36 | return GenerateModelForLinearTransformation();
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[10845] | 37 | } else if (transformation is ExponentialTransformation) {
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[10848] | 38 | return GenerateModelForExponentialTransformation();
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[10845] | 39 | } else if (transformation is LogarithmicTransformation) {
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[10853] | 40 | return GenerateModelForLogarithmicTransformation();
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[10845] | 41 | } else if (transformation is PowerTransformation) {
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[10853] | 42 | return GenerateModelForPowerTransformation();
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[10845] | 43 | } else if (transformation is ReciprocalTransformation) {
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[10853] | 44 | return GenerateModelForReciprocalTransformation();
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[10845] | 45 | } else if (transformation is ShiftStandardDistributionTransformation) {
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[10853] | 46 | return GenerateModelForShiftStandardDistributionTransformation();
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[10940] | 47 | } else if (transformation is CopyColumnTransformation) {
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[10943] | 48 | return GenerateTreeNodeForCopyColumnTransformation();
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[10845] | 49 | }
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[10846] | 50 | throw new NotImplementedException();
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[10845] | 51 | }
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| 52 |
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[10872] | 53 | public ISymbolicExpressionTreeNode GenerateInverseModel(ITransformation transformation) {
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[10846] | 54 | InitComponents(transformation);
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[10845] | 55 |
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| 56 | if (transformation is LinearTransformation) {
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[10848] | 57 | return GenerateInverseModelForLinearTransformation();
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[10845] | 58 | } else if (transformation is ExponentialTransformation) {
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[10848] | 59 | return GenerateInverseModelForExponentialTransformation();
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[10845] | 60 | } else if (transformation is LogarithmicTransformation) {
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[10853] | 61 | return GenerateInverseModelForLogarithmicTransformation();
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[10845] | 62 | } else if (transformation is PowerTransformation) {
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[10853] | 63 | return GenerateInverseModelForPowerTransformation();
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[10845] | 64 | } else if (transformation is ReciprocalTransformation) {
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[10853] | 65 | return GenerateInverseModelForReciprocalTransformation();
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[10845] | 66 | } else if (transformation is ShiftStandardDistributionTransformation) {
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[10866] | 67 | GenerateInverseModelForShiftStandardDistributionTransformation();
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[10943] | 68 | } else if (transformation is CopyColumnTransformation) {
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| 69 | return GenerateTreeNodeForCopyColumnTransformation();
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[10845] | 70 | }
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| 71 |
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[10846] | 72 | throw new NotImplementedException();
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[10845] | 73 | }
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| 74 |
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| 75 | #endregion
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| 76 |
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[10853] | 77 | // helper
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| 78 |
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[10872] | 79 | private ISymbolicExpressionTreeNode GenerateModelForLinearTransformation() {
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[10846] | 80 | var linearTransformation = (LinearTransformation)transformation;
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| 81 | var kValue = linearTransformation.Multiplier;
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| 82 | var dValue = linearTransformation.Addend;
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| 83 |
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| 84 | // k * x
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| 85 | var multiplicationNode = new Multiplication().CreateTreeNode();
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[10866] | 86 | var kNode = CreateConstantTreeNode("k", kValue);
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| 87 | var xNode = CreateVariableTreeNode(column, "x");
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[10846] | 88 | multiplicationNode.AddSubtree(kNode);
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| 89 | multiplicationNode.AddSubtree(xNode);
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| 90 |
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| 91 | // ( k * x ) + d
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| 92 | var additionNode = new Addition().CreateTreeNode();
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[10866] | 93 | var dNode = CreateConstantTreeNode("d", dValue);
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[10846] | 94 | additionNode.AddSubtree(multiplicationNode);
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| 95 | additionNode.AddSubtree(dNode);
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| 96 |
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[10872] | 97 | return additionNode;
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[10846] | 98 | }
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| 99 |
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[10872] | 100 | private ISymbolicExpressionTreeNode GenerateInverseModelForLinearTransformation() {
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[10848] | 101 | var linearTransformation = (LinearTransformation)transformation;
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| 102 | var kValue = linearTransformation.Multiplier;
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| 103 | var dValue = linearTransformation.Addend;
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| 104 |
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| 105 | // x - d
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| 106 | var substractionNode = new Subtraction().CreateTreeNode();
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[10866] | 107 | var dNode = CreateConstantTreeNode("d", dValue);
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| 108 | var xNode = CreateVariableTreeNode(column, "x");
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[10848] | 109 | substractionNode.AddSubtree(xNode);
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| 110 | substractionNode.AddSubtree(dNode);
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| 111 |
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| 112 | // ( x - d ) / k
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| 113 | var divisionNode = new Division().CreateTreeNode();
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[10866] | 114 | var kNode = CreateConstantTreeNode("k", kValue);
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[10848] | 115 | divisionNode.AddSubtree(substractionNode);
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| 116 | divisionNode.AddSubtree(kNode);
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| 117 |
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[10872] | 118 | return divisionNode;
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[10848] | 119 | }
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| 120 |
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| 121 |
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[10872] | 122 | private ISymbolicExpressionTreeNode GenerateModelForExponentialTransformation() {
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[10848] | 123 | var exponentialTransformation = (ExponentialTransformation)transformation;
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| 124 | var bValue = exponentialTransformation.Base;
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| 125 |
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[10853] | 126 | return GenTreePow_b_x(bValue);
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| 127 | }
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| 128 |
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[10872] | 129 | private ISymbolicExpressionTreeNode GenerateInverseModelForExponentialTransformation() {
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[10853] | 130 | var exponentialTransformation = (ExponentialTransformation)transformation;
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| 131 | var bValue = exponentialTransformation.Base;
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| 132 |
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| 133 | return GenTreeLog_x_b(bValue);
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| 134 | }
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| 135 |
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| 136 |
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[10872] | 137 | private ISymbolicExpressionTreeNode GenerateModelForLogarithmicTransformation() {
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[10853] | 138 | var logarithmicTransformation = (LogarithmicTransformation)transformation;
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| 139 | var bValue = logarithmicTransformation.Base;
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| 140 |
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| 141 | return GenTreeLog_x_b(bValue);
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| 142 | }
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| 143 |
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[10872] | 144 | private ISymbolicExpressionTreeNode GenerateInverseModelForLogarithmicTransformation() {
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[10853] | 145 | var logarithmicTransformation = (LogarithmicTransformation)transformation;
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| 146 | var bValue = logarithmicTransformation.Base;
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| 147 |
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| 148 | return GenTreePow_b_x(bValue);
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| 149 | }
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| 150 |
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| 151 |
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[10872] | 152 | private ISymbolicExpressionTreeNode GenerateModelForPowerTransformation() {
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[10853] | 153 | var powerTransformation = (PowerTransformation)transformation;
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| 154 | var expValue = powerTransformation.Exponent;
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| 155 |
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| 156 | // x ^ exp
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[10848] | 157 | var powerNode = new Power().CreateTreeNode();
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[10866] | 158 | var xNode = CreateVariableTreeNode(column, "x");
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| 159 | var expNode = CreateConstantTreeNode("exp", expValue);
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[10848] | 160 | powerNode.AddSubtree(xNode);
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[10853] | 161 | powerNode.AddSubtree(expNode);
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[10848] | 162 |
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[10872] | 163 | return powerNode;
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[10848] | 164 | }
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| 165 |
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[10872] | 166 | private ISymbolicExpressionTreeNode GenerateInverseModelForPowerTransformation() {
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[10853] | 167 | var powerTransformation = (PowerTransformation)transformation;
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| 168 | var expValue = powerTransformation.Exponent;
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[10848] | 169 |
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[10853] | 170 | // rt(x, b)
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| 171 | var rootNode = new Root().CreateTreeNode();
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[10866] | 172 | var xNode = CreateVariableTreeNode(column, "x");
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| 173 | var bNode = CreateConstantTreeNode("b", expValue);
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[10853] | 174 | rootNode.AddSubtree(xNode);
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| 175 | rootNode.AddSubtree(bNode);
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| 176 |
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[10872] | 177 | return rootNode;
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[10853] | 178 | }
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| 179 |
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| 180 |
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[10872] | 181 | private ISymbolicExpressionTreeNode GenerateModelForReciprocalTransformation() {
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[10853] | 182 | return GenTreeDiv_1_x();
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| 183 | }
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| 184 |
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[10872] | 185 | private ISymbolicExpressionTreeNode GenerateInverseModelForReciprocalTransformation() {
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[10853] | 186 | return GenTreeDiv_1_x();
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| 187 | }
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| 188 |
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[10872] | 189 |
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| 190 | private ISymbolicExpressionTreeNode GenerateModelForShiftStandardDistributionTransformation() {
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[10853] | 191 | var shiftStandardDistributionTransformation = (ShiftStandardDistributionTransformation)transformation;
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| 192 | var m_orgValue = shiftStandardDistributionTransformation.OriginalMean;
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| 193 | var s_orgValue = shiftStandardDistributionTransformation.OriginalStandardDeviation;
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| 194 | var m_tarValue = shiftStandardDistributionTransformation.Mean;
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| 195 | var s_tarValue = shiftStandardDistributionTransformation.StandardDeviation;
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| 196 |
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[10866] | 197 | return GenTreeShiftStdDist(column, m_orgValue, s_orgValue, m_tarValue, s_tarValue);
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| 198 | }
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[10853] | 199 |
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[10872] | 200 | private ISymbolicExpressionTreeNode GenerateInverseModelForShiftStandardDistributionTransformation() {
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[10866] | 201 | var shiftStandardDistributionTransformation = (ShiftStandardDistributionTransformation)transformation;
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| 202 | var m_orgValue = shiftStandardDistributionTransformation.OriginalMean;
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| 203 | var s_orgValue = shiftStandardDistributionTransformation.OriginalStandardDeviation;
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| 204 | var m_tarValue = shiftStandardDistributionTransformation.Mean;
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| 205 | var s_tarValue = shiftStandardDistributionTransformation.StandardDeviation;
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[10853] | 206 |
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[10866] | 207 | return GenTreeShiftStdDist(column, m_tarValue, s_tarValue, m_orgValue, s_orgValue);
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[10853] | 208 | }
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| 209 |
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[10943] | 210 | private ISymbolicExpressionTreeNode GenerateTreeNodeForCopyColumnTransformation() {
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| 211 | var copyColumnTransformation = (CopyColumnTransformation)transformation;
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| 212 | var copiedColumnName = copyColumnTransformation.CopiedColumnName;
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| 213 |
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| 214 | return CreateVariableTreeNode(copiedColumnName, copiedColumnName + "(original)");
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| 215 | }
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| 216 |
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[10853] | 217 | // helper's helper:
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| 218 |
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[10872] | 219 | private ISymbolicExpressionTreeNode GenTreeLog_x_b(double b) {
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[10848] | 220 | // log(x, b)
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| 221 | var logNode = new Logarithm().CreateTreeNode();
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[10866] | 222 | var bNode = CreateConstantTreeNode("b", b);
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| 223 | var xNode = CreateVariableTreeNode(column, "x");
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[10848] | 224 | logNode.AddSubtree(xNode);
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| 225 | logNode.AddSubtree(bNode);
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| 226 |
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[10872] | 227 | return logNode;
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[10848] | 228 | }
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| 229 |
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[10872] | 230 | private ISymbolicExpressionTreeNode GenTreePow_b_x(double b) {
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[10853] | 231 | // b ^ x
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| 232 | var powerNode = new Power().CreateTreeNode();
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[10866] | 233 | var bNode = CreateConstantTreeNode("b", b);
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| 234 | var xNode = CreateVariableTreeNode(column, "x");
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[10853] | 235 | powerNode.AddSubtree(bNode);
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| 236 | powerNode.AddSubtree(xNode);
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| 237 |
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[10872] | 238 | return powerNode;
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[10853] | 239 | }
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| 240 |
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[10872] | 241 | private ISymbolicExpressionTreeNode GenTreeDiv_1_x() {
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[10853] | 242 | // 1 / x
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| 243 | var divNode = new Division().CreateTreeNode();
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[10866] | 244 | var oneNode = CreateConstantTreeNode("1", 1.0);
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| 245 | var xNode = CreateVariableTreeNode(column, "x");
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[10853] | 246 | divNode.AddSubtree(oneNode);
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| 247 | divNode.AddSubtree(xNode);
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| 248 |
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[10872] | 249 | return divNode;
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[10853] | 250 | }
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[10872] | 251 |
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| 252 | private ISymbolicExpressionTreeNode GenTreeShiftStdDist(string variable, double m_org, double s_org, double m_tar, double s_tar) {
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[10866] | 253 | // x - m_org
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| 254 | var substractionNode = new Subtraction().CreateTreeNode();
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| 255 | var xNode = CreateVariableTreeNode(variable, "x");
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| 256 | var m_orgNode = CreateConstantTreeNode("m_org", m_org);
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| 257 | substractionNode.AddSubtree(xNode);
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| 258 | substractionNode.AddSubtree(m_orgNode);
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[10853] | 259 |
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[10866] | 260 | // (x - m_org) / s_org
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| 261 | var divisionNode = new Division().CreateTreeNode();
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| 262 | var s_orgNode = CreateConstantTreeNode("s_org", s_org);
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| 263 | divisionNode.AddSubtree(substractionNode);
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| 264 | divisionNode.AddSubtree(s_orgNode);
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[10853] | 265 |
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[10866] | 266 | // ((x - m_org) / s_org ) * s_tar
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| 267 | var multiplicationNode = new Multiplication().CreateTreeNode();
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| 268 | var s_tarNode = CreateConstantTreeNode("s_tar", s_tar);
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| 269 | multiplicationNode.AddSubtree(divisionNode);
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| 270 | multiplicationNode.AddSubtree(s_tarNode);
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| 271 |
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| 272 | // ((x - m_org) / s_org ) * s_tar + m_tar
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| 273 | var additionNode = new Addition().CreateTreeNode();
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| 274 | var m_tarNode = CreateConstantTreeNode("m_tar", m_tar);
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| 275 | additionNode.AddSubtree(multiplicationNode);
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| 276 | additionNode.AddSubtree(m_tarNode);
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| 277 |
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[10872] | 278 | return additionNode;
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[10866] | 279 | }
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| 280 |
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| 281 | private ConstantTreeNode CreateConstantTreeNode(string description, double value) {
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[10869] | 282 | return new ConstantTreeNode(new Constant()) { Value = value };
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[10866] | 283 | }
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[10872] | 284 |
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[10878] | 285 | private VariableTreeNode CreateVariableTreeNode(string name, string description) {
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| 286 | return new VariableTreeNode(new Variable(name, description)) { VariableName = name, Weight = 1.0 };
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[10866] | 287 | }
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| 288 |
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[10846] | 289 | private void InitComponents(ITransformation transformation) {
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| 290 | this.transformation = transformation;
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| 291 | column = transformation.Column;
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| 292 | }
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[10845] | 293 | }
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| 294 | }
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