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