1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2018 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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22 | using System.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Parameters;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 |
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30 | namespace HeuristicLab.Problems.DataAnalysis {
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31 | [Item("Z-Score Normalization", "Z-Score normalization transformation to standardize (target_mu = 0, target_sigma = 1) the values")]
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32 | [StorableClass]
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33 | public class ZNormalizationTransformation : Transformation<double> {
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34 | #region Parameters
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35 | private IFixedValueParameter<DoubleValue> TargetMeanParameter {
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36 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Target Mean"]; }
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37 | }
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38 | private IFixedValueParameter<DoubleValue> TargetStandardDeviationParameter {
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39 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Target Standard Deviation"]; }
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40 | }
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41 | private IFixedValueParameter<DoubleValue> OriginalMeanParameter {
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42 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Original Mean"]; }
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43 | }
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44 | private IFixedValueParameter<DoubleValue> OriginalStandardDeviationParameter {
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45 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Original Standard Deviation"]; }
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46 | }
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47 | #endregion
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48 |
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49 | #region Properties
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50 | public double TargetMean {
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51 | get { return TargetMeanParameter.Value.Value; }
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52 | set { TargetMeanParameter.Value.Value = value; }
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53 | }
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54 | public double TargetStandardDeviation {
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55 | get { return TargetStandardDeviationParameter.Value.Value; }
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56 | set { TargetStandardDeviationParameter.Value.Value = value; }
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57 | }
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58 | public double OriginalMean {
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59 | get { return OriginalMeanParameter.Value.Value; }
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60 | set { OriginalMeanParameter.Value.Value = value; }
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61 | }
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62 | public double OriginalStandardDeviation {
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63 | get { return OriginalStandardDeviationParameter.Value.Value; }
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64 | set { OriginalStandardDeviationParameter.Value.Value = value; }
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65 | }
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66 | #endregion
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67 |
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68 | #region Constructor, Cloning & Persistence
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69 | public ZNormalizationTransformation()
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70 | : base() {
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71 | Parameters.Add(new FixedValueParameter<DoubleValue>("Target Mean", new DoubleValue(0)));
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72 | Parameters.Add(new FixedValueParameter<DoubleValue>("Target Standard Deviation", new DoubleValue(1)));
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73 | Parameters.Add(new FixedValueParameter<DoubleValue>("Original Mean", new DoubleValue(double.NaN)) { Hidden = true });
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74 | Parameters.Add(new FixedValueParameter<DoubleValue>("Original Standard Deviation", new DoubleValue(double.NaN)) { Hidden = true });
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75 | }
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76 |
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77 | protected ZNormalizationTransformation(ZNormalizationTransformation original, Cloner cloner)
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78 | : base(original, cloner) {
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79 | }
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80 | public override IDeepCloneable Clone(Cloner cloner) {
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81 | return new ZNormalizationTransformation(this, cloner);
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82 | }
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83 |
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84 | [StorableConstructor]
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85 | protected ZNormalizationTransformation(bool deserializing)
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86 | : base(deserializing) {
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87 | }
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88 | #endregion
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89 |
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90 | public override bool Check(IEnumerable<double> data, out string errorMessage) {
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91 | if (data.StandardDeviationPop().IsAlmost(0.0)) {
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92 | errorMessage = "Z-Score Normalization cannot be applied for data with a standard deviation of zero.";
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93 | return false;
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94 | }
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95 | return base.Check(data, out errorMessage);
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96 | }
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97 |
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98 | public override void Configure(IEnumerable<double> data) {
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99 | OriginalMean = data.Average();
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100 | OriginalStandardDeviation = data.StandardDeviationPop();
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101 | base.Configure(data);
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102 | }
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103 |
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104 | public override IEnumerable<double> Apply(IEnumerable<double> data) {
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105 | if (double.IsNaN(OriginalMean) || double.IsNaN(OriginalStandardDeviation))
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106 | Configure(data);
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107 |
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108 | return Apply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
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109 | }
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110 |
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111 | public override IEnumerable<double> InverseApply(IEnumerable<double> data) {
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112 | return InverseApply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
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113 | }
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114 |
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115 | public static IEnumerable<double> Apply(IEnumerable<double> data, double targetMean, double targetStandardDeviation, double originalMean = double.NaN, double originalStandardDeviation = double.NaN) {
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116 | if (double.IsNaN(originalMean)) originalMean = data.Average();
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117 | if (double.IsNaN(originalStandardDeviation)) originalStandardDeviation = data.StandardDeviationPop();
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118 |
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119 | return data
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120 | .Select(x => (x - originalMean) / originalStandardDeviation) // standardize (0, 1)
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121 | .Select(x => x * targetStandardDeviation + targetMean);
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122 | }
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123 |
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124 | public static IEnumerable<double> InverseApply(IEnumerable<double> data, double targetMean, double targetStandardDeviation, double originalMean, double originalStandardDeviation) {
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125 | return Apply(data, originalMean, originalStandardDeviation, targetMean, targetStandardDeviation);
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126 | }
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127 | }
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128 | } |
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