[15846] | 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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[15884] | 22 | using System;
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[15846] | 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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| 31 | namespace HeuristicLab.Problems.DataAnalysis {
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[15865] | 32 | [Item("Z-Score Normalization", "Z-Score normalization transformation to standardize the values to a Target Mean and Target Standard Deviation.")]
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[15846] | 33 | [StorableClass]
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| 34 | public class ZNormalizationTransformation : Transformation<double> {
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| 35 | #region Parameters
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| 36 | private IFixedValueParameter<DoubleValue> TargetMeanParameter {
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| 37 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Target Mean"]; }
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| 38 | }
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| 39 | private IFixedValueParameter<DoubleValue> TargetStandardDeviationParameter {
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| 40 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Target Standard Deviation"]; }
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| 41 | }
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| 42 | private IFixedValueParameter<DoubleValue> OriginalMeanParameter {
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| 43 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Original Mean"]; }
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| 44 | }
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| 45 | private IFixedValueParameter<DoubleValue> OriginalStandardDeviationParameter {
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| 46 | get { return (IFixedValueParameter<DoubleValue>)Parameters["Original Standard Deviation"]; }
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| 47 | }
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| 48 | #endregion
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| 49 |
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| 50 | #region Properties
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| 51 | public double TargetMean {
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| 52 | get { return TargetMeanParameter.Value.Value; }
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| 53 | set { TargetMeanParameter.Value.Value = value; }
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| 54 | }
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| 55 | public double TargetStandardDeviation {
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| 56 | get { return TargetStandardDeviationParameter.Value.Value; }
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| 57 | set { TargetStandardDeviationParameter.Value.Value = value; }
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| 58 | }
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| 59 | public double OriginalMean {
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| 60 | get { return OriginalMeanParameter.Value.Value; }
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[15865] | 61 | private set { OriginalMeanParameter.Value.Value = value; }
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[15846] | 62 | }
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| 63 | public double OriginalStandardDeviation {
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| 64 | get { return OriginalStandardDeviationParameter.Value.Value; }
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[15865] | 65 | private set { OriginalStandardDeviationParameter.Value.Value = value; }
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[15846] | 66 | }
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| 67 | #endregion
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| 68 |
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| 69 | #region Constructor, Cloning & Persistence
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| 70 | public ZNormalizationTransformation()
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| 71 | : base() {
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| 72 | Parameters.Add(new FixedValueParameter<DoubleValue>("Target Mean", new DoubleValue(0)));
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| 73 | Parameters.Add(new FixedValueParameter<DoubleValue>("Target Standard Deviation", new DoubleValue(1)));
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| 74 | Parameters.Add(new FixedValueParameter<DoubleValue>("Original Mean", new DoubleValue(double.NaN)) { Hidden = true });
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| 75 | Parameters.Add(new FixedValueParameter<DoubleValue>("Original Standard Deviation", new DoubleValue(double.NaN)) { Hidden = true });
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| 76 | }
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| 77 |
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| 78 | protected ZNormalizationTransformation(ZNormalizationTransformation original, Cloner cloner)
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| 79 | : base(original, cloner) {
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| 80 | }
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| 81 | public override IDeepCloneable Clone(Cloner cloner) {
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| 82 | return new ZNormalizationTransformation(this, cloner);
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| 83 | }
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| 84 |
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| 85 | [StorableConstructor]
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| 86 | protected ZNormalizationTransformation(bool deserializing)
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| 87 | : base(deserializing) {
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| 88 | }
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| 89 | #endregion
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| 90 |
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| 91 | public override bool Check(IEnumerable<double> data, out string errorMessage) {
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| 92 | if (data.StandardDeviationPop().IsAlmost(0.0)) {
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| 93 | errorMessage = "Z-Score Normalization cannot be applied for data with a standard deviation of zero.";
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| 94 | return false;
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| 95 | }
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| 96 | return base.Check(data, out errorMessage);
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| 97 | }
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| 98 |
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| 99 | public override void Configure(IEnumerable<double> data) {
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[15884] | 100 | Configure(data, out double originalMean, out double originalStandardDeviation);
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| 101 | OriginalMean = originalMean;
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| 102 | OriginalStandardDeviation = originalStandardDeviation;
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[15846] | 103 | base.Configure(data);
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| 104 | }
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| 105 |
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| 106 | public override IEnumerable<double> Apply(IEnumerable<double> data) {
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[15884] | 107 | if (double.IsNaN(OriginalMean) || double.IsNaN(OriginalStandardDeviation))
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| 108 | throw new InvalidOperationException("Transformation is not configured");
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[15846] | 109 |
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| 110 | return Apply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
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| 111 | }
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| 112 |
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| 113 | public override IEnumerable<double> InverseApply(IEnumerable<double> data) {
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[15884] | 114 | if (double.IsNaN(OriginalMean) || double.IsNaN(OriginalStandardDeviation))
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| 115 | throw new InvalidOperationException("Transformation is not configured");
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| 116 |
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[15846] | 117 | return InverseApply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
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| 118 | }
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| 119 |
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[15865] | 120 |
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[15884] | 121 | public static void Configure(IEnumerable<double> data, out double originalMean, out double originalStandardDeviation) {
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| 122 | originalMean = data.Average();
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| 123 | originalStandardDeviation = data.StandardDeviationPop();
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| 124 | }
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| 125 |
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[15846] | 126 | public static IEnumerable<double> Apply(IEnumerable<double> data, double targetMean, double targetStandardDeviation, double originalMean = double.NaN, double originalStandardDeviation = double.NaN) {
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| 127 | if (double.IsNaN(originalMean)) originalMean = data.Average();
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| 128 | if (double.IsNaN(originalStandardDeviation)) originalStandardDeviation = data.StandardDeviationPop();
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| 129 |
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| 130 | return data
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| 131 | .Select(x => (x - originalMean) / originalStandardDeviation) // standardize (0, 1)
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| 132 | .Select(x => x * targetStandardDeviation + targetMean);
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| 133 | }
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| 134 |
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| 135 | public static IEnumerable<double> InverseApply(IEnumerable<double> data, double targetMean, double targetStandardDeviation, double originalMean, double originalStandardDeviation) {
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| 136 | return Apply(data, originalMean, originalStandardDeviation, targetMean, targetStandardDeviation);
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| 137 | }
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| 138 | }
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| 139 | } |
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