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