#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis {
[Item("Z-Score Normalization", "Z-Score normalization transformation to standardize (target_mu = 0, target_sigma = 1) the values")]
[StorableClass]
public class ZNormalizationTransformation : Transformation {
#region Parameters
private IFixedValueParameter TargetMeanParameter {
get { return (IFixedValueParameter)Parameters["Target Mean"]; }
}
private IFixedValueParameter TargetStandardDeviationParameter {
get { return (IFixedValueParameter)Parameters["Target Standard Deviation"]; }
}
private IFixedValueParameter OriginalMeanParameter {
get { return (IFixedValueParameter)Parameters["Original Mean"]; }
}
private IFixedValueParameter OriginalStandardDeviationParameter {
get { return (IFixedValueParameter)Parameters["Original Standard Deviation"]; }
}
#endregion
#region Properties
public double TargetMean {
get { return TargetMeanParameter.Value.Value; }
set { TargetMeanParameter.Value.Value = value; }
}
public double TargetStandardDeviation {
get { return TargetStandardDeviationParameter.Value.Value; }
set { TargetStandardDeviationParameter.Value.Value = value; }
}
public double OriginalMean {
get { return OriginalMeanParameter.Value.Value; }
set { OriginalMeanParameter.Value.Value = value; }
}
public double OriginalStandardDeviation {
get { return OriginalStandardDeviationParameter.Value.Value; }
set { OriginalStandardDeviationParameter.Value.Value = value; }
}
#endregion
#region Constructor, Cloning & Persistence
public ZNormalizationTransformation()
: base() {
Parameters.Add(new FixedValueParameter("Target Mean", new DoubleValue(0)));
Parameters.Add(new FixedValueParameter("Target Standard Deviation", new DoubleValue(1)));
Parameters.Add(new FixedValueParameter("Original Mean", new DoubleValue(double.NaN)) { Hidden = true });
Parameters.Add(new FixedValueParameter("Original Standard Deviation", new DoubleValue(double.NaN)) { Hidden = true });
}
protected ZNormalizationTransformation(ZNormalizationTransformation original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new ZNormalizationTransformation(this, cloner);
}
[StorableConstructor]
protected ZNormalizationTransformation(bool deserializing)
: base(deserializing) {
}
#endregion
public override bool Check(IEnumerable data, out string errorMessage) {
if (data.StandardDeviationPop().IsAlmost(0.0)) {
errorMessage = "Z-Score Normalization cannot be applied for data with a standard deviation of zero.";
return false;
}
return base.Check(data, out errorMessage);
}
public override void Configure(IEnumerable data) {
OriginalMean = data.Average();
OriginalStandardDeviation = data.StandardDeviationPop();
base.Configure(data);
}
public override IEnumerable Apply(IEnumerable data) {
if (double.IsNaN(OriginalMean) || double.IsNaN(OriginalStandardDeviation))
Configure(data);
return Apply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
}
public override IEnumerable InverseApply(IEnumerable data) {
return InverseApply(data, TargetMean, TargetStandardDeviation, OriginalMean, OriginalStandardDeviation);
}
public static IEnumerable Apply(IEnumerable data, double targetMean, double targetStandardDeviation, double originalMean = double.NaN, double originalStandardDeviation = double.NaN) {
if (double.IsNaN(originalMean)) originalMean = data.Average();
if (double.IsNaN(originalStandardDeviation)) originalStandardDeviation = data.StandardDeviationPop();
return data
.Select(x => (x - originalMean) / originalStandardDeviation) // standardize (0, 1)
.Select(x => x * targetStandardDeviation + targetMean);
}
public static IEnumerable InverseApply(IEnumerable data, double targetMean, double targetStandardDeviation, double originalMean, double originalStandardDeviation) {
return Apply(data, originalMean, originalStandardDeviation, targetMean, targetStandardDeviation);
}
}
}