#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 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.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
[Item("NCA Model", "")]
[StorableClass]
public class NcaModel : ClassificationModel, INcaModel {
public override IEnumerable VariablesUsedForPrediction {
get { return allowedInputVariables; }
}
[Storable]
private double[,] transformationMatrix;
public double[,] TransformationMatrix {
get { return (double[,])transformationMatrix.Clone(); }
}
[Storable]
private string[] allowedInputVariables;
[Storable]
private INearestNeighbourModel nnModel;
[Storable]
private double[] classValues;
[StorableConstructor]
protected NcaModel(bool deserializing) : base(deserializing) { }
protected NcaModel(NcaModel original, Cloner cloner)
: base(original, cloner) {
this.transformationMatrix = (double[,])original.transformationMatrix.Clone();
this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
this.nnModel = cloner.Clone(original.nnModel);
this.classValues = (double[])original.classValues.Clone();
}
public NcaModel(int k, double[,] transformationMatrix, IDataset dataset, IEnumerable rows, string targetVariable, IEnumerable allowedInputVariables, double[] classValues)
: base(targetVariable) {
Name = ItemName;
Description = ItemDescription;
this.transformationMatrix = (double[,])transformationMatrix.Clone();
this.allowedInputVariables = allowedInputVariables.ToArray();
this.classValues = (double[])classValues.Clone();
var ds = ReduceDataset(dataset, rows);
nnModel = new NearestNeighbourModel(ds, Enumerable.Range(0, ds.Rows), k, ds.VariableNames.Last(), ds.VariableNames.Take(transformationMatrix.GetLength(1)), classValues);
}
public override IDeepCloneable Clone(Cloner cloner) {
return new NcaModel(this, cloner);
}
public override IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows) {
var ds = ReduceDataset(dataset, rows);
return nnModel.GetEstimatedClassValues(ds, Enumerable.Range(0, ds.Rows));
}
public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
return new NcaClassificationSolution(this, new ClassificationProblemData(problemData));
}
INcaClassificationSolution INcaModel.CreateClassificationSolution(IClassificationProblemData problemData) {
return new NcaClassificationSolution(this, new ClassificationProblemData(problemData));
}
public double[,] Reduce(IDataset dataset, IEnumerable rows) {
var data = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
var targets = dataset.GetDoubleValues(TargetVariable, rows).ToArray();
var result = new double[data.GetLength(0), transformationMatrix.GetLength(1) + 1];
for (int i = 0; i < data.GetLength(0); i++)
for (int j = 0; j < data.GetLength(1); j++) {
for (int x = 0; x < transformationMatrix.GetLength(1); x++) {
result[i, x] += data[i, j] * transformationMatrix[j, x];
}
result[i, transformationMatrix.GetLength(1)] = targets[i];
}
return result;
}
public Dataset ReduceDataset(IDataset dataset, IEnumerable rows) {
return new Dataset(Enumerable
.Range(0, transformationMatrix.GetLength(1))
.Select(x => "X" + x.ToString())
.Concat(TargetVariable.ToEnumerable()),
Reduce(dataset, rows));
}
}
}