#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Linear discriminant analysis classification algorithm.
///
[Item("Linear Discriminant Analysis", "Linear discriminant analysis classification algorithm.")]
[Creatable("Data Analysis")]
[StorableClass]
public sealed class LinearDiscriminantAnalysis : FixedDataAnalysisAlgorithm {
private const string LinearDiscriminantAnalysisSolutionResultName = "Linear discriminant analysis solution";
[StorableConstructor]
private LinearDiscriminantAnalysis(bool deserializing) : base(deserializing) { }
private LinearDiscriminantAnalysis(LinearDiscriminantAnalysis original, Cloner cloner)
: base(original, cloner) {
}
public LinearDiscriminantAnalysis()
: base() {
Problem = new ClassificationProblem();
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() { }
public override IDeepCloneable Clone(Cloner cloner) {
return new LinearDiscriminantAnalysis(this, cloner);
}
#region Fisher LDA
protected override void Run() {
var solution = CreateLinearDiscriminantAnalysisSolution(Problem.ProblemData);
Results.Add(new Result(LinearDiscriminantAnalysisSolutionResultName, "The linear discriminant analysis.", solution));
}
public static IClassificationSolution CreateLinearDiscriminantAnalysisSolution(IClassificationProblemData problemData) {
Dataset dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable allowedInputVariables = problemData.AllowedInputVariables;
int samplesStart = problemData.TrainingPartition.Start;
int samplesEnd = problemData.TrainingPartition.End;
IEnumerable rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart);
int nClasses = problemData.ClassNames.Count();
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
if (inputMatrix.Cast().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
throw new NotSupportedException("Linear discriminant analysis does not support NaN or infinity values in the input dataset.");
// change class values into class index
int targetVariableColumn = inputMatrix.GetLength(1) - 1;
List classValues = problemData.ClassValues.OrderBy(x => x).ToList();
for (int row = 0; row < inputMatrix.GetLength(0); row++) {
inputMatrix[row, targetVariableColumn] = classValues.IndexOf(inputMatrix[row, targetVariableColumn]);
}
int info;
double[] w;
alglib.fisherlda(inputMatrix, inputMatrix.GetLength(0), allowedInputVariables.Count(), nClasses, out info, out w);
if (info < 1) throw new ArgumentException("Error in calculation of linear discriminant analysis solution");
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int col = 0;
foreach (string column in allowedInputVariables) {
VariableTreeNode vNode = (VariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
vNode.VariableName = column;
vNode.Weight = w[col];
addition.AddSubtree(vNode);
col++;
}
ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
cNode.Value = w[w.Length - 1];
addition.AddSubtree(cNode);
var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, problemData);
return solution;
}
#endregion
private static SymbolicDiscriminantFunctionClassificationModel CreateDiscriminantFunctionModel(ISymbolicExpressionTree tree,
ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
IClassificationProblemData problemData,
IEnumerable rows) {
return new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter);
}
}
}