#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); } } }