#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; using System.Collections.Generic; using System.Linq; using System.Threading; 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 (LDA)", "Linear discriminant analysis classification algorithm (wrapper for ALGLIB).")] [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 100)] [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(CancellationToken cancellationToken) { var solution = CreateLinearDiscriminantAnalysisSolution(Problem.ProblemData); Results.Add(new Result(LinearDiscriminantAnalysisSolutionResultName, "The linear discriminant analysis.", solution)); } public static IClassificationSolution CreateLinearDiscriminantAnalysisSolution(IClassificationProblemData problemData) { var dataset = problemData.Dataset; string targetVariable = problemData.TargetVariable; IEnumerable allowedInputVariables = problemData.AllowedInputVariables; IEnumerable rows = problemData.TrainingIndices; int nClasses = problemData.ClassNames.Count(); var doubleVariableNames = allowedInputVariables.Where(dataset.VariableHasType).ToArray(); var factorVariableNames = allowedInputVariables.Where(dataset.VariableHasType).ToArray(); double[,] inputMatrix = dataset.ToArray(doubleVariableNames.Concat(new string[] { targetVariable }), rows); var factorVariables = dataset.GetFactorVariableValues(factorVariableNames, rows); var factorMatrix = dataset.ToArray(factorVariables, rows); inputMatrix = factorMatrix.HorzCat(inputMatrix); if (inputMatrix.ContainsNanOrInfinity()) 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), inputMatrix.GetLength(1) - 1, nClasses, out info, out w); if (info < 1) throw new ArgumentException("Error in calculation of linear discriminant analysis solution"); var nFactorCoeff = factorMatrix.GetLength(1); var tree = LinearModelToTreeConverter.CreateTree(factorVariables, w.Take(nFactorCoeff).ToArray(), doubleVariableNames, w.Skip(nFactorCoeff).Take(doubleVariableNames.Length).ToArray()); var model = CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeLinearInterpreter(), problemData, rows); SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone()); return solution; } #endregion private static SymbolicDiscriminantFunctionClassificationModel CreateDiscriminantFunctionModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IClassificationProblemData problemData, IEnumerable rows) { var model = new SymbolicDiscriminantFunctionClassificationModel(problemData.TargetVariable, tree, interpreter, new AccuracyMaximizationThresholdCalculator()); model.RecalculateModelParameters(problemData, rows); return model; } } }