#region License Information /* HeuristicLab * Copyright (C) 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 HEAL.Attic; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { /// /// Represents a multinomial logit model for classification /// [StorableType("AC4174A4-9FBC-4B07-9239-1E0E6F86034D")] [Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")] public sealed class MultinomialLogitModel : ClassificationModel { private alglib.logitmodel logitModel; public alglib.logitmodel Model { get { return logitModel; } set { if (value != logitModel) { if (value == null) throw new ArgumentNullException(); logitModel = value; OnChanged(EventArgs.Empty); } } } public override IEnumerable VariablesUsedForPrediction { get { return allowedInputVariables; } } [Storable] private string[] allowedInputVariables; [Storable] private double[] classValues; [Storable] private List>> factorVariables; [StorableConstructor] private MultinomialLogitModel(StorableConstructorFlag _) : base(_) { logitModel = new alglib.logitmodel(); } private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner) : base(original, cloner) { logitModel = new alglib.logitmodel(); logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone(); allowedInputVariables = (string[])original.allowedInputVariables.Clone(); classValues = (double[])original.classValues.Clone(); this.factorVariables = original.factorVariables.Select(kvp => new KeyValuePair>(kvp.Key, new List(kvp.Value))).ToList(); } public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable doubleInputVariables, IEnumerable>> factorVariables, double[] classValues) : base(targetVariable) { this.name = ItemName; this.description = ItemDescription; this.logitModel = logitModel; this.allowedInputVariables = doubleInputVariables.ToArray(); this.factorVariables = factorVariables.Select(kvp => new KeyValuePair>(kvp.Key, new List(kvp.Value))).ToList(); this.classValues = (double[])classValues.Clone(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.3 #region Backwards compatible code, remove with 3.4 factorVariables = new List>>(); #endregion } public override IDeepCloneable Clone(Cloner cloner) { return new MultinomialLogitModel(this, cloner); } public override IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows) { double[,] inputData = dataset.ToArray(allowedInputVariables, rows); double[,] factorData = dataset.ToArray(factorVariables, rows); inputData = factorData.HorzCat(inputData); int n = inputData.GetLength(0); int columns = inputData.GetLength(1); double[] x = new double[columns]; double[] y = new double[classValues.Length]; for (int row = 0; row < n; row++) { for (int column = 0; column < columns; column++) { x[column] = inputData[row, column]; } alglib.mnlprocess(logitModel, x, ref y); // find class for with the largest probability value int maxProbClassIndex = 0; double maxProb = y[0]; for (int i = 1; i < y.Length; i++) { if (maxProb < y[i]) { maxProb = y[i]; maxProbClassIndex = i; } } yield return classValues[maxProbClassIndex]; } } public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return new MultinomialLogitClassificationSolution(this, new ClassificationProblemData(problemData)); } #region events public event EventHandler Changed; private void OnChanged(EventArgs e) { var handlers = Changed; if (handlers != null) handlers(this, e); } #endregion #region persistence [Storable] private double[] LogitModelW { get { return logitModel.innerobj.w; } set { logitModel.innerobj.w = value; } } #endregion } }