#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.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Represents a multinomial logit model for classification
///
[StorableClass]
[Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")]
public sealed class MultinomialLogitModel : NamedItem, IClassificationModel {
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);
}
}
}
[Storable]
private string targetVariable;
[Storable]
private string[] allowedInputVariables;
[Storable]
private double[] classValues;
[StorableConstructor]
private MultinomialLogitModel(bool deserializing)
: base(deserializing) {
if (deserializing)
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();
targetVariable = original.targetVariable;
allowedInputVariables = (string[])original.allowedInputVariables.Clone();
classValues = (double[])original.classValues.Clone();
}
public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable allowedInputVariables, double[] classValues)
: base() {
this.name = ItemName;
this.description = ItemDescription;
this.logitModel = logitModel;
this.targetVariable = targetVariable;
this.allowedInputVariables = allowedInputVariables.ToArray();
this.classValues = (double[])classValues.Clone();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new MultinomialLogitModel(this, cloner);
}
public IEnumerable GetEstimatedClassValues(Dataset dataset, IEnumerable rows) {
double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
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 MultinomialLogitClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
return new MultinomialLogitClassificationSolution(problemData, this);
}
IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
return CreateClassificationSolution(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
}
}