#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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { /// /// Represents a neural network ensembel model for regression and classification /// [StorableClass] [Item("NeuralNetworkEnsembleModel", "Represents a neural network ensemble for regression and classification.")] public sealed class NeuralNetworkEnsembleModel : ClassificationModel, INeuralNetworkEnsembleModel { private alglib.mlpensemble mlpEnsemble; public alglib.mlpensemble MultiLayerPerceptronEnsemble { get { return mlpEnsemble; } set { if (value != mlpEnsemble) { if (value == null) throw new ArgumentNullException(); mlpEnsemble = value; OnChanged(EventArgs.Empty); } } } public override IEnumerable VariablesUsedForPrediction { get { return allowedInputVariables; } } [Storable] private string targetVariable; [Storable] private string[] allowedInputVariables; [Storable] private double[] classValues; [StorableConstructor] private NeuralNetworkEnsembleModel(bool deserializing) : base(deserializing) { if (deserializing) mlpEnsemble = new alglib.mlpensemble(); } private NeuralNetworkEnsembleModel(NeuralNetworkEnsembleModel original, Cloner cloner) : base(original, cloner) { mlpEnsemble = new alglib.mlpensemble(); string serializedEnsemble; alglib.mlpeserialize(original.mlpEnsemble, out serializedEnsemble); alglib.mlpeunserialize(serializedEnsemble, out this.mlpEnsemble); targetVariable = original.targetVariable; allowedInputVariables = (string[])original.allowedInputVariables.Clone(); if (original.classValues != null) this.classValues = (double[])original.classValues.Clone(); } public NeuralNetworkEnsembleModel(alglib.mlpensemble mlpEnsemble, string targetVariable, IEnumerable allowedInputVariables, double[] classValues = null) : base(targetVariable) { this.name = ItemName; this.description = ItemDescription; this.mlpEnsemble = mlpEnsemble; this.targetVariable = targetVariable; this.allowedInputVariables = allowedInputVariables.ToArray(); if (classValues != null) this.classValues = (double[])classValues.Clone(); } public override IDeepCloneable Clone(Cloner cloner) { return new NeuralNetworkEnsembleModel(this, cloner); } public IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { double[,] inputData = dataset.ToArray(allowedInputVariables, rows); int n = inputData.GetLength(0); int columns = inputData.GetLength(1); double[] x = new double[columns]; double[] y = new double[1]; for (int row = 0; row < n; row++) { for (int column = 0; column < columns; column++) { x[column] = inputData[row, column]; } // mlpeprocess writes data in mlpEnsemble and is therefore not thread-safe lock (mlpEnsemble) { alglib.mlpeprocess(mlpEnsemble, x, ref y); } yield return y[0]; } } public override IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows) { double[,] inputData = dataset.ToArray(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]; } // mlpeprocess writes data in mlpEnsemble and is therefore not thread-safe lock (mlpEnsemble) { alglib.mlpeprocess(mlpEnsemble, 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 IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new NeuralNetworkEnsembleRegressionSolution(this, new RegressionEnsembleProblemData(problemData)); } public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return new NeuralNetworkEnsembleClassificationSolution(this, new ClassificationEnsembleProblemData(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 string MultiLayerPerceptronEnsembleNetwork { get { string serializedNetwork; alglib.mlpeserialize(this.mlpEnsemble, out serializedNetwork); return serializedNetwork; } set { alglib.mlpeunserialize(value, out this.mlpEnsemble); } } [Storable] private double[] MultiLayerPerceptronEnsembleColumnMeans { get { return mlpEnsemble.innerobj.columnmeans; } set { mlpEnsemble.innerobj.columnmeans = value; mlpEnsemble.innerobj.network.columnmeans = value; } } [Storable] private double[] MultiLayerPerceptronEnsembleColumnSigmas { get { return mlpEnsemble.innerobj.columnsigmas; } set { mlpEnsemble.innerobj.columnsigmas = value; mlpEnsemble.innerobj.network.columnsigmas = value; } } [Storable(AllowOneWay = true)] private double[] MultiLayerPerceptronEnsembleDfdnet { set { mlpEnsemble.innerobj.network.dfdnet = value; } } [Storable] private int MultiLayerPerceptronEnsembleSize { get { return mlpEnsemble.innerobj.ensemblesize; } set { mlpEnsemble.innerobj.ensemblesize = value; mlpEnsemble.innerobj.ensemblesize = value; } } [Storable(AllowOneWay = true)] private double[] MultiLayerPerceptronEnsembleNeurons { set { mlpEnsemble.innerobj.network.neurons = value; } } [Storable(AllowOneWay = true)] private double[] MultiLayerPerceptronEnsembleSerializedMlp { set { mlpEnsemble.innerobj.network.dfdnet = value; } } [Storable(AllowOneWay = true)] private int[] MultiLayerPerceptronStuctinfo { set { mlpEnsemble.innerobj.network.structinfo = value; } } [Storable] private double[] MultiLayerPerceptronWeights { get { return mlpEnsemble.innerobj.weights; } set { mlpEnsemble.innerobj.weights = value; mlpEnsemble.innerobj.network.weights = value; } } [Storable] private double[] MultiLayerPerceptronY { get { return mlpEnsemble.innerobj.y; } set { mlpEnsemble.innerobj.y = value; mlpEnsemble.innerobj.network.y = value; } } #endregion } }