#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 model for regression and classification /// [StorableClass] [Item("NeuralNetworkModel", "Represents a neural network for regression and classification.")] public sealed class NeuralNetworkModel : ClassificationModel, INeuralNetworkModel { private alglib.multilayerperceptron multiLayerPerceptron; public alglib.multilayerperceptron MultiLayerPerceptron { get { return multiLayerPerceptron; } set { if (value != multiLayerPerceptron) { if (value == null) throw new ArgumentNullException(); multiLayerPerceptron = value; OnChanged(EventArgs.Empty); } } } public override IEnumerable VariablesUsedForPrediction { get { return allowedInputVariables; } } [Storable] private string[] allowedInputVariables; [Storable] private double[] classValues; [StorableConstructor] private NeuralNetworkModel(bool deserializing) : base(deserializing) { if (deserializing) multiLayerPerceptron = new alglib.multilayerperceptron(); } private NeuralNetworkModel(NeuralNetworkModel original, Cloner cloner) : base(original, cloner) { multiLayerPerceptron = new alglib.multilayerperceptron(); multiLayerPerceptron.innerobj.chunks = (double[,])original.multiLayerPerceptron.innerobj.chunks.Clone(); multiLayerPerceptron.innerobj.columnmeans = (double[])original.multiLayerPerceptron.innerobj.columnmeans.Clone(); multiLayerPerceptron.innerobj.columnsigmas = (double[])original.multiLayerPerceptron.innerobj.columnsigmas.Clone(); multiLayerPerceptron.innerobj.derror = (double[])original.multiLayerPerceptron.innerobj.derror.Clone(); multiLayerPerceptron.innerobj.dfdnet = (double[])original.multiLayerPerceptron.innerobj.dfdnet.Clone(); multiLayerPerceptron.innerobj.neurons = (double[])original.multiLayerPerceptron.innerobj.neurons.Clone(); multiLayerPerceptron.innerobj.nwbuf = (double[])original.multiLayerPerceptron.innerobj.nwbuf.Clone(); multiLayerPerceptron.innerobj.structinfo = (int[])original.multiLayerPerceptron.innerobj.structinfo.Clone(); multiLayerPerceptron.innerobj.weights = (double[])original.multiLayerPerceptron.innerobj.weights.Clone(); multiLayerPerceptron.innerobj.x = (double[])original.multiLayerPerceptron.innerobj.x.Clone(); multiLayerPerceptron.innerobj.y = (double[])original.multiLayerPerceptron.innerobj.y.Clone(); allowedInputVariables = (string[])original.allowedInputVariables.Clone(); if (original.classValues != null) this.classValues = (double[])original.classValues.Clone(); } public NeuralNetworkModel(alglib.multilayerperceptron multiLayerPerceptron, string targetVariable, IEnumerable allowedInputVariables, double[] classValues = null) : base(targetVariable) { this.name = ItemName; this.description = ItemDescription; this.multiLayerPerceptron = multiLayerPerceptron; this.allowedInputVariables = allowedInputVariables.ToArray(); if (classValues != null) this.classValues = (double[])classValues.Clone(); } public override IDeepCloneable Clone(Cloner cloner) { return new NeuralNetworkModel(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]; } alglib.mlpprocess(multiLayerPerceptron, 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]; } alglib.mlpprocess(multiLayerPerceptron, 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 NeuralNetworkRegressionSolution(this, new RegressionProblemData(problemData)); } public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return new NeuralNetworkClassificationSolution(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[,] MultiLayerPerceptronChunks { get { return multiLayerPerceptron.innerobj.chunks; } set { multiLayerPerceptron.innerobj.chunks = value; } } [Storable] private double[] MultiLayerPerceptronColumnMeans { get { return multiLayerPerceptron.innerobj.columnmeans; } set { multiLayerPerceptron.innerobj.columnmeans = value; } } [Storable] private double[] MultiLayerPerceptronColumnSigmas { get { return multiLayerPerceptron.innerobj.columnsigmas; } set { multiLayerPerceptron.innerobj.columnsigmas = value; } } [Storable] private double[] MultiLayerPerceptronDError { get { return multiLayerPerceptron.innerobj.derror; } set { multiLayerPerceptron.innerobj.derror = value; } } [Storable] private double[] MultiLayerPerceptronDfdnet { get { return multiLayerPerceptron.innerobj.dfdnet; } set { multiLayerPerceptron.innerobj.dfdnet = value; } } [Storable] private double[] MultiLayerPerceptronNeurons { get { return multiLayerPerceptron.innerobj.neurons; } set { multiLayerPerceptron.innerobj.neurons = value; } } [Storable] private double[] MultiLayerPerceptronNwbuf { get { return multiLayerPerceptron.innerobj.nwbuf; } set { multiLayerPerceptron.innerobj.nwbuf = value; } } [Storable] private int[] MultiLayerPerceptronStuctinfo { get { return multiLayerPerceptron.innerobj.structinfo; } set { multiLayerPerceptron.innerobj.structinfo = value; } } [Storable] private double[] MultiLayerPerceptronWeights { get { return multiLayerPerceptron.innerobj.weights; } set { multiLayerPerceptron.innerobj.weights = value; } } [Storable] private double[] MultiLayerPerceptronX { get { return multiLayerPerceptron.innerobj.x; } set { multiLayerPerceptron.innerobj.x = value; } } [Storable] private double[] MultiLayerPerceptronY { get { return multiLayerPerceptron.innerobj.y; } set { multiLayerPerceptron.innerobj.y = value; } } #endregion } }