#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
}
}