[6577] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[7259] | 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[6577] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 | using HeuristicLab.Problems.DataAnalysis;
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| 32 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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| 34 | using HeuristicLab.Parameters;
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| 35 |
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| 36 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 37 | /// <summary>
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[6579] | 38 | /// Neural network classification data analysis algorithm.
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[6577] | 39 | /// </summary>
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[6580] | 40 | [Item("Neural Network Classification", "Neural network classification data analysis algorithm (wrapper for ALGLIB). Further documentation: http://www.alglib.net/dataanalysis/neuralnetworks.php")]
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[6577] | 41 | [Creatable("Data Analysis")]
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| 42 | [StorableClass]
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[6579] | 43 | public sealed class NeuralNetworkClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
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[6578] | 44 | private const string DecayParameterName = "Decay";
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| 45 | private const string HiddenLayersParameterName = "HiddenLayers";
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| 46 | private const string NodesInFirstHiddenLayerParameterName = "NodesInFirstHiddenLayer";
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| 47 | private const string NodesInSecondHiddenLayerParameterName = "NodesInSecondHiddenLayer";
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| 48 | private const string RestartsParameterName = "Restarts";
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[6579] | 49 | private const string NeuralNetworkRegressionModelResultName = "Neural network classification solution";
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[6578] | 50 |
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| 51 | #region parameter properties
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| 52 | public IFixedValueParameter<DoubleValue> DecayParameter {
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| 53 | get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
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| 54 | }
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| 55 | public ConstrainedValueParameter<IntValue> HiddenLayersParameter {
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| 56 | get { return (ConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
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| 57 | }
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| 58 | public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
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| 59 | get { return (IFixedValueParameter<IntValue>)Parameters[NodesInFirstHiddenLayerParameterName]; }
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| 60 | }
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| 61 | public IFixedValueParameter<IntValue> NodesInSecondHiddenLayerParameter {
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| 62 | get { return (IFixedValueParameter<IntValue>)Parameters[NodesInSecondHiddenLayerParameterName]; }
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| 63 | }
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| 64 | public IFixedValueParameter<IntValue> RestartsParameter {
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| 65 | get { return (IFixedValueParameter<IntValue>)Parameters[RestartsParameterName]; }
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| 66 | }
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| 67 | #endregion
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| 68 |
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| 69 | #region properties
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| 70 | public double Decay {
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| 71 | get { return DecayParameter.Value.Value; }
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| 72 | set {
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| 73 | if (value < 0.001 || value > 100) throw new ArgumentException("The decay parameter should be set to a value between 0.001 and 100.", "Decay");
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| 74 | DecayParameter.Value.Value = value;
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| 75 | }
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| 76 | }
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| 77 | public int HiddenLayers {
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| 78 | get { return HiddenLayersParameter.Value.Value; }
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| 79 | set {
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| 80 | if (value < 0 || value > 2) throw new ArgumentException("The number of hidden layers should be set to 0, 1, or 2.", "HiddenLayers");
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| 81 | HiddenLayersParameter.Value = (from v in HiddenLayersParameter.ValidValues
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| 82 | where v.Value == value
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| 83 | select v)
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| 84 | .Single();
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| 85 | }
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| 86 | }
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| 87 | public int NodesInFirstHiddenLayer {
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| 88 | get { return NodesInFirstHiddenLayerParameter.Value.Value; }
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| 89 | set {
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| 90 | if (value < 1) throw new ArgumentException("The number of nodes in the first hidden layer must be at least one.", "NodesInFirstHiddenLayer");
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| 91 | NodesInFirstHiddenLayerParameter.Value.Value = value;
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| 92 | }
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| 93 | }
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| 94 | public int NodesInSecondHiddenLayer {
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| 95 | get { return NodesInSecondHiddenLayerParameter.Value.Value; }
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| 96 | set {
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| 97 | if (value < 1) throw new ArgumentException("The number of nodes in the first second layer must be at least one.", "NodesInSecondHiddenLayer");
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| 98 | NodesInSecondHiddenLayerParameter.Value.Value = value;
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| 99 | }
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| 100 | }
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| 101 | public int Restarts {
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| 102 | get { return RestartsParameter.Value.Value; }
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| 103 | set {
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| 104 | if (value < 0) throw new ArgumentException("The number of restarts must be positive.", "Restarts");
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| 105 | RestartsParameter.Value.Value = value;
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| 106 | }
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| 107 | }
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| 108 | #endregion
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| 109 |
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| 110 |
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[6577] | 111 | [StorableConstructor]
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[6579] | 112 | private NeuralNetworkClassification(bool deserializing) : base(deserializing) { }
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| 113 | private NeuralNetworkClassification(NeuralNetworkClassification original, Cloner cloner)
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[6577] | 114 | : base(original, cloner) {
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[6720] | 115 | RegisterEventHandlers();
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[6577] | 116 | }
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[6579] | 117 | public NeuralNetworkClassification()
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[6577] | 118 | : base() {
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[6720] | 119 | var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] {
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| 120 | (IntValue)new IntValue(0).AsReadOnly(),
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| 121 | (IntValue)new IntValue(1).AsReadOnly(),
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| 122 | (IntValue)new IntValue(2).AsReadOnly() });
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[6578] | 123 | var selectedHiddenLayerValue = (from v in validHiddenLayerValues
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| 124 | where v.Value == 1
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| 125 | select v)
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| 126 | .Single();
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| 127 | Parameters.Add(new FixedValueParameter<DoubleValue>(DecayParameterName, "The decay parameter for the training phase of the neural network. This parameter determines the strengh of regularization and should be set to a value between 0.001 (weak regularization) to 100 (very strong regularization). The correct value should be determined via cross-validation.", new DoubleValue(1)));
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| 128 | Parameters.Add(new ConstrainedValueParameter<IntValue>(HiddenLayersParameterName, "The number of hidden layers for the neural network (0, 1, or 2)", validHiddenLayerValues, selectedHiddenLayerValue));
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| 129 | Parameters.Add(new FixedValueParameter<IntValue>(NodesInFirstHiddenLayerParameterName, "The number of nodes in the first hidden layer. This value is not used if the number of hidden layers is zero.", new IntValue(10)));
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| 130 | Parameters.Add(new FixedValueParameter<IntValue>(NodesInSecondHiddenLayerParameterName, "The number of nodes in the second hidden layer. This value is not used if the number of hidden layers is zero or one.", new IntValue(10)));
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| 131 | Parameters.Add(new FixedValueParameter<IntValue>(RestartsParameterName, "The number of restarts for learning.", new IntValue(2)));
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| 132 |
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[6720] | 133 | RestartsParameter.Hidden = true;
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| 134 | NodesInSecondHiddenLayerParameter.Hidden = true;
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| 135 |
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| 136 | RegisterEventHandlers();
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| 137 |
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[6579] | 138 | Problem = new ClassificationProblem();
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[6577] | 139 | }
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[6720] | 140 |
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| 141 | private void RegisterEventHandlers() {
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| 142 | HiddenLayersParameter.Value.ValueChanged += HiddenLayersParameterValueValueChanged;
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| 143 | HiddenLayersParameter.ValueChanged += HiddenLayersParameterValueChanged;
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| 144 | }
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| 145 |
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[6577] | 146 | [StorableHook(HookType.AfterDeserialization)]
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[6720] | 147 | private void AfterDeserialization() {
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| 148 | RegisterEventHandlers();
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| 149 | }
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[6577] | 150 |
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| 151 | public override IDeepCloneable Clone(Cloner cloner) {
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[6579] | 152 | return new NeuralNetworkClassification(this, cloner);
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[6577] | 153 | }
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[6720] | 154 | private void HiddenLayersParameterValueChanged(object source, EventArgs e) {
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| 155 | HiddenLayersParameter.Value.ValueChanged += HiddenLayersParameterValueValueChanged;
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| 156 | HiddenLayersParameterValueValueChanged(this, EventArgs.Empty);
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| 157 | }
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[6577] | 158 |
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[6720] | 159 | private void HiddenLayersParameterValueValueChanged(object source, EventArgs e) {
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| 160 | if (HiddenLayers == 0) {
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| 161 | NodesInFirstHiddenLayerParameter.Hidden = true;
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| 162 | NodesInSecondHiddenLayerParameter.Hidden = true;
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| 163 | } else if (HiddenLayers == 1) {
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| 164 | NodesInFirstHiddenLayerParameter.Hidden = false;
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| 165 | NodesInSecondHiddenLayerParameter.Hidden = true;
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| 166 | } else {
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| 167 | NodesInFirstHiddenLayerParameter.Hidden = false;
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| 168 | NodesInSecondHiddenLayerParameter.Hidden = false;
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| 169 | }
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| 170 | }
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| 171 |
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[6577] | 172 | #region neural network
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| 173 | protected override void Run() {
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[6579] | 174 | double rmsError, avgRelError, relClassError;
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| 175 | var solution = CreateNeuralNetworkClassificationSolution(Problem.ProblemData, HiddenLayers, NodesInFirstHiddenLayer, NodesInSecondHiddenLayer, Decay, Restarts, out rmsError, out avgRelError, out relClassError);
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[6577] | 176 | Results.Add(new Result(NeuralNetworkRegressionModelResultName, "The neural network regression solution.", solution));
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| 177 | Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network regression solution on the training set.", new DoubleValue(rmsError)));
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| 178 | Results.Add(new Result("Average relative error", "The average of relative errors of the neural network regression solution on the training set.", new PercentValue(avgRelError)));
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[6579] | 179 | Results.Add(new Result("Relative classification error", "The percentage of misclassified samples.", new PercentValue(relClassError)));
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[6577] | 180 | }
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| 181 |
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[6579] | 182 | public static IClassificationSolution CreateNeuralNetworkClassificationSolution(IClassificationProblemData problemData, int nLayers, int nHiddenNodes1, int nHiddenNodes2, double decay, int restarts,
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| 183 | out double rmsError, out double avgRelError, out double relClassError) {
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[6577] | 184 | Dataset dataset = problemData.Dataset;
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| 185 | string targetVariable = problemData.TargetVariable;
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| 186 | IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
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| 187 | IEnumerable<int> rows = problemData.TrainingIndizes;
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| 188 | double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
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| 189 | if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
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[6579] | 190 | throw new NotSupportedException("Neural network classification does not support NaN or infinity values in the input dataset.");
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[6577] | 191 |
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| 192 | int nRows = inputMatrix.GetLength(0);
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[6579] | 193 | int nFeatures = inputMatrix.GetLength(1) - 1;
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[6740] | 194 | double[] classValues = dataset.GetDoubleValues(targetVariable).Distinct().OrderBy(x => x).ToArray();
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[6579] | 195 | int nClasses = classValues.Count();
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| 196 | // map original class values to values [0..nClasses-1]
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| 197 | Dictionary<double, double> classIndizes = new Dictionary<double, double>();
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| 198 | for (int i = 0; i < nClasses; i++) {
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| 199 | classIndizes[classValues[i]] = i;
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| 200 | }
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| 201 | for (int row = 0; row < nRows; row++) {
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| 202 | inputMatrix[row, nFeatures] = classIndizes[inputMatrix[row, nFeatures]];
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| 203 | }
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[6577] | 204 |
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[6580] | 205 | alglib.multilayerperceptron multiLayerPerceptron = null;
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| 206 | if (nLayers == 0) {
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| 207 | alglib.mlpcreatec0(allowedInputVariables.Count(), nClasses, out multiLayerPerceptron);
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| 208 | } else if (nLayers == 1) {
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| 209 | alglib.mlpcreatec1(allowedInputVariables.Count(), nHiddenNodes1, nClasses, out multiLayerPerceptron);
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| 210 | } else if (nLayers == 2) {
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| 211 | alglib.mlpcreatec2(allowedInputVariables.Count(), nHiddenNodes1, nHiddenNodes2, nClasses, out multiLayerPerceptron);
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| 212 | } else throw new ArgumentException("Number of layers must be zero, one, or two.", "nLayers");
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| 213 | alglib.mlpreport rep;
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| 214 |
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[6577] | 215 | int info;
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| 216 | // using mlptrainlm instead of mlptraines or mlptrainbfgs because only one parameter is necessary
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| 217 | alglib.mlptrainlm(multiLayerPerceptron, inputMatrix, nRows, decay, restarts, out info, out rep);
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[6579] | 218 | if (info != 2) throw new ArgumentException("Error in calculation of neural network classification solution");
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[6577] | 219 |
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| 220 | rmsError = alglib.mlprmserror(multiLayerPerceptron, inputMatrix, nRows);
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[6578] | 221 | avgRelError = alglib.mlpavgrelerror(multiLayerPerceptron, inputMatrix, nRows);
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[6579] | 222 | relClassError = alglib.mlpclserror(multiLayerPerceptron, inputMatrix, nRows) / (double)nRows;
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[6577] | 223 |
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[6649] | 224 | var problemDataClone = (IClassificationProblemData)problemData.Clone();
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| 225 | return new NeuralNetworkClassificationSolution(problemDataClone, new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables, problemDataClone.ClassValues.ToArray()));
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[6577] | 226 | }
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| 227 | #endregion
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| 228 | }
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| 229 | }
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