[2562] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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.Collections.Generic;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.Modeling;
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| 26 | using System;
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| 27 | using System.Xml;
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| 28 | using HeuristicLab.DataAnalysis;
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[2985] | 29 | using System.Linq;
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[2562] | 30 |
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| 31 | namespace HeuristicLab.ArtificialNeuralNetworks {
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| 32 | public class Predictor : PredictorBase {
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| 33 | private MultiLayerPerceptron perceptron;
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| 34 | public Predictor() : base() { } // for persistence
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| 35 | public Predictor(MultiLayerPerceptron perceptron, double lowerPredictionLimit, double upperPredictionLimit)
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| 36 | : base(lowerPredictionLimit, upperPredictionLimit) {
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| 37 | this.perceptron = perceptron;
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| 38 | }
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| 39 |
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[2619] | 40 | public override IEnumerable<double> Predict(Dataset input, int start, int end) {
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[2562] | 41 |
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| 42 | if (start < 0 || end <= start) throw new ArgumentException("start must be larger than zero and strictly smaller than end");
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| 43 | if (end > input.Rows) throw new ArgumentOutOfRangeException("number of rows in input is smaller then end");
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[2619] | 44 |
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| 45 | for (int i = 0; i < end - start; i++) {
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| 46 | double[] output = new double[1];
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[2985] | 47 | double[] inputRow = new double[GetInputVariables().Count()];
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| 48 | int c = 0;
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| 49 | foreach (var inputVariable in GetInputVariables()) {
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| 50 | int inputVariableIndex = input.GetVariableIndex(inputVariable);
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| 51 | inputRow[c++] = input.GetValue(i + start, inputVariableIndex);
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[2562] | 52 | }
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[2985] | 53 | double estimatedValue;
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| 54 | try {
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| 55 | alglib.mlpbase.multilayerperceptron p = perceptron.Perceptron;
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| 56 | alglib.mlpbase.mlpprocess(ref p, ref inputRow, ref output);
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| 57 | perceptron.Perceptron = p;
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| 58 | estimatedValue = output[0];
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| 59 | }
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| 60 | catch (ArithmeticException) {
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| 61 | estimatedValue = UpperPredictionLimit;
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| 62 | }
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| 63 | yield return Math.Max(Math.Min(estimatedValue, UpperPredictionLimit), LowerPredictionLimit);
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[2562] | 64 | }
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| 65 | }
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| 66 |
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| 67 | public override IEnumerable<string> GetInputVariables() {
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| 68 | return perceptron.InputVariables;
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| 69 | }
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| 70 |
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| 71 |
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| 72 | public override object Clone(IDictionary<Guid, object> clonedObjects) {
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| 73 | Predictor clone = (Predictor)base.Clone(clonedObjects);
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| 74 | clone.perceptron = (MultiLayerPerceptron)Auxiliary.Clone(perceptron, clonedObjects);
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| 75 | return clone;
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| 76 | }
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| 77 |
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| 78 | public override System.Xml.XmlNode GetXmlNode(string name, System.Xml.XmlDocument document, IDictionary<Guid, IStorable> persistedObjects) {
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| 79 | XmlNode node = base.GetXmlNode(name, document, persistedObjects);
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| 80 | node.AppendChild(PersistenceManager.Persist("Perceptron", perceptron, document, persistedObjects));
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| 81 | return node;
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| 82 | }
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| 83 |
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| 84 | public override void Populate(System.Xml.XmlNode node, IDictionary<Guid, IStorable> restoredObjects) {
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| 85 | base.Populate(node, restoredObjects);
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| 86 | perceptron = (MultiLayerPerceptron)PersistenceManager.Restore(node.SelectSingleNode("Perceptron"), restoredObjects);
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| 87 | }
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| 88 | }
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| 89 | }
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