[3442] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using System.Drawing;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.PluginInfrastructure;
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| 33 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 34 | using HeuristicLab.Problems.DataAnalysis;
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| 35 | using HeuristicLab.Operators;
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| 36 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 37 |
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| 38 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
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| 39 | [StorableClass]
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[3462] | 40 | [Item("SymbolicRegressionModel", "A symbolic regression model represents an entity that provides estimated values based on input values.")]
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| 41 | public class SymbolicRegressionModel : Item {
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[3442] | 42 | [Storable]
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| 43 | private SymbolicExpressionTree tree;
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| 44 | public SymbolicExpressionTree SymbolicExpressionTree {
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| 45 | get { return tree; }
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| 46 | }
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| 47 | [Storable]
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[3462] | 48 | private ISymbolicExpressionTreeInterpreter interpreter;
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| 49 | public ISymbolicExpressionTreeInterpreter Interpreter {
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| 50 | get { return interpreter; }
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[3442] | 51 | }
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[3462] | 52 | [Storable]
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| 53 | private List<string> inputVariables;
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| 54 | public IEnumerable<string> InputVariables {
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| 55 | get { return inputVariables.AsEnumerable(); }
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| 56 | }
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[3442] | 57 | public SymbolicRegressionModel() : base() { } // for cloning
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| 58 |
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[3462] | 59 | public SymbolicRegressionModel(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree tree, IEnumerable<string> inputVariables)
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[3442] | 60 | : base() {
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| 61 | this.tree = tree;
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[3462] | 62 | this.interpreter = interpreter;
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| 63 | this.inputVariables = inputVariables.ToList();
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[3442] | 64 | }
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| 65 |
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[3462] | 66 | public IEnumerable<double> GetEstimatedValues(Dataset dataset, int start, int end) {
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| 67 | return interpreter.GetSymbolicExpressionTreeValues(tree, dataset, Enumerable.Range(start, end - start));
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| 68 | }
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[3442] | 69 |
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[3462] | 70 | public override IDeepCloneable Clone(Cloner cloner) {
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| 71 | var clone = (SymbolicRegressionModel)base.Clone(cloner);
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[3493] | 72 | clone.tree = (SymbolicExpressionTree)cloner.Clone(tree);
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| 73 | clone.interpreter = (ISymbolicExpressionTreeInterpreter)cloner.Clone(interpreter);
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[3462] | 74 | clone.inputVariables = new List<string>(inputVariables);
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| 75 | return clone;
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[3442] | 76 | }
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| 77 | }
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| 78 | }
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