1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
4 | *
|
---|
5 | * This file is part of HeuristicLab.
|
---|
6 | *
|
---|
7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
8 | * it under the terms of the GNU General Public License as published by
|
---|
9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
10 | * (at your option) any later version.
|
---|
11 | *
|
---|
12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
15 | * GNU General Public License for more details.
|
---|
16 | *
|
---|
17 | * You should have received a copy of the GNU General Public License
|
---|
18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
19 | */
|
---|
20 | #endregion
|
---|
21 |
|
---|
22 | using System;
|
---|
23 | using System.Collections.Generic;
|
---|
24 | using System.Linq;
|
---|
25 | using HeuristicLab.Common;
|
---|
26 | using HeuristicLab.Core;
|
---|
27 | using HeuristicLab.Data;
|
---|
28 | using HeuristicLab.Parameters;
|
---|
29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
30 | using HeuristicLab.Optimization;
|
---|
31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
32 | using HeuristicLab.Problems.DataAnalysis;
|
---|
33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
|
---|
34 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
|
---|
35 |
|
---|
36 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
37 | /// <summary>
|
---|
38 | /// Nonlinear regression data analysis algorithm.
|
---|
39 | /// </summary>
|
---|
40 | [Item("Nonlinear Regression (NLR)", "Nonlinear regression (curve fitting) data analysis algorithm (wrapper for ALGLIB).")]
|
---|
41 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
|
---|
42 | [StorableClass]
|
---|
43 | public sealed class NonlinearRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
|
---|
44 | private const string LinearRegressionModelResultName = "Regression solution";
|
---|
45 | private const string ModelStructureParameterName = "Model structure";
|
---|
46 | private const string IterationsParameterName = "Iterations";
|
---|
47 |
|
---|
48 | public IFixedValueParameter<StringValue> ModelStructureParameter {
|
---|
49 | get { return (IFixedValueParameter<StringValue>)Parameters[ModelStructureParameterName]; }
|
---|
50 | }
|
---|
51 | public IFixedValueParameter<IntValue> IterationsParameter {
|
---|
52 | get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
|
---|
53 | }
|
---|
54 |
|
---|
55 | public string ModelStructure {
|
---|
56 | get { return ModelStructureParameter.Value.Value; }
|
---|
57 | set { ModelStructureParameter.Value.Value = value; }
|
---|
58 | }
|
---|
59 |
|
---|
60 | public int Iterations {
|
---|
61 | get { return IterationsParameter.Value.Value; }
|
---|
62 | set { IterationsParameter.Value.Value = value; }
|
---|
63 | }
|
---|
64 |
|
---|
65 |
|
---|
66 | [StorableConstructor]
|
---|
67 | private NonlinearRegression(bool deserializing) : base(deserializing) { }
|
---|
68 | private NonlinearRegression(NonlinearRegression original, Cloner cloner)
|
---|
69 | : base(original, cloner) {
|
---|
70 | }
|
---|
71 | public NonlinearRegression()
|
---|
72 | : base() {
|
---|
73 | Problem = new RegressionProblem();
|
---|
74 | Parameters.Add(new FixedValueParameter<StringValue>(ModelStructureParameterName, "The function for which the parameters must be fit (only numeric constants are tuned).", new StringValue("1.0 * x*x + 0.0")));
|
---|
75 | Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "The maximum number of iterations for constants optimization.", new IntValue(200)));
|
---|
76 | }
|
---|
77 | [StorableHook(HookType.AfterDeserialization)]
|
---|
78 | private void AfterDeserialization() { }
|
---|
79 |
|
---|
80 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
81 | return new NonlinearRegression(this, cloner);
|
---|
82 | }
|
---|
83 |
|
---|
84 | #region nonlinear regression
|
---|
85 | protected override void Run() {
|
---|
86 | var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations);
|
---|
87 | Results.Add(new Result(LinearRegressionModelResultName, "The nonlinear regression solution.", solution));
|
---|
88 | Results.Add(new Result("Root mean square error (train)", "The root of the mean of squared errors of the regression solution on the training set.", new DoubleValue(solution.TrainingRootMeanSquaredError)));
|
---|
89 | Results.Add(new Result("Root mean square error (test)", "The root of the mean of squared errors of the regression solution on the test set.", new DoubleValue(solution.TestRootMeanSquaredError)));
|
---|
90 | }
|
---|
91 |
|
---|
92 | public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations) {
|
---|
93 | var parser = new InfixExpressionParser();
|
---|
94 | var tree = parser.Parse(modelStructure);
|
---|
95 | var simplifier = new SymbolicDataAnalysisExpressionTreeSimplifier();
|
---|
96 |
|
---|
97 | if (!SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree)) throw new ArgumentException("The optimizer does not support the specified model structure.");
|
---|
98 |
|
---|
99 | var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
|
---|
100 | SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices,
|
---|
101 | applyLinearScaling: false, maxIterations: maxIterations,
|
---|
102 | updateVariableWeights: false, updateConstantsInTree: true);
|
---|
103 |
|
---|
104 |
|
---|
105 | var scaledModel = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone());
|
---|
106 | scaledModel.Scale(problemData);
|
---|
107 | SymbolicRegressionSolution solution = new SymbolicRegressionSolution(scaledModel, (IRegressionProblemData)problemData.Clone());
|
---|
108 | solution.Model.Name = "Regression Model";
|
---|
109 | solution.Name = "Regression Solution";
|
---|
110 | return solution;
|
---|
111 | }
|
---|
112 | #endregion
|
---|
113 | }
|
---|
114 | }
|
---|