[14386] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2016 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 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.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.DataAnalysis;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 32 | /// <summary>
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| 33 | /// Represents a Radial Basis Function regression model.
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| 34 | /// </summary>
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| 35 | [StorableClass]
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| 36 | [Item("RBFModel", "Represents a Gaussian process posterior.")]
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| 37 | public sealed class RadialBasisFunctionModel : RegressionModel, IConfidenceRegressionModel {
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| 38 | public override IEnumerable<string> VariablesUsedForPrediction
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| 39 | {
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| 40 | get { return allowedInputVariables; }
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| 41 | }
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| 42 |
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| 43 | [Storable]
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| 44 | private string[] allowedInputVariables;
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| 45 | public string[] AllowedInputVariables
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| 46 | {
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| 47 | get { return allowedInputVariables; }
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| 48 | }
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| 49 |
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| 50 | [Storable]
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| 51 | private double[] alpha;
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| 52 | [Storable]
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| 53 | private IDataset trainingDataset; // it is better to store the original training dataset completely because this is more efficient in persistence
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| 54 | [Storable]
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| 55 | private int[] trainingRows;
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| 56 | [Storable]
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| 57 | private IKernelFunction<double[]> kernel;
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| 58 | [Storable]
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| 59 | private DoubleMatrix gramInvert;
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| 60 |
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| 61 |
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| 62 | [StorableConstructor]
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| 63 | private RadialBasisFunctionModel(bool deserializing) : base(deserializing) { }
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| 64 | private RadialBasisFunctionModel(RadialBasisFunctionModel original, Cloner cloner)
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| 65 | : base(original, cloner) {
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| 66 | // shallow copies of arrays because they cannot be modified
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| 67 | trainingRows = original.trainingRows;
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| 68 | allowedInputVariables = original.allowedInputVariables;
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| 69 | alpha = original.alpha;
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| 70 | trainingDataset = original.trainingDataset;
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| 71 | kernel = original.kernel;
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| 72 | }
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| 73 | public RadialBasisFunctionModel(IDataset dataset, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<int> rows, IKernelFunction<double[]> kernel)
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| 74 | : base(targetVariable) {
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| 75 | name = ItemName;
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| 76 | description = ItemDescription;
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| 77 | this.allowedInputVariables = allowedInputVariables.ToArray();
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| 78 | trainingRows = rows.ToArray();
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| 79 | trainingDataset = dataset;
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| 80 | this.kernel = (IKernelFunction<double[]>)kernel.Clone();
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| 81 | try {
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| 82 | var data = ExtractData(dataset, trainingRows);
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| 83 | var qualities = dataset.GetDoubleValues(targetVariable, trainingRows).ToArray();
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| 84 | int info;
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| 85 | alglib.densesolverlsreport denseSolveRep;
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| 86 | var gr = BuildGramMatrix(data);
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| 87 | alglib.rmatrixsolvels(gr, data.Length + 1, data.Length + 1, qualities.Concat(new[] { 0.0 }).ToArray(), 0.0, out info, out denseSolveRep, out alpha);
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| 88 | if (info != 1) throw new ArgumentException("Could not create Model.");
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| 89 | gramInvert = new DoubleMatrix(gr).Invert();
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| 90 | }
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| 91 | catch (alglib.alglibexception ae) {
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| 92 | // wrap exception so that calling code doesn't have to know about alglib implementation
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| 93 | throw new ArgumentException("There was a problem in the calculation of the RBF process model", ae);
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| 94 | }
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| 95 | }
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| 96 | private double[][] ExtractData(IDataset dataset, IEnumerable<int> rows) {
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| 97 | return rows.Select(r => allowedInputVariables.Select(v => dataset.GetDoubleValue(v, r)).ToArray()).ToArray();
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| 98 | }
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| 99 |
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| 100 | public override IDeepCloneable Clone(Cloner cloner) {
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| 101 | return new RadialBasisFunctionModel(this, cloner);
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| 102 | }
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| 103 |
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| 104 | #region IRegressionModel Members
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| 105 | public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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| 106 | var solutions = ExtractData(dataset, rows);
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| 107 | var data = ExtractData(trainingDataset, trainingRows);
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| 108 | return solutions.Select(solution => alpha.Zip(data, (a, d) => a * kernel.Get(solution, d)).Sum() + 1 * alpha[alpha.Length - 1]).ToArray();
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| 109 | }
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| 110 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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| 111 | return new RadialBasisFunctionRegressionSolution(this, new RegressionProblemData(problemData));
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| 112 | }
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| 113 | #endregion
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| 114 |
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| 115 | public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
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| 116 | var data = ExtractData(trainingDataset, trainingRows);
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| 117 | return ExtractData(dataset, rows).Select(x => GetVariance(x, data));
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| 118 | }
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| 119 | public double LeaveOneOutCrossValidationRootMeanSquaredError() {
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| 120 | return Math.Sqrt(alpha.Select((t, i) => t / gramInvert[i, i]).Sum(d => d * d) / gramInvert.Rows);
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| 121 | }
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| 122 |
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| 123 | #region helpers
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| 124 | private double[,] BuildGramMatrix(double[][] data) {
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| 125 | var size = data.Length + 1;
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| 126 | var gram = new double[size, size];
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| 127 | for (var i = 0; i < size; i++)
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| 128 | for (var j = i; j < size; j++) {
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| 129 | if (j == size - 1 && i == size - 1) gram[i, j] = 0;
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| 130 | else if (j == size - 1 || i == size - 1) gram[j, i] = gram[i, j] = 1;
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| 131 | else gram[j, i] = gram[i, j] = kernel.Get(data[i], data[j]); //symmteric Matrix --> half of the work
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| 132 | }
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| 133 | return gram;
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| 134 | }
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| 135 | private double GetVariance(double[] solution, IEnumerable<double[]> data) {
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| 136 | var phiT = data.Select(x => kernel.Get(x, solution)).Concat(new[] { 1.0 }).ToColumnVector();
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| 137 | var res = phiT.Transpose().Mul(gramInvert.Mul(phiT))[0, 0];
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| 138 | return res > 0 ? res : 0;
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| 139 | }
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| 140 | #endregion
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| 141 | }
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| 142 | }
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