[15964] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 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.Diagnostics;
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| 25 | using System.Linq;
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[15968] | 26 | using HeuristicLab.Analysis;
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| 27 | using HeuristicLab.Collections;
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[15964] | 28 | using HeuristicLab.Common;
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| 29 | using HeuristicLab.Core;
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[15968] | 30 | using HeuristicLab.Data;
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[15964] | 31 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[15968] | 32 | using HeuristicLab.Optimization;
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[15964] | 33 | using HeuristicLab.Parameters;
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| 34 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 35 | using HeuristicLab.Problems.DataAnalysis;
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[16126] | 36 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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[15964] | 37 | using HeuristicLab.Problems.Instances;
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| 38 |
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| 39 | namespace HeuristicLab.Problems.DynamicalSystemsModelling {
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| 40 | public class Vector {
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| 41 | public readonly static Vector Zero = new Vector(new double[0]);
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| 42 |
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| 43 | public static Vector operator +(Vector a, Vector b) {
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| 44 | if (a == Zero) return b;
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| 45 | if (b == Zero) return a;
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| 46 | Debug.Assert(a.arr.Length == b.arr.Length);
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| 47 | var res = new double[a.arr.Length];
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| 48 | for (int i = 0; i < res.Length; i++)
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| 49 | res[i] = a.arr[i] + b.arr[i];
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| 50 | return new Vector(res);
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| 51 | }
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| 52 | public static Vector operator -(Vector a, Vector b) {
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| 53 | if (b == Zero) return a;
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| 54 | if (a == Zero) return -b;
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| 55 | Debug.Assert(a.arr.Length == b.arr.Length);
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| 56 | var res = new double[a.arr.Length];
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| 57 | for (int i = 0; i < res.Length; i++)
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| 58 | res[i] = a.arr[i] - b.arr[i];
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| 59 | return new Vector(res);
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| 60 | }
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| 61 | public static Vector operator -(Vector v) {
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| 62 | if (v == Zero) return Zero;
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| 63 | for (int i = 0; i < v.arr.Length; i++)
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| 64 | v.arr[i] = -v.arr[i];
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| 65 | return v;
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| 66 | }
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| 67 |
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| 68 | public static Vector operator *(double s, Vector v) {
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| 69 | if (v == Zero) return Zero;
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| 70 | if (s == 0.0) return Zero;
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| 71 | var res = new double[v.arr.Length];
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| 72 | for (int i = 0; i < res.Length; i++)
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| 73 | res[i] = s * v.arr[i];
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| 74 | return new Vector(res);
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| 75 | }
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| 76 | public static Vector operator *(Vector v, double s) {
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| 77 | return s * v;
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| 78 | }
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| 79 | public static Vector operator /(double s, Vector v) {
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| 80 | if (s == 0.0) return Zero;
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| 81 | if (v == Zero) throw new ArgumentException("Division by zero vector");
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| 82 | var res = new double[v.arr.Length];
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| 83 | for (int i = 0; i < res.Length; i++)
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| 84 | res[i] = 1.0 / v.arr[i];
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| 85 | return new Vector(res);
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| 86 | }
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| 87 | public static Vector operator /(Vector v, double s) {
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| 88 | return v * 1.0 / s;
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| 89 | }
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| 90 |
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| 91 |
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| 92 | private readonly double[] arr; // backing array;
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| 93 |
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| 94 | public Vector(double[] v) {
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| 95 | this.arr = v;
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| 96 | }
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| 97 |
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| 98 | public void CopyTo(double[] target) {
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| 99 | Debug.Assert(arr.Length <= target.Length);
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| 100 | Array.Copy(arr, target, arr.Length);
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| 101 | }
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| 102 | }
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| 103 |
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| 104 | [Item("Dynamical Systems Modelling Problem", "TODO")]
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| 105 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 900)]
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| 106 | [StorableClass]
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[15968] | 107 | public sealed class Problem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
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[15964] | 108 |
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| 109 | #region parameter names
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[15968] | 110 | private const string ProblemDataParameterName = "Data";
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| 111 | private const string TargetVariablesParameterName = "Target variables";
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| 112 | private const string FunctionSetParameterName = "Function set";
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| 113 | private const string MaximumLengthParameterName = "Size limit";
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| 114 | private const string MaximumParameterOptimizationIterationsParameterName = "Max. parameter optimization iterations";
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[15970] | 115 | private const string NumberOfLatentVariablesParameterName = "Number of latent variables";
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| 116 | private const string NumericIntegrationStepsParameterName = "Steps for numeric integration";
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[15964] | 117 | #endregion
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| 118 |
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| 119 | #region Parameter Properties
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| 120 | IParameter IDataAnalysisProblem.ProblemDataParameter { get { return ProblemDataParameter; } }
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| 121 |
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| 122 | public IValueParameter<IRegressionProblemData> ProblemDataParameter {
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| 123 | get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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| 124 | }
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[15968] | 125 | public IValueParameter<ReadOnlyCheckedItemCollection<StringValue>> TargetVariablesParameter {
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| 126 | get { return (IValueParameter<ReadOnlyCheckedItemCollection<StringValue>>)Parameters[TargetVariablesParameterName]; }
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| 127 | }
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| 128 | public IValueParameter<ReadOnlyCheckedItemCollection<StringValue>> FunctionSetParameter {
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| 129 | get { return (IValueParameter<ReadOnlyCheckedItemCollection<StringValue>>)Parameters[FunctionSetParameterName]; }
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| 130 | }
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| 131 | public IFixedValueParameter<IntValue> MaximumLengthParameter {
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| 132 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumLengthParameterName]; }
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| 133 | }
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| 134 | public IFixedValueParameter<IntValue> MaximumParameterOptimizationIterationsParameter {
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| 135 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumParameterOptimizationIterationsParameterName]; }
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| 136 | }
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[15970] | 137 | public IFixedValueParameter<IntValue> NumberOfLatentVariablesParameter {
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| 138 | get { return (IFixedValueParameter<IntValue>)Parameters[NumberOfLatentVariablesParameterName]; }
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| 139 | }
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| 140 | public IFixedValueParameter<IntValue> NumericIntegrationStepsParameter {
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| 141 | get { return (IFixedValueParameter<IntValue>)Parameters[NumericIntegrationStepsParameterName]; }
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| 142 | }
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[15964] | 143 | #endregion
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| 144 |
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| 145 | #region Properties
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| 146 | public IRegressionProblemData ProblemData {
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| 147 | get { return ProblemDataParameter.Value; }
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| 148 | set { ProblemDataParameter.Value = value; }
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| 149 | }
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| 150 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData { get { return ProblemData; } }
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| 151 |
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[15968] | 152 | public ReadOnlyCheckedItemCollection<StringValue> TargetVariables {
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| 153 | get { return TargetVariablesParameter.Value; }
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| 154 | }
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| 155 |
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| 156 | public ReadOnlyCheckedItemCollection<StringValue> FunctionSet {
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| 157 | get { return FunctionSetParameter.Value; }
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| 158 | }
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| 159 |
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| 160 | public int MaximumLength {
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| 161 | get { return MaximumLengthParameter.Value.Value; }
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| 162 | }
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| 163 | public int MaximumParameterOptimizationIterations {
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| 164 | get { return MaximumParameterOptimizationIterationsParameter.Value.Value; }
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| 165 | }
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[15970] | 166 | public int NumberOfLatentVariables {
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| 167 | get { return NumberOfLatentVariablesParameter.Value.Value; }
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| 168 | }
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| 169 | public int NumericIntegrationSteps {
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| 170 | get { return NumericIntegrationStepsParameter.Value.Value; }
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| 171 | }
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| 172 |
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[15968] | 173 | #endregion
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| 174 |
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[15964] | 175 | public event EventHandler ProblemDataChanged;
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| 176 |
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| 177 | public override bool Maximization {
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| 178 | get { return false; } // we minimize NMSE
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| 179 | }
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| 180 |
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| 181 | #region item cloning and persistence
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| 182 | // persistence
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| 183 | [StorableConstructor]
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| 184 | private Problem(bool deserializing) : base(deserializing) { }
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| 185 | [StorableHook(HookType.AfterDeserialization)]
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| 186 | private void AfterDeserialization() {
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| 187 | RegisterEventHandlers();
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| 188 | }
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| 189 |
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| 190 | // cloning
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| 191 | private Problem(Problem original, Cloner cloner)
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| 192 | : base(original, cloner) {
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| 193 | RegisterEventHandlers();
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| 194 | }
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| 195 | public override IDeepCloneable Clone(Cloner cloner) { return new Problem(this, cloner); }
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| 196 | #endregion
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| 197 |
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| 198 | public Problem()
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| 199 | : base() {
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[15968] | 200 | var targetVariables = new CheckedItemCollection<StringValue>().AsReadOnly(); // HACK: it would be better to provide a new class derived from IDataAnalysisProblem
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| 201 | var functions = CreateFunctionSet();
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[15970] | 202 | Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, "The data captured from the dynamical system. Use CSV import functionality to import data.", new RegressionProblemData()));
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[15968] | 203 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemCollection<StringValue>>(TargetVariablesParameterName, "Target variables (overrides setting in ProblemData)", targetVariables));
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| 204 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemCollection<StringValue>>(FunctionSetParameterName, "The list of allowed functions", functions));
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[15970] | 205 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumLengthParameterName, "The maximally allowed length of each expression. Set to a small value (5 - 25). Default = 10", new IntValue(10)));
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| 206 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumParameterOptimizationIterationsParameterName, "The maximum number of iterations for optimization of parameters (using L-BFGS). More iterations makes the algorithm slower, fewer iterations might prevent convergence in the optimization scheme. Default = 100", new IntValue(100)));
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| 207 | Parameters.Add(new FixedValueParameter<IntValue>(NumberOfLatentVariablesParameterName, "Latent variables (unobserved variables) allow us to produce expressions which are integrated up and can be used in other expressions. They are handled similarly to target variables in forward simulation / integration. The difference to target variables is that there are no data to which the calculated values of latent variables are compared. Set to a small value (0 .. 5) as necessary (default = 0)", new IntValue(0)));
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| 208 | Parameters.Add(new FixedValueParameter<IntValue>(NumericIntegrationStepsParameterName, "Number of steps in the numeric integration that are taken from one row to the next (set to 1 to 100). More steps makes the algorithm slower, less steps worsens the accuracy of the numeric integration scheme.", new IntValue(10)));
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[15964] | 209 |
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| 210 | RegisterEventHandlers();
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[15968] | 211 | InitAllParameters();
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[16152] | 212 |
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| 213 | // TODO: do not clear selection of target variables when the input variables are changed
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| 214 | // TODO: UI hangs when selecting / deselecting input variables because the encoding is updated on each item
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[15964] | 215 | }
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| 216 |
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| 217 |
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[15968] | 218 | public override double Evaluate(Individual individual, IRandom random) {
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| 219 | var trees = individual.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
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| 220 |
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[15964] | 221 | var problemData = ProblemData;
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| 222 | var rows = ProblemData.TrainingIndices.ToArray();
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[15968] | 223 | var targetVars = TargetVariables.CheckedItems.Select(i => i.Value).ToArray();
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[15970] | 224 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
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| 225 | var targetValues = new double[rows.Length, targetVars.Length];
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| 226 |
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[15968] | 227 | // collect values of all target variables
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| 228 | var colIdx = 0;
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[15970] | 229 | foreach (var targetVar in targetVars) {
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[15968] | 230 | int rowIdx = 0;
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[15970] | 231 | foreach (var value in problemData.Dataset.GetDoubleValues(targetVar, rows)) {
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[15968] | 232 | targetValues[rowIdx, colIdx] = value;
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| 233 | rowIdx++;
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| 234 | }
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| 235 | colIdx++;
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| 236 | }
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[15964] | 237 |
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| 238 | var nodeIdx = new Dictionary<ISymbolicExpressionTreeNode, int>();
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[15968] | 239 |
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| 240 | foreach (var tree in trees) {
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| 241 | foreach (var node in tree.Root.IterateNodesPrefix().Where(n => IsConstantNode(n))) {
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| 242 | nodeIdx.Add(node, nodeIdx.Count);
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| 243 | }
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[15964] | 244 | }
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| 245 |
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| 246 | var theta = nodeIdx.Select(_ => random.NextDouble() * 2.0 - 1.0).ToArray(); // init params randomly from Unif(-1,1)
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| 247 |
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| 248 | double[] optTheta = new double[0];
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| 249 | if (theta.Length > 0) {
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| 250 | alglib.minlbfgsstate state;
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| 251 | alglib.minlbfgsreport report;
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| 252 | alglib.minlbfgscreate(Math.Min(theta.Length, 5), theta, out state);
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[15968] | 253 | alglib.minlbfgssetcond(state, 0.0, 0.0, 0.0, MaximumParameterOptimizationIterations);
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[16126] | 254 | alglib.minlbfgsoptimize(state, EvaluateObjectiveAndGradient, null,
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[15970] | 255 | new object[] { trees, targetVars, problemData, nodeIdx, targetValues, rows, NumericIntegrationSteps, latentVariables }); //TODO: create a type
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[15964] | 256 | alglib.minlbfgsresults(state, out optTheta, out report);
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| 257 |
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| 258 | /*
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| 259 | *
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| 260 | * L-BFGS algorithm results
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| 261 |
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| 262 | INPUT PARAMETERS:
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| 263 | State - algorithm state
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| 264 |
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| 265 | OUTPUT PARAMETERS:
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| 266 | X - array[0..N-1], solution
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| 267 | Rep - optimization report:
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| 268 | * Rep.TerminationType completetion code:
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| 269 | * -7 gradient verification failed.
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| 270 | See MinLBFGSSetGradientCheck() for more information.
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| 271 | * -2 rounding errors prevent further improvement.
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| 272 | X contains best point found.
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| 273 | * -1 incorrect parameters were specified
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| 274 | * 1 relative function improvement is no more than
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| 275 | EpsF.
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| 276 | * 2 relative step is no more than EpsX.
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| 277 | * 4 gradient norm is no more than EpsG
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| 278 | * 5 MaxIts steps was taken
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| 279 | * 7 stopping conditions are too stringent,
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| 280 | further improvement is impossible
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| 281 | * Rep.IterationsCount contains iterations count
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| 282 | * NFEV countains number of function calculations
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| 283 | */
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| 284 | if (report.terminationtype < 0) return double.MaxValue;
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| 285 | }
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| 286 |
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| 287 | // perform evaluation for optimal theta to get quality value
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| 288 | double[] grad = new double[optTheta.Length];
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| 289 | double optQuality = double.NaN;
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[16126] | 290 | EvaluateObjectiveAndGradient(optTheta, ref optQuality, grad,
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[15970] | 291 | new object[] { trees, targetVars, problemData, nodeIdx, targetValues, rows, NumericIntegrationSteps, latentVariables });
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[15964] | 292 | if (double.IsNaN(optQuality) || double.IsInfinity(optQuality)) return 10E6; // return a large value (TODO: be consistent by using NMSE)
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[15968] | 293 |
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| 294 | individual["OptTheta"] = new DoubleArray(optTheta); // write back optimized parameters so that we can use them in the Analysis method
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[15964] | 295 | return optQuality;
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| 296 | }
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| 297 |
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| 298 | private static void EvaluateObjectiveAndGradient(double[] x, ref double f, double[] grad, object obj) {
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[15968] | 299 | var trees = (ISymbolicExpressionTree[])((object[])obj)[0];
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| 300 | var targetVariables = (string[])((object[])obj)[1];
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| 301 | var problemData = (IRegressionProblemData)((object[])obj)[2];
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| 302 | var nodeIdx = (Dictionary<ISymbolicExpressionTreeNode, int>)((object[])obj)[3];
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| 303 | var targetValues = (double[,])((object[])obj)[4];
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| 304 | var rows = (int[])((object[])obj)[5];
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[15970] | 305 | var numericIntegrationSteps = (int)((object[])obj)[6];
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| 306 | var latentVariables = (string[])((object[])obj)[7];
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[15964] | 307 |
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| 308 | var predicted = Integrate(
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[15968] | 309 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
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[15964] | 310 | problemData.Dataset,
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| 311 | problemData.AllowedInputVariables.ToArray(),
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[15968] | 312 | targetVariables,
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[15970] | 313 | latentVariables,
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[15968] | 314 | rows,
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| 315 | nodeIdx, // TODO: is it Ok to use rows here ?
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[15970] | 316 | x, numericIntegrationSteps).ToArray();
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[15964] | 317 |
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[15968] | 318 |
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| 319 | // for normalized MSE = 1/variance(t) * MSE(t, pred)
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[15971] | 320 | // TODO: Perf. (by standardization of target variables before evaluation of all trees)
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[15968] | 321 | var invVar = Enumerable.Range(0, targetVariables.Length)
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| 322 | .Select(c => rows.Select(row => targetValues[row, c])) // colums vectors
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| 323 | .Select(vec => vec.Variance())
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| 324 | .Select(v => 1.0 / v)
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| 325 | .ToArray();
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| 326 |
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| 327 | // objective function is NMSE
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[15964] | 328 | f = 0.0;
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| 329 | int n = predicted.Length;
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| 330 | double invN = 1.0 / n;
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| 331 | var g = Vector.Zero;
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[15968] | 332 | int r = 0;
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| 333 | foreach (var y_pred in predicted) {
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[15970] | 334 | for (int c = 0; c < y_pred.Length; c++) {
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| 335 |
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[15968] | 336 | var y_pred_f = y_pred[c].Item1;
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[15970] | 337 | var y = targetValues[r, c];
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[15964] | 338 |
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[15968] | 339 | var res = (y - y_pred_f);
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| 340 | var ressq = res * res;
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| 341 | f += ressq * invN * invVar[c];
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| 342 | g += -2.0 * res * y_pred[c].Item2 * invN * invVar[c];
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| 343 | }
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| 344 | r++;
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[15964] | 345 | }
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| 346 |
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| 347 | g.CopyTo(grad);
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| 348 | }
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| 349 |
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[15968] | 350 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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| 351 | base.Analyze(individuals, qualities, results, random);
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[15964] | 352 |
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[15968] | 353 | if (!results.ContainsKey("Prediction (training)")) {
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| 354 | results.Add(new Result("Prediction (training)", typeof(ReadOnlyItemList<DataTable>)));
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| 355 | }
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| 356 | if (!results.ContainsKey("Prediction (test)")) {
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| 357 | results.Add(new Result("Prediction (test)", typeof(ReadOnlyItemList<DataTable>)));
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| 358 | }
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| 359 | if (!results.ContainsKey("Models")) {
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| 360 | results.Add(new Result("Models", typeof(ReadOnlyItemList<ISymbolicExpressionTree>)));
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| 361 | }
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| 362 |
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| 363 | // TODO extract common functionality from Evaluate and Analyze
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| 364 | var bestIndividualAndQuality = this.GetBestIndividual(individuals, qualities);
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[15970] | 365 | var optTheta = ((DoubleArray)bestIndividualAndQuality.Item1["OptTheta"]).ToArray(); // see evaluate
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[15968] | 366 | var trees = bestIndividualAndQuality.Item1.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
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| 367 | var nodeIdx = new Dictionary<ISymbolicExpressionTreeNode, int>();
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| 368 |
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| 369 |
|
---|
| 370 | foreach (var tree in trees) {
|
---|
| 371 | foreach (var node in tree.Root.IterateNodesPrefix().Where(n => IsConstantNode(n))) {
|
---|
| 372 | nodeIdx.Add(node, nodeIdx.Count);
|
---|
| 373 | }
|
---|
| 374 | }
|
---|
| 375 | var problemData = ProblemData;
|
---|
| 376 | var targetVars = TargetVariables.CheckedItems.Select(i => i.Value).ToArray();
|
---|
[15970] | 377 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
|
---|
[15968] | 378 |
|
---|
| 379 | var trainingList = new ItemList<DataTable>();
|
---|
| 380 | var trainingRows = ProblemData.TrainingIndices.ToArray();
|
---|
| 381 | var trainingPrediction = Integrate(
|
---|
| 382 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
|
---|
| 383 | problemData.Dataset,
|
---|
| 384 | problemData.AllowedInputVariables.ToArray(),
|
---|
| 385 | targetVars,
|
---|
[15970] | 386 | latentVariables,
|
---|
[15968] | 387 | trainingRows,
|
---|
| 388 | nodeIdx,
|
---|
[16126] | 389 | optTheta,
|
---|
[15970] | 390 | NumericIntegrationSteps).ToArray();
|
---|
[15968] | 391 |
|
---|
| 392 | for (int colIdx = 0; colIdx < targetVars.Length; colIdx++) {
|
---|
| 393 | var targetVar = targetVars[colIdx];
|
---|
[15970] | 394 | var trainingDataTable = new DataTable(targetVar + " prediction (training)");
|
---|
[15968] | 395 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, trainingRows));
|
---|
| 396 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, trainingPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
| 397 | trainingDataTable.Rows.Add(actualValuesRow);
|
---|
| 398 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
| 399 | trainingList.Add(trainingDataTable);
|
---|
| 400 | }
|
---|
| 401 |
|
---|
| 402 | // TODO: DRY for training and test
|
---|
| 403 | var testList = new ItemList<DataTable>();
|
---|
| 404 | var testRows = ProblemData.TestIndices.ToArray();
|
---|
| 405 | var testPrediction = Integrate(
|
---|
| 406 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
|
---|
| 407 | problemData.Dataset,
|
---|
| 408 | problemData.AllowedInputVariables.ToArray(),
|
---|
| 409 | targetVars,
|
---|
[15970] | 410 | latentVariables,
|
---|
[15968] | 411 | testRows,
|
---|
| 412 | nodeIdx,
|
---|
[16126] | 413 | optTheta,
|
---|
[15970] | 414 | NumericIntegrationSteps).ToArray();
|
---|
[15968] | 415 |
|
---|
| 416 | for (int colIdx = 0; colIdx < targetVars.Length; colIdx++) {
|
---|
| 417 | var targetVar = targetVars[colIdx];
|
---|
| 418 | var testDataTable = new DataTable(targetVar + " prediction (test)");
|
---|
| 419 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, testRows));
|
---|
| 420 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, testPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
| 421 | testDataTable.Rows.Add(actualValuesRow);
|
---|
| 422 | testDataTable.Rows.Add(predictedValuesRow);
|
---|
| 423 | testList.Add(testDataTable);
|
---|
| 424 | }
|
---|
| 425 |
|
---|
| 426 | results["Prediction (training)"].Value = trainingList.AsReadOnly();
|
---|
| 427 | results["Prediction (test)"].Value = testList.AsReadOnly();
|
---|
[16126] | 428 |
|
---|
[16152] | 429 | #region simplification of models
|
---|
| 430 | // TODO the dependency of HeuristicLab.Problems.DataAnalysis.Symbolic is not ideal
|
---|
[16126] | 431 | var modelList = new ItemList<ISymbolicExpressionTree>();
|
---|
| 432 | foreach (var tree in trees) {
|
---|
| 433 | var shownTree = (ISymbolicExpressionTree)tree.Clone();
|
---|
| 434 | var constantsNodeOrig = tree.IterateNodesPrefix().Where(IsConstantNode);
|
---|
| 435 | var constantsNodeShown = shownTree.IterateNodesPrefix().Where(IsConstantNode);
|
---|
| 436 |
|
---|
| 437 | foreach (var n in constantsNodeOrig.Zip(constantsNodeShown, (original, shown) => new { original, shown })) {
|
---|
| 438 | double constantsVal = optTheta[nodeIdx[n.original]];
|
---|
| 439 |
|
---|
| 440 | ConstantTreeNode replacementNode = new ConstantTreeNode(new Constant()) { Value = constantsVal };
|
---|
| 441 |
|
---|
| 442 | var parentNode = n.shown.Parent;
|
---|
| 443 | int replacementIndex = parentNode.IndexOfSubtree(n.shown);
|
---|
| 444 | parentNode.RemoveSubtree(replacementIndex);
|
---|
| 445 | parentNode.InsertSubtree(replacementIndex, replacementNode);
|
---|
| 446 | }
|
---|
| 447 |
|
---|
| 448 | modelList.Add(shownTree);
|
---|
| 449 | }
|
---|
| 450 | results["Models"].Value = modelList.AsReadOnly();
|
---|
[16152] | 451 | #endregion
|
---|
[15968] | 452 | }
|
---|
| 453 |
|
---|
| 454 |
|
---|
| 455 | #region interpretation
|
---|
| 456 | private static IEnumerable<Tuple<double, Vector>[]> Integrate(
|
---|
[15970] | 457 | ISymbolicExpressionTree[] trees, IDataset dataset, string[] inputVariables, string[] targetVariables, string[] latentVariables, IEnumerable<int> rows,
|
---|
| 458 | Dictionary<ISymbolicExpressionTreeNode, int> nodeIdx, double[] parameterValues, int numericIntegrationSteps = 100) {
|
---|
[15964] | 459 |
|
---|
[15970] | 460 | int NUM_STEPS = numericIntegrationSteps ;
|
---|
[15964] | 461 | double h = 1.0 / NUM_STEPS;
|
---|
| 462 |
|
---|
| 463 | // return first value as stored in the dataset
|
---|
[15968] | 464 | yield return targetVariables
|
---|
| 465 | .Select(targetVar => Tuple.Create(dataset.GetDoubleValue(targetVar, rows.First()), Vector.Zero))
|
---|
| 466 | .ToArray();
|
---|
| 467 |
|
---|
[15964] | 468 | // integrate forward starting with known values for the target in t0
|
---|
| 469 |
|
---|
| 470 | var variableValues = new Dictionary<string, Tuple<double, Vector>>();
|
---|
| 471 | var t0 = rows.First();
|
---|
| 472 | foreach (var varName in inputVariables) {
|
---|
| 473 | variableValues.Add(varName, Tuple.Create(dataset.GetDoubleValue(varName, t0), Vector.Zero));
|
---|
| 474 | }
|
---|
| 475 | foreach (var varName in targetVariables) {
|
---|
| 476 | variableValues.Add(varName, Tuple.Create(dataset.GetDoubleValue(varName, t0), Vector.Zero));
|
---|
| 477 | }
|
---|
[15970] | 478 | // add value entries for latent variables which are also integrated
|
---|
| 479 | foreach(var latentVar in latentVariables) {
|
---|
| 480 | variableValues.Add(latentVar, Tuple.Create(0.0, Vector.Zero)); // we don't have observations for latent variables -> assume zero as starting value
|
---|
| 481 | }
|
---|
| 482 | var calculatedVariables = targetVariables.Concat(latentVariables); // TODO: must conincide with the order of trees in the encoding
|
---|
[15964] | 483 |
|
---|
| 484 | foreach (var t in rows.Skip(1)) {
|
---|
| 485 | for (int step = 0; step < NUM_STEPS; step++) {
|
---|
| 486 | var deltaValues = new Dictionary<string, Tuple<double, Vector>>();
|
---|
[15970] | 487 | foreach (var tup in trees.Zip(calculatedVariables, Tuple.Create)) {
|
---|
[15964] | 488 | var tree = tup.Item1;
|
---|
| 489 | var targetVarName = tup.Item2;
|
---|
| 490 | // skip programRoot and startSymbol
|
---|
| 491 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), variableValues, nodeIdx, parameterValues);
|
---|
| 492 | deltaValues.Add(targetVarName, res);
|
---|
| 493 | }
|
---|
| 494 |
|
---|
| 495 | // update variableValues for next step
|
---|
| 496 | foreach (var kvp in deltaValues) {
|
---|
| 497 | var oldVal = variableValues[kvp.Key];
|
---|
| 498 | variableValues[kvp.Key] = Tuple.Create(
|
---|
| 499 | oldVal.Item1 + h * kvp.Value.Item1,
|
---|
| 500 | oldVal.Item2 + h * kvp.Value.Item2
|
---|
| 501 | );
|
---|
| 502 | }
|
---|
| 503 | }
|
---|
| 504 |
|
---|
[15970] | 505 | // only return the target variables for calculation of errors
|
---|
[15968] | 506 | yield return targetVariables
|
---|
| 507 | .Select(targetVar => variableValues[targetVar])
|
---|
| 508 | .ToArray();
|
---|
[15964] | 509 |
|
---|
| 510 | // update for next time step
|
---|
| 511 | foreach (var varName in inputVariables) {
|
---|
| 512 | variableValues[varName] = Tuple.Create(dataset.GetDoubleValue(varName, t), Vector.Zero);
|
---|
| 513 | }
|
---|
| 514 | }
|
---|
| 515 | }
|
---|
| 516 |
|
---|
| 517 | private static Tuple<double, Vector> InterpretRec(
|
---|
| 518 | ISymbolicExpressionTreeNode node,
|
---|
| 519 | Dictionary<string, Tuple<double, Vector>> variableValues,
|
---|
| 520 | Dictionary<ISymbolicExpressionTreeNode, int> nodeIdx,
|
---|
| 521 | double[] parameterValues
|
---|
| 522 | ) {
|
---|
| 523 |
|
---|
| 524 | switch (node.Symbol.Name) {
|
---|
| 525 | case "+": {
|
---|
[15970] | 526 | var l = InterpretRec(node.GetSubtree(0), variableValues, nodeIdx, parameterValues); // TODO capture all parameters into a state type for interpretation
|
---|
[15964] | 527 | var r = InterpretRec(node.GetSubtree(1), variableValues, nodeIdx, parameterValues);
|
---|
| 528 |
|
---|
| 529 | return Tuple.Create(l.Item1 + r.Item1, l.Item2 + r.Item2);
|
---|
| 530 | }
|
---|
| 531 | case "*": {
|
---|
[15968] | 532 | var l = InterpretRec(node.GetSubtree(0), variableValues, nodeIdx, parameterValues);
|
---|
[15964] | 533 | var r = InterpretRec(node.GetSubtree(1), variableValues, nodeIdx, parameterValues);
|
---|
| 534 |
|
---|
| 535 | return Tuple.Create(l.Item1 * r.Item1, l.Item2 * r.Item1 + l.Item1 * r.Item2);
|
---|
| 536 | }
|
---|
| 537 |
|
---|
| 538 | case "-": {
|
---|
[15968] | 539 | var l = InterpretRec(node.GetSubtree(0), variableValues, nodeIdx, parameterValues);
|
---|
[15964] | 540 | var r = InterpretRec(node.GetSubtree(1), variableValues, nodeIdx, parameterValues);
|
---|
| 541 |
|
---|
| 542 | return Tuple.Create(l.Item1 - r.Item1, l.Item2 - r.Item2);
|
---|
| 543 | }
|
---|
| 544 | case "%": {
|
---|
[15968] | 545 | var l = InterpretRec(node.GetSubtree(0), variableValues, nodeIdx, parameterValues);
|
---|
[15964] | 546 | var r = InterpretRec(node.GetSubtree(1), variableValues, nodeIdx, parameterValues);
|
---|
| 547 |
|
---|
| 548 | // protected division
|
---|
| 549 | if (r.Item1.IsAlmost(0.0)) {
|
---|
| 550 | return Tuple.Create(0.0, Vector.Zero);
|
---|
| 551 | } else {
|
---|
| 552 | return Tuple.Create(
|
---|
| 553 | l.Item1 / r.Item1,
|
---|
| 554 | l.Item1 * -1.0 / (r.Item1 * r.Item1) * r.Item2 + 1.0 / r.Item1 * l.Item2 // (f/g)' = f * (1/g)' + 1/g * f' = f * -1/g² * g' + 1/g * f'
|
---|
| 555 | );
|
---|
| 556 | }
|
---|
| 557 | }
|
---|
| 558 | default: {
|
---|
| 559 | // distinguish other cases
|
---|
| 560 | if (IsConstantNode(node)) {
|
---|
| 561 | var vArr = new double[parameterValues.Length]; // backing array for vector
|
---|
| 562 | vArr[nodeIdx[node]] = 1.0;
|
---|
| 563 | var g = new Vector(vArr);
|
---|
| 564 | return Tuple.Create(parameterValues[nodeIdx[node]], g);
|
---|
| 565 | } else {
|
---|
| 566 | // assume a variable name
|
---|
| 567 | var varName = node.Symbol.Name;
|
---|
| 568 | return variableValues[varName];
|
---|
| 569 | }
|
---|
| 570 | }
|
---|
| 571 | }
|
---|
| 572 | }
|
---|
[15968] | 573 | #endregion
|
---|
[15964] | 574 |
|
---|
| 575 | #region events
|
---|
[15968] | 576 | /*
|
---|
| 577 | * Dependencies between parameters:
|
---|
| 578 | *
|
---|
| 579 | * ProblemData
|
---|
| 580 | * |
|
---|
| 581 | * V
|
---|
[15970] | 582 | * TargetVariables FunctionSet MaximumLength NumberOfLatentVariables
|
---|
| 583 | * | | | |
|
---|
| 584 | * V V | |
|
---|
| 585 | * Grammar <---------------+-------------------
|
---|
[15968] | 586 | * |
|
---|
| 587 | * V
|
---|
| 588 | * Encoding
|
---|
| 589 | */
|
---|
[15964] | 590 | private void RegisterEventHandlers() {
|
---|
[15968] | 591 | ProblemDataParameter.ValueChanged += ProblemDataParameter_ValueChanged;
|
---|
| 592 | if (ProblemDataParameter.Value != null) ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
| 593 |
|
---|
| 594 | TargetVariablesParameter.ValueChanged += TargetVariablesParameter_ValueChanged;
|
---|
| 595 | if (TargetVariablesParameter.Value != null) TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
| 596 |
|
---|
| 597 | FunctionSetParameter.ValueChanged += FunctionSetParameter_ValueChanged;
|
---|
| 598 | if (FunctionSetParameter.Value != null) FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
| 599 |
|
---|
| 600 | MaximumLengthParameter.Value.ValueChanged += MaximumLengthChanged;
|
---|
[15970] | 601 |
|
---|
| 602 | NumberOfLatentVariablesParameter.Value.ValueChanged += NumLatentVariablesChanged;
|
---|
[15964] | 603 | }
|
---|
| 604 |
|
---|
[15970] | 605 | private void NumLatentVariablesChanged(object sender, EventArgs e) {
|
---|
| 606 | UpdateGrammarAndEncoding();
|
---|
| 607 | }
|
---|
| 608 |
|
---|
[15968] | 609 | private void MaximumLengthChanged(object sender, EventArgs e) {
|
---|
| 610 | UpdateGrammarAndEncoding();
|
---|
| 611 | }
|
---|
| 612 |
|
---|
| 613 | private void FunctionSetParameter_ValueChanged(object sender, EventArgs e) {
|
---|
| 614 | FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
| 615 | }
|
---|
| 616 |
|
---|
| 617 | private void CheckedFunctionsChanged(object sender, CollectionItemsChangedEventArgs<StringValue> e) {
|
---|
| 618 | UpdateGrammarAndEncoding();
|
---|
| 619 | }
|
---|
| 620 |
|
---|
| 621 | private void TargetVariablesParameter_ValueChanged(object sender, EventArgs e) {
|
---|
| 622 | TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
| 623 | }
|
---|
| 624 |
|
---|
| 625 | private void CheckedTargetVariablesChanged(object sender, CollectionItemsChangedEventArgs<StringValue> e) {
|
---|
| 626 | UpdateGrammarAndEncoding();
|
---|
| 627 | }
|
---|
| 628 |
|
---|
[15964] | 629 | private void ProblemDataParameter_ValueChanged(object sender, EventArgs e) {
|
---|
[15968] | 630 | ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
[15964] | 631 | OnProblemDataChanged();
|
---|
| 632 | OnReset();
|
---|
| 633 | }
|
---|
| 634 |
|
---|
| 635 | private void ProblemData_Changed(object sender, EventArgs e) {
|
---|
[15968] | 636 | OnProblemDataChanged();
|
---|
[15964] | 637 | OnReset();
|
---|
| 638 | }
|
---|
| 639 |
|
---|
| 640 | private void OnProblemDataChanged() {
|
---|
[15968] | 641 | UpdateTargetVariables(); // implicitly updates other dependent parameters
|
---|
[15964] | 642 | var handler = ProblemDataChanged;
|
---|
| 643 | if (handler != null) handler(this, EventArgs.Empty);
|
---|
| 644 | }
|
---|
| 645 |
|
---|
[15968] | 646 | #endregion
|
---|
| 647 |
|
---|
| 648 | #region helper
|
---|
| 649 |
|
---|
| 650 | private void InitAllParameters() {
|
---|
| 651 | UpdateTargetVariables(); // implicitly updates the grammar and the encoding
|
---|
| 652 | }
|
---|
| 653 |
|
---|
| 654 | private ReadOnlyCheckedItemCollection<StringValue> CreateFunctionSet() {
|
---|
| 655 | var l = new CheckedItemCollection<StringValue>();
|
---|
| 656 | l.Add(new StringValue("+").AsReadOnly());
|
---|
| 657 | l.Add(new StringValue("*").AsReadOnly());
|
---|
| 658 | l.Add(new StringValue("%").AsReadOnly());
|
---|
| 659 | l.Add(new StringValue("-").AsReadOnly());
|
---|
| 660 | return l.AsReadOnly();
|
---|
| 661 | }
|
---|
| 662 |
|
---|
| 663 | private static bool IsConstantNode(ISymbolicExpressionTreeNode n) {
|
---|
| 664 | return n.Symbol.Name.StartsWith("θ");
|
---|
| 665 | }
|
---|
[15970] | 666 | private static bool IsLatentVariableNode(ISymbolicExpressionTreeNode n) {
|
---|
| 667 | return n.Symbol.Name.StartsWith("λ");
|
---|
| 668 | }
|
---|
[15968] | 669 |
|
---|
| 670 |
|
---|
| 671 | private void UpdateTargetVariables() {
|
---|
| 672 | var currentlySelectedVariables = TargetVariables.CheckedItems.Select(i => i.Value).ToArray();
|
---|
| 673 |
|
---|
| 674 | var newVariablesList = new CheckedItemCollection<StringValue>(ProblemData.Dataset.VariableNames.Select(str => new StringValue(str).AsReadOnly()).ToArray()).AsReadOnly();
|
---|
| 675 | var matchingItems = newVariablesList.Where(item => currentlySelectedVariables.Contains(item.Value)).ToArray();
|
---|
| 676 | foreach (var matchingItem in matchingItems) {
|
---|
| 677 | newVariablesList.SetItemCheckedState(matchingItem, true);
|
---|
| 678 | }
|
---|
| 679 | TargetVariablesParameter.Value = newVariablesList;
|
---|
| 680 | }
|
---|
| 681 |
|
---|
| 682 | private void UpdateGrammarAndEncoding() {
|
---|
| 683 | var encoding = new MultiEncoding();
|
---|
| 684 | var g = CreateGrammar();
|
---|
| 685 | foreach (var targetVar in TargetVariables.CheckedItems) {
|
---|
[15970] | 686 | encoding = encoding.Add(new SymbolicExpressionTreeEncoding(targetVar + "_tree", g, MaximumLength, MaximumLength)); // only limit by length
|
---|
[15968] | 687 | }
|
---|
[15970] | 688 | for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
| 689 | encoding = encoding.Add(new SymbolicExpressionTreeEncoding("λ" + i + "_tree", g, MaximumLength, MaximumLength));
|
---|
| 690 | }
|
---|
[15968] | 691 | Encoding = encoding;
|
---|
| 692 | }
|
---|
| 693 |
|
---|
| 694 | private ISymbolicExpressionGrammar CreateGrammar() {
|
---|
[15964] | 695 | // whenever ProblemData is changed we create a new grammar with the necessary symbols
|
---|
| 696 | var g = new SimpleSymbolicExpressionGrammar();
|
---|
[15968] | 697 | g.AddSymbols(FunctionSet.CheckedItems.Select(i => i.Value).ToArray(), 2, 2);
|
---|
[15964] | 698 |
|
---|
| 699 | // TODO
|
---|
| 700 | //g.AddSymbols(new[] {
|
---|
| 701 | // "exp",
|
---|
| 702 | // "log", // log( <expr> ) // TODO: init a theta to ensure the value is always positive
|
---|
| 703 | // "exp_minus" // exp((-1) * <expr>
|
---|
| 704 | //}, 1, 1);
|
---|
| 705 |
|
---|
[15968] | 706 | foreach (var variableName in ProblemData.AllowedInputVariables.Union(TargetVariables.CheckedItems.Select(i => i.Value)))
|
---|
[15964] | 707 | g.AddTerminalSymbol(variableName);
|
---|
| 708 |
|
---|
| 709 | // generate symbols for numeric parameters for which the value is optimized using AutoDiff
|
---|
| 710 | // we generate multiple symbols to balance the probability for selecting a numeric parameter in the generation of random trees
|
---|
| 711 | var numericConstantsFactor = 2.0;
|
---|
[15968] | 712 | for (int i = 0; i < numericConstantsFactor * (ProblemData.AllowedInputVariables.Count() + TargetVariables.CheckedItems.Count()); i++) {
|
---|
[15964] | 713 | g.AddTerminalSymbol("θ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
| 714 | }
|
---|
[15970] | 715 |
|
---|
| 716 | // generate symbols for latent variables
|
---|
| 717 | for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
| 718 | g.AddTerminalSymbol("λ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
| 719 | }
|
---|
| 720 |
|
---|
[15968] | 721 | return g;
|
---|
[15964] | 722 | }
|
---|
[15968] | 723 |
|
---|
[15964] | 724 | #endregion
|
---|
| 725 |
|
---|
| 726 | #region Import & Export
|
---|
| 727 | public void Load(IRegressionProblemData data) {
|
---|
| 728 | Name = data.Name;
|
---|
| 729 | Description = data.Description;
|
---|
| 730 | ProblemData = data;
|
---|
| 731 | }
|
---|
| 732 |
|
---|
| 733 | public IRegressionProblemData Export() {
|
---|
| 734 | return ProblemData;
|
---|
| 735 | }
|
---|
| 736 | #endregion
|
---|
| 737 |
|
---|
| 738 | }
|
---|
| 739 | }
|
---|