[16399] | 1 | using System;
|
---|
| 2 | using System.Collections.Generic;
|
---|
| 3 | using System.Linq;
|
---|
| 4 | using HeuristicLab.Common;
|
---|
| 5 | using HeuristicLab.Core;
|
---|
| 6 | using HeuristicLab.Data;
|
---|
| 7 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
| 8 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 9 | using HeuristicLab.Problems.DataAnalysis;
|
---|
| 10 | using HeuristicLab.Random;
|
---|
| 11 |
|
---|
| 12 | namespace HeuristicLab.Problems.DynamicalSystemsModelling {
|
---|
| 13 | [StorableClass]
|
---|
| 14 | public class Solution : Item {
|
---|
| 15 | [Storable]
|
---|
| 16 | private ISymbolicExpressionTree[] trees;
|
---|
| 17 | public ISymbolicExpressionTree[] Trees {
|
---|
| 18 | get { return trees; }
|
---|
| 19 | }
|
---|
| 20 | // [Storable]
|
---|
| 21 | // private double[] theta;
|
---|
| 22 |
|
---|
| 23 | [Storable]
|
---|
| 24 | private IRegressionProblemData problemData;
|
---|
| 25 | public IRegressionProblemData ProblemData {
|
---|
| 26 | get { return problemData; }
|
---|
| 27 | }
|
---|
| 28 | [Storable]
|
---|
| 29 | private string[] targetVars;
|
---|
| 30 | public string[] TargetVariables {
|
---|
| 31 | get { return targetVars; }
|
---|
| 32 | }
|
---|
| 33 | [Storable]
|
---|
| 34 | private string[] latentVariables;
|
---|
| 35 | public string[] LatentVariables {
|
---|
| 36 | get { return latentVariables; }
|
---|
| 37 | }
|
---|
| 38 | [Storable]
|
---|
| 39 | private IEnumerable<IntRange> trainingEpisodes;
|
---|
| 40 | public IEnumerable<IntRange> TrainingEpisodes {
|
---|
| 41 | get { return trainingEpisodes; }
|
---|
| 42 | }
|
---|
| 43 | [Storable]
|
---|
| 44 | private string odeSolver;
|
---|
| 45 | [Storable]
|
---|
| 46 | private int numericIntegrationSteps;
|
---|
| 47 |
|
---|
| 48 | [StorableConstructor]
|
---|
| 49 | private Solution(bool deserializing) : base(deserializing) { }
|
---|
| 50 | [StorableHook(HookType.AfterDeserialization)]
|
---|
| 51 | private void AfterDeserialization() {
|
---|
| 52 | }
|
---|
| 53 |
|
---|
| 54 | // cloning
|
---|
| 55 | private Solution(Solution original, Cloner cloner)
|
---|
| 56 | : base(original, cloner) {
|
---|
| 57 | this.trees = new ISymbolicExpressionTree[original.trees.Length];
|
---|
| 58 | for (int i = 0; i < trees.Length; i++) this.trees[i] = cloner.Clone(original.trees[i]);
|
---|
| 59 | // this.theta = new double[original.theta.Length];
|
---|
| 60 | // Array.Copy(original.theta, this.theta, this.theta.Length);
|
---|
| 61 | this.problemData = cloner.Clone(original.problemData);
|
---|
| 62 | this.targetVars = original.TargetVariables.ToArray();
|
---|
| 63 | this.latentVariables = original.LatentVariables.ToArray();
|
---|
| 64 | this.trainingEpisodes = original.TrainingEpisodes.Select(te => cloner.Clone(te)).ToArray();
|
---|
| 65 | this.odeSolver = original.odeSolver;
|
---|
| 66 | this.numericIntegrationSteps = original.numericIntegrationSteps;
|
---|
| 67 | }
|
---|
| 68 |
|
---|
| 69 | public Solution(ISymbolicExpressionTree[] trees,
|
---|
| 70 | IRegressionProblemData problemData,
|
---|
| 71 | string[] targetVars, string[] latentVariables, IEnumerable<IntRange> trainingEpisodes,
|
---|
| 72 | string odeSolver, int numericIntegrationSteps) : base() {
|
---|
| 73 | this.trees = trees;
|
---|
| 74 |
|
---|
| 75 | this.problemData = problemData;
|
---|
| 76 | this.targetVars = targetVars;
|
---|
| 77 | this.latentVariables = latentVariables;
|
---|
| 78 | this.trainingEpisodes = trainingEpisodes;
|
---|
| 79 | this.odeSolver = odeSolver;
|
---|
| 80 | this.numericIntegrationSteps = numericIntegrationSteps;
|
---|
| 81 | }
|
---|
| 82 |
|
---|
| 83 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 84 | return new Solution(this, cloner);
|
---|
| 85 | }
|
---|
| 86 |
|
---|
[16400] | 87 | public IEnumerable<double[]> Predict(IntRange episode, int forecastHorizon, out double snmse) {
|
---|
[16399] | 88 | var forecastEpisode = new IntRange(episode.Start, episode.End + forecastHorizon);
|
---|
[16400] | 89 |
|
---|
[16399] | 90 | double[] optL0;
|
---|
| 91 | var random = new FastRandom(12345);
|
---|
[16400] | 92 | Problem.OptimizeForEpisodes(trees, problemData, targetVars, latentVariables, random, new[] { forecastEpisode }, 100, numericIntegrationSteps, odeSolver, out optL0, out snmse);
|
---|
[16399] | 93 | var predictions = Problem.Integrate(
|
---|
| 94 | trees,
|
---|
| 95 | problemData.Dataset,
|
---|
| 96 | problemData.AllowedInputVariables.ToArray(),
|
---|
| 97 | targetVars,
|
---|
| 98 | latentVariables,
|
---|
| 99 | new[] { forecastEpisode },
|
---|
| 100 | optL0,
|
---|
| 101 | odeSolver,
|
---|
| 102 | numericIntegrationSteps).ToArray();
|
---|
| 103 | return predictions.Select(p => p.Select(pi => pi.Item1).ToArray()).ToArray();
|
---|
| 104 | }
|
---|
| 105 | }
|
---|
| 106 | } |
---|