#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Linq;
using System.Threading;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Random;
using System.Collections.Generic;
using HEAL.Attic;
namespace HeuristicLab.Problems.DynamicalSystemsModelling {
[Item("OdeParameterIdentification", "TODO")]
[Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
[StorableType("93C151A2-9579-4013-9E67-7611F9378962")]
public sealed class OdeParameterIdentification : FixedDataAnalysisAlgorithm {
private const string RegressionSolutionResultName = "Regression solution";
private const string ModelStructureParameterName = "Model structure";
private const string IterationsParameterName = "Iterations";
private const string RestartsParameterName = "Restarts";
private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
private const string SeedParameterName = "Seed";
private const string InitParamsRandomlyParameterName = "InitializeParametersRandomly";
public IValueParameter ModelStructureParameter {
get { return (IValueParameter)Parameters[ModelStructureParameterName]; }
}
public IFixedValueParameter IterationsParameter {
get { return (IFixedValueParameter)Parameters[IterationsParameterName]; }
}
public IFixedValueParameter SetSeedRandomlyParameter {
get { return (IFixedValueParameter)Parameters[SetSeedRandomlyParameterName]; }
}
public IFixedValueParameter SeedParameter {
get { return (IFixedValueParameter)Parameters[SeedParameterName]; }
}
public IFixedValueParameter RestartsParameter {
get { return (IFixedValueParameter)Parameters[RestartsParameterName]; }
}
public IFixedValueParameter InitParametersRandomlyParameter {
get { return (IFixedValueParameter)Parameters[InitParamsRandomlyParameterName]; }
}
public StringArray ModelStructure {
get { return ModelStructureParameter.Value; }
set { ModelStructureParameter.Value = value; }
}
public int Iterations {
get { return IterationsParameter.Value.Value; }
set { IterationsParameter.Value.Value = value; }
}
public int Restarts {
get { return RestartsParameter.Value.Value; }
set { RestartsParameter.Value.Value = value; }
}
public int Seed {
get { return SeedParameter.Value.Value; }
set { SeedParameter.Value.Value = value; }
}
public bool SetSeedRandomly {
get { return SetSeedRandomlyParameter.Value.Value; }
set { SetSeedRandomlyParameter.Value.Value = value; }
}
public bool InitializeParametersRandomly {
get { return InitParametersRandomlyParameter.Value.Value; }
set { InitParametersRandomlyParameter.Value.Value = value; }
}
[StorableConstructor]
private OdeParameterIdentification(StorableConstructorFlag _) : base(_) { }
private OdeParameterIdentification(OdeParameterIdentification original, Cloner cloner)
: base(original, cloner) {
}
public OdeParameterIdentification()
: base() {
Problem = new Problem();
Parameters.Add(new ValueParameter(ModelStructureParameterName, "The function for which the parameters must be fit (only numeric constants are tuned).", new StringArray(new string[] { "1.0 * x*x + 0.0" })));
Parameters.Add(new FixedValueParameter(IterationsParameterName, "The maximum number of iterations for constants optimization.", new IntValue(200)));
Parameters.Add(new FixedValueParameter(RestartsParameterName, "The number of independent random restarts (>0)", new IntValue(10)));
Parameters.Add(new FixedValueParameter(SeedParameterName, "The PRNG seed value.", new IntValue()));
Parameters.Add(new FixedValueParameter(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
Parameters.Add(new FixedValueParameter(InitParamsRandomlyParameterName, "Switch to determine if the real-valued model parameters should be initialized randomly in each restart.", new BoolValue(false)));
SetParameterHiddenState();
InitParametersRandomlyParameter.Value.ValueChanged += (sender, args) => {
SetParameterHiddenState();
};
}
private void SetParameterHiddenState() {
var hide = !InitializeParametersRandomly;
RestartsParameter.Hidden = hide;
SeedParameter.Hidden = hide;
SetSeedRandomlyParameter.Hidden = hide;
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new OdeParameterIdentification(this, cloner);
}
#region nonlinear regression
protected override void Run(CancellationToken cancellationToken) {
if (SetSeedRandomly) Seed = (new System.Random()).Next();
var rand = new MersenneTwister((uint)Seed);
if (InitializeParametersRandomly) {
throw new NotImplementedException();
// var qualityTable = new DataTable("RMSE table");
// qualityTable.VisualProperties.YAxisLogScale = true;
// var trainRMSERow = new DataRow("RMSE (train)");
// trainRMSERow.VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
// var testRMSERow = new DataRow("RMSE test");
// testRMSERow.VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
//
// qualityTable.Rows.Add(trainRMSERow);
// qualityTable.Rows.Add(testRMSERow);
// Results.Add(new Result(qualityTable.Name, qualityTable.Name + " for all restarts", qualityTable));
// CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
//
// for (int r = 0; r < Restarts; r++) {
// CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
// trainRMSERow.Values.Add(solution.TrainingRootMeanSquaredError);
// testRMSERow.Values.Add(solution.TestRootMeanSquaredError);
// if (solution.TrainingRootMeanSquaredError < bestSolution.TrainingRootMeanSquaredError) {
// bestSolution = solution;
// }
// }
} else {
CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
}
// Results.Add(new Result(RegressionSolutionResultName, "The nonlinear regression solution.", bestSolution));
// 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(bestSolution.TrainingRootMeanSquaredError)));
// 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(bestSolution.TestRootMeanSquaredError)));
}
public void CreateSolution(Problem problem, string[] modelStructure, int maxIterations, IRandom rand) {
var parser = new InfixExpressionParser();
var trees = modelStructure.Select(expr => parser.Parse(expr)).ToArray();
var names = problem.Encoding.Encodings.Select(enc => enc.Name).ToArray();
if (trees.Length != names.Length) throw new ArgumentException("The number of expressions must match the number of target variables exactly");
var scope = new Scope();
for (int i = 0; i < names.Length; i++) {
scope.Variables.Add(new Core.Variable(names[i], trees[i]));
}
var ind = problem.Encoding.GetIndividual(scope);
var quality = problem.Evaluate(ind, rand);
problem.Analyze(new[] { ind }, new[] { quality }, Results, rand);
}
#endregion
}
}