#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "MeanModel", Description = "A mean function for Gaussian processes that uses a regression solution created with a different algorithm to calculate the mean.")] // essentially an adaptor which maps from IMeanFunction to IRegressionSolution public sealed class MeanModel : ParameterizedNamedItem, IMeanFunction { public IValueParameter RegressionSolutionParameter { get { return (IValueParameter)Parameters["RegressionSolution"]; } } public IRegressionSolution RegressionSolution { get { return RegressionSolutionParameter.Value; } set { RegressionSolutionParameter.Value = value; } } [StorableConstructor] private MeanModel(bool deserializing) : base(deserializing) { } private MeanModel(MeanModel original, Cloner cloner) : base(original, cloner) { } public MeanModel() : base("MeanModel", "A mean function for Gaussian processes that uses a regression solution created with a different algorithm to calculate the mean.") { Parameters.Add(new ValueParameter("RegressionSolution", "The solution containing the model that should be used for the mean prediction.")); } public MeanModel(IRegressionSolution solution) : this() { // here we cannot check if the model is actually compatible (uses only input variables that are available) // we only assume that the list of allowed inputs in the regression solution is the same as the list of allowed // inputs in the Gaussian process. // later we might get an error or bad behaviour when the mean function is evaluated RegressionSolution = solution; } public override IDeepCloneable Clone(Cloner cloner) { return new MeanModel(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { return 0; // no support for hyperparameters for regression models yet } public void SetParameter(double[] p) { if (p.Length > 0) throw new ArgumentException("No parameters allowed for model-based mean function.", "p"); } public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable columnIndices) { if (p.Length > 0) throw new ArgumentException("No parameters allowed for model-based mean function.", "p"); var solution = RegressionSolution; var variableNames = solution.ProblemData.AllowedInputVariables.ToArray(); if (variableNames.Length != columnIndices.Count()) throw new ArgumentException("The number of input variables does not match in MeanModel"); var variableValues = variableNames.Select(_ => new List() { 0.0 }).ToArray(); // or of zeros // uses modifyable dataset to pass values to the model var ds = new ModifiableDataset(variableNames, variableValues); var mf = new ParameterizedMeanFunction(); var model = solution.Model; // effort for parameter access only once mf.Mean = (x, i) => { ds.ReplaceRow(0, Util.GetRow(x, i, columnIndices).OfType()); return model.GetEstimatedValues(ds, 0.ToEnumerable()).Single(); // evaluate the model on the specified row only }; mf.Gradient = (x, i, k) => { if (k > 0) throw new ArgumentException(); return 0.0; }; return mf; } } }