#region License Information /* HeuristicLab * Copyright (C) 2002-2017 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.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { //mulitdimensional extension of http://www2.stat.duke.edu/~tjl13/s101/slides/unit6lec3H.pdf [StorableClass] internal sealed class PreconstructedLinearModel : RegressionModel, IConfidenceRegressionModel { [Storable] public Dictionary Coefficients { get; private set; } [Storable] public double Intercept { get; private set; } [Storable] private Dictionary Center { get; set; } [Storable] private Dictionary Variances { get; set; } [Storable] private double ResidualVariance { get; set; } [Storable] private int SampleSize { get; set; } public override IEnumerable VariablesUsedForPrediction { get { return Coefficients.Keys; } } #region HLConstructors [StorableConstructor] private PreconstructedLinearModel(bool deserializing) : base(deserializing) { } private PreconstructedLinearModel(PreconstructedLinearModel original, Cloner cloner) : base(original, cloner) { if (original.Coefficients != null) Coefficients = original.Coefficients.ToDictionary(x => x.Key, x => x.Value); Intercept = original.Intercept; if (original.Center != null) Center = original.Center.ToDictionary(x => x.Key, x => x.Value); if (original.Variances != null) Variances = original.Variances.ToDictionary(x => x.Key, x => x.Value); ResidualVariance = original.ResidualVariance; SampleSize = original.SampleSize; } private PreconstructedLinearModel(Dictionary coefficients, double intercept, string targetvariable) : base(targetvariable) { Coefficients = coefficients.ToDictionary(x => x.Key, x => x.Value); Intercept = intercept; Variances = new Dictionary(); ResidualVariance = 0; SampleSize = 0; } public PreconstructedLinearModel(double intercept, string targetvariable) : base(targetvariable) { Coefficients = new Dictionary(); Intercept = intercept; Variances = new Dictionary(); ResidualVariance = 0; SampleSize = 0; } public override IDeepCloneable Clone(Cloner cloner) { return new PreconstructedLinearModel(this, cloner); } #endregion public static PreconstructedLinearModel CreateConfidenceLinearModel(IRegressionProblemData pd, out double rmse, out double cvRmse) { var inputMatrix = pd.Dataset.ToArray(pd.AllowedInputVariables.Concat(new[] {pd.TargetVariable}), pd.AllIndices); alglib.linearmodel lm; alglib.lrreport ar; var nFeatures = inputMatrix.GetLength(1) - 1; double[] coefficients; // last coefficient is for the constant int retVal; alglib.lrbuild(inputMatrix, inputMatrix.GetLength(0), nFeatures, out retVal, out lm, out ar); if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution"); rmse = ar.rmserror; cvRmse = ar.cvrmserror; alglib.lrunpack(lm, out coefficients, out nFeatures); return new PreconstructedLinearModel(pd.AllowedInputVariables.Zip(coefficients, (s, d) => new {s, d}).ToDictionary(x => x.s, x => x.d), coefficients[nFeatures], pd.TargetVariable); } public override IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { return rows.Select(row => GetEstimatedValue(dataset, row)); } public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new RegressionSolution(this, problemData); } public IEnumerable GetEstimatedVariances(IDataset dataset, IEnumerable rows) { return rows.Select(i => GetEstimatedVariance(dataset, i)); } #region helpers private double GetEstimatedValue(IDataset dataset, int row) { return Intercept + (Coefficients.Count == 0 ? 0 : Coefficients.Sum(s => s.Value * dataset.GetDoubleValue(s.Key, row))); } private double GetEstimatedVariance(IDataset dataset, int row) { if (SampleSize == 0) return 0.0; var sum = (from var in Variances let d = dataset.GetDoubleValue(var.Key, row) - Center[var.Key] select d * d / var.Value).Sum(); return ResidualVariance * (1.0 / SampleSize + sum / (SampleSize - 1)); } #endregion } }