#region License Information /* HeuristicLab * Copyright (C) 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; namespace HeuristicLab.Common { public class EmpiricalCumulativeDistributionFunction { private static readonly AbscissaComparer abscissaComparer = new AbscissaComparer(); private static readonly OrdinateComparer ordinateComparer = new OrdinateComparer(); private List> ecdf; public IEnumerable> SupportingPoints { get { return ecdf; } } public EmpiricalCumulativeDistributionFunction(IList sample) { ecdf = new List>(); var len = sample.Count; var cumulative = 0; var localcumulative = 0; var prev = double.NaN; foreach (var p in sample.OrderBy(x => x)) { if (double.IsNaN(p) || double.IsInfinity(p)) continue; if (!double.IsNaN(prev) && prev < p) { cumulative += localcumulative; localcumulative = 0; ecdf.Add(Point2D.Create(prev, cumulative / (double)len)); } prev = p; localcumulative++; } if (!double.IsNaN(prev)) { cumulative += localcumulative; ecdf.Add(Point2D.Create(prev, cumulative / (double)len)); } } public EmpiricalCumulativeDistributionFunction(IEnumerable> ecdf) { this.ecdf = new List>(); var prev = Point2D.Empty; foreach (var point in ecdf) { if (point.Y < 0 || point.Y > 1 || double.IsNaN(point.X) || double.IsInfinity(point.X) || point.IsEmpty || (!prev.IsEmpty && (point.X <= prev.X || point.Y <= prev.Y))) throw new ArgumentException("Invalid supporting points of a cumulative distribution function. Must be strictly monotonically increasing in both X and Y with X in R and Y in [0;1].", "ecdf"); this.ecdf.Add(point); prev = point; } } public double Evaluate(double x) { if (ecdf.Count == 0) return double.NaN; if (x < ecdf[0].X) return 0; var last = ecdf[ecdf.Count - 1]; if (x >= last.X) return last.Y; var index = ecdf.BinarySearch(Point2D.Create(x, 0), abscissaComparer); if (index >= 0) return ecdf[index].Y; return ecdf[~index - 1].Y; } public double InterpolateLinear(double x) { if (ecdf.Count == 0) return double.NaN; if (x < ecdf[0].X) return 0; var last = ecdf[ecdf.Count - 1]; if (x >= last.X) return last.Y; var index = ecdf.BinarySearch(Point2D.Create(x, 0), abscissaComparer); if (index >= 0) return ecdf[index].Y; var prev = ecdf[~index - 1]; var next = ecdf[~index]; return prev.Y + (next.Y - prev.Y) * ((x - prev.X) / (next.X - prev.X)); } public double InterpolateNearest(double x) { if (ecdf.Count == 0) return double.NaN; if (x < ecdf[0].X) return 0; var last = ecdf[ecdf.Count - 1]; if (x >= last.X) return last.Y; var index = ecdf.BinarySearch(Point2D.Create(x, 0), abscissaComparer); if (index >= 0) return ecdf[index].Y; var prev = ecdf[~index - 1]; var next = ecdf[~index]; if (x - prev.X < next.X - x) return prev.Y; return next.Y; } public double Inverse(double y) { if (ecdf.Count == 0) return double.NaN; if (y < 0 || y > 1) throw new ArgumentException("parameter must be in interval [0;1]", "y"); if (ecdf[ecdf.Count - 1].Y < y) return double.PositiveInfinity; var index = ecdf.BinarySearch(Point2D.Create(0, y), ordinateComparer); if (index >= 0) return ecdf[index].X; return ecdf[Math.Max(~index - 1, 0)].X; } private class AbscissaComparer : Comparer> { public override int Compare(Point2D x, Point2D y) { return x.X.CompareTo(y.X); } } private class OrdinateComparer : Comparer> { public override int Compare(Point2D x, Point2D y) { return x.Y.CompareTo(y.Y); } } } }