#region License Information
/* HeuristicLab
* Copyright (C) 2002-2016 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableClass]
[Item("CircularKernel", "A circular kernel function")]
public class CircularKernel : KernelBase {
#region HLConstructors & Boilerplate
[StorableConstructor]
protected CircularKernel(bool deserializing) : base(deserializing) { }
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() { }
protected CircularKernel(CircularKernel original, Cloner cloner) : base(original, cloner) { }
public CircularKernel() {
Parameters.Add(new FixedValueParameter(BetaParameterName, "The beta in the kernel function 2*pi*(acos(-d)-d*(1-n²)^(0.5)) where n = ||x-c|| and d = n/beta", new DoubleValue(2)));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CircularKernel(this, cloner);
}
#endregion
protected override double Get(double norm) {
if (Math.Abs(Beta) < double.Epsilon) return double.NaN;
if (norm >= Beta) return 0;
var d = norm / Beta;
return Math.Acos(-d) - d * Math.Sqrt(1 - d * d) - Math.PI / 2;
}
protected override double GetGradient(double norm) {
if (Math.Abs(Beta) < double.Epsilon) return double.NaN;
if (Beta < norm) return 0;
return -2*Math.Pow(norm,3)/(Math.Pow(Beta,4)*Math.Sqrt(1-norm*norm/(Beta*Beta)));
}
}
}