#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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.NK {
[Item("ExponentialDistributionWeightsInitializer", "Assigns exponentially decreasing weights using the rate parameter lambda.")]
[StorableType("59DC441A-28C4-4C0A-B66D-3F73FAD6AF1B")]
public sealed class ExponentialDistributionWeightsInitializer : ParameterizedNamedItem, IWeightsInitializer {
public IValueParameter LambdaParameter {
get { return (IValueParameter)Parameters["Lambda"]; }
}
[StorableConstructor]
private ExponentialDistributionWeightsInitializer(bool deserializing) : base(deserializing) { }
private ExponentialDistributionWeightsInitializer(ExponentialDistributionWeightsInitializer original, Cloner cloner)
: base(original, cloner) {
}
public ExponentialDistributionWeightsInitializer() {
Parameters.Add(new ValueParameter("Lambda", "The rate parameter of the exponential distribution.", new DoubleValue(1.0)));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new ExponentialDistributionWeightsInitializer(this, cloner);
}
private static double f(double x, double lambda) {
if (x < 0.0)
return 0.0;
return lambda * Math.Exp(-lambda * x);
}
public IEnumerable GetWeights(int nComponents) {
double lambda = LambdaParameter.Value.Value;
for (int i = 0; i < nComponents; i++)
yield return f(i, lambda);
}
}
}