mirror of
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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
97 lines
3.5 KiB
C#
97 lines
3.5 KiB
C#
// Accord Statistics Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.Statistics.Distributions.Fitting
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{
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using System;
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using Accord.Compat;
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/// <summary>
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/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.CauchyDistribution">
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/// Cauchy distributions</see>.
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/// </summary>
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///
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[Serializable]
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public class CauchyOptions : IFittingOptions
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{
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/// <summary>
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/// Gets or sets a value indicating whether the distribution parameters
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/// should be estimated using maximum likelihood. Default is true.
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/// </summary>
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///
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/// <remarks>
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/// The Cauchy distribution parameters can be estimated in many ways. One
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/// approach is to use order statistics to derive approximations to the
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/// location and scale parameters by analysis the interquartile range of
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/// the data. The other approach is to use Maximum Likelihood to estimate
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/// the parameters. The MLE does not exists in simple algebraic form, so
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/// it has to be estimated using numeric optimization.
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/// </remarks>
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///
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/// <value><c>true</c> if the parameters should be estimated by ML; otherwise, <c>false</c>.</value>
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///
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public bool MaximumLikelihood { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the scale
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/// parameter should be estimated. Default is true.
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/// </summary>
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///
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/// <value><c>true</c> if the scale parameter should be estimated; otherwise, <c>false</c>.</value>
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///
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public bool EstimateScale { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the location
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/// parameter should be estimated. Default is true.
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/// </summary>
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///
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/// <value><c>true</c> if the location parameter should be estimated; otherwise, <c>false</c>.</value>
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///
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public bool EstimateLocation { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="CauchyOptions"/> class.
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/// </summary>
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///
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public CauchyOptions()
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{
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MaximumLikelihood = true;
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EstimateLocation = true;
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EstimateScale = true;
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}
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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public object Clone()
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{
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return MemberwiseClone();
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}
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}
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}
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