mirror of
https://github.com/hanzoai/jube-fork.git
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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.
145 lines
5.0 KiB
C#
145 lines
5.0 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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using System.Threading.Tasks;
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/// <summary>
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/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.Mixture{T}">univariate</see>
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/// and <see cref="Accord.Statistics.Distributions.Multivariate.MultivariateMixture{T}">multivariate</see>
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/// <see cref="Accord.Statistics.Distributions.IMixture{T}">mixture distributions</see>.
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/// </summary>
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///
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[Serializable]
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public class MixtureOptions : IFittingOptions
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{
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/// <summary>
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/// Gets or sets the convergence criterion for the
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/// Expectation-Maximization algorithm. Default is 1e-3.
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/// </summary>
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///
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/// <value>The convergence threshold.</value>
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///
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public double Threshold { get; set; }
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/// <summary>
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/// Gets or sets the maximum number of iterations
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/// to be performed by the Expectation-Maximization
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/// algorithm. Default is zero (iterate until convergence).
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/// </summary>
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///
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public int MaxIterations
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{
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#pragma warning disable 612, 618
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get { return Iterations; }
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set { Iterations = value; }
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#pragma warning restore 612, 618
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}
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/// <summary>
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/// Gets or sets the maximum number of iterations
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/// to be performed by the Expectation-Maximization
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/// algorithm. Default is zero (iterate until convergence).
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/// </summary>
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///
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[Obsolete("Please use MaxIterations instead.")]
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public int Iterations { get; set; }
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/// <summary>
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/// Gets or sets the parallelization options to be used when fitting.
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/// </summary>
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///
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/// <value>The parallel options.</value>
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///
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public ParallelOptions ParallelOptions { get; set; }
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/// <summary>
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/// Gets or sets the fitting options for the inner
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/// component distributions of the mixture density.
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/// </summary>
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///
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/// <value>The fitting options for inner distributions.</value>
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///
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public IFittingOptions InnerOptions { get; set; }
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/// <summary>
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/// Gets or sets whether to make computations using the log
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/// -domain. This might improve accuracy on large datasets.
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/// </summary>
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///
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public bool Logarithm { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="MixtureOptions"/> class.
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/// </summary>
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///
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public MixtureOptions()
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{
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this.Threshold = 1e-3;
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this.ParallelOptions = new ParallelOptions();
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="MixtureOptions"/> class.
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/// </summary>
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///
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/// <param name="threshold">The convergence criterion for the
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/// Expectation-Maximization algorithm. Default is 1e-3.</param>
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///
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public MixtureOptions(double threshold)
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: this()
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{
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Threshold = threshold;
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="MixtureOptions"/> class.
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/// </summary>
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///
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/// <param name="threshold">The convergence criterion for the
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/// Expectation-Maximization algorithm. Default is 1e-3.</param>
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/// <param name="innerOptions">The fitting options for the inner
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/// component distributions of the mixture density.</param>
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///
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public MixtureOptions(double threshold, IFittingOptions innerOptions)
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: this(threshold)
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{
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InnerOptions = innerOptions;
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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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