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.
106 lines
3.9 KiB
C#
106 lines
3.9 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 Multivariate;
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using Univariate;
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using Accord.Compat;
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using Accord.Math.Decompositions;
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/// <summary>
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/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.NormalDistribution">
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/// Normal distributions</see>.
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/// </summary>
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///
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[Serializable]
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public class NormalOptions : IFittingOptions, IComponentOptions
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{
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/// <summary>
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/// Gets or sets the regularization step to avoid singular or non-positive definite
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/// covariance matrices. Default is 0. Setting this property to a small constant like
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/// 1e-6 is more efficient than setting <see cref="Robust"/> to true.
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/// </summary>
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///
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/// <value>The regularization step.</value>
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///
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public double Regularization { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the covariance
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/// matrix to be estimated should be assumed to be diagonal.
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/// </summary>
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///
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/// <value><c>true</c> to estimate a diagonal covariance matrix; otherwise, <c>false</c>.</value>
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///
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public bool Diagonal { get; set; }
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/// <summary>
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/// Gets or sets whether the estimation function should allow non-positive definite covariance
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/// matrices by using the <see cref="SingularValueDecomposition">Singular Value Decomposition</see>
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/// instead of the default <see cref="CholeskyDecomposition">Cholesky</see>. Enabling this property
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/// can significantly increase learning times.
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/// </summary>
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///
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public bool Robust { get; set; }
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/// <summary>
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/// Gets or sets whether the normal distributions should have only a single, shared
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/// covariance matrix among all components in a mixture. Setting this property only
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/// has effect if the distributions are part of a <see cref="Mixture{T}"/> or
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/// <see cref="MultivariateMixture{T}"/>
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/// </summary>
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///
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public bool Shared { get; set; }
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/// <summary>
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/// Gets or sets a post processing step can be called after all component
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/// distributions have been fitted (or their .Fit() method has been called).
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/// </summary>
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///
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public Action<IDistribution[], double[]> Postprocessing { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="NormalOptions"/> class.
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/// </summary>
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///
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public NormalOptions()
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{
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Regularization = 0;
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Diagonal = false;
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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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