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.
123 lines
4.8 KiB
C#
123 lines
4.8 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
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{
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using System;
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using Accord.Statistics.Distributions.Fitting;
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/// <summary>
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/// Common interface for distributions which can be estimated from data.
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/// </summary>
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///
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/// <typeparam name="TObservations">The type of the observations, such as <see cref="System.Double"/>.</typeparam>
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/// <typeparam name="TOptions">The type of the options specifying object.</typeparam>
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///
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public interface IFittableDistribution<in TObservations, in TOptions> :
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IFittable<TObservations, TOptions>,
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IFittableDistribution<TObservations>
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where TOptions : class, IFittingOptions
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{
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}
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/// <summary>
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/// Common interface for distributions which can be estimated from data.
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/// </summary>
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///
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/// <typeparam name="TObservations">The type of the observations, such as <see cref="System.Double"/>.</typeparam>
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/// <typeparam name="TOptions">The type of the options specifying object.</typeparam>
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///
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public interface IFittable<in TObservations, in TOptions> :
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IFittable<TObservations>
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where TOptions : class, IFittingOptions
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{
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
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/// <param name="options">Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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void Fit(TObservations[] observations, double[] weights = null, TOptions options = default(TOptions));
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}
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/// <summary>
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/// Common interface for distributions which can be estimated from data.
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/// </summary>
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///
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/// <typeparam name="TObservations">The type of the observations, such as <see cref="System.Double"/>.</typeparam>
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///
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public interface IFittableDistribution<in TObservations> :
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IFittable<TObservations>, IDistribution<TObservations>
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{
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}
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/// <summary>
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/// Common interface for distributions which can be estimated from data.
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/// </summary>
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///
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public interface IUnivariateFittableDistribution :
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IFittableDistribution<double>,
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IUnivariateDistribution<double>,
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IUnivariateDistribution
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{
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}
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/// <summary>
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/// Common interface for distributions which can be estimated from data.
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/// </summary>
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///
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/// <typeparam name="TObservations">The type of the observations, such as <see cref="System.Double"/>.</typeparam>
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///
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public interface IFittable<in TObservations>
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{
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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///
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void Fit(TObservations[] observations);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
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///
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void Fit(TObservations[] observations, double[] weights);
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}
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}
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