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.
183 lines
6.2 KiB
C#
183 lines
6.2 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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using Accord.Statistics.Distributions.Multivariate;
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/// <summary>
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/// Common interface for multivariate probability distributions.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This interface is implemented by both multivariate <see cref="MultivariateDiscreteDistribution">
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/// Discrete Distributions</see> and <see cref="MultivariateContinuousDistribution">Continuous
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/// Distributions</see>.</para>
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///
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/// <para>
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/// For Univariate distributions, see <see cref="IUnivariateDistribution"/>.</para>
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/// </remarks>
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///
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/// <seealso cref="MultivariateNormalDistribution"/>
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/// <seealso cref="DirichletDistribution"/>
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/// <seealso cref="MultivariateEmpiricalDistribution"/>
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/// <seealso cref="WishartDistribution"/>
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///
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public interface IMultivariateDistribution : IDistribution
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{
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/// <summary>
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/// Gets the number of variables for the distribution.
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/// </summary>
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///
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int Dimension { get; }
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/// <summary>
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/// Gets the Mean vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the mean values for this distribution.</value>
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///
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double[] Mean { get; }
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/// <summary>
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/// Gets the Median vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the median values for this distribution.</value>
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///
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double[] Median { get; }
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/// <summary>
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/// Gets the Mode vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the mode values for this distribution.</value>
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///
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double[] Mode { get; }
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/// <summary>
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/// Gets the Variance vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the variance values for this distribution.</value>
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///
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double[] Variance { get; }
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/// <summary>
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/// Gets the Variance-Covariance matrix for the distribution.
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/// </summary>
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///
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/// <value>An multidimensional array of double-precision values
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/// containing the covariance values for this distribution.</value>
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///
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double[,] Covariance { get; }
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}
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/// <summary>
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/// Common interface for multivariate probability distributions.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This interface is implemented by both multivariate <see cref="MultivariateDiscreteDistribution">
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/// Discrete Distributions</see> and <see cref="MultivariateContinuousDistribution">Continuous
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/// Distributions</see>. However, unlike <see cref="IMultivariateDistribution"/>, this interface
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/// has a generic parameter that allows to define the type of the distribution values (i.e.
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/// <see cref="T:double"/>).</para>
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///
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/// <para>
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/// For Univariate distributions, see <see cref="IUnivariateDistribution"/>.</para>
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/// </remarks>
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///
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/// <seealso cref="MultivariateNormalDistribution"/>
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/// <seealso cref="DirichletDistribution"/>
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/// <seealso cref="MultivariateEmpiricalDistribution"/>
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/// <seealso cref="WishartDistribution"/>
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///
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public interface IMultivariateDistribution<in TObservation>
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: IDistribution<TObservation>, IDistribution
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{
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/// <summary>
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/// Gets the number of variables for the distribution.
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/// </summary>
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///
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int Dimension { get; }
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/// <summary>
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/// Gets the Mean vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the mean values for this distribution.</value>
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///
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double[] Mean { get; }
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/// <summary>
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/// Gets the Median vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the median values for this distribution.</value>
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///
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double[] Median { get; }
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/// <summary>
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/// Gets the Mode vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the mode values for this distribution.</value>
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///
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double[] Mode { get; }
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/// <summary>
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/// Gets the Variance vector for the distribution.
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/// </summary>
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///
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/// <value>An array of double-precision values containing
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/// the variance values for this distribution.</value>
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///
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double[] Variance { get; }
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/// <summary>
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/// Gets the Variance-Covariance matrix for the distribution.
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/// </summary>
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///
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/// <value>An multidimensional array of double-precision values
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/// containing the covariance values for this distribution.</value>
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///
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double[,] Covariance { get; }
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}
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}
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