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.
101 lines
3.9 KiB
C#
101 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.Sampling
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{
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using Accord.Math.Random;
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using Accord.Statistics.Distributions.Multivariate;
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using Accord.Statistics.Distributions.Univariate;
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using System;
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/// <summary>
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/// Metropolis-Hasting sampling algorithm.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// References:
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/// <list type="bullet">
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/// <item><description><a href="https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm">
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/// Wikipedia, The Free Encyclopedia. Metropolis-Hastings algorithm.
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/// Available on: https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm </a></description></item>
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/// <item><description><a href="http://en.wikipedia.org/wiki/Joint_probability_distribution">
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/// Darren Wilkinson, Metropolis Hastings MCMC when the proposal and target have differing support.
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/// Available on: https://darrenjw.wordpress.com/2012/06/04/metropolis-hastings-mcmc-when-the-proposal-and-target-have-differing-support/ </a></description></item>
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/// </list></para>
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/// </remarks>
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///
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public class MetropolisHasting<TObservation, TProposalDistribution>
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: MetropolisHasting<TObservation>
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where TProposalDistribution : ISampleableDistribution<TObservation[]>
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{
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private TProposalDistribution proposal;
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/// <summary>
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/// Gets or sets the move proposal distribution.
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/// </summary>
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///
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public new TProposalDistribution Proposal
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{
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get { return proposal; }
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="MetropolisHasting"/> algorithm.
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/// </summary>
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///
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/// <param name="dimensions">The number of dimensions in each observation.</param>
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/// <param name="logDensity">A function specifying the log probability density of the distribution to be sampled.</param>
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/// <param name="proposal">The proposal distribution that is used to generate new parameter samples to be explored.</param>
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///
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public MetropolisHasting(int dimensions, Func<TObservation[], double> logDensity, TProposalDistribution proposal)
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{
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Initialize(dimensions, logDensity, proposal);
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="MetropolisHasting{TObservation, TProposalDistribution}"/> class.
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/// </summary>
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///
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protected MetropolisHasting()
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{
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}
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/// <summary>
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/// Initializes the algorithm.
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/// </summary>
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///
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protected void Initialize(int dimensions, Func<TObservation[], double> logDensity, TProposalDistribution proposal)
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{
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this.proposal = proposal;
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Initialize(dimensions, logDensity, generate);
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}
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private TObservation[] generate(TObservation[] current, TObservation[] next)
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{
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return proposal.Generate(result: next);
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}
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}
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}
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