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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.
85 lines
3.6 KiB
C#
85 lines
3.6 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.Models.Markov.Learning
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{
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using Accord.Statistics.Models.Markov;
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using Accord.Statistics.Distributions;
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using Accord.Statistics.Distributions.Fitting;
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using System;
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/// <summary>
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/// Baum-Welch learning algorithms for learning Hidden Markov Models.
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/// </summary>
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///
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/// <typeparam name="TDistribution">The type of the emission distributions in the model.</typeparam>
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/// <typeparam name="TObservation">The type of the observations (i.e. int for a discrete model).</typeparam>
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/// <typeparam name="TOptions">The type of fitting options accepted by this distribution.</typeparam>
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///
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/// <remarks>
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/// Please see the <see cref="BaumWelchLearning{TDistribution, TObservation}"/> documentation page for
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/// the actual documentation of this class, including examples.
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/// </remarks>
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///
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public class BaumWelchLearning<TDistribution, TObservation, TOptions> :
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BaseBaumWelchLearningOptions<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation, TOptions>,
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IConvergenceLearning
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where TDistribution : IFittableDistribution<TObservation, TOptions>
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where TOptions : class, IFittingOptions, new()
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
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/// </summary>
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///
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public BaumWelchLearning()
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{
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
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/// </summary>
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///
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/// <param name="model">The model to be learned.</param>
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///
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public BaumWelchLearning(HiddenMarkovModel<TDistribution, TObservation> model)
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: base(model)
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{
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}
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/// <summary>
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/// Creates an instance of the model to be learned. Inheritors of this abstract
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/// class must define this method so new models can be created from the training data.
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/// </summary>
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///
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protected override HiddenMarkovModel<TDistribution, TObservation> Create(TObservation[][] x)
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{
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if (Topology == null)
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throw new InvalidOperationException("Please set the Topology property before trying to learn a new hidden Markov model.");
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if (Emissions == null)
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throw new InvalidOperationException("Please set the Emissions property before trying to learn a new hidden Markov model.");
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return new HiddenMarkovModel<TDistribution, TObservation>(Topology, Emissions);
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}
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}
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}
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