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.
225 lines
8.8 KiB
C#
225 lines
8.8 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.Models.Markov.Learning
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{
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#pragma warning disable 612, 618
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using System;
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using Accord.Math;
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using Accord.Statistics.Models.Markov;
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using Accord.Statistics.Distributions.Univariate;
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using Accord.Statistics.Distributions.Multivariate;
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using Accord.Statistics.Distributions.Fitting;
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/// <summary>
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/// Baum-Welch learning algorithm for <see cref="HiddenMarkovModel">
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/// discrete-density Hidden Markov Models</see>.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// The Baum-Welch algorithm is an <see cref="IUnsupervisedLearning">unsupervised algorithm</see>
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/// used to learn a single hidden Markov model object from a set of observation sequences. It works
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/// by using a variant of the <see cref="Mixture{T}.Fit(double[], double[], MixtureOptions)">
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/// Expectation-Maximization</see> algorithm to search a set of model parameters (i.e. the matrix
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/// of <see cref="IHiddenMarkovModel.Transitions">transition probabilities A</see>, the matrix
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/// of <see cref="HiddenMarkovModel.Emissions">emission probabilities B</see>, and the
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/// <see cref="IHiddenMarkovModel.Probabilities">initial probability vector π</see>) that
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/// would result in a model having a high likelihood of being able
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/// to <see cref="HiddenMarkovModel{TDistribution, TObservation}.Generate(int)">generate</see> a set of training
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/// sequences given to this algorithm.</para>
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///
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/// <para>
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/// For increased accuracy, this class performs all computations using log-probabilities.</para>
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///
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/// <para>
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/// For a more thorough explanation on <see cref="HiddenMarkovModel">hidden Markov models</see>
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/// with practical examples on gesture recognition, please see
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/// <a href="http://www.codeproject.com/Articles/541428/Sequence-Classifiers-in-Csharp-Part-I-Hidden-Marko">
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/// Sequence Classifiers in C#, Part I: Hidden Markov Models</a> [1].</para>
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///
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/// <para>
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/// [1]: <a href="http://www.codeproject.com/Articles/541428/Sequence-Classifiers-in-Csharp-Part-I-Hidden-Marko">
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/// http://www.codeproject.com/Articles/541428/Sequence-Classifiers-in-Csharp-Part-I-Hidden-Marko </a>
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/// </para>
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/// </remarks>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Statistics\Models\Markov\HiddenMarkovModelTest.cs" region="doc_learn"/>
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/// </example>
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///
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/// <seealso cref="HiddenMarkovModel"/>
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/// <seealso cref="HiddenMarkovModel{TDistribution}"/>
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/// <seealso cref="BaumWelchLearning{TDistribution}"/>
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///
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public class BaumWelchLearning : BaseBaumWelchLearningOptions<HiddenMarkovModel, GeneralDiscreteDistribution, int, GeneralDiscreteOptions>
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{
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/// <summary>
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/// Gets or sets the number of symbols that should be used whenever
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/// this learning algorithm needs to create a new model. This property
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/// must be set before learning.
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/// </summary>
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///
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/// <value>The number of symbols.</value>
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///
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public int NumberOfSymbols { get; set; }
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/// <summary>
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/// Creates a new instance of the Baum-Welch learning algorithm.
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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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/// Creates a new instance of the Baum-Welch learning algorithm.
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/// </summary>
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///
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public BaumWelchLearning(HiddenMarkovModel model)
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: base(model)
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{
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NumberOfSymbols = model.NumberOfSymbols;
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}
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/// <summary>
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/// Obsolete.
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/// </summary>
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[Obsolete("Please use Learn(x, y) instead.")]
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public double Run(int[] observations)
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{
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Learn(new[] { observations });
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return LogLikelihood;
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}
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/// <summary>
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/// Obsolete.
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/// </summary>
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[Obsolete("Please use Learn(x, y) instead.")]
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public double Run(int[][] observations)
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{
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Learn(observations);
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return LogLikelihood;
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}
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/// <summary>
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/// Creates a Baum-Welch with default configurations for
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/// hidden Markov models with normal mixture densities.
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/// </summary>
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///
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public static BaumWelchLearning<Mixture<NormalDistribution>> FromMixtureModel(
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HiddenMarkovModel<Mixture<NormalDistribution>> model, NormalOptions options)
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{
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MixtureOptions mixOptions = new MixtureOptions()
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{
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Iterations = 1,
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InnerOptions = options,
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//ParallelOptions = ParallelOptions, // TODO:
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};
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return new BaumWelchLearning<Mixture<NormalDistribution>>(model)
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{
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FittingOptions = mixOptions
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};
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}
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/// <summary>
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/// Creates a Baum-Welch with default configurations for
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/// hidden Markov models with normal mixture densities.
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/// </summary>
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///
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public static BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>> FromMixtureModel(
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HiddenMarkovModel<MultivariateMixture<MultivariateNormalDistribution>> model, NormalOptions options)
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{
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MixtureOptions mixOptions = new MixtureOptions()
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{
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Iterations = 1,
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InnerOptions = options
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};
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return new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(model)
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{
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FittingOptions = mixOptions
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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 Create(int[][] 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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this.NumberOfSymbols = x.Max() + 1;
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return new HiddenMarkovModel(Topology, NumberOfSymbols);
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}
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// TODO: Uncomment the following lines
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///// <summary>
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///// Creates a Baum-Welch with default configurations for
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///// hidden Markov models with normal mixture densities.
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///// </summary>
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/////
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//public static BaumWelchLearning<Mixture<NormalDistribution>> FromMixtureModel(
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// HiddenMarkovModel<Mixture<NormalDistribution, double>> model, NormalOptions options)
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//{
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// MixtureOptions mixOptions = new MixtureOptions()
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// {
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// Iterations = 1,
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// InnerOptions = options,
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// //ParallelOptions = ParallelOptions, // TODO:
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// };
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// return new BaumWelchLearning<Mixture<NormalDistribution>>(model)
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// {
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// FittingOptions = mixOptions
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// };
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//}
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///// <summary>
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///// Creates a Baum-Welch with default configurations for
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///// hidden Markov models with normal mixture densities.
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///// </summary>
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/////
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//public static BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution, double[]>> FromMixtureModel(
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// HiddenMarkovModel<MultivariateMixture<MultivariateNormalDistribution>> model, NormalOptions options)
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//{
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// MixtureOptions mixOptions = new MixtureOptions()
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// {
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// Iterations = 1,
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// InnerOptions = options
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// };
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// return new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(model)
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// {
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// FittingOptions = mixOptions
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// };
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//}
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}
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}
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