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.
79 lines
3.5 KiB
C#
79 lines
3.5 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
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{
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using System.Runtime.CompilerServices;
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using Accord.Statistics.Models;
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using Accord.Statistics.Models.Fields;
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using Accord.Statistics.Models.Markov;
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using Accord.Statistics.Models.Markov.Hybrid;
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using Accord.Statistics.Models.Regression;
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using Accord.Statistics.Models.Regression.Linear;
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/// <summary>
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/// Contains statistical models with direct applications in machine learning, such as
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/// <see cref="HiddenMarkovModel">Hidden Markov Models</see>, <see cref="ConditionalRandomField{T}">
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/// Conditional Random Fields</see>, <see cref="HiddenConditionalRandomField{T}">Hidden Conditional
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/// Random Fields</see> and <see cref="ILinearRegression">linear</see> and <see cref="LogisticRegression">
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/// logistic regressions</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 main algorithms and techniques available on this namespaces are certainly
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/// the <see cref="Accord.Statistics.Models.Markov">hidden Markov models</see>.
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/// The Accord.NET Framework contains one of the most popular and well-tested
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/// offerings for creating, training and validating Markov models using either
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/// discrete observations or any arbitrary discrete, continuous or mixed <see
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/// cref="Accord.Statistics.Distributions">probability distributions</see> to
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/// model the observations.</para>
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///
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/// <para>
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/// This namespace also brings <see cref="Accord.Statistics.Models.Fields">
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/// Conditional Random Fields</see>, that alongside the Markov models can be
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/// used to build <see cref="HiddenMarkovClassifier">sequence classifiers</see>,
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/// perform gesture recognition, and can even be combined with neural networks
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/// to create <see cref="HybridMarkovClassifier">hybrid models</see>. Other
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/// models include <see cref="Accord.Statistics.Models.Regression">regression
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/// and survival models</see>.</para>
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///
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///
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/// <para>
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/// The namespace class diagram is shown below. </para>
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/// <img src="..\diagrams\classes\Accord.Statistics.Models.png" />
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///
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/// <para>
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/// Please note that class diagrams for each of the inner namespaces are
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/// also available within their own documentation pages.</para>
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/// </remarks>
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///
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/// <seealso cref="Accord.Statistics.Models.Fields"/>
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/// <seealso cref="Accord.Statistics.Models.Markov"/>
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/// <seealso cref="Accord.Statistics.Models.Regression"/>
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///
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[CompilerGenerated]
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class NamespaceDoc
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{
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}
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}
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