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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.
257 lines
12 KiB
C#
257 lines
12 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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#pragma warning disable 612, 618
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namespace Accord.Statistics.Models.Markov
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{
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using System;
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using System.IO;
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using System.Runtime.Serialization.Formatters.Binary;
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using Accord.Statistics.Distributions;
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using Accord.Statistics.Models.Markov.Topology;
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using Accord.Statistics.Models.Markov.Learning;
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using System.Collections.Generic;
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using Accord.Compat;
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/// <summary>
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/// Arbitrary-density Hidden Markov Model Set for Sequence Classification.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This class uses a set of <see cref="HiddenMarkovModel{TDistribution}">density hidden
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/// Markov models</see> to classify sequences of real (double-precision floating point)
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/// numbers or arrays of those numbers. Each model will try to learn and recognize each
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/// of the different output classes. For examples and details on how to learn such models,
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/// please take a look on the documentation for
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/// <see cref="HiddenMarkovClassifierLearning{TDistribution}"/>.</para>
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///
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/// <para>
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/// For the discrete version of this classifier, please see its non-generic counterpart
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/// <see cref="HiddenMarkovClassifier"/>.</para>
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/// </remarks>
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///
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/// <example>
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/// <para>
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/// Examples are available at the respective learning algorithm pages. For
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/// example, see <see cref="HiddenMarkovClassifierLearning{TDistribution, TObservation}"/>. </para>
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/// </example>
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///
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/// <seealso cref="HiddenMarkovClassifierLearning{TDistribution}"/>
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/// <seealso cref="HiddenMarkovClassifier"/>
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///
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[Serializable]
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public class HiddenMarkovClassifier<TDistribution, TObservation> :
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BaseHiddenMarkovClassifier<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation>,
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IEnumerable<HiddenMarkovModel<TDistribution, TObservation>>
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where TDistribution : IDistribution<TObservation>
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{
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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///
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public HiddenMarkovClassifier(int classes)
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: base(classes)
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{
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="states">An array specifying the number of hidden states for each
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/// of the classifiers. By default, and Ergodic topology will be used.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, int[] states, Func<int, int, TDistribution> initial)
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: base(classes)
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{
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology topology, Func<int, int, TDistribution> initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology, (stateIndex) => initial(i, stateIndex));
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="states">An array specifying the number of hidden states for each
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/// of the classifiers. By default, and Ergodic topology will be used.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, int[] states, TDistribution initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(states[i], initial);
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology topology, TDistribution initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology, initial);
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology topology, TDistribution[] initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology, initial[i]);
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology[] topology, TDistribution[] initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology[i], initial[i]);
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology[] topology, TDistribution initial)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology[i], initial);
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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/// <param name="names">The class labels for each of the models.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology[] topology, TDistribution initial, string[] names)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology[i], initial) { Tag = names[i] };
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}
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="models">
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/// The models specializing in each of the classes of
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/// the classification problem.</param>
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///
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public HiddenMarkovClassifier(HiddenMarkovModel<TDistribution, TObservation>[] models)
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: base(models) { }
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/// <summary>
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/// Creates a new Sequence Classifier with the given number of classes.
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/// </summary>
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///
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/// <param name="classes">The number of classes in the classifier.</param>
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/// <param name="topology">The topology of the hidden states. A forward-only topology
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/// is indicated to sequence classification problems, such as speech recognition.</param>
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/// <param name="initial">The initial probability distributions for the hidden states.
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/// For multivariate continuous density distributions, such as Normal mixtures, the
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/// choice of initial values is crucial for a good performance.</param>
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/// <param name="names">The class labels for each of the models.</param>
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///
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public HiddenMarkovClassifier(int classes, ITopology topology, TDistribution initial, string[] names)
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: base(classes)
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{
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for (int i = 0; i < classes; i++)
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Models[i] = new HiddenMarkovModel<TDistribution, TObservation>(topology, initial) { Tag = names[i] };
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}
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}
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}
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