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.
246 lines
8.8 KiB
C#
246 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.Distributions.Multivariate
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{
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using System;
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using Accord.Statistics.Distributions.Fitting;
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using Accord.Statistics.Models.Markov;
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/// <summary>
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/// Hidden Markov Model probability distribution.
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/// </summary>
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///
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public class HiddenMarkovDistribution : MultivariateDiscreteDistribution
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{
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HiddenMarkovModel model;
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/// <summary>
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/// Initializes a new instance of the <see cref="HiddenMarkovDistribution"/> class.
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/// </summary>
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///
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/// <param name="model">The model.</param>
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///
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public HiddenMarkovDistribution(HiddenMarkovModel model)
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: base(0)
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{
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this.model = model;
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}
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/// <summary>
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/// Gets the mean for this distribution.
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/// </summary>
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///
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/// <value>
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/// An array of double-precision values containing
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/// the mean values for this distribution.
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/// </value>
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///
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public override double[] Mean
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{
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get { throw new NotSupportedException(); }
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}
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/// <summary>
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/// Gets the mean for this distribution.
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/// </summary>
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///
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/// <value>
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/// An array of double-precision values containing
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/// the variance values for this distribution.
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/// </value>
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///
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public override double[] Variance
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{
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get { throw new NotSupportedException(); }
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}
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/// <summary>
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/// Gets the variance for this distribution.
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/// </summary>
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///
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/// <value>
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/// An multidimensional array of double-precision values
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/// containing the covariance values for this distribution.
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/// </value>
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///
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public override double[,] Covariance
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{
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get { throw new NotSupportedException(); }
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}
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/// <summary>
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/// Gets the cumulative distribution function (cdf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">A single point in the distribution range.</param>
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///
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/// <remarks>
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/// The Cumulative Distribution Function (CDF) describes the cumulative
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/// probability that a given value or any value smaller than it will occur.
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/// </remarks>
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///
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protected internal override double InnerDistributionFunction(int[] x)
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{
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throw new NotSupportedException();
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}
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/// <summary>
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/// Gets the support interval for this distribution.
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/// </summary>
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/// <value>A <see cref="IntRange" /> containing
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/// the support interval for this distribution.</value>
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public override IntRange[] Support
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{
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get { return new [] { new IntRange(0, model.NumberOfSymbols) }; }
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}
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/// <summary>
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/// Gets the probability mass function (pmf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">A single point in the distribution range.</param>
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///
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/// <returns>
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/// The probability of <c>x</c> occurring
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/// in the current distribution.
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/// </returns>
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///
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/// <remarks>
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/// The Probability Mass Function (PMF) describes the
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/// probability that a given value <c>x</c> will occur.
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/// </remarks>
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///
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protected internal override double InnerProbabilityMassFunction(int[] x)
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{
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return Math.Exp(model.LogLikelihood(x));
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}
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/// <summary>
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/// Gets the log-probability mass function (pmf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">A single point in the distribution range.</param>
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///
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/// <returns>
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/// The logarithm of the probability of <c>x</c>
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/// occurring in the current distribution.
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/// </returns>
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///
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/// <remarks>
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/// The Probability Mass Function (PMF) describes the
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/// probability that a given value <c>x</c> will occur.
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/// </remarks>
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///
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protected internal override double InnerLogProbabilityMassFunction(int[] x)
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{
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return model.LogLikelihood(x);
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}
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
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/// <param name="options">Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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public override void Fit(double[][] observations, double[] weights, Fitting.IFittingOptions options)
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{
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HiddenMarkovOptions normalOptions = options as HiddenMarkovOptions;
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if (options != null && normalOptions == null)
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throw new ArgumentException("The specified options' type is invalid.", "options");
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Fit(observations, weights, normalOptions);
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}
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
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/// <param name="options">Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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public void Fit(double[][] observations, double[] weights, HiddenMarkovOptions options)
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{
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if (options == null)
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throw new ArgumentNullException("options");
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options.Learning(model, observations, weights);
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}
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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///
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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///
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public override object Clone()
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{
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return model.Clone();
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}
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/// <summary>
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/// Returns a <see cref="System.String" /> that represents this instance.
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/// </summary>
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///
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/// <param name="format">The format.</param>
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/// <param name="formatProvider">The format provider.</param>
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///
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/// <returns>
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/// A <see cref="System.String" /> that represents this instance.
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/// </returns>
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///
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public override string ToString(string format, IFormatProvider formatProvider)
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{
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return String.Format(formatProvider, "HMM(X; A, B, π)");
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}
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}
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}
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