mirror of
https://github.com/hanzoai/jube-fork.git
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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.
91 lines
3.2 KiB
C#
91 lines
3.2 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.Hybrid
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{
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using System;
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/// <summary>
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/// Common interface for Hybrid Hidden Markov Models.
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/// </summary>
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///
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public interface IHybridMarkovModel
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{
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/// <summary>
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/// Calculates the most likely sequence of hidden states
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/// that produced the given observation sequence.
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/// </summary>
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/// <remarks>
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/// Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence
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/// O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si
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/// that produced this observation sequence O. This can be computed efficiently
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/// using the Viterbi algorithm.
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/// </remarks>
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/// <param name="observations">
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/// A sequence of observations.</param>
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/// <param name="logLikelihood">
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/// The state optimized probability.</param>
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/// <returns>
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/// The sequence of states that most likely produced the sequence.
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/// </returns>
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///
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int[] Decode(double[][] observations, out double logLikelihood);
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/// <summary>
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/// Calculates the probability that this model has generated the given sequence.
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/// </summary>
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/// <remarks>
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/// Evaluation problem. Given the HMM M = (A, B, pi) and the observation
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/// sequence O = {o1, o2, ..., oK}, calculate the probability that model
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/// M has generated sequence O. This can be computed efficiently using the
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/// Forward algorithm. </remarks>
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/// <param name="observations">
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/// A sequence of observations. </param>
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/// <returns>
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/// The probability that the given sequence has been generated by this model.
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/// </returns>
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///
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double Evaluate(double[][] observations);
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/// <summary>
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/// Gets the expected number of dimensions in each observation.
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/// </summary>
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///
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int Dimension { get; }
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/// <summary>
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/// Gets the number of states of this model.
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/// </summary>
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///
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int States { get; }
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/// <summary>
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/// Gets or sets a user-defined tag.
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/// </summary>
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///
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object Tag { get; set; }
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}
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}
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