mirror of
https://github.com/hanzoai/jube-fork.git
synced 2026-11-03 01:01:41 +00:00
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
3.2 KiB
C#
79 lines
3.2 KiB
C#
// Accord Statistics Library
|
|
// The Accord.NET Framework
|
|
// http://accord-framework.net
|
|
//
|
|
// Copyright © César Souza, 2009-2017
|
|
// cesarsouza at gmail.com
|
|
//
|
|
// This library is free software; you can redistribute it and/or
|
|
// modify it under the terms of the GNU Lesser General Public
|
|
// License as published by the Free Software Foundation; either
|
|
// version 2.1 of the License, or (at your option) any later version.
|
|
//
|
|
// This library is distributed in the hope that it will be useful,
|
|
// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
|
// Lesser General Public License for more details.
|
|
//
|
|
// You should have received a copy of the GNU Lesser General Public
|
|
// License along with this library; if not, write to the Free Software
|
|
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
|
|
//
|
|
|
|
namespace Accord.Statistics.Models.Markov.Topology
|
|
{
|
|
|
|
/// <summary>
|
|
/// Hidden Markov model topology (architecture) specification.
|
|
/// </summary>
|
|
///
|
|
/// <remarks>
|
|
/// <para>
|
|
/// An Hidden Markov Model Topology specifies how many states and which
|
|
/// initial probabilities a Markov model should have. Two common topologies
|
|
/// can be discussed in terms of transition state probabilities and are
|
|
/// available to construction through the <see cref="Ergodic"/> and
|
|
/// <see cref="Forward"/> classes implementing this interface.</para>
|
|
///
|
|
/// <para>Topology specification is important with regard to both learning and
|
|
/// performance: A model with too many states (and thus too many settable
|
|
/// parameters) will require too much training data while an model with an
|
|
/// insufficient number of states will prohibit the HMM from capturing subtle
|
|
/// statistical patterns.</para>
|
|
///
|
|
///
|
|
/// <para>
|
|
/// References:
|
|
/// <list type="bullet">
|
|
/// <item><description>
|
|
/// Alexander Schliep, "Learning Hidden Markov Model Topology".</description></item>
|
|
/// <item><description>
|
|
/// Richard Hughey and Anders Krogh, "Hidden Markov models for sequence analysis:
|
|
/// extension and analysis of the basic method", CABIOS 12(2):95-107, 1996. Available in:
|
|
/// http://compbio.soe.ucsc.edu/html_format_papers/hughkrogh96/cabios.html</description></item>
|
|
/// </list></para>
|
|
///
|
|
/// </remarks>
|
|
///
|
|
/// <seealso cref="HiddenMarkovModel"/>
|
|
/// <seealso cref="Accord.Statistics.Models.Markov.Topology.Ergodic"/>
|
|
/// <seealso cref="Accord.Statistics.Models.Markov.Topology.Forward"/>
|
|
/// <seealso cref="Accord.Statistics.Models.Markov.Topology.Custom"/>
|
|
///
|
|
public interface ITopology
|
|
{
|
|
|
|
/// <summary>
|
|
/// Gets the number of states in this topology.
|
|
/// </summary>
|
|
int States { get; set; }
|
|
|
|
/// <summary>
|
|
/// Creates the state transitions matrix and the
|
|
/// initial state probabilities for this topology.
|
|
/// </summary>
|
|
int Create(bool logarithm, out double[,] transitionMatrix, out double[] initialState);
|
|
|
|
}
|
|
}
|