Files
richard-churchman cc0a2bee21 Extensive modifications to the serialisation of Neural Network models to support Newtonsoft Json.net rather than Binary Serializer.
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.
2023-12-24 10:22:22 +01:00

88 lines
2.9 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.Distributions.Fitting
{
using System;
using Accord.Compat;
/// <summary>
/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.
/// GeneralDiscreteDistribution">general discrete (categorical) distributions</see>.
/// </summary>
///
[Serializable]
public class GeneralDiscreteOptions : IFittingOptions
{
/// <summary>
/// Gets or sets the minimum allowed probability
/// in the frequency tables specifying the discrete
/// distribution. Default is 1e-10.
/// </summary>
///
public double Minimum { get; set; }
/// <summary>
/// Gets ors sets whether to use Laplace's rule of succession
/// to avoid zero probabilities. Default is false.
/// </summary>
///
public bool UseLaplaceRule { get; set; }
/// <summary>
/// Gets or sets how much percent of the previous value for
/// the distribution should be kept in its updated value. Default is 0.
/// </summary>
///
public double Regularization { get; set; }
/// <summary>
/// Gets or sets whether current frequency values in the distribution
/// should be considered as priors during the next time the distribution
/// is estimated. Default is false.
/// </summary>
///
public bool UsePreviousValuesAsPriors { get; set; }
/// <summary>
/// Initializes a new instance of the <see cref="GeneralDiscreteOptions"/> class.
/// </summary>
///
public GeneralDiscreteOptions()
{
Minimum = 1e-10;
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
public object Clone()
{
return MemberwiseClone();
}
}
}