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

63 lines
2.4 KiB
C#

// Accord Neural Net 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.Neuro.ActivationFunctions
{
/// <summary>
/// Common interface for stochastic activation functions.
/// </summary>
///
/// <seealso cref="BernoulliFunction"/>
/// <seealso cref="GaussianFunction"/>
///
public interface IStochasticFunction : IActivationFunction
{
/// <summary>
/// Samples a value from the function given a input value.
/// </summary>
///
/// <param name="x">Function input value.</param>
///
/// <returns>Draws a random value from the function.</returns>
///
double Generate(double x);
/// <summary>
/// Samples a value from the function given a function output value.
/// </summary>
///
/// <param name="y">The function output value. This is the value which was obtained
/// with the help of the <see cref="IActivationFunction.Function(double)"/> method.</param>
///
/// <remarks><para>The method calculates the same output value as the
/// <see cref="Generate"/> method, but it takes not the input <b>x</b> value
/// itself, but the function value, which was calculated previously with help
/// of the <see cref="IActivationFunction.Function(double)"/> method.</para>
/// </remarks>
///
/// <returns>Draws a random value from the function.</returns>
///
double Generate2(double y);
}
}