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.
119 lines
3.8 KiB
C#
119 lines
3.8 KiB
C#
// Accord Neural Net 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.Neuro
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{
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using System;
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using AForge;
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using Accord.Compat;
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/// <summary>
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/// Rectified linear activation function.
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/// </summary>
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///
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/// <remarks>
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/// <para>This class implements a rectified linear activation
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/// function as given by the piecewise formula:</para>
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///
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/// <code lang="none">
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/// f(x) = x, if x > 0
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/// f(x) = 0, otherwise
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/// </code>
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///
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/// <para>
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/// This function is non-differentiable at zero.
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/// </para>
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/// </remarks>
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///
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[Serializable]
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public class RectifiedLinearFunction : IActivationFunction
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="RectifiedLinearFunction"/> class.
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/// </summary>
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///
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public RectifiedLinearFunction()
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{
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}
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/// <summary>
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/// Calculates function value.
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/// </summary>
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///
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/// <param name="x">Function input value.</param>
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///
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/// <returns>Function output value, <i>f(x)</i>.</returns>
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///
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/// <remarks>The method calculates function value at point <paramref name="x"/>.</remarks>
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///
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public double Function(double x)
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{
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if (x > 0)
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return x;
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return 0;
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}
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/// <summary>
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/// Calculates function derivative.
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/// </summary>
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///
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/// <param name="x">Function input value.</param>
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///
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/// <returns>Function derivative, <i>f'(x)</i>.</returns>
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///
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/// <remarks>The method calculates function derivative at point <paramref name="x"/>.</remarks>
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///
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public double Derivative(double x)
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{
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if (x > 0)
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return 1;
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return 0;
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}
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/// <summary>
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/// Calculates function derivative.
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/// </summary>
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///
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/// <param name="y">Function output value - the value, which was obtained
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/// with the help of <see cref="Function"/> method.</param>
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///
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/// <returns>Function derivative, <i>f'(x)</i>.</returns>
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///
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/// <remarks><para>The method calculates the same derivative value as the
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/// <see cref="Derivative"/> method, but it takes not the input <b>x</b> value
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/// itself, but the function value, which was calculated previously with
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/// the help of <see cref="Function"/> method.</para>
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///
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/// <para><note>Some applications require as function value, as derivative value,
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/// so they can save the amount of calculations using this method to calculate derivative.</note></para>
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/// </remarks>
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///
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public double Derivative2(double y)
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{
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if (y > 0)
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return 1;
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return 0;
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}
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}
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} |