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.
157 lines
4.6 KiB
C#
157 lines
4.6 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.Links
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{
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using System;
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using Accord.Math;
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using Accord.Compat;
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/// <summary>
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/// Natural logarithm of natural logarithm link function.
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/// </summary>
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///
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[Serializable]
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public class LogLogLinkFunction : ILinkFunction
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{
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/// <summary>
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/// Linear scaling coefficient a (intercept).
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/// </summary>
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///
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public double A { get; private set; }
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/// <summary>
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/// Linear scaling coefficient b (slope).
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/// </summary>
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///
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public double B { get; private set; }
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/// <summary>
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/// Creates a new Log-Log link function.
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/// </summary>
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///
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/// <param name="beta">The beta value.</param>
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/// <param name="constant">The constant value.</param>
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///
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public LogLogLinkFunction(double beta, double constant)
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{
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this.B = beta;
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this.A = constant;
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}
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/// <summary>
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/// Creates a new Log-Log link function.
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/// </summary>
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///
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public LogLogLinkFunction() : this(1, 0) { }
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/// <summary>
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/// Creates a Complementary Log-Log link function.
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/// </summary>
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///
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public static LogLogLinkFunction Complementary()
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{
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return new LogLogLinkFunction(-1, 1);
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}
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/// <summary>
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/// The Log-log link function.
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/// </summary>
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///
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/// <param name="x">An input value.</param>
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///
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/// <returns>The transformed input value.</returns>
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///
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public double Function(double x)
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{
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return (Math.Log(-Math.Log(x)) - A) / B;
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}
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/// <summary>
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/// The Log-log mean (activation) function.
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/// </summary>
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///
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/// <param name="x">A transformed value.</param>
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///
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/// <returns>The reverse transformed value.</returns>
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///
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public double Inverse(double x)
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{
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return Math.Exp(-Math.Exp(B * x + A));
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}
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/// <summary>The logarithm of the inverse of the link function.</summary>
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/// <param name="x">A transformed value.</param>
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/// <returns>The log of the reverse transformed value.</returns>
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public double Log(double x)
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{
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return -Math.Exp(B * x + A);
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}
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/// <summary>
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/// First derivative of the <see cref="Inverse"/> function.
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/// </summary>
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///
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/// <param name="x">The input value.</param>
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///
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/// <returns>The first derivative of the input value.</returns>
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///
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public double Derivative(double x)
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{
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// -(b+1) e^(b x-e^(b x+x)+x)
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double z = B * x + A;
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double w = z - Math.Exp(z);
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return -B * Math.Exp(w);
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}
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/// <summary>
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/// First derivative of the <see cref="Inverse"/>
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/// function expressed in terms of it's output.
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/// </summary>
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///
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/// <param name="y">The reverse transformed value.</param>
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///
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/// <returns>The first derivative of the input value.</returns>
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///
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public double Derivative2(double y)
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{
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double w = Math.Log(y);
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return B * y * w;
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}
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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///
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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///
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public object Clone()
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{
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return MemberwiseClone();
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}
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}
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}
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