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.
159 lines
4.8 KiB
C#
159 lines
4.8 KiB
C#
// AForge Math Library
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// AForge.NET framework
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// http://www.aforgenet.com/framework/
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//
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// Copyright © Andrew Kirillov, 2005-2009
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// [email protected]
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//
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namespace AForge.Math
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{
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using Accord.Math;
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using System;
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/// <summary>
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/// Gaussian function.
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/// </summary>
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///
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/// <remarks><para>The class is used to calculate 1D and 2D Gaussian functions for
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/// specified <see cref="Sigma"/> (s) value:</para>
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///
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/// <code lang="none">
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/// 1-D: f(x) = exp( x * x / ( -2 * s * s ) ) / ( s * sqrt( 2 * PI ) )
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///
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/// 2-D: f(x, y) = exp( x * x + y * y / ( -2 * s * s ) ) / ( s * s * 2 * PI )
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/// </code>
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///
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/// </remarks>
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///
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[Obsolete("Please use Accord.Math.Normal instead.")]
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public class Gaussian
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{
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// sigma value
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private double sigma = 1.0;
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// squared sigma
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private double sqrSigma = 1.0;
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/// <summary>
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/// Sigma value.
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/// </summary>
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///
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/// <remarks><para>Sigma property of Gaussian function.</para>
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///
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/// <para>Default value is set to <b>1</b>. Minimum allowed value is <b>0.00000001</b>.</para>
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/// </remarks>
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///
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public double Sigma
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{
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get { return sigma; }
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set
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{
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sigma = Math.Max(0.00000001, value);
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sqrSigma = sigma * sigma;
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}
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Gaussian"/> class.
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/// </summary>
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///
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public Gaussian() { }
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/// <summary>
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/// Initializes a new instance of the <see cref="Gaussian"/> class.
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/// </summary>
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///
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/// <param name="sigma">Sigma value.</param>
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///
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public Gaussian(double sigma)
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{
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Sigma = sigma;
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}
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/// <summary>
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/// 1-D Gaussian function.
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/// </summary>
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///
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/// <param name="x">x value.</param>
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///
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/// <returns>Returns function's value at point <paramref name="x"/>.</returns>
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///
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/// <remarks><para>The function calculates 1-D Gaussian function:</para>
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///
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/// <code lang="none">
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/// f(x) = exp( x * x / ( -2 * s * s ) ) / ( s * sqrt( 2 * PI ) )
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/// </code>
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/// </remarks>
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///
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[Obsolete("Please use Normal.Gaussian instead.")]
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public double Function(double x)
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{
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return Normal.Gaussian(sqrSigma, x);
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}
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/// <summary>
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/// 2-D Gaussian function.
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/// </summary>
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///
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/// <param name="x">x value.</param>
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/// <param name="y">y value.</param>
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///
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/// <returns>Returns function's value at point (<paramref name="x"/>, <paramref name="y"/>).</returns>
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///
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/// <remarks><para>The function calculates 2-D Gaussian function:</para>
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///
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/// <code lang="none">
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/// f(x, y) = exp( x * x + y * y / ( -2 * s * s ) ) / ( s * s * 2 * PI )
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/// </code>
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/// </remarks>
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///
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[Obsolete("Please use Normal.Gaussian2D instead.")]
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public double Function2D(double x, double y)
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{
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return Normal.Gaussian2D(sqrSigma, x, y);
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}
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/// <summary>
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/// 1-D Gaussian kernel.
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/// </summary>
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///
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/// <param name="size">Kernel size (should be odd), [3, 101].</param>
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///
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/// <returns>Returns 1-D Gaussian kernel of the specified size.</returns>
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///
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/// <remarks><para>The function calculates 1-D Gaussian kernel, which is array
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/// of Gaussian function's values in the [-r, r] range of x value, where
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/// r=floor(<paramref name="size"/>/2).
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/// </para></remarks>
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///
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/// <exception cref="ArgumentException">Wrong kernel size.</exception>
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///
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[Obsolete("Please use Normal.Kernel instead.")]
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public double[] Kernel(int size)
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{
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return Normal.Kernel(sqrSigma, size);
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}
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/// <summary>
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/// 2-D Gaussian kernel.
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/// </summary>
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///
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/// <param name="size">Kernel size (should be odd), [3, 101].</param>
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///
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/// <returns>Returns 2-D Gaussian kernel of specified size.</returns>
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///
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/// <remarks><para>The function calculates 2-D Gaussian kernel, which is array
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/// of Gaussian function's values in the [-r, r] range of x,y values, where
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/// r=floor(<paramref name="size"/>/2).
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/// </para></remarks>
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///
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/// <exception cref="ArgumentException">Wrong kernel size.</exception>
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///
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[Obsolete("Please use Normal.Kernel2D instead.")]
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public double[,] Kernel2D(int size)
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{
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return Normal.Kernel2D(sqrSigma, size);
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}
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}
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}
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