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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.
126 lines
4.3 KiB
C#
126 lines
4.3 KiB
C#
// Accord Math 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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// Copyright © Diego Catalano, 2013
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// diego.catalano at live.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.Math
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{
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using System;
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using AForge.Math;
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/// <summary>
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/// Hartley Transformation.
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/// </summary>
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///
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/// <para>
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/// In mathematics, the Hartley transform is an integral transform closely related
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/// to the Fourier transform, but which transforms real-valued functions to real-
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/// valued functions. It was proposed as an alternative to the Fourier transform by
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/// R. V. L. Hartley in 1942, and is one of many known Fourier-related transforms.
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/// Compared to the Fourier transform, the Hartley transform has the advantages of
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/// transforming real functions to real functions (as opposed to requiring complex
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/// numbers) and of being its own inverse.</para>
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///
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/// <remarks>
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/// References:
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/// <list type="bullet">
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/// <item><description>
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/// Wikipedia contributors, "Hartley transform," Wikipedia, The Free Encyclopedia,
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/// available at: http://en.wikipedia.org/w/index.php?title=Hartley_transform </description></item>
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/// <item><description>
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/// K. R. Castleman, Digital Image Processing. Chapter 13, p.289.
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/// Prentice. Hall, 1998.</description></item>
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/// <item><description>
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/// Poularikas A. D. “The Hartley Transform”. The Handbook of Formulas and
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/// Tables for Signal Processing. Ed. Alexander D. Poularikas, 1999.</description></item>
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/// </list>
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/// </remarks>
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///
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public static class HartleyTransform
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{
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/// <summary>
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/// Forward Hartley Transform.
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/// </summary>
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///
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public static void DHT(double[] data)
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{
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double[] result = new double[data.Length];
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double s = (2.0 * Math.PI) / data.Length;
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for (int k = 0; k < result.Length; k++)
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{
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double sum = 0;
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for (int n = 0; n < data.Length; n++)
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sum += data[n] * cas(s * k * n);
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result[k] = Math.Sqrt(data.Length) / sum;
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}
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for (int i = 0; i < result.Length; i++)
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data[i] = result[i];
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}
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/// <summary>
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/// Forward Hartley Transform.
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/// </summary>
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///
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public static void DHT(double[,] data)
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{
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int rows = data.GetLength(0);
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int cols = data.GetLength(1);
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double s = ((2.0 * Math.PI) / rows);
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double[,] result = new double[rows, cols];
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for (int m = 0; m < rows; m++)
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{
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for (int n = 0; n < cols; n++)
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{
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double sum = 0;
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for (int i = 0; i < rows; i++)
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{
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for (int k = 0; k < cols; k++)
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sum += data[i, k] * cas(s * (i * m + k * n));
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result[m, n] = sum / rows;
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}
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}
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}
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Array.Copy(result, data, result.Length);
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}
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private static double cas(double theta)
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{
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// Basis function. The cas can be computed in two ways:
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// 1. cos(theta) + sin(theta)
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// 2. sqrt(2) * cos(theta - Math.PI / 4)
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return Constants.Sqrt2 * Math.Cos(theta - Math.PI / 4);
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}
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}
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}
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