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.
334 lines
9.7 KiB
C#
334 lines
9.7 KiB
C#
// Accord Wavelet 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 © Gregoire Pau, 2006
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// gregoire.pau at ebi.ac.uk
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//
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// Based on the original C implementation by Gregoire Pau,
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// redistributed with modifications under the LGPL with
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// authorization of original author.
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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.Wavelets
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{
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using System;
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/// <summary>
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/// Cohen-Daubechies-Feauveau Wavelet Transform
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/// </summary>
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///
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public class CDF97 : IWavelet
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{
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// Constants as used by Gregoire P.
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const double alpha = -1.586134342;
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const double beta = -0.05298011854;
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const double gamma = 0.8829110762;
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const double delta = 0.4435068522;
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const double zeta = 1.149604398;
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private int levels;
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/// <summary>
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/// Constructs a new Cohen-Daubechies-Feauveau Wavelet Transform.
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/// </summary>
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///
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/// <param name="levels">The number of iterations for the 2D transform.</param>
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///
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public CDF97(int levels)
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{
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this.levels = levels;
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}
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/// <summary>
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/// 1-D Forward Discrete Wavelet Transform.
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/// </summary>
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///
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public void Forward(double[] data)
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{
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FWT97(data);
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}
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/// <summary>
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/// 2-D Forward Discrete Wavelet Transform.
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/// </summary>
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///
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public void Forward(double[,] data)
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{
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FWT97(data, levels);
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}
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/// <summary>
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/// 1-D Backward (Inverse) Discrete Wavelet Transform.
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/// </summary>
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///
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public void Backward(double[] data)
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{
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IWT97(data);
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}
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/// <summary>
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/// 2-D Backward (Inverse) Discrete Wavelet Transform.
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/// </summary>
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public void Backward(double[,] data)
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{
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IWT97(data, levels);
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}
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/// <summary>
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/// Forward biorthogonal 9/7 wavelet transform
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/// </summary>
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public static void FWT97(double[] x)
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{
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int n = x.Length;
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// Predict 1
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for (int i = 1; i < n - 2; i += 2)
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x[i] += alpha * (x[i - 1] + x[i + 1]);
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x[n - 1] += 2 * alpha * x[n - 2];
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// Update 1
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for (int i = 2; i < n; i += 2)
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x[i] += beta * (x[i - 1] + x[i + 1]);
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x[0] += 2 * beta * x[1];
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// Predict 2
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for (int i = 1; i < n - 2; i += 2)
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x[i] += gamma * (x[i - 1] + x[i + 1]);
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x[n - 1] += 2 * gamma * x[n - 2];
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// Update 2
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for (int i = 2; i < n; i += 2)
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x[i] += delta * (x[i - 1] + x[i + 1]);
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x[0] += 2.0 * delta * x[1];
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// Scale
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for (int i = 0; i < n; i++)
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{
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if ((i % 2) != 0)
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x[i] *= (1 / zeta);
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else x[i] /= (1 / zeta);
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}
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// Pack
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var tempbank = new double[n];
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for (int i = 0; i < n; i++)
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{
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if ((i % 2) == 0)
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tempbank[i / 2] = x[i];
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else tempbank[n / 2 + i / 2] = x[i];
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}
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for (int i = 0; i < n; i++)
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x[i] = tempbank[i];
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}
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/// <summary>
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/// Inverse biorthogonal 9/7 wavelet transform
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/// </summary>
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///
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public static void IWT97(double[] x)
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{
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int n = x.Length;
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// Unpack
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var tempbank = new double[n];
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for (int i = 0; i < n / 2; i++)
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{
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tempbank[i * 2] = x[i];
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tempbank[i * 2 + 1] = x[i + n / 2];
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}
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for (int i = 0; i < n; i++)
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x[i] = tempbank[i];
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// Undo scale
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for (int i = 0; i < n; i++)
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{
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if ((i % 2) != 0)
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x[i] *= zeta;
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else x[i] /= zeta;
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}
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// Undo update 2
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for (int i = 2; i < n; i += 2)
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x[i] -= delta * (x[i - 1] + x[i + 1]);
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x[0] -= 2.0 * delta * x[1];
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// Undo predict 2
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for (int i = 1; i < n - 2; i += 2)
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x[i] -= gamma * (x[i - 1] + x[i + 1]);
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x[n - 1] -= 2.0 * gamma * x[n - 2];
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// Undo update 1
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for (int i = 2; i < n; i += 2)
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x[i] -= beta * (x[i - 1] + x[i + 1]);
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x[0] -= 2.0 * beta * x[1];
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// Undo predict 1
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for (int i = 1; i < n - 2; i += 2)
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x[i] -= alpha * (x[i - 1] + x[i + 1]);
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x[n - 1] -= 2.0 * alpha * x[n - 2];
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}
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/// <summary>
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/// Forward biorthogonal 9/7 2D wavelet transform
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/// </summary>
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///
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public static double[,] FWT97(double[,] data, int levels)
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{
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int w = data.GetLength(0);
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int h = data.GetLength(1);
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for (int i = 0; i < levels; i++)
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{
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fwt2d(data, w, h);
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fwt2d(data, w, h);
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w >>= 1;
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h >>= 1;
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}
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return data;
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}
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/// <summary>
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/// Inverse biorthogonal 9/7 2D wavelet transform
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/// </summary>
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///
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public static double[,] IWT97(double[,] data, int levels)
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{
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int w = data.GetLength(0);
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int h = data.GetLength(1);
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for (int i = 0; i < levels - 1; i++)
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{
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h >>= 1;
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w >>= 1;
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}
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for (int i = 0; i < levels; i++)
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{
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data = iwt2d(data, w, h);
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data = iwt2d(data, w, h);
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h <<= 1;
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w <<= 1;
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}
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return data;
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}
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private static double[,] fwt2d(double[,] x, int width, int height)
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{
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for (int j = 0; j < width; j++)
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{
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// Predict 1
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for (int i = 1; i < height - 1; i += 2)
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x[i, j] += alpha * (x[i - 1, j] + x[i + 1, j]);
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x[height - 1, j] += 2 * alpha * x[height - 2, j];
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// Update 1
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for (int i = 2; i < height; i += 2)
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x[i, j] += beta * (x[i - 1, j] + x[i + 1, j]);
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x[0, j] += 2 * beta * x[1, j];
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// Predict 2
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for (int i = 1; i < height - 1; i += 2)
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x[i, j] += gamma * (x[i - 1, j] + x[i + 1, j]);
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x[height - 1, j] += 2 * gamma * x[height - 2, j];
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// Update 2
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for (int i = 2; i < height; i += 2)
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x[i, j] += delta * (x[i - 1, j] + x[i + 1, j]);
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x[0, j] += 2 * delta * x[1, j];
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}
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// Pack
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var tempbank = new double[width, height];
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for (int i = 0; i < height; i++)
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{
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for (int j = 0; j < width; j++)
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{
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if ((i % 2) == 0)
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tempbank[j, i / 2] = (1 / zeta) * x[i, j];
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else
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tempbank[j, i / 2 + height / 2] = (zeta / 2) * x[i, j];
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}
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}
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for (int i = 0; i < width; i++)
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for (int j = 0; j < width; j++)
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x[i, j] = tempbank[i, j];
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return x;
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}
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private static double[,] iwt2d(double[,] x, int width, int height)
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{
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// Unpack
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var tempbank = new double[width, height];
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for (int j = 0; j < width / 2; j++)
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{
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for (int i = 0; i < height; i++)
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{
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tempbank[j * 2, i] = zeta * x[i, j];
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tempbank[j * 2 + 1, i] = (2 / zeta) * x[i, j + width / 2];
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}
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}
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for (int i = 0; i < width; i++)
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for (int j = 0; j < height; j++)
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x[i, j] = tempbank[i, j];
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for (int j = 0; j < width; j++)
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{
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// Undo update 2
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for (int i = 2; i < height; i += 2)
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x[i, j] -= delta * (x[i - 1, j] + x[i + 1, j]);
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x[0, j] -= 2 * delta * x[1, j];
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// Undo predict 2
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for (int i = 1; i < height - 1; i += 2)
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x[i, j] -= gamma * (x[i - 1, j] + x[i + 1, j]);
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x[height - 1, j] -= 2 * gamma * x[height - 2, j];
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// Undo update 1
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for (int i = 2; i < height; i += 2)
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x[i, j] -= beta * (x[i - 1, j] + x[i + 1, j]);
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x[0, j] -= 2 * beta * x[1, j];
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// Undo predict 1
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for (int i = 1; i < height - 1; i += 2)
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x[i, j] -= alpha * (x[i - 1, j] + x[i + 1, j]);
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x[height - 1, j] -= 2 * alpha * x[height - 2, j];
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}
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return x;
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}
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}
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}
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