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.
239 lines
6.4 KiB
C#
239 lines
6.4 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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// 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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using Accord.Math;
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/// <summary>
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/// Haar Wavelet Transform.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// References:
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/// <list type="bullet">
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/// <item><description>
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/// Musawir Ali, An Introduction to Wavelets and the Haar Transform.
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/// Available on: http://www.cs.ucf.edu/~mali/haar/ </description></item>
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/// </list>
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/// </para>
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/// </remarks>
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///
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public class Haar : IWavelet
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{
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private const double SQRT2 = Accord.Math.Constants.Sqrt2;
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private const double w0 = 0.5;
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private const double w1 = -0.5;
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private const double s0 = 0.5;
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private const double s1 = 0.5;
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private int levels;
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/// <summary>
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/// Constructs a new Haar Wavelet Transform.
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/// </summary>
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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 Haar(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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FWT(data);
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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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IWT(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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FWT(data, levels);
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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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///
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public void Backward(double[,] data)
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{
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IWT(data, levels);
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}
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/// <summary>
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/// Discrete Haar Wavelet Transform
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/// </summary>
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///
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public static void FWT(double[] data)
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{
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double[] temp = new double[data.Length];
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int h = data.Length >> 1;
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for (int i = 0; i < h; i++)
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{
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int k = (i << 1);
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temp[i] = data[k] * s0 + data[k + 1] * s1;
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temp[i + h] = data[k] * w0 + data[k + 1] * w1;
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}
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for (int i = 0; i < data.Length; i++)
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data[i] = temp[i];
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}
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/// <summary>
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/// Inverse Haar Wavelet Transform
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/// </summary>
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///
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public static void IWT(double[] data)
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{
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double[] temp = new double[data.Length];
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int h = data.Length >> 1;
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for (int i = 0; i < h; i++)
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{
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int k = (i << 1);
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temp[k] = (data[i] * s0 + data[i + h] * w0) / w0;
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temp[k + 1] = (data[i] * s1 + data[i + h] * w1) / s0;
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}
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for (int i = 0; i < data.Length; i++)
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data[i] = temp[i];
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}
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/// <summary>
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/// Discrete Haar Wavelet 2D Transform
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/// </summary>
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///
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public static void FWT(double[,] data, int iterations)
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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[] row;
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double[] col;
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for (int k = 0; k < iterations; k++)
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{
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int lev = 1 << k;
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int levCols = cols / lev;
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int levRows = rows / lev;
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row = new double[levCols];
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for (int i = 0; i < levRows; i++)
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{
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for (int j = 0; j < row.Length; j++)
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row[j] = data[i, j];
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FWT(row);
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for (int j = 0; j < row.Length; j++)
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data[i, j] = row[j];
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}
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col = new double[levRows];
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for (int j = 0; j < levCols; j++)
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{
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for (int i = 0; i < col.Length; i++)
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col[i] = data[i, j];
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FWT(col);
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for (int i = 0; i < col.Length; i++)
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data[i, j] = col[i];
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}
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}
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}
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/// <summary>
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/// Inverse Haar Wavelet 2D Transform
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/// </summary>
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///
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public static void IWT(double[,] data, int iterations)
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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[] col;
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double[] row;
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for (int k = iterations - 1; k >= 0; k--)
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{
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int lev = 1 << k;
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int levCols = cols / lev;
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int levRows = rows / lev;
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col = new double[levRows];
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for (int j = 0; j < levCols; j++)
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{
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for (int i = 0; i < col.Length; i++)
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col[i] = data[i, j];
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IWT(col);
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for (int i = 0; i < col.Length; i++)
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data[i, j] = col[i];
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}
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row = new double[levCols];
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for (int i = 0; i < levRows; i++)
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{
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for (int j = 0; j < row.Length; j++)
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row[j] = data[i, j];
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IWT(row);
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for (int j = 0; j < row.Length; j++)
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data[i, j] = row[j];
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}
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}
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}
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}
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}
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