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.
144 lines
4.8 KiB
C#
144 lines
4.8 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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// 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 Accord.Compat;
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using System.Numerics;
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using Accord.Math.Transforms;
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/// <summary>
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/// Obsolete. Please use <see cref="HilbertTransform2"/> instead.
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/// </summary>
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///
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[Obsolete("Please use HilbertTransform2 instead.")]
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public static class HilbertTransform
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{
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/// <summary>
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/// Performs the Fast Hilbert Transform over a double[] array.
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/// </summary>
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///
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public static void FHT(double[] data, FourierTransform.Direction direction)
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{
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int N = data.Length;
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// Forward operation
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if (direction == FourierTransform.Direction.Forward)
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{
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// Copy the input to a complex array which can be processed
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// in the complex domain by the FFT
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Complex[] cdata = new Complex[N];
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for (int i = 0; i < N; i++)
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cdata[i] = new Complex(data[i], 0.0);
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// Perform FFT
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FourierTransform.FFT(cdata, FourierTransform.Direction.Forward);
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//double positive frequencies
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for (int i = 1; i < (N / 2); i++)
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{
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cdata[i] *= 2.0;
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}
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// zero out negative frequencies
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// (leaving out the dc component)
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for (int i = (N / 2) + 1; i < N; i++)
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{
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cdata[i] = Complex.Zero;
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}
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// Reverse the FFT
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FourierTransform.FFT(cdata, FourierTransform.Direction.Backward);
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// Convert back to our initial double array
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for (int i = 0; i < N; i++)
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data[i] = cdata[i].Imaginary;
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}
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else // Backward operation
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{
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// The inverse Hilbert can be calculated by
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// negating the transform and reapplying the
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// transformation.
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//
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// H^–1{h(t)} = –H{h(t)}
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FHT(data, FourierTransform.Direction.Forward);
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for (int i = 0; i < data.Length; i++)
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data[i] = -data[i];
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}
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}
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/// <summary>
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/// Performs the Fast Hilbert Transform over a complex[] array.
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/// </summary>
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///
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public static void FHT(Complex[] data, FourierTransform.Direction direction)
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{
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int N = data.Length;
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// Forward operation
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if (direction == FourierTransform.Direction.Forward)
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{
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// Makes a copy of the data so we don't lose the
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// original information to build our final signal
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Complex[] shift = (Complex[])data.Clone();
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// Perform FFT
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FourierTransform.FFT(shift, FourierTransform.Direction.Backward);
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//double positive frequencies
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for (int i = 1; i < (N / 2); i++)
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{
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shift[i] *= 2.0;
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}
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// zero out negative frequencies
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// (leaving out the dc component)
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for (int i = (N / 2) + 1; i < N; i++)
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{
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shift[i] = Complex.Zero;
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}
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// Reverse the FFT
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FourierTransform.FFT(shift, FourierTransform.Direction.Forward);
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// Put the Hilbert transform in the Imaginary part
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// of the input signal, creating a Analytic Signal
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for (int i = 0; i < N; i++)
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data[i] = new Complex(data[i].Real, shift[i].Imaginary);
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}
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else // Backward operation
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{
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// Just discard the imaginary part
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for (int i = 0; i < data.Length; i++)
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data[i] = new Complex(data[i].Real, 0.0);
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}
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}
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}
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}
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