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.
210 lines
6.6 KiB
C#
210 lines
6.6 KiB
C#
// Accord Core 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.Random
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{
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using Accord.Compat;
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using System.Threading.Tasks;
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/// <summary>
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/// Normal random number generator using the Ziggurat method.
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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><a href="http://people.sc.fsu.edu/~jburkardt/c_src/ziggurat/ziggurat.html">
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/// John Burkard, Ziggurat Random Number Generator (RNG). Available on:
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/// http://people.sc.fsu.edu/~jburkardt/c_src/ziggurat/ziggurat.c (LGPL) </a></description></item>
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/// <item><description>
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/// Philip Leong, Guanglie Zhang, Dong-U Lee, Wayne Luk, John Villasenor,
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/// A comment on the implementation of the ziggurat method,
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/// Journal of Statistical Software, Volume 12, Number 7, February 2005.
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/// </description></item>
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/// <item><description>
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/// George Marsaglia, Wai Wan Tsang, The Ziggurat Method for Generating Random Variables,
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/// Journal of Statistical Software, Volume 5, Number 8, October 2000, seven pages. </description></item>
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/// </list></para>
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/// </remarks>
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///
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public class ZigguratNormalGenerator : IRandomNumberGenerator<double>
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{
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private ZigguratUniformOneGenerator u;
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uint[] kn;
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double[] fn;
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double[] wn;
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/// <summary>
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/// Initializes a new instance of the <see cref="ZigguratNormalGenerator"/> class.
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/// </summary>
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///
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/// <param name="seed">The random seed to use. Default is to use the next value from
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/// the <see cref="Generator">the framework-wide random generator</see>.</param>
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///
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public ZigguratNormalGenerator(int seed)
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{
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u = new ZigguratUniformOneGenerator(seed);
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kn = new uint[128];
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fn = new double[128];
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wn = new double[128];
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setup();
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="ZigguratNormalGenerator"/> class.
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/// </summary>
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///
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public ZigguratNormalGenerator()
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: this(Generator.Random.Next())
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{
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}
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/// <summary>
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/// Generates a random vector of observations from the current distribution.
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/// </summary>
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///
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/// <param name="samples">The number of samples to generate.</param>
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///
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/// <returns>
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/// A random vector of observations drawn from this distribution.
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/// </returns>
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///
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public double[] Generate(int samples)
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{
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return Generate(samples, new double[samples]);
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}
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/// <summary>
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/// Generates a random vector of observations from the current distribution.
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/// </summary>
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///
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/// <param name="samples">The number of samples to generate.</param>
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/// <param name="result">The location where to store the samples.</param>
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///
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/// <returns>
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/// A random vector of observations drawn from this distribution.
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/// </returns>
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///
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public double[] Generate(int samples, double[] result)
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{
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for (int i = 0; i < samples; i++)
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result[i] = Generate();
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return result;
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}
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/// <summary>
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/// Generates a random vector of observations from the current distribution.
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/// </summary>
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///
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/// <returns>
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/// A random vector of observations drawn from this distribution.
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/// </returns>
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///
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public double Generate()
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{
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const double r = 3.442620;
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int hz = unchecked((int)u.Next());
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uint iz = (uint)(hz & 127);
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if (Math.Abs(hz) < kn[iz])
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return hz * wn[iz];
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for (; ; )
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{
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double x;
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double y;
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if (iz == 0)
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{
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for (; ; )
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{
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x = -0.2904764 * Math.Log(u.Generate());
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y = -Math.Log(u.Generate());
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if (x * x <= y + y)
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{
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break;
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}
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}
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if (hz <= 0)
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return -r - x;
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return +r + x;
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}
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x = hz * wn[iz];
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if (fn[iz] + u.Generate() * (fn[iz - 1] - fn[iz]) < Math.Exp(-0.5 * x * x))
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return x;
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hz = unchecked((int)u.Next());
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iz = (uint)(hz & 127);
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if (Math.Abs(hz) < kn[iz])
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return hz * wn[iz];
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}
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// throw new InvalidOperationException("Execution should not reach here.");
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}
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void setup()
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{
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double dn = 3.442619855899;
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const double m1 = 2147483648.0;
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double tn = 3.442619855899;
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const double vn = 9.91256303526217E-03;
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double q = vn / Math.Exp(-0.5 * dn * dn);
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kn[0] = (uint)((dn / q) * m1);
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kn[1] = 0;
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wn[0] = q / m1;
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wn[127] = dn / m1;
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fn[0] = 1.0;
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fn[127] = Math.Exp(-0.5 * dn * dn);
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for (int i = 126; 1 <= i; i--)
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{
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dn = Math.Sqrt(-2.0 * Math.Log(vn / dn + Math.Exp(-0.5 * dn * dn)));
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kn[i + 1] = (uint)((dn / tn) * m1);
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tn = dn;
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fn[i] = Math.Exp(-0.5 * dn * dn);
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wn[i] = dn / m1;
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}
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return;
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}
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}
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}
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