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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.
72 lines
3.5 KiB
C#
72 lines
3.5 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.Optimization
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{
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using System.Runtime.CompilerServices;
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/// <summary>
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/// Contains classes for constrained and unconstrained optimization. Includes
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/// <see cref="ConjugateGradient">Conjugate Gradient (CG)</see>, <see cref="BoundedBroydenFletcherGoldfarbShanno">
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/// Bounded</see> and <see cref="BroydenFletcherGoldfarbShanno">Unbounded Broyden–Fletcher–Goldfarb–Shanno (BFGS)</see>,
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/// gradient-free optimization methods such as <see cref="Cobyla"/> and the <see cref="GoldfarbIdnani">Goldfarb-Idnani
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/// </see> solver for Quadratic Programming (QP) problems.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This namespace contains different methods for solving both constrained and unconstrained
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/// optimization problems. For unconstrained optimization, methods available include
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/// <see cref="ConjugateGradient">Conjugate Gradient (CG)</see>, <see cref="BoundedBroydenFletcherGoldfarbShanno">
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/// Bounded</see> and <see cref="BroydenFletcherGoldfarbShanno">Unbounded Broyden–Fletcher–Goldfarb–Shanno (BFGS)</see>,
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/// <see cref="ResilientBackpropagation">Resilient Backpropagation</see> and a simplified implementation of the
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/// <see cref="TrustRegionNewtonMethod">Trust Region Newton Method (TRON)</see>.</para>
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///
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/// <para>
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/// For constrained optimization problems, methods available include the <see cref="AugmentedLagrangian">
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/// Augmented Lagrangian method</see> for general non-linear optimization, <see cref="Cobyla"/> for
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/// gradient-free non-linear optimization, and the <see cref="GoldfarbIdnani">Goldfarb-Idnani</see>
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/// method for solving Quadratic Programming (QP) problems.</para>
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///
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/// <para>
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/// This namespace also contains optimizers specialized for least squares problems, such as <see cref="GaussNewton">
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/// Gauss Newton</see> and the <see cref="LevenbergMarquardt">Levenberg-Marquart</see> least squares solvers.</para>
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///
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/// <para>
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/// For univariate problems, standard search algorithms are also available, such as <see cref="BrentSearch">
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/// Brent</see> and <see cref="BinarySearch">Binary search</see>.</para>
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///
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/// <para>
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/// The namespace class diagram is shown below. </para>
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/// <img src="..\diagrams\classes\Accord.Math.Optimization.png" />
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/// </remarks>
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///
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/// <seealso cref="Accord.Math"/>
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/// <seealso cref="Accord.Math.Differentiation"/>
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/// <seealso cref="Accord.Math.Integration"/>
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///
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[CompilerGenerated]
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class NamespaceDoc
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{
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}
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}
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