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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.
70 lines
2.2 KiB
C#
70 lines
2.2 KiB
C#
// Accord Machine Learning 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.Collections
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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 Accord.Compat;
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/// <summary>
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/// List of k-dimensional tree nodes.
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/// </summary>
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///
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/// <typeparam name="T">The type of the value being stored.</typeparam>
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///
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/// <remarks>
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/// This class is used to store neighbor nodes when running one of the
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/// search algorithms for <see cref="KDTree{T}">k-dimensional trees</see>.
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/// </remarks>
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///
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/// <seealso cref="KDTree{T}"/>
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/// <seealso cref="NodeDistance{T}"/>
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///
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[Serializable]
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public class KDTreeNodeList<T> : List<NodeDistance<T>>
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where T : IComparable<T>, IEquatable<T>
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="KDTreeNodeList<T>"/>
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/// class that is empty.
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/// </summary>
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///
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public KDTreeNodeList()
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{
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="KDTreeNodeList<T>"/>
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/// class that is empty and has the specified capacity.
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/// </summary>
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///
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public KDTreeNodeList(int capacity)
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: base(capacity)
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{
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}
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}
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}
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