mirror of
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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.
136 lines
4.1 KiB
C#
136 lines
4.1 KiB
C#
// Accord Statistics 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.Statistics.Distributions.Fitting
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{
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using System;
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using Accord.Compat;
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/// <summary>
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/// Triangular distribution's mode estimation method.
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/// </summary>
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///
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public enum TriangularEstimationMethod
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{
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/// <summary>
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/// Estimates the mode using the mean-maximum-minimum method.
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/// </summary>
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///
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MeanMaxMin,
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/// <summary>
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/// Estimates the mode using the standard algorithm.
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/// </summary>
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///
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Standard,
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/// <summary>
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/// Estimates the mode using the bisection algorithm.
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/// </summary>
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///
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Bisection
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}
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/// <summary>
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/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.TriangularDistribution">
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/// Triangular distributions</see>.
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/// </summary>
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///
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[Serializable]
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public class TriangularOptions : IFittingOptions
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{
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/// <summary>
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/// Gets or sets the index of the minimum observed
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/// value, if already known. Default is -1.
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/// </summary>
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///
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public int MinIndex { get; set; }
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/// <summary>
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/// Gets or sets the index of the maximum observed
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/// value, if already known. Default is -1.
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/// </summary>
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///
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public int MaxIndex { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the observations are already sorted.
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/// </summary>
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///
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/// <value>
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/// Set to <c>true</c> if the observations are sorted; otherwise, <c>false</c>.
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/// </value>
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///
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public bool IsSorted { get; set; }
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/// <summary>
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/// Gets or sets the mode estimation method to use. Default
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/// is <see cref="TriangularEstimationMethod.MeanMaxMin"/>.
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/// </summary>
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///
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public TriangularEstimationMethod Method { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the maximum
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/// value should be treated as fixed and not be estimated.
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/// Default is true.
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/// </summary>
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///
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public bool FixMax { get; set; }
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/// <summary>
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/// Gets or sets a value indicating whether the minimum
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/// value should be treated as fixed and not be estimated.
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/// Default is true.
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/// </summary>
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///
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public bool FixMin { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="TriangularOptions"/> class.
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/// </summary>
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///
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public TriangularOptions()
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{
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IsSorted = false;
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Method = TriangularEstimationMethod.MeanMaxMin;
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MinIndex = -1;
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MaxIndex = -1;
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FixMin = true;
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FixMax = true;
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}
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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public object Clone()
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{
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return MemberwiseClone();
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}
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}
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}
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