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.
89 lines
3.3 KiB
C#
89 lines
3.3 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.Statistics.Distributions.Univariate;
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using Accord.Compat;
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/// <summary>
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/// Smoothing rule function definition for <see cref="EmpiricalDistribution">
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/// Empirical distributions</see>.
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/// </summary>
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///
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/// <param name="observations">The observations for the empirical distribution.</param>
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/// <param name="weights">The fractional importance for each sample. Those values must sum up to one.</param>
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/// <param name="repeats">The number of times each sample should be repeated.</param>
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///
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/// <returns>An estimative of the smoothing parameter.</returns>
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///
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public delegate double SmoothingRule(double[] observations, double[] weights = null, int[] repeats = null);
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/// <summary>
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/// Estimation options for <see cref="Accord.Statistics.Distributions.Univariate.EmpiricalDistribution">
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/// Empirical distributions</see>.
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/// </summary>
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///
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[Serializable]
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public class EmpiricalOptions : IFittingOptions
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{
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/// <summary>
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/// Gets or sets the smoothing rule used to compute the smoothing
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/// parameter in the <see cref="EmpiricalDistribution"/>. Default
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/// is to use the <see cref="EmpiricalDistribution.SmoothingRule(double[], double[], int[])">
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/// normal distribution bandwidth approximation</see>.
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/// </summary>
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///
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public SmoothingRule SmoothingRule { get; set; }
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/// <summary>
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/// Gets or sets whether the empirical distribution should be take the
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/// observation and weight vectors directly instead of making a copy
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/// beforehand.
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/// </summary>
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///
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public bool InPlace { get; set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="NormalOptions"/> class.
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/// </summary>
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///
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public EmpiricalOptions()
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{
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SmoothingRule = EmpiricalDistribution.SmoothingRule;
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InPlace = false;
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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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