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.
130 lines
4.8 KiB
C#
130 lines
4.8 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.MachineLearning
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{
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using Accord.MachineLearning.Performance;
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using System;
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using Accord.Compat;
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/// <summary>
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/// Summary statistics for a Split-set validation trial.
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/// </summary>
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///
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[Serializable]
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public class SplitSetStatistics<TModel>
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where TModel : class
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{
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/// <summary>
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/// Gets the model created with the
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/// </summary>
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public TModel Model { get; private set; }
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/// <summary>
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/// Gets the values acquired during the cross-validation.
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/// Most often those will be the errors for each folding.
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/// </summary>
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///
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public double Value { get; private set; }
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/// <summary>
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/// Gets the variance for each value acquired during the cross-validation.
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/// Most often those will be the error variance for each folding.
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/// </summary>
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///
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public double Variance { get; private set; }
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/// <summary>
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/// Gets the number of samples used to compute the variance
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/// of the values acquired during the validation.
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/// </summary>
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///
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public int Size { get; private set; }
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/// <summary>
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/// Gets the standard deviation of the performance statistic.
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/// </summary>
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///
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public double StandardDeviation { get { return Math.Sqrt(Variance); } }
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/// <summary>
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/// Gets or sets a tag for user-defined information.
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/// </summary>
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///
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public object Tag { get; set; }
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/// <summary>
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/// Create a new split-set statistics class.
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/// </summary>
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///
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/// <param name="model">The generated model.</param>
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/// <param name="size">The number of samples used to compute the statistic.</param>
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/// <param name="value">The performance statistic gathered during the run.</param>
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/// <param name="variance">The variance of the performance statistic during the run.</param>
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///
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public SplitSetStatistics(TModel model, int size, double value, double variance)
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{
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this.Size = size;
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this.Model = model;
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this.Value = value;
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this.Variance = variance;
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}
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}
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/// <summary>
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/// Obsolete. Please refer to <see cref="SplitSetValidation{TModel, TInput, TOutput}"/> instead.
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/// </summary>
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///
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[Obsolete("Please refer to SplitSetValidation<TModel, TInput> instead.")]
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public class SplitSetStatistics : SplitSetStatistics<object>
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{
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/// <summary>
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/// Create a new split-set statistics class.
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/// </summary>
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///
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/// <param name="model">The generated model.</param>
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/// <param name="size">The number of samples used to compute the statistic.</param>
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/// <param name="value">The performance statistic gathered during the run.</param>
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/// <param name="variance">The variance of the performance statistic during the run.</param>
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///
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public SplitSetStatistics(object model, int size, double value, double variance)
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: base(model, size, value, variance) { }
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/// <summary>
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/// Create a new split-set statistics class.
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/// </summary>
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///
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/// <param name="model">The generated model.</param>
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/// <param name="size">The number of samples used to compute the statistic.</param>
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/// <param name="value">The performance statistic gathered during the run.</param>
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/// <param name="variance">The variance of the performance statistic during the run.</param>
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///
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public static SplitSetStatistics<TModel> Create<TModel>(TModel model, int size,
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double value, double variance) where TModel : class
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{
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return new SplitSetStatistics<TModel>(model, size, value, variance);
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}
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}
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}
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