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.
97 lines
3.3 KiB
C#
97 lines
3.3 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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/// <summary>
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/// Obsolete. Please refer to <see cref="Bootstrap{TModel, TInput, TOutput}"/> instead.
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/// </summary>
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///
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[Obsolete("Please refer to Bootstrap<TModel, TInput, TOutput> instead.")]
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public class BootstrapValues
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{
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/// <summary>
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/// Gets the validation value for the model.
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/// </summary>
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///
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public double ValidationValue { get; private set; }
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/// <summary>
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/// Gets the variance of the validation
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/// value for the model, if available.
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/// </summary>
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///
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public double ValidationVariance { get; private set; }
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/// <summary>
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/// Gets the training value for the model.
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/// </summary>
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///
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public double TrainingValue { get; private set; }
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/// <summary>
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/// Gets the variance of the training
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/// value for the model, if available.
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/// </summary>
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///
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public double TrainingVariance { get; private set; }
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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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/// <param name="trainingValue">The training value for the model.</param>
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/// <param name="validationValue">The validation value for the model.</param>
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///
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public BootstrapValues(double trainingValue, double validationValue)
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{
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this.TrainingValue = trainingValue;
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this.ValidationValue = validationValue;
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}
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/// <param name="trainingValue">The training value for the model.</param>
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/// <param name="validationValue">The validation value for the model.</param>
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/// <param name="trainingVariance">The variance of the training values.</param>
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/// <param name="validationVariance">The variance of the validation values.</param>
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///
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public BootstrapValues(double trainingValue, double trainingVariance,
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double validationValue, double validationVariance)
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{
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this.TrainingValue = trainingValue;
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this.ValidationValue = validationValue;
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this.TrainingVariance = trainingVariance;
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this.ValidationVariance = validationVariance;
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}
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}
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}
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