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.
131 lines
5.6 KiB
C#
131 lines
5.6 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.MachineLearning
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{
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using Accord.Math;
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using Accord.Statistics;
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using Accord.Compat;
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using System.Threading;
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using System.Threading.Tasks;
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/// <summary>
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/// Common base class for supervised learning algorithms for
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/// <see cref="IBinaryClassifier{TInput}">binary classifiers</see>.
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/// </summary>
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///
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/// <typeparam name="TModel">The type for the model being learned.</typeparam>
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/// <typeparam name="TInput">The type for the input data that enters the model.</typeparam>
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///
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public abstract class BinaryLearningBase<TModel, TInput> :
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ISupervisedBinaryLearning<TModel, TInput>
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where TModel : IBinaryClassifier<TInput>
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{
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/// <summary>
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/// Gets or sets a cancellation token that can be used to
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/// stop the learning algorithm while it is running.
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/// </summary>
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///
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public virtual CancellationToken Token { get; set; }
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/// <summary>
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/// Gets or sets the classifier being learned.
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/// </summary>
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///
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public TModel Model { get; set; }
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/// <summary>
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/// Learns a model that can map the given inputs to the given outputs.
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/// </summary>
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///
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/// <param name="x">The model inputs.</param>
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/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
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/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
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///
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/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
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///
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public TModel Learn(TInput[] x, double[] y, double[] weights = null)
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{
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return Learn(x, Classes.Decide(y), weights);
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}
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/// <summary>
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/// Learns a model that can map the given inputs to the given outputs.
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/// </summary>
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///
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/// <param name="x">The model inputs.</param>
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/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
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/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
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///
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/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
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///
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public TModel Learn(TInput[] x, int[] y, double[] weights = null)
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{
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return Learn(x, Classes.Decide(y), weights);
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}
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/// <summary>
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/// Learns a model that can map the given inputs to the given outputs.
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/// </summary>
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///
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/// <param name="x">The model inputs.</param>
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/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
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/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
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///
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/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
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///
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public TModel Learn(TInput[] x, int[][] y, double[] weights = null)
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{
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return Learn(x, Classes.Decide(y.GetColumn(0)), weights);
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}
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/// <summary>
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/// Learns a model that can map the given inputs to the given outputs.
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/// </summary>
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///
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/// <param name="x">The model inputs.</param>
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/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
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/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
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///
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/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
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///
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public TModel Learn(TInput[] x, bool[][] y, double[] weights = null)
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{
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return Learn(x, y.GetColumn(0), weights);
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}
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/// <summary>
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/// Learns a model that can map the given inputs to the given outputs.
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/// </summary>
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///
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/// <param name="x">The model inputs.</param>
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/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
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/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
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///
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/// <returns>A model that has learned how to produce <paramref name="y"/> given <paramref name="x"/>.</returns>
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///
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public abstract TModel Learn(TInput[] x, bool[] y, double[] weights = null);
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}
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}
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