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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.
49 lines
1.5 KiB
C#
49 lines
1.5 KiB
C#
// AForge Neural Net Library
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// AForge.NET framework
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// http://www.aforgenet.com/framework/
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//
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// Copyright © AForge.NET, 2007-2012
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// [email protected]
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//
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namespace Accord.Neuro.Learning
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{
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using System;
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/// <summary>
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/// Supervised learning interface.
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/// </summary>
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///
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/// <remarks><para>The interface describes methods, which should be implemented
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/// by all supervised learning algorithms. Supervised learning is such
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/// type of learning algorithms, where system's desired output is known on
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/// the learning stage. So, given sample input values and desired outputs,
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/// system should adopt its internals to produce correct (or close to correct)
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/// result after the learning step is complete.</para></remarks>
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///
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public interface ISupervisedLearning
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{
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/// <summary>
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/// Runs learning iteration.
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/// </summary>
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///
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/// <param name="input">Input vector.</param>
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/// <param name="output">Desired output vector.</param>
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///
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/// <returns>Returns learning error.</returns>
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///
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double Run( double[] input, double[] output );
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/// <summary>
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/// Runs learning epoch.
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/// </summary>
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///
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/// <param name="input">Array of input vectors.</param>
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/// <param name="output">Array of output vectors.</param>
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///
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/// <returns>Returns sum of learning errors.</returns>
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///
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double RunEpoch( double[][] input, double[][] output );
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}
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}
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