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.
108 lines
4.1 KiB
C#
108 lines
4.1 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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/// <summary>
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/// Common interface for classification models. Classification models
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/// learn how to produce a class-label (or a set of class labels) <c>y</c>
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/// from an input vector <c>x</c>.
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/// </summary>
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///
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public interface IClassifier
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{
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/// <summary>
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/// Gets or sets the number of classes expected and recognized by the classifier.
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/// </summary>
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///
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int NumberOfClasses
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{
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get;
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set;
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}
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}
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/// <summary>
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/// Common interface for classification models. Classification models
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/// learn how to produce a class-label (or a set of class labels) <c>y</c>
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/// from an input vector <c>x</c>.
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/// </summary>
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///
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/// <typeparam name="TInput">The data type for the input data. Default is double[].</typeparam>
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/// <typeparam name="TClasses">The data type for the class labels. Default is int.</typeparam>
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///
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public interface IClassifier<in TInput, TClasses> : IClassifier,
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ITransform<TInput, TClasses>
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{
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/// <summary>
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/// Computes a class-label decision for a given <paramref name="input"/>.
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/// </summary>
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///
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/// <param name="input">The input vector that should be classified into
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/// one of the <see cref="ITransform.NumberOfOutputs"/> possible classes.</param>
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///
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/// <returns>A class-label that best described <paramref name="input"/> according
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/// to this classifier.</returns>
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///
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TClasses Decide(TInput input);
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/// <summary>
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/// Computes class-label decisions for each vector in the given <paramref name="input"/>.
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/// </summary>
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///
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/// <param name="input">The input vectors that should be classified into
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/// one of the <see cref="ITransform.NumberOfOutputs"/> possible classes.</param>
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///
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/// <returns>The class-labels that best describe each <paramref name="input"/>
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/// vectors according to this classifier.</returns>
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///
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TClasses[] Decide(TInput[] input);
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/// <summary>
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/// Computes class-label decisions for each vector in the given <paramref name="input"/>.
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/// </summary>
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///
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/// <param name="input">The input vectors that should be classified into
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/// one of the <see cref="ITransform.NumberOfOutputs"/> possible classes.</param>
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/// <param name="result">The location where to store the class-labels.</param>
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///
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/// <returns>The class-labels that best describe each <paramref name="input"/>
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/// vectors according to this classifier.</returns>
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///
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TClasses[] Decide(TInput[] input, TClasses[] result);
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}
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// TODO: Incorporate this interface in the current architecture
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// in the hope it can make learning easier.
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/*
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public interface IClassifier<in TInput, TClasses, TLearning> :
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IClassifier<TInput, TClasses>
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where TLearning : class, new()
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{
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TLearning Fit(TInput[] input, TClasses[] outputs, double[] weights = null, TLearning teacher = null);
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}
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*/
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}
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