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.
95 lines
3.3 KiB
C#
95 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 © Darko Jurić, 2013
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// https://code.google.com/p/accord/issues/detail?id=27
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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.Boosting.Learners
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{
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using System;
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using Accord.Math.Comparers;
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using Accord.Math;
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using Accord.Statistics;
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/// <summary>
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/// Adapter for models that do not implement a .Decide function.
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/// </summary>
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///
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/// <typeparam name="TModel">The type for the weak classifier model.</typeparam>
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///
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[Obsolete("This class will be removed.")]
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public class Weak<TModel> : BinaryClassifierBase<double[]>
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{
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/// <summary>
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/// Gets or sets the weak decision model.
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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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/// Gets or sets the decision function used by the <see cref="Model"/>.
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/// </summary>
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///
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public Func<TModel, double[], int> Function { get; set; }
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/// <summary>
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/// Creates a new Weak classifier given a
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/// classification model and its decision function.
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/// </summary>
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///
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/// <param name="model">The classifier.</param>
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/// <param name="function">The classifier decision function.</param>
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///
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public Weak(TModel model, Func<TModel, double[], int> function)
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{
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this.Model = model;
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this.Function = function;
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}
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/// <summary>
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/// Computes the classifier decision for a given input.
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/// </summary>
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///
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/// <param name="inputs">The input vector.</param>
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///
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/// <returns>The model's decision label.</returns>
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///
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[Obsolete("Please use the Decide() method instead.")]
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public int Compute(double[] inputs)
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{
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return Function(Model, inputs);
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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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/// <param name="input">The input vector that should be classified into
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/// one of the <see cref="P:Accord.MachineLearning.ITransform.NumberOfOutputs" /> possible classes.</param>
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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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public override bool Decide(double[] input)
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{
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return Classes.Decide(Function(Model, input));
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}
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}
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}
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