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.
276 lines
9.1 KiB
C#
276 lines
9.1 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
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{
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using System;
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using System.Collections.Generic;
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using Accord.Compat;
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using Accord.Statistics;
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// TODO: Divide into separate classes
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/// <summary>
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/// Weighted Weak Classifier.
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/// </summary>
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///
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/// <typeparam name="TModel">The type of the weak classifier.</typeparam>
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///
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[Serializable]
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public class Weighted<TModel> : Weighted<TModel, double[]>
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where TModel : IClassifier<double[], int>
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{
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}
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/// <summary>
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/// Weighted Weak Classifier.
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/// </summary>
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///
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/// <typeparam name="TModel">The type of the weak classifier.</typeparam>
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/// <typeparam name="TInput">The type of the input vectors accepted by the classifier.</typeparam>
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///
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[Serializable]
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public class Weighted<TModel, TInput>
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where TModel : IClassifier<TInput, int>
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{
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/// <summary>
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/// Gets or sets the weight associated
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/// with the weak <see cref="Model"/>.
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/// </summary>
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///
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public double Weight { get; set; }
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/// <summary>
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/// Gets or sets the <see cref="IWeakClassifier">weak
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/// classifier</see> associated with the <see cref="Weight"/>.
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/// </summary>
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///
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public TModel Model { get; set; }
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}
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/// <summary>
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/// Boosted classification model.
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/// </summary>
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///
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/// <typeparam name="TModel">The type of the weak classifier.</typeparam>
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///
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[Serializable]
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public class Boost<TModel> : BoostBase<TModel, Weighted<TModel>, double[]>
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where TModel : IClassifier<double[], int>
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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public Boost()
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{
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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/// <param name="weights">The initial boosting weights.</param>
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/// <param name="models">The initial weak classifiers.</param>
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///
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public Boost(IList<double> weights, IList<TModel> models)
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: base(weights, models)
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{
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}
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/// <summary>
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/// Computes the output class label for a given input.
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/// </summary>
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///
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/// <param name="input">The input vector.</param>
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///
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/// <returns>The most likely class label for the given input.</returns>
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///
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[Obsolete("Please use the Decide() method instead.")]
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public int Compute(double[] input)
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{
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return Decide(input) ? +1 : -1;
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}
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}
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/// <summary>
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/// Boosted classification model.
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/// </summary>
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///
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/// <typeparam name="TModel">The type of the weak classifier.</typeparam>
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/// <typeparam name="TInput">The type of the input vectors accepted by the classifier.</typeparam>
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///
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[Serializable]
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public class Boost<TModel, TInput> : BoostBase<TModel, Weighted<TModel, TInput>, TInput>
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where TModel : IClassifier<TInput, int>
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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public Boost()
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{
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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/// <param name="weights">The initial boosting weights.</param>
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/// <param name="models">The initial weak classifiers.</param>
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///
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public Boost(IList<double> weights, IList<TModel> models)
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: base(weights, models)
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{
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}
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}
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/// <summary>
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/// Boosted classification model.
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/// </summary>
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///
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/// <typeparam name="TModel">The type of the weak classifier.</typeparam>
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/// <typeparam name="TWeighted">The type of the weighted classifier.</typeparam>
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/// <typeparam name="TInput">The type of the input vectors accepted by the classifier.</typeparam>
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///
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[Serializable]
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public class BoostBase<TModel, TWeighted, TInput> : BinaryClassifierBase<TInput>, IEnumerable<TWeighted>
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where TModel : IClassifier<TInput, int>
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where TWeighted : Weighted<TModel, TInput>, new()
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{
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/// <summary>
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/// Gets the list of weighted weak models
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/// contained in this boosted classifier.
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/// </summary>
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///
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public List<TWeighted> Models { get; private set; }
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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public BoostBase()
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{
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Models = new List<TWeighted>();
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Boost<TModel>"/> class.
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/// </summary>
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///
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/// <param name="weights">The initial boosting weights.</param>
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/// <param name="models">The initial weak classifiers.</param>
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///
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public BoostBase(IList<double> weights, IList<TModel> models)
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{
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if (weights.Count != models.Count)
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throw new DimensionMismatchException("models", "The number of models and weights must match.");
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for (int i = 0; i < weights.Count; i++)
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{
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if (models[i].NumberOfClasses != 2)
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throw new ArgumentException("Only binary classifiers are supported at this time.");
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Models.Add(new TWeighted() { Weight = weights[i], Model = models[i] });
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}
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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(TInput input)
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{
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double sum = 0.0;
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foreach (var pair in Models)
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{
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if (pair.Model.Decide(input) > 0)
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sum += pair.Weight;
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else
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sum -= pair.Weight;
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}
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return Classes.Decide(sum);
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}
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/// <summary>
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/// Adds a new weak classifier and its corresponding
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/// weight to the end of this boosted classifier.
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/// </summary>
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///
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/// <param name="weight">The weight of the weak classifier.</param>
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/// <param name="model">The weak classifier</param>
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///
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public void Add(double weight, TModel model)
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{
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Models.Add(new TWeighted() { Weight = weight, Model = model });
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}
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/// <summary>
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/// Gets or sets the <see cref="Accord.MachineLearning.Boosting
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/// .Weighted<TModel>"/> at the specified index.
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/// </summary>
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///
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public TWeighted this[int index]
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{
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get { return Models[index]; }
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set { Models[index] = value; }
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}
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/// <summary>
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/// Returns an enumerator that iterates through this collection.
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/// </summary>
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///
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/// <returns>
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/// An <see cref="T:System.Collections.IEnumerator"/> object that can be used to iterate through the collection.
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/// </returns>
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///
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public IEnumerator<TWeighted> GetEnumerator()
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{
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return Models.GetEnumerator();
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}
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/// <summary>
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/// Returns an enumerator that iterates through this collection.
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/// </summary>
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///
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/// <returns>
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/// An <see cref="T:System.Collections.IEnumerator"/> object that can be used to iterate through the collection.
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/// </returns>
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///
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System.Collections.IEnumerator System.Collections.IEnumerable.GetEnumerator()
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{
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return Models.GetEnumerator();
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}
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}
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}
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