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.
262 lines
9.4 KiB
C#
262 lines
9.4 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 © 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 System;
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using System.Linq;
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using Accord.Math;
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using Accord.MachineLearning.Performance;
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using Accord.Compat;
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using System.Threading.Tasks;
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/// <summary>
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/// Obsolete. Please refer to <see cref="Bootstrap{TModel, TInput, TOutput}"/> instead.
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/// </summary>
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///
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[Obsolete("Please refer to Bootstrap<TModel, TInput, TOutput> instead.")]
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public delegate BootstrapValues BootstrapFittingFunction(int[] trainingSamples, int[] validationSamples);
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/// <summary>
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/// Obsolete. Please use <see cref="Bootstrap{TModel, TInput, TOutput}"/> instead.
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/// </summary>
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///
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[Obsolete("Please use Bootstrap<TModel, TInput, TOutput> instead.")]
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[Serializable]
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public class Bootstrap
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{
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private int[][] subsampleIndices;
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private int samples;
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private bool parallel = true;
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[NonSerialized]
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BootstrapFittingFunction fitting;
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/// <summary>
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/// Gets the number B of bootstrap samplings
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/// to be drawn from the population dataset.
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/// </summary>
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///
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public int B { get { return subsampleIndices.Length; } }
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/// <summary>
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/// Gets the total number of samples in the population dataset.
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/// </summary>
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///
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public int Samples { get { return samples; } }
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/// <summary>
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/// Gets the bootstrap samples drawn from
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/// the population dataset as indices.
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/// </summary>
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///
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public int[][] Subsamples { get { return subsampleIndices; } }
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/// <summary>
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/// Gets or sets the model fitting function.
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/// </summary>
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/// <remarks>
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/// The fitting function should accept an array of integers containing the
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/// indexes for the training samples, an array of integers containing the
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/// indexes for the validation samples and should return information about
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/// the model fitted using those two subsets of the available data.
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/// </remarks>
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///
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public BootstrapFittingFunction Fitting
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{
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get { return fitting; }
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set { fitting = value; }
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}
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/// <summary>
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/// Gets or sets a value indicating whether to use parallel
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/// processing through the use of multiple threads or not.
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/// Default is true.
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/// </summary>
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///
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/// <value><c>true</c> to use multiple threads; otherwise, <c>false</c>.</value>
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///
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public bool RunInParallel
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{
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get { return parallel; }
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set { parallel = value; }
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}
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/// <summary>
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/// Creates a new Bootstrap estimation algorithm.
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/// </summary>
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///
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/// <param name="size">The size of the complete dataset.</param>
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/// <param name="subsamples">The number B of bootstrap resamplings to perform.</param>
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///
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public Bootstrap(int size, int subsamples)
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: this(size, subsamples, size) { }
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/// <summary>
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/// Creates a new Bootstrap estimation algorithm.
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/// </summary>
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///
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/// <param name="size">The size of the complete dataset.</param>
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/// <param name="subsamples">The number B of bootstrap resamplings to perform.</param>
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/// <param name="subsampleSize">The number of samples in each subsample. Default
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/// is to use the total number of samples in the population dataset.</param>.
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///
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public Bootstrap(int size, int subsamples, int subsampleSize)
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{
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this.samples = size;
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this.subsampleIndices = new int[subsamples][];
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this.subsampleIndices = Bootstrap.Samplings(size, subsamples, subsampleSize);
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}
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/// <summary>
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/// Creates a new Bootstrap estimation algorithm.
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/// </summary>
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///
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/// <param name="size">The size of the complete dataset.</param>
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/// <param name="resamplings">The indices of the bootstrap samplings.</param>
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///
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public Bootstrap(int size, int[][] resamplings)
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{
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this.samples = size;
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this.subsampleIndices = resamplings;
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}
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/// <summary>
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/// Gets the indices for the training and validation
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/// sets for the specified validation fold index.
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/// </summary>
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///
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/// <param name="index">The index of the validation fold.</param>
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/// <param name="trainingSet">The indices for the observations in the training set.</param>
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/// <param name="validationSet">The indices for the observations in the validation set.</param>
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///
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public void CreatePartitions(int index, out int[] trainingSet, out int[] validationSet)
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{
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if (index < 0 || index >= Subsamples.Length)
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throw new ArgumentOutOfRangeException("index");
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// Create indices for the foldings
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// The training set is already computed
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trainingSet = Subsamples[index];
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// The validation set is the complement of the training set
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validationSet = Vector.Range(0, Samples).Except(trainingSet).ToArray();
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}
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/// <summary>
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/// Computes the cross validation algorithm.
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/// </summary>
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///
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public BootstrapResult Compute()
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{
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if (Fitting == null)
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throw new InvalidOperationException("Fitting function must have been previously defined.");
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var models = new BootstrapValues[Subsamples.Length];
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if (RunInParallel)
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{
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Parallel.For(0, Subsamples.Length, i =>
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{
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int[] trainingSet, validationSet;
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// Create training and validation sets
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CreatePartitions(i, out trainingSet, out validationSet);
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// Fit and evaluate the model
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models[i] = fitting(trainingSet, validationSet);
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});
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}
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else
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{
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for (int i = 0; i < Subsamples.Length; i++)
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{
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int[] trainingSet, validationSet;
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// Create training and validation sets
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CreatePartitions(i, out trainingSet, out validationSet);
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// Fit and evaluate the model
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models[i] = fitting(trainingSet, validationSet);
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}
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}
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// Return cross-validation statistics
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return new BootstrapResult(this, models);
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}
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/// <summary>
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/// Gets the number of instances in training and validation
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/// sets for the specified validation fold index.
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/// </summary>
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///
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/// <param name="index">The index of the bootstrap sample.</param>
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/// <param name="trainingCount">The number of instances in the training set.</param>
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/// <param name="validationCount">The number of instances in the validation set.</param>
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///
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public void GetPartitionSize(int index, out int trainingCount, out int validationCount)
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{
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if (index < 0 || index >= subsampleIndices.Length)
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throw new ArgumentOutOfRangeException("index");
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trainingCount = subsampleIndices[index].Length;
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validationCount = samples - trainingCount;
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}
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/// <summary>
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/// Draws the bootstrap samples from the population.
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/// </summary>
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///
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/// <param name="size">The size of the samples to be drawn.</param>
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/// <param name="resamplings">The number of samples to drawn.</param>
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/// <param name="subsampleSize">The size of the samples to be drawn.</param>
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///
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/// <returns>The indices of the samples in the original set.</returns>
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///
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public static int[][] Samplings(int size, int resamplings, int subsampleSize)
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{
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// Create samples with replacement
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int[][] idx = new int[resamplings][];
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// For each fold to be created
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for (int i = 0; i < idx.Length; i++)
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{
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idx[i] = new int[subsampleSize];
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// Generate a random sample (with replacement)
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for (int j = 0; j < idx[i].Length; j++)
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idx[i][j] = Accord.Math.Random.Generator.Random.Next(0, size);
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}
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return idx;
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}
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}
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}
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