mirror of
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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.
109 lines
3.8 KiB
C#
109 lines
3.8 KiB
C#
// Accord Math 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.Math.Optimization.Losses
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{
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using System;
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using Accord.Compat;
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/// <summary>
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/// Negative log-likelihood loss.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// The log-likelihood loss can be used to measure the performance of unsupervised
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/// model fitting algorithms. It simply computes the sum of all log-likelihood values
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/// produced by the model.</para>
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///
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/// <para>
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/// If you would like to measure the performance of a supervised classification model
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/// based on their probability predictions, please refer to the <see cref="BinaryCrossEntropyLoss"/>
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/// and <see cref="CategoryCrossEntropyLoss"/> for binary and multi-class decision problems,
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/// respectively.</para>
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/// </remarks>
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///
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/// <example>
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/// <para>The following example shows how to learn an one-class SVM
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/// and measure its performance using the log-likelihood loss class.</para>
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/// <code source="Unit Tests\Accord.Tests.MachineLearning\VectorMachines\OneclassSupportVectorLearningTest.cs" region="doc_learn" />
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/// </example>
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///
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/// <seealso cref="BinaryCrossEntropyLoss"/>
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/// <seealso cref="CategoryCrossEntropyLoss"/>
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///
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[Serializable]
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public class LogLikelihoodLoss : ILoss<double[][]>, ILoss<double[]>
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="LogLikelihoodLoss"/> class.
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/// </summary>
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///
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public LogLikelihoodLoss()
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{
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}
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/// <summary>
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/// Computes the loss between the expected values (ground truth)
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/// and the given actual values that have been predicted.
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/// </summary>
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///
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/// <param name="actual">The actual values that have been predicted.</param>
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///
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/// <returns>
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/// The loss value between the expected values and
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/// the actual predicted values.
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/// </returns>
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///
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public double Loss(double[][] actual)
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{
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double error = 0;
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for (int i = 0; i < actual.Length; i++)
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for (int j = 0; j < actual[i].Length; j++)
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error += actual[i][j];
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return error;
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}
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/// <summary>
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/// Computes the loss between the expected values (ground truth)
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/// and the given actual values that have been predicted.
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/// </summary>
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/// <param name="actual">The actual values that have been predicted.</param>
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/// <returns>
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/// The loss value between the expected values and
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/// the actual predicted values.
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/// </returns>
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public double Loss(double[] actual)
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{
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double error = 0;
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for (int i = 0; i < actual.Length; i++)
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error += actual[i];
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return error;
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}
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}
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}
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