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.
188 lines
6.6 KiB
C#
188 lines
6.6 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 Accord.Math;
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using Accord.Statistics;
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using System;
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using Accord.Compat;
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/// <summary>
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/// Mean Accuracy loss, also known as zero-one-loss per
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/// class. Equivalent to <see cref="ZeroOneLoss"/> but
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/// for multi-label classifiers.
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/// </summary>
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///
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[Serializable]
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public class HammingLoss : LossBase<int[][]>,
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ILoss<bool[][]>, ILoss<double[][]>, ILoss<int[]>
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{
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private bool mean = true;
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private int total;
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/// <summary>
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/// Gets or sets a value indicating whether the mean
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/// accuracy loss should be computed. Default is true.
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/// </summary>
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///
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/// <value>
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/// <c>true</c> if the mean accuracy loss should be computed; otherwise, <c>false</c>.
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/// </value>
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///
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public bool Mean
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{
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get { return mean; }
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set { mean = value; }
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="HammingLoss"/> class.
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/// </summary>
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/// <param name="expected">The expected outputs (ground truth).</param>
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public HammingLoss(double[][] expected)
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{
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this.Expected = expected.ToInt32();
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for (int i = 0; i < expected.Length; i++)
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total += expected[i].Length;
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="HammingLoss"/> class.
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/// </summary>
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/// <param name="expected">The expected outputs (ground truth).</param>
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public HammingLoss(int[][] expected)
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{
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this.Expected = expected;
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for (int i = 0; i < expected.Length; i++)
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total += expected[i].Length;
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="HammingLoss"/> class.
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/// </summary>
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/// <param name="expected">The expected outputs (ground truth).</param>
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public HammingLoss(int[] expected)
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: this(Jagged.OneHot(expected))
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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="HammingLoss"/> class.
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/// </summary>
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/// <param name="expected">The expected outputs (ground truth).</param>
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public HammingLoss(bool[][] expected)
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{
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this.Expected = expected.ToInt32();
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for (int i = 0; i < expected.Length; i++)
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total += expected[i].Length;
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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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int error = 0;
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for (int i = 0; i < Expected.Length; i++)
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for (int j = 0; j < Expected[i].Length; j++)
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if (Classes.Decide(Expected[i][j]) != Classes.Decide(actual[i][j]))
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error++;
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if (mean)
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return error / (double)total;
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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 override double Loss(int[][] actual)
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{
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int error = 0;
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for (int i = 0; i < Expected.Length; i++)
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for (int j = 0; j < Expected[i].Length; j++)
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if (Classes.Decide(Expected[i][j]) != Classes.Decide(actual[i][j]))
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error++;
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if (mean)
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return error / (double)total;
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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(int[] actual)
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{
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int error = 0;
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for (int i = 0; i < Expected.Length; i++)
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if (Expected[i][0] != actual[i])
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error++;
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if (mean)
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return error / (double)total;
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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(bool[][] actual)
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{
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int error = 0;
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for (int i = 0; i < Expected.Length; i++)
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for (int j = 0; j < Expected[i].Length; j++)
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if (Classes.Decide(Expected[i][j]) != actual[i][j])
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error++;
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if (mean)
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return error / (double)total;
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return error;
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}
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}
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}
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