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.
189 lines
6.4 KiB
C#
189 lines
6.4 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.Distances
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{
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using Accord.Math.Decompositions;
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using System;
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using System.Runtime.CompilerServices;
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using Accord.Compat;
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/// <summary>
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/// Squared Mahalanobis distance.
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/// </summary>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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[Serializable]
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public struct SquareMahalanobis : IMetric<double[]>, ICloneable
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{
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CholeskyDecomposition chol;
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SingularValueDecomposition svd;
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double[,] precision;
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/// <summary>
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/// Initializes a new instance of the <see cref="Mahalanobis"/> class.
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/// </summary>
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///
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/// <param name="chol">A Cholesky decomposition of the covariance matrix.</param>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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public SquareMahalanobis(CholeskyDecomposition chol)
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{
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this.chol = chol;
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this.svd = null;
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this.precision = null;
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Mahalanobis"/> class.
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/// </summary>
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///
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/// <param name="svd">A Singular Value decomposition of the covariance matrix.</param>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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public SquareMahalanobis(SingularValueDecomposition svd)
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{
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this.chol = null;
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this.svd = svd;
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this.precision = null;
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="Mahalanobis"/> class.
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/// </summary>
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///
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/// <param name="precision">The precision matrix (the inverse of the covariance matrix).</param>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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public SquareMahalanobis(double[,] precision)
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{
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this.chol = null;
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this.svd = null;
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this.precision = precision;
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}
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/// <summary>
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/// Creates a new Square-Mahalanobis distance from a covariance matrix.
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/// </summary>
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///
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/// <param name="covariance">A covariance matrix.</param>
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///
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/// <returns>
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/// A square Mahalanobis distance using the <see cref="SingularValueDecomposition"/>
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/// of the given covariance matrix.
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/// </returns>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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public static SquareMahalanobis FromCovarianceMatrix(double[,] covariance)
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{
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return new SquareMahalanobis(new CholeskyDecomposition(covariance));
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}
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/// <summary>
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/// Creates a new Square-Mahalanobis distance from a precision matrix.
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/// </summary>
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///
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/// <param name="precision">A precision matrix.</param>
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///
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/// <returns>
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/// A square Mahalanobis distance using the given precision matrix.
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/// </returns>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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public static SquareMahalanobis FromPrecisionMatrix(double[,] precision)
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{
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return new SquareMahalanobis(precision);
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}
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/// <summary>
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/// Computes the distance <c>d(x,y)</c> between points
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/// <paramref name="x"/> and <paramref name="y"/>.
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/// </summary>
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///
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/// <param name="x">The first point <c>x</c>.</param>
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/// <param name="y">The second point <c>y</c>.</param>
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///
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/// <returns>
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/// A double-precision value representing the distance <c>d(x,y)</c>
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/// between <paramref name="x"/> and <paramref name="y"/> according
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/// to the distance function implemented by this class.
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/// </returns>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.Math\DistanceTest.cs" region="doc_square_mahalanobis_3" />
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/// </example>
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///
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#if NET45 || NET46 || NET462 || NETSTANDARD2_0
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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#endif
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public double Distance(double[] x, double[] y)
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{
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double[] d = new double[x.Length];
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for (int i = 0; i < x.Length; i++)
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d[i] = x[i] - y[i];
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double[] z;
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if (svd != null)
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z = svd.Solve(d);
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else if (chol != null)
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z = chol.Solve(d);
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else
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z = precision.Dot(d);
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double sum = 0.0;
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for (int i = 0; i < d.Length; i++)
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sum += d[i] * z[i];
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return Math.Abs(sum);
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}
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/// <summary>
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/// Creates a new object that is a copy of the current instance.
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/// </summary>
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/// <returns>A new object that is a copy of this instance.</returns>
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public object Clone()
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{
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return MemberwiseClone();
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}
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}
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}
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