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.
337 lines
12 KiB
C#
337 lines
12 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 Accord.Statistics;
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using Accord.Statistics.Distributions.Multivariate;
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using Accord.Statistics.Distributions.Univariate;
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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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/// Bhattacharyya distance.
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/// </summary>
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///
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[Serializable]
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public sealed class Bhattacharyya :
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IDistance<double[]>,
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IDistance<double[,]>, IDistance<double[][]>,
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IDistance<GeneralDiscreteDistribution>,
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IDistance<UnivariateDiscreteDistribution>,
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IDistance<MultivariateNormalDistribution>
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{
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/// <summary>
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/// Initializes a new instance of the <see cref="Bhattacharyya"/> class.
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/// </summary>
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///
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public Bhattacharyya()
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{
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}
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/// <summary>
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/// Bhattacharyya distance between two histograms.
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/// </summary>
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///
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/// <param name="x">The first histogram.</param>
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/// <param name="y">The second histogram.</param>
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///
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/// <returns>
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/// The Bhattacharyya between the two histograms.
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/// </returns>
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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 b = 0;
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for (int i = 0; i < x.Length; i++)
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b += System.Math.Sqrt(x[i] * y[i]);
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return System.Math.Sqrt(1.0 - b);
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}
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/// <summary>
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/// Bhattacharyya distance between two histograms.
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/// </summary>
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///
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/// <param name="x">The first histogram.</param>
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/// <param name="y">The second histogram.</param>
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///
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/// <returns>
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/// The Bhattacharyya between the two histograms.
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/// </returns>
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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(GeneralDiscreteDistribution x, GeneralDiscreteDistribution y)
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{
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double b = 0;
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for (int i = 0; i < x.Length; i++)
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b += System.Math.Sqrt(x.Frequencies[i] * y.Frequencies[i]);
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return System.Math.Sqrt(1.0 - b);
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}
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/// <summary>
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/// Bhattacharyya distance between two histograms.
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/// </summary>
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///
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/// <param name="x">The first histogram.</param>
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/// <param name="y">The second histogram.</param>
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///
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/// <returns>
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/// The Bhattacharyya between the two histograms.
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/// </returns>
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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(UnivariateDiscreteDistribution x, UnivariateDiscreteDistribution y)
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{
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double b = 0;
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foreach (int i in x.Support.Intersection(y.Support))
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b += System.Math.Sqrt(x.ProbabilityMassFunction(i) * y.ProbabilityMassFunction(i));
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return System.Math.Sqrt(1.0 - b);
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}
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/// <summary>
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/// Bhattacharyya distance between two datasets, assuming
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/// their contents can be modelled by multivariate Gaussians.
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/// </summary>
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///
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/// <param name="x">The first dataset.</param>
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/// <param name="y">The second dataset.</param>
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///
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/// <returns>
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/// The Bhattacharyya between the two datasets.
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/// </returns>
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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[] meanX = x.Mean(dimension: 0);
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double[] meanY = y.Mean(dimension: 0);
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double[][] covX = x.Covariance(meanX);
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double[][] covY = y.Covariance(meanY);
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return Distance(meanX, covX, meanY, covY);
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}
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/// <summary>
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/// Bhattacharyya distance between two datasets, assuming
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/// their contents can be modelled by multivariate Gaussians.
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/// </summary>
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///
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/// <param name="x">The first dataset.</param>
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/// <param name="y">The second dataset.</param>
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///
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/// <returns>
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/// The Bhattacharyya between the two datasets.
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/// </returns>
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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[] meanX = x.Mean(dimension: 0);
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double[] meanY = y.Mean(dimension: 0);
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double[,] covX = x.Covariance(meanX);
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double[,] covY = y.Covariance(meanY);
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return Distance(meanX, covX, meanY, covY);
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}
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/// <summary>
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/// Bhattacharyya distance between two Gaussian distributions.
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/// </summary>
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///
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/// <param name="meanX">Mean for the first distribution.</param>
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/// <param name="covX">Covariance matrix for the first distribution.</param>
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/// <param name="meanY">Mean for the second distribution.</param>
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/// <param name="covY">Covariance matrix for the second distribution.</param>
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///
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/// <returns>The Bhattacharyya distance between the two distributions.</returns>
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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[] meanX, double[,] covX, double[] meanY, double[,] covY)
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{
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int n = meanX.Length;
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double lnDetX = covX.LogPseudoDeterminant();
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double lnDetY = covY.LogPseudoDeterminant();
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return Distance(meanX, covX, lnDetX, meanY, covY, lnDetY);
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}
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/// <summary>
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/// Bhattacharyya distance between two Gaussian distributions.
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/// </summary>
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///
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/// <param name="meanX">Mean for the first distribution.</param>
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/// <param name="covX">Covariance matrix for the first distribution.</param>
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/// <param name="meanY">Mean for the second distribution.</param>
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/// <param name="covY">Covariance matrix for the second distribution.</param>
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///
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/// <returns>The Bhattacharyya distance between the two distributions.</returns>
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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[] meanX, double[][] covX, double[] meanY, double[][] covY)
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{
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int n = meanX.Length;
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double lnDetX = covX.LogPseudoDeterminant();
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double lnDetY = covY.LogPseudoDeterminant();
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return Distance(meanX, covX, lnDetX, meanY, covY, lnDetY);
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}
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/// <summary>
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/// Bhattacharyya distance between two Gaussian distributions.
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/// </summary>
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///
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/// <param name="meanX">Mean for the first distribution.</param>
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/// <param name="covX">Covariance matrix for the first distribution.</param>
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/// <param name="meanY">Mean for the second distribution.</param>
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/// <param name="covY">Covariance matrix for the second distribution.</param>
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/// <param name="lnDetCovX">The logarithm of the determinant for
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/// the covariance matrix of the first distribution.</param>
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/// <param name="lnDetCovY">The logarithm of the determinant for
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/// the covariance matrix of the second distribution.</param>
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///
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/// <returns>The Bhattacharyya distance between the two distributions.</returns>
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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(
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double[] meanX, double[,] covX, double lnDetCovX,
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double[] meanY, double[,] covY, double lnDetCovY)
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{
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int n = meanX.Length;
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// P = (covX + covY) / 2
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var P = new double[n, n];
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for (int i = 0; i < n; i++)
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for (int j = 0; j < n; j++)
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P[i, j] = (covX[i, j] + covY[i, j]) / 2.0;
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var svd = new SingularValueDecomposition(P);
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double detP = svd.LogPseudoDeterminant;
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double[] d = new double[meanX.Length];
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for (int i = 0; i < meanX.Length; i++)
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d[i] = meanX[i] - meanY[i];
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double[] z = svd.Solve(d);
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double r = 0.0;
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for (int i = 0; i < d.Length; i++)
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r += d[i] * z[i];
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double mahalanobis = Math.Abs(r);
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double a = (1.0 / 8.0) * mahalanobis;
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double b = (0.5) * (detP - 0.5 * (lnDetCovX + lnDetCovY));
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return a + b;
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}
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/// <summary>
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/// Bhattacharyya distance between two Gaussian distributions.
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/// </summary>
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///
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/// <param name="meanX">Mean for the first distribution.</param>
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/// <param name="covX">Covariance matrix for the first distribution.</param>
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/// <param name="meanY">Mean for the second distribution.</param>
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/// <param name="covY">Covariance matrix for the second distribution.</param>
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/// <param name="lnDetCovX">The logarithm of the determinant for
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/// the covariance matrix of the first distribution.</param>
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/// <param name="lnDetCovY">The logarithm of the determinant for
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/// the covariance matrix of the second distribution.</param>
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///
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/// <returns>The Bhattacharyya distance between the two distributions.</returns>
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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(
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double[] meanX, double[][] covX, double lnDetCovX,
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double[] meanY, double[][] covY, double lnDetCovY)
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{
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int n = meanX.Length;
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// P = (covX + covY) / 2
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var P = Jagged.Zeros(n, n);
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for (int i = 0; i < n; i++)
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for (int j = 0; j < n; j++)
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P[i][j] = (covX[i][j] + covY[i][j]) / 2.0;
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var svd = new JaggedSingularValueDecomposition(P);
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double detP = svd.LogPseudoDeterminant;
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double[] d = new double[meanX.Length];
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for (int i = 0; i < meanX.Length; i++)
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d[i] = meanX[i] - meanY[i];
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double[] z = svd.Solve(d);
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double r = 0.0;
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for (int i = 0; i < d.Length; i++)
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r += d[i] * z[i];
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double mahalanobis = Math.Abs(r);
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double a = (1.0 / 8.0) * mahalanobis;
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double b = (0.5) * (detP - 0.5 * (lnDetCovX + lnDetCovY));
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return a + b;
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}
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/// <summary>
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/// Bhattacharyya distance between two Gaussian distributions.
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/// </summary>
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///
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/// <param name="x">The first Normal distribution.</param>
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/// <param name="y">The second Normal distribution.</param>
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///
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/// <returns>The Bhattacharyya distance between the two distributions.</returns>
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///
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public double Distance(MultivariateNormalDistribution x, MultivariateNormalDistribution y)
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{
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return Distance(x.Mean, x.Covariance, y.Mean, y.Covariance);
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}
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}
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}
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