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.
266 lines
8.7 KiB
C#
266 lines
8.7 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 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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/// Square-Euclidean distance and similarity. Please note that this
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/// distance is not a metric as it doesn't obey the triangle inequality.
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/// </summary>
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///
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/// <seealso cref="Euclidean"/>
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///
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[Serializable]
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public struct SquareEuclidean :
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IDistance<double[]>, ISimilarity<double[]>,
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IDistance<double>, ISimilarity<double>,
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IDistance<Sparse<double>>, ISimilarity<Sparse<double>>, ICloneable
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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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#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 sum = 0.0;
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for (int i = 0; i < x.Length; i++)
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{
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double u = x[i] - y[i];
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sum += u * u;
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}
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return sum;
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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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#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 u = x - y;
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return u * u;
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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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#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(Sparse<double> x, Sparse<double> y)
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{
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return Sparse(x, y);
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}
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/// <summary>
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/// Gets the Square Euclidean distance between two points.
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/// </summary>
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///
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/// <param name="x1">The first coordinate of first point in space.</param>
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/// <param name="y1">The second coordinate of first point in space.</param>
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/// <param name="x2">The first coordinate of second point in space.</param>
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/// <param name="y2">The second coordinate of second point in space.</param>
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///
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/// <returns>The Square Euclidean distance between x and y.</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 x1, double y1, double x2, double y2)
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{
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double dx = x1 - x2;
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double dy = y1 - y2;
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return dx * dx + dy * dy;
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}
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/// <summary>
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/// Gets a similarity measure between two points.
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/// </summary>
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///
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/// <param name="x">The first point to be compared.</param>
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/// <param name="y">The second point to be compared.</param>
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///
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/// <returns>A similarity measure between x and y.</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 Similarity(double[] x, double[] y)
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{
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return 1.0 / (1.0 + Distance(x, y));
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}
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/// <summary>
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/// Gets a similarity measure between two points.
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/// </summary>
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///
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/// <param name="x">The first point to be compared.</param>
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/// <param name="y">The second point to be compared.</param>
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///
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/// <returns>A similarity measure between x and y.</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 Similarity(Sparse<double> x, Sparse<double> y)
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{
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return 1.0 / (1.0 + Distance(x, y));
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}
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/// <summary>
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/// Gets a similarity measure between two points.
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/// </summary>
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///
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/// <param name="x">The first point to be compared.</param>
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/// <param name="y">The second point to be compared.</param>
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///
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/// <returns>A similarity measure between x and y.</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 Similarity(double x, double y)
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{
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return 1.0 / (1.0 + Distance(x, y));
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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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public static double Sparse(Sparse<double> x, Sparse<double> y)
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{
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double sum = 0;
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int i = 0, j = 0;
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while (i < x.Indices.Length && j < y.Indices.Length)
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{
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int posx = x.Indices[i];
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int posy = y.Indices[j];
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if (posx == posy)
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{
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double d = x.Values[i] - y.Values[j];
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sum += d * d;
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i++;
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j++;
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}
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else if (posx < posy)
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{
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double d = x.Values[i];
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sum += d * d;
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i++;
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}
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else if (posx > posy)
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{
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double d = y.Values[j];
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sum += d * d;
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j++;
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}
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}
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for (; i < x.Values.Length; i++)
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sum += x.Values[i] * x.Values[i];
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for (; j < y.Values.Length; j++)
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sum += y.Values[j] * y.Values[j];
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return 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 new SquareEuclidean();
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}
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}
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}
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