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.
322 lines
10 KiB
C#
322 lines
10 KiB
C#
// Accord Statistics 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.Statistics.Kernels
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{
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using System;
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using AForge;
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using Accord.Math.Distances;
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using Accord.Math;
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using Accord.Compat;
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/// <summary>
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/// Laplacian Kernel.
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/// </summary>
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///
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[Serializable]
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public sealed class Laplacian : KernelBase, IKernel,
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IRadialBasisKernel, IDistance, ICloneable, IEstimable, IReverseDistance,
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IKernel<Sparse<double>>, IDistance<Sparse<double>>
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{
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private double sigma;
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private double gamma;
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/// <summary>
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/// Constructs a new Laplacian Kernel
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/// </summary>
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///
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public Laplacian()
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: this(1) { }
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/// <summary>
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/// Constructs a new Laplacian Kernel
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/// </summary>
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///
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/// <param name="sigma">The sigma slope value.</param>
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///
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public Laplacian(double sigma)
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{
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this.Sigma = sigma;
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}
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/// <summary>
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/// Gets or sets the sigma value for the kernel. When setting
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/// sigma, gamma gets updated accordingly (gamma = 0.5*/sigma^2).
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/// </summary>
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///
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public double Sigma
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{
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get { return sigma; }
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set
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{
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sigma = value;
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gamma = 1.0 / sigma;
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}
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}
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/// <summary>
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/// Gets or sets the gamma value for the kernel. When setting
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/// gamma, sigma gets updated accordingly (gamma = 0.5*/sigma^2).
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/// </summary>
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///
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public double Gamma
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{
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get { return gamma; }
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set
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{
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gamma = value;
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sigma = 1.0 / gamma;
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}
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}
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/// <summary>
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/// Laplacian Kernel function.
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/// </summary>
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///
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/// <param name="x">Vector <c>x</c> in input space.</param>
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/// <param name="y">Vector <c>y</c> in input space.</param>
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///
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/// <returns>Dot product in feature (kernel) space.</returns>
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///
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public override double Function(double[] x, double[] y)
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{
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// Optimization in case x and y are
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// exactly the same object reference.
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if (x == y)
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return 1.0;
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double norm = 0.0;
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for (int i = 0; i < x.Length; i++)
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{
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double d = x[i] - y[i];
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norm += d * d;
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}
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norm = Math.Sqrt(norm);
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return Math.Exp(-gamma * norm);
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}
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/// <summary>
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/// Laplacian Kernel function.
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/// </summary>
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///
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/// <param name="x">Vector <c>x</c> in input space.</param>
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/// <param name="y">Vector <c>y</c> in input space.</param>
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///
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/// <returns>Dot product in feature (kernel) space.</returns>
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///
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public double Function(Sparse<double> x, Sparse<double> y)
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{
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// Optimization in case x and y are
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// exactly the same object reference.
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if (x == y)
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return 1.0;
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double norm = Accord.Math.Distance.Euclidean(x, y);
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return Math.Exp(-gamma * norm);
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}
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/// <summary>
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/// Laplacian Kernel function.
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/// </summary>
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///
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/// <param name="z">Distance <c>z</c> in input space.</param>
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///
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/// <returns>Dot product in feature (kernel) space.</returns>
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///
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public double Function(double z)
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{
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return Math.Exp(-gamma * z);
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}
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/// <summary>
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/// Computes the squared distance in input space
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/// between two points given in feature space.
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/// </summary>
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///
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/// <param name="x">Vector <c>x</c> in feature (kernel) space.</param>
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/// <param name="y">Vector <c>y</c> in feature (kernel) space.</param>
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///
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/// <returns>Squared distance between <c>x</c> and <c>y</c> in input space.</returns>
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///
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public override double Distance(double[] x, double[] y)
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{
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if (x == y)
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return 0.0;
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double norm = 0.0;
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for (int i = 0; i < x.Length; i++)
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{
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double d = x[i] - y[i];
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norm += d * d;
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}
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norm = Math.Sqrt(norm);
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return 2 - 2 * Math.Exp(-gamma * norm);
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}
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/// <summary>
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/// Computes the squared distance in input space
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/// between two points given in feature space.
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/// </summary>
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///
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/// <param name="x">Vector <c>x</c> in feature (kernel) space.</param>
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/// <param name="y">Vector <c>y</c> in feature (kernel) space.</param>
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///
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/// <returns>Squared distance between <c>x</c> and <c>y</c> in input space.</returns>
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///
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public double Distance(Sparse<double> x, Sparse<double> y)
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{
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if (x == y)
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return 0.0;
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double norm = Accord.Math.Distance.Euclidean(x, y);
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return 2 - 2 * Math.Exp(-gamma * norm);
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}
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/// <summary>
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/// Computes the squared distance in input space
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/// between two points given in feature space.
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/// </summary>
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///
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/// <param name="x">Vector <c>x</c> in feature (kernel) space.</param>
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/// <param name="y">Vector <c>y</c> in feature (kernel) space.</param>
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///
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/// <returns>
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/// Squared distance between <c>x</c> and <c>y</c> in input space.
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/// </returns>
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///
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public double ReverseDistance(double[] x, double[] y)
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{
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if (x == y)
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return 0.0;
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double norm = 0.0;
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for (int i = 0; i < x.Length; i++)
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{
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double d = x[i] - y[i];
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norm += d * d;
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}
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return (1.0 / -gamma) * Math.Log(1.0 - 0.5 * norm);
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}
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/// <summary>
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/// Computes the distance in input space given
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/// a distance computed in feature space.
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/// </summary>
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///
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/// <param name="df">Distance in feature space.</param>
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/// <returns>Distance in input space.</returns>
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///
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public double ReverseDistance(double df)
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{
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return (1.0 / -gamma) * Math.Log(1.0 - 0.5 * df);
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}
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/// <summary>
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/// Estimate appropriate values for sigma given a data set.
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/// </summary>
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///
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/// <remarks>
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/// This method uses a simple heuristic to obtain appropriate values
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/// for sigma in a radial basis function kernel. The heuristic is shown
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/// by Caputo, Sim, Furesjo and Smola, "Appearance-based object
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/// recognition using SVMs: which kernel should I use?", 2002.
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/// </remarks>
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///
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/// <param name="inputs">The data set.</param>
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///
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/// <returns>A Laplacian kernel initialized with an appropriate sigma value.</returns>
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///
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public static Laplacian Estimate(double[][] inputs)
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{
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DoubleRange range;
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return Estimate(inputs, inputs.Length, out range);
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}
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/// <summary>
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/// Estimate appropriate values for sigma given a data set.
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/// </summary>
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///
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/// <remarks>
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/// This method uses a simple heuristic to obtain appropriate values
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/// for sigma in a radial basis function kernel. The heuristic is shown
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/// by Caputo, Sim, Furesjo and Smola, "Appearance-based object
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/// recognition using SVMs: which kernel should I use?", 2002.
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/// </remarks>
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///
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/// <param name="inputs">The data set.</param>
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/// <param name="samples">The number of random samples to analyze.</param>
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/// <param name="range">The range of suitable values for sigma.</param>
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///
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/// <returns>A Laplacian kernel initialized with an appropriate sigma value.</returns>
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///
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public static Laplacian Estimate(double[][] inputs, int samples, out DoubleRange range)
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{
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if (samples > inputs.Length)
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throw new ArgumentOutOfRangeException("samples");
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double[] distances = Gaussian.Distances(inputs, samples);
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double q1 = distances[(int)Math.Ceiling(0.15 * distances.Length)];
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double q9 = distances[(int)Math.Ceiling(0.85 * distances.Length)];
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double qm = Measures.Median(distances, alreadySorted: true);
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range = new DoubleRange(q1, q9);
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return new Laplacian(sigma: qm);
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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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///
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/// <returns>
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/// A new object that is a copy of this instance.
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/// </returns>
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///
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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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void IEstimable<double[]>.Estimate(double[][] inputs)
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{
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var l = Estimate(inputs);
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this.Sigma = l.Sigma;
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}
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}
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}
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