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.
131 lines
3.7 KiB
C#
131 lines
3.7 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 Accord.Compat;
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/// <summary>
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/// Power Kernel, also known as the (Unrectified) Triangular Kernel.
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/// </summary>
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///
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/// <remarks>
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/// The Power kernel is also known as the (unrectified) triangular kernel.
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/// It is an example of scale-invariant kernel (Sahbi and Fleuret, 2004)
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/// and is also only conditionally positive definite.
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/// </remarks>
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///
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[Serializable]
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public sealed class Power : KernelBase, IKernel, IRadialBasisKernel, ICloneable
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{
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private double degree;
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/// <summary>
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/// Gets or sets the kernel's degree.
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/// </summary>
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///
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public double Degree
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{
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get { return degree; }
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set { degree = value; }
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}
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/// <summary>
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/// Constructs a new Power Kernel.
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/// </summary>
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///
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/// <param name="degree">The kernel's degree.</param>
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///
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public Power(int degree)
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{
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this.degree = degree;
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}
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/// <summary>
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/// Constructs a new Power Kernel.
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/// </summary>
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///
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/// <param name="degree">The kernel's degree.</param>
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///
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public Power(double degree)
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{
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this.degree = degree;
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}
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/// <summary>
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/// Power 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 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 -System.Math.Pow(norm, degree / 2.0);
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}
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/// <summary>
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/// Power 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 -System.Math.Pow(z, degree);
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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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}
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}
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