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
3.9 KiB
C#
131 lines
3.9 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.Math;
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using Accord.Math.Distances;
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using Accord.Compat;
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/// <summary>
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/// Normalized Polynomial Kernel. This class is equivalent to the
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/// Normalized>Polynomial> kernel but has more efficient
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/// implementation.
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/// </summary>
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///
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[Serializable]
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public sealed class NormalizedPolynomial : KernelBase, IKernel,
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IDistance, ICloneable
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{
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private int degree;
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private double constant;
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/// <summary>
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/// Constructs a new Normalized Polynomial kernel of a given degree.
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/// </summary>
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///
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/// <param name="degree">The polynomial degree for this kernel.</param>
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/// <param name="constant">The polynomial constant for this kernel. Default is 1.</param>
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///
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public NormalizedPolynomial(int degree, double constant)
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{
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this.degree = degree;
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this.constant = constant;
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}
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/// <summary>
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/// Constructs a new Normalized Polynomial kernel of a given degree.
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/// </summary>
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///
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/// <param name="degree">The polynomial degree for this kernel.</param>
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///
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public NormalizedPolynomial(int degree)
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: this(degree, 1.0) { }
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/// <summary>
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/// Gets or sets the kernel's polynomial degree.
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/// </summary>
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///
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public int Degree
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{
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get { return degree; }
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set
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{
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if (degree <= 0)
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throw new ArgumentOutOfRangeException("value", "Degree must be positive.");
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degree = value;
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}
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}
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/// <summary>
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/// Gets or sets the kernel's polynomial constant term.
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/// </summary>
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///
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public double Constant
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{
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get { return constant; }
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set { constant = value; }
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}
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/// <summary>
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/// Normalized polynomial 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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/// <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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double sum = constant;
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double sumX = constant;
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double sumY = constant;
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for (int i = 0; i < x.Length; i++)
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{
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sum += x[i] * y[i];
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sumX += x[i] * x[i];
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sumY += y[i] * y[i];
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}
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return Math.Pow(sum / (sumX + sumY), 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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