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.
244 lines
7.5 KiB
C#
244 lines
7.5 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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/// Quadratic Kernel.
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/// </summary>
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///
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[Serializable]
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public sealed class Quadratic : KernelBase, IKernel,
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IDistance, IReverseDistance, ICloneable, ITransform
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{
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private double constant;
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/// <summary>
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/// Constructs a new Quadratic kernel.
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/// </summary>
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///
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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 Quadratic(double constant)
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{
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this.constant = constant;
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}
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/// <summary>
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/// Constructs a new Quadratic kernel.
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/// </summary>
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///
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public Quadratic()
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: this(1.0) { }
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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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/// Quadratic 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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double sum = constant;
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for (int i = 0; i < x.Length; i++)
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sum += x[i] * y[i];
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return sum * sum;
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}
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/// <summary>
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/// Quadratic 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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double sum = constant + z;
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return sum * sum;
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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>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 sumx = constant, sumy = constant, sum = constant;
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for (int i = 0; i < x.Length; i++)
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{
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sumx += x[i] * x[i];
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sumy += y[i] * y[i];
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sum += x[i] * y[i];
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}
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return sumx * sumx + sumy * sumy - 2 * sum * sum;
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}
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/// <summary>
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/// Computes the 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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/// <returns>Distance between <c>x</c> and <c>y</c> in input space.</returns>
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///
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public double ReverseDistance(double[] x, double[] y)
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{
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double sumx = 0;
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double sumy = 0;
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double sum = 0;
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for (int i = 0; i < x.Length; i++)
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{
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sumx += x[i] * x[i];
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sumy += y[i] * y[i];
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sum += x[i] * y[i];
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}
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return Math.Sqrt(sumx) + Math.Sqrt(sumy) - 2.0 * Math.Sqrt(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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///
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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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/// <summary>
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/// Projects an input point into feature space.
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/// </summary>
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///
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/// <param name="input">The input point to be projected into feature space.</param>
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///
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/// <returns>
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/// The feature space representation of the given <paramref name="input"/> point.
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/// </returns>
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///
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public double[] Transform(double[] input)
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{
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return Transform(input, constant);
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}
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/// <summary>
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/// Projects a set of input points into feature space.
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/// </summary>
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///
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/// <param name="inputs">The input points to be projected into feature space.</param>
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///
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/// <returns>
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/// The feature space representation of the given <paramref name="inputs"/> points.
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/// </returns>
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///
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public double[][] Transform(double[][] inputs)
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{
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double[][] r = new double[inputs.Length][];
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for (int i = 0; i < inputs.Length; i++)
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r[i] = Transform(inputs[i]);
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return r;
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}
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/// <summary>
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/// Projects an input point into feature space.
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/// </summary>
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///
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/// <param name="input">The input point to be projected into feature space.</param>
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/// <param name="constant">The <see cref="Constant"/> parameter of the kernel.</param>
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///
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/// <returns>
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/// The feature space representation of the given <paramref name="input"/> point.
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/// </returns>
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///
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public static double[] Transform(double[] input, double constant)
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{
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int n = input.Length;
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int m = (n * (n + 1)) / 2;
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double[] features = (constant == 0) ?
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new double[m] : new double[m + n + 1];
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for (int i = 0; i < input.Length; i++)
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features[i] = input[i] * input[i];
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int c = input.Length;
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for (int i = 0; i < input.Length; i++)
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for (int j = i + 1; j < input.Length; j++)
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features[c++] = Constants.Sqrt2 * input[i] * input[j];
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if (constant != 0)
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{
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double sqrt2c = Math.Sqrt(2 * constant);
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for (int i = 0; i < input.Length; i++)
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features[m + i] = input[i] * sqrt2c;
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features[features.Length - 1] = constant;
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}
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return features;
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}
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}
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}
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