mirror of
https://github.com/hanzoai/jube-fork.git
synced 2026-10-30 18:33:33 +00:00
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.
492 lines
16 KiB
C#
492 lines
16 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 Accord.Math;
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using Accord.Math.Distances;
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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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/// Linear Kernel.
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/// </summary>
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///
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[Serializable]
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public struct Linear : IKernel, IDistance, ILinear, ICloneable, IReverseDistance,
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ITransform, IKernel<Sparse<double>>, ILinear<Sparse<double>>, IDistance<Sparse<double>>
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{
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private double constant;
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/// <summary>
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/// Constructs a new Linear kernel.
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/// </summary>
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///
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/// <param name="constant">A constant intercept term. Default is 0.</param>
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///
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public Linear(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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/// Gets or sets the kernel's intercept term. Default is 0.
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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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/// Linear 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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#if NET45 || NET46 || NET462 || NETSTANDARD2_0
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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#endif
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public 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 < y.Length; i++)
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sum += x[i] * y[i];
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return sum;
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}
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/// <summary>
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/// Linear 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 z + constant;
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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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#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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if (x == y)
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return 0.0;
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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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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 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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#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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if (x == y)
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return 0.0;
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return Accord.Math.Distance.SquareEuclidean(x, y);
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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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double sumx = constant;
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double sumy = constant;
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double 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 + sumy - 2.0 * sum;
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}
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/// <summary>
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/// Elementwise addition of a and b, storing in result.
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/// </summary>
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///
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/// <param name="a">The first vector to add.</param>
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/// <param name="b">The second vector to add.</param>
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/// <param name="result">An array to store the result.</param>
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/// <returns>The same vector passed as result.</returns>
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///
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public void Add(double[] a, double[] b, double[] result)
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{
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a.Add(b, result: result);
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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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/// Elementwise multiplication of scalar a and vector b, storing in result.
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/// </summary>
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///
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/// <param name="a">The scalar to be multiplied.</param>
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/// <param name="b">The vector to be multiplied.</param>
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/// <param name="accumulate">An array to store the result.</param>
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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 void Product(double a, double[] b, double[] accumulate)
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{
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if (a == 0)
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return;
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for (int j = 0; j < b.Length; j++)
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accumulate[j] += a * b[j];
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}
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/// <summary>
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/// Compress a set of support vectors and weights into a single
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/// parameter vector.
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/// </summary>
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///
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/// <param name="weights">The weights associated with each support vector.</param>
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/// <param name="supportVectors">The support vectors.</param>
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/// <param name="c">The constant (bias) value.</param>
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///
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/// <returns>A single parameter vector.</returns>
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///
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public double[] Compress(double[] weights, double[][] supportVectors, out double c)
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{
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double[] result = Matrix.Dot(weights, supportVectors);
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c = -constant;
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return result;
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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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if (constant == 0)
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return input;
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var feature = new double[input.Length + 3];
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for (int i = 0; i < input.Length; i++)
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feature[i] = input[i];
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feature[input.Length] = constant;
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return feature;
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}
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/// <summary>
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/// The kernel function.
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/// </summary>
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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>
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/// Dot product in feature (kernel) space.
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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 Function(Sparse<double> x, Sparse<double> y)
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{
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return x.Dot(y) + constant;
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}
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/// <summary>
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/// The kernel function.
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/// </summary>
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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>
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/// Dot product in feature (kernel) space.
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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 Function(double[] y, Sparse<double> x)
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{
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return x.Dot(y) + constant;
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}
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/// <summary>
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/// Elementwise addition of a and b, storing in result.
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/// </summary>
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///
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/// <param name="a">The first vector to add.</param>
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/// <param name="b">The second vector to add.</param>
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/// <param name="result">An array to store the result.</param>
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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 void Add(Sparse<double> a, double[] b, double[] result)
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{
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a.Add(b, result);
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}
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/// <summary>
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/// Elementwise multiplication of scalar a and vector b, storing in result.
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/// </summary>
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/// <param name="a">The scalar to be multiplied.</param>
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/// <param name="b">The vector to be multiplied.</param>
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/// <param name="accumulate">An array to store the result.</param>
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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 void Product(double a, Sparse<double> b, double[] accumulate)
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{
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if (a == 0)
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return;
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for (int j = 0; j < b.Indices.Length; j++)
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accumulate[b.Indices[j]] += a * b.Values[j];
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}
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///// <summary>
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///// Elementwise multiplication of scalar a and vector b, storing in result.
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///// </summary>
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///// <param name="a">The scalar to be multiplied.</param>
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///// <param name="b">The vector to be multiplied.</param>
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///// <param name="accumulate">An array to store the result.</param>
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//public void Product(double a, Sparse<double> b, Sparse<double> accumulate)
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//{
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// if (a == 0)
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// return;
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// for (int j = 0; j < b.Indices.Length; j++)
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// accumulate.Values[b.Indices[j]] += a * b.Values[j];
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//}
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/// <summary>
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/// Compress a set of support vectors and weights into a single
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/// parameter vector.
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/// </summary>
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/// <param name="weights">The weights associated with each support vector.</param>
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/// <param name="supportVectors">The support vectors.</param>
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/// <param name="c">The constant (bias) value.</param>
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/// <returns>
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/// A single parameter vector.
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/// </returns>
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public Sparse<double> Compress(double[] weights, Sparse<double>[] supportVectors, out double c)
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{
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return Accord.Math.Sparse.FromDense(Compress(weights, supportVectors.ToDense(), out c));
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}
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/// <summary>
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/// Gets the number of parameters in the input vectors.
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/// </summary>
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///
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public int GetLength(double[][] inputs)
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{
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return inputs.Columns(max: true);
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}
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/// <summary>
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/// Gets the number of parameters in the input vectors.
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/// </summary>
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///
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public int GetLength(Sparse<double>[] inputs)
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{
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return inputs.Columns();
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}
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/// <summary>
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/// Creates an input vector from the given double values.
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/// </summary>
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///
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public double[] CreateVector(double[] values)
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{
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return Accord.Math.Vector.Create(values);
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}
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/// <summary>
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/// Creates an input vector with the given dimensions.
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/// </summary>
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///
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public double[] CreateVector(int dimensions)
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{
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return new double[dimensions];
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}
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Sparse<double> ILinear<Sparse<double>>.CreateVector(double[] values)
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{
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return Accord.Math.Sparse.FromDense(values);
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}
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//Sparse<double> ILinear<Sparse<double>>.CreateVector(int dimensions)
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//{
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// return Accord.Math.Sparse.FromDense(new double[dimensions], removeZeros: false);
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//}
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/// <summary>
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/// Elementwise multiplication of vector a and vector b, accumulating in result.
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/// </summary>
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///
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/// <param name="a">The vector to be multiplied.</param>
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/// <param name="b">The vector to be multiplied.</param>
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/// <param name="accumulate">An array to store the result.</param>
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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 void Product(double[] a, Sparse<double> b, double[] accumulate)
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{
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for (int j = 0; j < b.Indices.Length; j++)
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accumulate[b.Indices[j]] += a[b.Indices[j]] * b.Values[j];
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}
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/// <summary>
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/// Elementwise multiplication of vector a and vector b, accumulating in result.
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/// </summary>
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///
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/// <param name="a">The vector to be multiplied.</param>
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/// <param name="b">The vector to be multiplied.</param>
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/// <param name="accumulate">An array to store the result.</param>
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///
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public void Product(double[] a, double[] b, double[] accumulate)
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{
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for (int i = 0; i < a.Length; i++)
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accumulate[i] += a[i] * b[i];
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}
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/// <summary>
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/// Converts the input vectors to a double-precision representation.
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/// </summary>
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///
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public double[][] ToDouble(double[][] input)
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{
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return input;
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}
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/// <summary>
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/// Converts the input vectors to a double-precision representation.
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/// </summary>
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///
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public double[][] ToDouble(Sparse<double>[] input)
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{
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return Accord.Math.Sparse.ToDense(input);
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}
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}
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}
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