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.
112 lines
3.3 KiB
C#
112 lines
3.3 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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/// Additive combination of kernels.
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/// </summary>
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///
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[Serializable]
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public sealed class Additive : KernelBase, IKernel
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{
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private IKernel[] kernels;
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private double[] weights;
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/// <summary>
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/// Gets the combination of kernels to use.
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/// </summary>
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///
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public IKernel[] Kernels
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{
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get { return kernels; }
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}
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/// <summary>
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/// Gets the weight array to use in the weighted kernel sum.
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/// </summary>
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///
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public double[] Weights
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{
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get { return weights; }
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}
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/// <summary>
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/// Constructs a new additive kernel.
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/// </summary>
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///
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/// <param name="kernels">Kernels to combine.</param>
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///
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public Additive(params IKernel[] kernels)
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{
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this.kernels = kernels;
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this.weights = new double[kernels.Length];
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for (int i = 0; i < kernels.Length; i++)
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weights[i] = 1;
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}
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/// <summary>
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/// Constructs a new additive kernel.
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/// </summary>
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///
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/// <param name="kernels">Kernels to combine.</param>
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///
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/// <param name="weights">
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/// Weight values for each of the kernels.
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/// Default is to assign equal weights.</param>
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///
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public Additive(IKernel[] kernels, params double[] weights)
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{
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if (kernels.Length != weights.Length)
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{
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throw new ArgumentException("The number of weights should equals the number of kernels.",
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"weights");
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}
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this.kernels = kernels;
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this.weights = weights;
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}
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/// <summary>
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/// Additive Kernel Combination 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 = 0.0;
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for (int i = 0; i < kernels.Length; i++)
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sum += weights[i] * kernels[i].Function(x, y);
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return sum;
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}
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}
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}
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