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.
127 lines
3.9 KiB
C#
127 lines
3.9 KiB
C#
// Accord Math 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.Math.Decompositions
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{
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using Accord;
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/// <summary>
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/// Gram-Schmidt Orthogonalization.
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/// </summary>
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///
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public class GramSchmidtOrthogonalization
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{
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private double[,] q;
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private double[,] r;
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/// <summary>
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/// Initializes a new instance of the <see cref="GramSchmidtOrthogonalization"/> class.
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/// </summary>
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///
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/// <param name="value">The matrix <c>A</c> to be decomposed.</param>
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///
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public GramSchmidtOrthogonalization(double[,] value)
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: this(value, true)
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{
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="GramSchmidtOrthogonalization"/> class.
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/// </summary>
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///
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/// <param name="value">The matrix <c>A</c> to be decomposed.</param>
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/// <param name="modified">True to use modified Gram-Schmidt; false
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/// otherwise. Default is true (and is the recommended setup).</param>
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///
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public GramSchmidtOrthogonalization(double[,] value, bool modified)
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{
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if (value.GetLength(0) != value.GetLength(1))
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throw new DimensionMismatchException("value", "Matrix must be square.");
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int size = value.GetLength(0);
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q = new double[size, size];
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r = new double[size, size];
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if (modified)
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{
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for (int j = 0; j < size; j++)
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{
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double[] v = value.GetColumn(j);
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for (int i = 0; i < j; i++)
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{
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r[i, j] = q.GetColumn(i).Dot(v);
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var t = r[i, j].Multiply(q.GetColumn(i));
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v.Subtract(t, result: v);
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}
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r[j, j] = Norm.Euclidean(v);
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q.SetColumn(j, v.Divide(r[j, j]));
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}
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}
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else
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{
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for (int j = 0; j < size; j++)
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{
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double[] v = value.GetColumn(j);
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double[] a = value.GetColumn(j);
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for (int i = 0; i < j; i++)
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{
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r[i, j] = q.GetColumn(j).Dot(a);
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v = v.Subtract(r[i, j].Multiply(q.GetColumn(i)));
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}
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r[j, j] = Norm.Euclidean(v);
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q.SetColumn(j, v.Divide(r[j, j]));
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}
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}
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}
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/// <summary>
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/// Returns the orthogonal factor matrix <c>Q</c>.
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/// </summary>
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///
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public double[,] OrthogonalFactor
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{
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get { return q; }
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}
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/// <summary>
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/// Returns the upper triangular factor matrix <c>R</c>.
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/// </summary>
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///
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public double[,] UpperTriangularFactor
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{
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get { return r; }
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}
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}
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}
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