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
4.6 KiB
C#
127 lines
4.6 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.Optimization
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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.Expressions;
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using System.Text.RegularExpressions;
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using System.Text;
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/// <summary>
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/// Constraint with only quadratic terms.
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/// </summary>
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///
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public class QuadraticConstraint : NonlinearConstraint, IConstraint
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{
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/// <summary>
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/// Gets the matrix of <c>A</c> quadratic terms
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/// for the constraint <c>x'Ax + x'b</c>.
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/// </summary>
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///
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public double[,] QuadraticTerms { get; private set; }
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/// <summary>
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/// Gets the vector <c>b</c> of linear terms
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/// for the constraint <c>x'Ax + x'b</c>.
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/// </summary>
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///
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public double[] LinearTerms { get; private set; }
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/// <summary>
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/// Constructs a new quadratic constraint in the form <c>x'Ax + x'b</c>.
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/// </summary>
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///
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/// <param name="objective">The objective function to which this constraint refers.</param>
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/// <param name="quadraticTerms">The matrix of <c>A</c> quadratic terms.</param>
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/// <param name="linearTerms">The vector <c>b</c> of linear terms.</param>
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/// <param name="shouldBe">How the left hand side of the constraint should be compared to
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/// the given <paramref name="value"/>.</param>
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/// <param name="value">The right hand side of the constraint equation.</param>
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/// <param name="withinTolerance">The tolerance for violations of the constraint. Equality
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/// constraints should set this to a small positive value. Default is 0.</param>
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///
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public QuadraticConstraint(IObjectiveFunction objective,
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double[,] quadraticTerms, double[] linearTerms = null,
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ConstraintType shouldBe = ConstraintType.LesserThanOrEqualTo,
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double value = 0, double withinTolerance = 0.0)
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{
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int n = objective.NumberOfVariables;
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if (quadraticTerms == null)
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throw new ArgumentNullException("quadraticTerms");
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if (quadraticTerms.GetLength(0) != quadraticTerms.GetLength(1))
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throw new DimensionMismatchException("quadraticTerms", "Matrix must be square.");
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if (quadraticTerms.GetLength(0) != n)
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throw new DimensionMismatchException("quadraticTerms",
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"Matrix rows must match the number of variables in the objective function.");
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if (linearTerms != null)
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{
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if (linearTerms.Length != n)
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throw new DimensionMismatchException("linearTerms",
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"The length of the linear terms vector must match the "+
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"number of variables in the objective function.");
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}
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else
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{
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linearTerms = new double[n];
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}
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this.QuadraticTerms = quadraticTerms;
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this.LinearTerms = linearTerms;
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Create(n, this.function, shouldBe, value, this.gradient, withinTolerance);
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}
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private double function(double[] x)
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{
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return x.DotAndDot(QuadraticTerms, x) + LinearTerms.Dot(x);
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}
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private double[] gradient(double[] x)
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{
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double[] g = new double[x.Length];
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for (int i = 0; i < x.Length; i++)
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{
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// Calculate quadratic terms
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g[i] = 2.0 * x[i] * QuadraticTerms[i, i];
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for (int j = 0; j < x.Length; j++)
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if (i != j) g[i] += x[j] * (QuadraticTerms[i, j] + QuadraticTerms[j, i]);
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// Calculate for linear terms
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g[i] += LinearTerms[i];
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}
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return g;
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}
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}
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}
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