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.
146 lines
4.3 KiB
C#
146 lines
4.3 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.Testing
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{
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using System;
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using Accord.Math;
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using Accord.Statistics.Analysis;
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using Accord.Compat;
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/// <summary>
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/// Bhapkar test of homogeneity for contingency tables.
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/// </summary>
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///
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/// <remarks>
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/// The Bhapkar test is a more powerful alternative to the
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/// <see cref="StuartMaxwellTest">Stuart-Maxwell test</see>.
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///
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/// <para>
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/// This is a <see cref="ChiSquareTest">Chi-square kind of test</see>.</para>
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///
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/// <para>
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/// References:
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/// <list type="bullet">
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/// <item><description>
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/// Bhapkar, V.P. (1966). A note on the equivalence of two test criteria
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/// for hypotheses in categorical data. Journal of the American Statistical
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/// Association, 61, 228-235.</description></item>
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/// </list></para>
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/// </remarks>
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///
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/// <seealso cref="ChiSquareTest"/>
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///
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[Serializable]
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public class BhapkarTest : ChiSquareTest
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{
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double[] d;
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double[,] S;
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double[,] invS;
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/// <summary>
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/// Gets the delta vector <c>d</c> used
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/// in the test calculations.
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/// </summary>
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///
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public double[] Delta
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{
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get { return d; }
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}
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/// <summary>
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/// Gets the covariance matrix <c>S</c>
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/// used in the test calculations.
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/// </summary>
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///
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public double[,] Covariance
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{
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get { return S; }
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}
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/// <summary>
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/// Gets the inverse covariance matrix
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/// <c>S^-1</c> used in the calculations.
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/// </summary>
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///
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public double[,] Precision
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{
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get { return invS; }
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}
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/// <summary>
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/// Creates a new Bhapkar test.
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/// </summary>
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///
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/// <param name="matrix">The contingency table to test.</param>
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///
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public BhapkarTest(GeneralConfusionMatrix matrix)
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{
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int classes = matrix.NumberOfClasses;
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int samples = matrix.NumberOfSamples;
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int df = classes - 1;
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int[] rowMarginals = matrix.RowTotals;
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int[] colMarginals = matrix.ColumnTotals;
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d = new double[df];
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for (int i = 0; i < d.Length; i++)
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d[i] = rowMarginals[i] - colMarginals[i];
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S = new double[df, df];
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for (int i = 0; i < df; i++)
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{
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for (int j = 0; j < df; j++)
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{
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if (i == j)
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{
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double u = (rowMarginals[i] - colMarginals[i]);
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double pii = matrix.Matrix[i, i];
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S[i, i] = rowMarginals[i] + colMarginals[i] - 2.0 * pii - u * u / (double)samples;
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}
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else
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{
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double pij = matrix.Matrix[i, j];
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double pji = matrix.Matrix[j, i];
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S[i, j] = -(pij + pji) - (rowMarginals[i] - colMarginals[i]) * (rowMarginals[j] - colMarginals[j]) / (double)samples;
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}
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}
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}
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invS = S.PseudoInverse();
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double chiSquare = d.DotAndDot(invS, d);
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Compute(chiSquare, df);
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}
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}
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}
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