mirror of
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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.
83 lines
3.4 KiB
C#
83 lines
3.4 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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/// <summary>
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/// Wilcoxon signed-rank test for paired samples.
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/// </summary>
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///
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/// <seealso cref="WilcoxonSignedRankTest"/>
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/// <seealso cref="Accord.Statistics.Distributions.Univariate.WilcoxonDistribution"/>
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///
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public class TwoSampleWilcoxonSignedRankTest : WilcoxonTest
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{
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/// <summary>
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/// Gets the alternative hypothesis under test. If the test is
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/// <see cref="IHypothesisTest.Significant"/>, the null hypothesis can be rejected
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/// in favor of this alternative hypothesis.
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/// </summary>
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///
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public TwoSampleHypothesis Hypothesis { get; protected set; }
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/// <summary>
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/// Tests whether the medians of two paired samples are different.
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/// </summary>
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///
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/// <param name="sample1">The first sample.</param>
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/// <param name="sample2">The second sample.</param>
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/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
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/// <param name="exact">True to compute the exact distribution. May require a significant
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/// amount of processing power for large samples (n > 12). If left at null, whether to
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/// compute the exact or approximate distribution will depend on the number of samples.
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/// Default is null.</param>
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/// <param name="adjustForTies">Whether to account for ties when computing the
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/// rank statistics or not. Default is true.</param>
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///
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public TwoSampleWilcoxonSignedRankTest(double[] sample1, double[] sample2,
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TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent,
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bool? exact = null, bool adjustForTies = true)
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{
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if (sample1.Length != sample2.Length)
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throw new DimensionMismatchException("sample2", "Both samples should be of the same size.");
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int[] signs = new int[sample1.Length];
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double[] diffs = new double[sample1.Length];
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// 1. Compute absolute difference and sign function
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for (int i = 0; i < sample1.Length; i++)
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{
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double d = sample1[i] - sample2[i];
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signs[i] = Math.Sign(d);
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diffs[i] = Math.Abs(d);
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}
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this.Hypothesis = alternate;
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base.Compute(signs, diffs, (DistributionTail)alternate, exact, adjustForTies: adjustForTies);
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}
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}
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}
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