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.
238 lines
9.3 KiB
C#
238 lines
9.3 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.Distributions.Univariate;
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using Accord.Compat;
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/// <summary>
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/// Multinomial test (approximated).
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// In statistics, the multinomial test is the test of the null hypothesis that the
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/// parameters of a multinomial distribution equal specified values. The test can be
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/// approximated using a <see cref="ChiSquareDistribution">chi-square distribution</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><a href="http://en.wikipedia.org/wiki/Multinomial_test">
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/// Wikipedia, The Free Encyclopedia. Multinomial Test. Available on:
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/// http://en.wikipedia.org/wiki/Multinomial_test </a></description></item>
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/// </list></para>
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/// </remarks>
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///
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/// <example>
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///
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/// <para>
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/// The following example is based on the example available on About.com Statistics,
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/// <a href="http://statistics.about.com/od/Inferential-Statistics/a/An-Example-Of-Chi-Square-Test-For-A-Multinomial-Experiment.htm">An Example of Chi-Square Test for a Multinomial Experiment</a> By Courtney Taylor.</para>
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/// <para>
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/// In this example, we would like to test if a die is fair. For this, we
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/// will be rolling the die 600 times, annotating the result every time
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/// the die falls. In the end, we got a one 106 times, a two 90 times, a
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/// three 98 times, a four 102 times, a five 100 times and a six 104 times:</para>
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///
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/// <code>
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/// int[] sample = { 106, 90, 98, 102, 100, 104 };
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///
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/// // If the die was fair, we should note that we would be expecting the
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/// // probabilities to be all equal to 1 / 6:
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///
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/// double[] hypothesizedProportion =
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/// {
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/// // 1 2 3 4 5 6
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/// 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0, 1 / 6.0,
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/// };
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///
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/// // Now, we create our test using the samples and the expected proportion
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/// MultinomialTest test = new MultinomialTest(sample, hypothesizedProportion);
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///
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/// double chiSquare = test.Statistic; // 1.6
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/// bool significant = test.Significant; // false
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/// </code>
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///
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/// <para>
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/// Since the test didn't come up significant, it means that we
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/// don't have enough evidence to to reject the null hypothesis
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/// that the die is fair.</para>
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/// </example>
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///
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/// <seealso cref="ChiSquareTest"/>
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/// <seealso cref="Accord.Statistics.Distributions.Univariate.ChiSquareDistribution"/>
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///
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[Serializable]
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public class MultinomialTest : ChiSquareTest
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{
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/// <summary>
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/// Gets the observed sample proportions.
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/// </summary>
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///
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public double[] ObservedProportions { get; private set; }
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/// <summary>
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/// Gets the hypothesized population proportions.
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/// </summary>
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///
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public double[] HypothesizedProportions { get; private set; }
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sampleProportions">The proportions for each category in the sample.</param>
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/// <param name="sampleSize">The number of observations in the sample.</param>
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///
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public MultinomialTest(double[] sampleProportions, int sampleSize)
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{
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double[] hypothesizedProportions = new double[sampleProportions.Length];
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for (int i = 0; i < hypothesizedProportions.Length; i++)
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hypothesizedProportions[i] = 1.0 / hypothesizedProportions.Length;
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Compute(sampleSize, sampleProportions, hypothesizedProportions);
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}
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sampleCounts">The number of occurrences for each category in the sample.</param>
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///
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public MultinomialTest(int[] sampleCounts)
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{
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double[] hypothesizedProportions = new double[sampleCounts.Length];
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for (int i = 0; i < hypothesizedProportions.Length; i++)
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hypothesizedProportions[i] = 1.0 / hypothesizedProportions.Length;
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int sampleSize = sampleCounts.Sum();
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double[] sampleProportions = sampleCounts.Divide(sampleSize);
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Compute(sampleSize, sampleProportions, hypothesizedProportions);
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}
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sampleCounts">The number of occurrences for each category in the sample.</param>
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/// <param name="hypothesizedProportions">The hypothesized category proportions. Default is
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/// to assume uniformly equal proportions.</param>
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///
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public MultinomialTest(int[] sampleCounts, double[] hypothesizedProportions)
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{
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if (sampleCounts.Length != hypothesizedProportions.Length)
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throw new DimensionMismatchException("hypothesizedProportions");
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int sampleSize = sampleCounts.Sum();
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double[] sampleProportions = sampleCounts.Divide(sampleSize);
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Compute(sampleSize, sampleProportions, hypothesizedProportions);
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}
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sampleProportions">The proportions for each category in the sample.</param>
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/// <param name="sampleSize">The number of observations in the sample.</param>
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/// <param name="hypothesizedProportions">The hypothesized category proportions. Default is
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/// to assume uniformly equal proportions.</param>
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///
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public MultinomialTest(double[] sampleProportions, int sampleSize, double[] hypothesizedProportions)
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{
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if (sampleProportions.Length != hypothesizedProportions.Length)
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throw new DimensionMismatchException("hypothesizedProportions");
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Compute(sampleSize, sampleProportions, hypothesizedProportions);
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}
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sample">The categories for each observation in the sample.</param>
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/// <param name="categories">The number of possible categories.</param>
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///
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public MultinomialTest(int[] sample, int categories)
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{
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double[] hypothesizedProportions = new double[categories];
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for (int i = 0; i < hypothesizedProportions.Length; i++)
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hypothesizedProportions[i] = 1.0 / hypothesizedProportions.Length;
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double[] observed = new double[categories];
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for (int i = 0; i < observed.Length; i++)
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observed[sample[i]]++;
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Compute(sample.Length, observed, hypothesizedProportions);
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}
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/// <summary>
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/// Creates a new Multinomial test.
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/// </summary>
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///
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/// <param name="sample">The categories for each observation in the sample.</param>
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/// <param name="categories">The number of possible categories.</param>
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/// <param name="hypothesizedProportions">The hypothesized category proportions. Default is
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/// to assume uniformly equal proportions.</param>
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///
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public MultinomialTest(int[] sample, int categories, double[] hypothesizedProportions)
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{
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double[] observed = new double[categories];
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for (int i = 0; i < observed.Length; i++)
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observed[sample[i]]++;
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Compute(sample.Length, observed, hypothesizedProportions);
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}
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/// <summary>
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/// Computes the Multinomial test.
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/// </summary>
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///
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protected void Compute(int sampleSize, double[] observed, double[] expected)
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{
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// Approximate using a Chi-Square distribution
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this.ObservedProportions = observed;
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this.HypothesizedProportions = expected;
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double sum = 0;
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for (int i = 0; i < ObservedProportions.Length; i++)
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{
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double e = sampleSize * expected[i];
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double u = sampleSize * observed[i] - e;
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sum += (u * u) / e;
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}
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base.Compute(sum, observed.Length - 1);
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}
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}
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}
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