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.
115 lines
4.1 KiB
C#
115 lines
4.1 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.Statistics.Analysis;
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using Accord.Compat;
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/// <summary>
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/// Hypothesis test for two Receiver-Operating
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/// Characteristic (ROC) curve areas (ROC-AUC).
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/// </summary>
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///
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/// <seealso cref="TwoSampleZTest"/>
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/// <seealso cref="ZTest"/>
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///
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[Serializable]
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public class TwoReceiverOperatingCurveTest : TwoSampleZTest
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{
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/// <summary>
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/// First Receiver-Operating Characteristic curve.
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/// </summary>
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///
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public ReceiverOperatingCharacteristic Curve1 { get; private set; }
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/// <summary>
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/// First Receiver-Operating Characteristic curve.
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/// </summary>
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///
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public ReceiverOperatingCharacteristic Curve2 { get; private set; }
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/// <summary>
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/// Gets the summed Kappa variance
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/// for the two contingency tables.
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/// </summary>
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///
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public double OverallVariance { get; private set; }
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/// <summary>
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/// Gets the variance for the first Kappa value.
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/// </summary>
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///
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public double Variance1 { get; private set; }
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/// <summary>
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/// Gets the variance for the second Kappa value.
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/// </summary>
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///
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public double Variance2 { get; private set; }
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/// <summary>
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/// Creates a new test for two ROC curves.
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/// </summary>
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///
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/// <param name="curve1">The first ROC curve.</param>
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/// <param name="curve2">The second ROC curve.</param>
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/// <param name="hypothesizedDifference">The hypothesized difference between the two areas.</param>
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/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
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///
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public TwoReceiverOperatingCurveTest(ReceiverOperatingCharacteristic curve1, ReceiverOperatingCharacteristic curve2,
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double hypothesizedDifference = 0, TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
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{
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this.Curve1 = curve1;
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this.Curve2 = curve2;
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double[] Vx1 = curve1.NegativeAccuracies;
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double[] Vy1 = curve1.PositiveAccuracies;
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double[] Vx2 = curve2.NegativeAccuracies;
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double[] Vy2 = curve2.PositiveAccuracies;
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double covx = Measures.Covariance(Vx1, Vx2);
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double covy = Measures.Covariance(Vy1, Vy2);
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double cov = covx / Vx1.Length + covy / Vy1.Length;
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this.EstimatedValue1 = curve1.Area;
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this.EstimatedValue2 = curve2.Area;
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this.ObservedDifference = EstimatedValue1 - EstimatedValue2;
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this.HypothesizedDifference = hypothesizedDifference;
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this.Variance1 = curve1.Variance;
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this.Variance2 = curve2.Variance;
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this.OverallVariance = Variance1 + Variance2 - 2 * cov;
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this.StandardError = System.Math.Sqrt(OverallVariance);
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// Compute Z statistic
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double z = (ObservedDifference - HypothesizedDifference) / StandardError;
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Compute(z, alternate);
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}
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}
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}
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