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86 lines
3.4 KiB
86 lines
3.4 KiB
// <copyright file="StandardErrorTest.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2013 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using System;
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using System.Linq;
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using NUnit.Framework;
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namespace MathNet.Numerics.UnitTests.GoodnessOfFit
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{
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[TestFixture, Category("Regression")]
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public class StandardErrorTest
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{
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[Test]
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public void ComputesPopulationStandardErrorOfTheRegression()
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{
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// Definition as described at: http://onlinestatbook.com/lms/regression/accuracy.html
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var xes = new[] { 1.0, 2, 3, 4, 5 };
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var ys = new[] { 1, 2, 1.3, 3.75, 2.25 };
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var fit = Fit.Line(xes, ys);
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var a = fit.Item1;
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var b = fit.Item2;
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var predictedYs = xes.Select(x => a + b * x);
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var standardError = Numerics.GoodnessOfFit.PopulationStandardError(predictedYs, ys);
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Assert.AreEqual(0.747, standardError, 1e-3);
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}
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[Test]
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public void ComputesStandardErrorOfTheRegression()
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{
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// Definition as described at: http://onlinestatbook.com/lms/regression/accuracy.html
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var xes = new[] { 1.0, 2, 3, 4, 5 };
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var ys = new[] { 1, 2, 1.3, 3.75, 2.25 };
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var fit = Fit.Line(xes, ys);
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var a = fit.Item1;
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var b = fit.Item2;
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var predictedYs = xes.Select(x => a + b * x);
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var standardError = Numerics.GoodnessOfFit.StandardError(predictedYs, ys, degreesOfFreedom: 2);
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Assert.AreEqual(0.964, standardError, 1e-3);
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}
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[Test]
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public void PopulationStandardErrorShouldThrowIfInputsSequencesDifferInLength()
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{
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var y1 = new[] { 0.0, 1 };
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var y2 = new[] { 1.0 };
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Assert.Throws<ArgumentOutOfRangeException>(() => Numerics.GoodnessOfFit.PopulationStandardError(y1, y2));
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}
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[Test]
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public void StandardErrorShouldThrowIfSampleSizeIsSmallerThanGivenDegreesOfFreedom()
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{
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var modelled = new[] { 1.0 };
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var observed = new[] { 1.0 };
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Assert.Throws<ArgumentOutOfRangeException>(() => Numerics.GoodnessOfFit.StandardError(modelled, observed, 2));
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}
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}
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}
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