richard-allen
5 years ago
6 changed files with 984 additions and 519 deletions
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// <copyright file="DescriptiveStatisticsTests.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) 2009-2016 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.Collections.Generic; |
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using System.Linq; |
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#if NET5_0_OR_GREATER
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using System.Text.Json; |
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using System.Text.Json.Serialization; |
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#endif
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using NUnit.Framework; |
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using MathNet.Numerics.Statistics; |
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namespace MathNet.Numerics.UnitTests.StatisticsTests |
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{ |
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/// <summary>
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/// Descriptive statistics tests.
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/// </summary>
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/// <remarks>NOTE: this class is not included into Silverlight version, because it uses data from local files.
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/// In Silverlight access to local files is forbidden, except several cases.</remarks>
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[TestFixture, Category("Statistics")] |
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public class WeightedDescriptiveStatisticsTests |
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{ |
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/// <summary>
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/// Statistics data.
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/// </summary>
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readonly IDictionary<string, StatTestData> _data = new Dictionary<string, StatTestData>(); |
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/// <summary>
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/// Initializes a new instance of the DescriptiveStatisticsTests class.
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/// </summary>
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public WeightedDescriptiveStatisticsTests() |
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{ |
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_data.Add("lottery", new StatTestData("NIST.Lottery.dat")); |
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_data.Add("lew", new StatTestData("NIST.Lew.dat")); |
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_data.Add("mavro", new StatTestData("NIST.Mavro.dat")); |
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_data.Add("michelso", new StatTestData("NIST.Michelso.dat")); |
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_data.Add("numacc1", new StatTestData("NIST.NumAcc1.dat")); |
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_data.Add("numacc2", new StatTestData("NIST.NumAcc2.dat")); |
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_data.Add("numacc3", new StatTestData("NIST.NumAcc3.dat")); |
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_data.Add("numacc4", new StatTestData("NIST.NumAcc4.dat")); |
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_data.Add("meixner", new StatTestData("NIST.Meixner.dat")); |
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} |
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/// <summary>
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/// Constructor with <c>null</c> throws <c>ArgumentNullException</c>.
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/// </summary>
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[Test] |
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public void ConstructorThrowArgumentNullException() |
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{ |
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const IEnumerable<Tuple<double, double>> WeightedData = null; |
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const IEnumerable<(double, double)> WeightedData2 = null; |
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Assert.That(() => new WeightedDescriptiveStatistics(WeightedData), Throws.TypeOf<ArgumentNullException>()); |
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Assert.That(() => new WeightedDescriptiveStatistics(WeightedData, true), Throws.TypeOf<ArgumentNullException>()); |
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Assert.That(() => new WeightedDescriptiveStatistics(WeightedData2), Throws.TypeOf<ArgumentNullException>()); |
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Assert.That(() => new WeightedDescriptiveStatistics(WeightedData2, true), Throws.TypeOf<ArgumentNullException>()); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> Double.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="digits">Digits count.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", 12, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", 12, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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[TestCase("meixner", 8, -0.016649617280859657, 0.8171318629552635, -0.002042931016531602, -4.825626912281697, 5.3018298664184913, 10000)] |
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public void IEnumerableTuple(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.Data.Select(x => Tuple.Create(1.0, x))); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 10); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 8); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 8); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> Double.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="digits">Digits count.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", 12, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", 12, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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[TestCase("meixner", 8, -0.016649617280859657, 0.8171318629552635, -0.002042931016531602, -4.825626912281697, 5.3018298664184913, 10000)] |
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public void IEnumerableValueTuple(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.Data.Select(x => (1.0, x))); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 10); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 8); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 8); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> Double high accuracy.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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public void IEnumerableTupleHighAccuracy(string dataSet, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.Data.Select(x => Tuple.Create(1.0, x)), true); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, 14); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 9); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 9); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> Double high accuracy.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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public void IEnumerableValueTupleHighAccuracy(string dataSet, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.Data.Select(x => (1.0, x)), true); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, 14); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 9); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 9); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> double low accuracy.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="digits">Digits count.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", 14, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", 14, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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public void IEnumerableTupleLowAccuracy(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.Data.Select(x => Tuple.Create(1.0, x)), false); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 7); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 7); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> <c>Nullable</c> double.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="digits">Digits count.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", 14, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
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[TestCase("lew", 14, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
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[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
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[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
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[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
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[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
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[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
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[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
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public void IEnumerableZeroWeightTuple(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
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{ |
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var data = _data[dataSet]; |
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var stats = new WeightedDescriptiveStatistics(data.DataWithNulls.Select(x => x.HasValue ? Tuple.Create(1.0, x.Value) : Tuple.Create(0.0, 3.14159))); |
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AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
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AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
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AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 7); |
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AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 7); |
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Assert.AreEqual(stats.Minimum, min); |
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Assert.AreEqual(stats.Maximum, max); |
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Assert.AreEqual(stats.Count, count); |
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Assert.AreEqual(stats.TotalWeight, count); |
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} |
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/// <summary>
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/// <c>IEnumerable</c> <c>Nullable</c> double high accuracy.
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/// </summary>
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/// <param name="dataSet">Dataset name.</param>
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/// <param name="skewness">Skewness value.</param>
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/// <param name="kurtosis">Kurtosis value.</param>
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/// <param name="median">Median value.</param>
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/// <param name="min">Min value.</param>
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/// <param name="max">Max value.</param>
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/// <param name="count">Count value.</param>
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[TestCase("lottery", -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
||||
|
[TestCase("lew", -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
||||
|
[TestCase("mavro", 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
||||
|
[TestCase("michelso", -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
||||
|
[TestCase("numacc1", 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
||||
|
[TestCase("numacc2", 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
||||
|
[TestCase("numacc3", 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
||||
|
[TestCase("numacc4", 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
||||
|
public void IEnumerableZeroWeightTupleHighAccuracy(string dataSet, double skewness, double kurtosis, double median, double min, double max, int count) |
||||
|
{ |
||||
|
var data = _data[dataSet]; |
||||
|
var stats = new WeightedDescriptiveStatistics(data.DataWithNulls.Select(x => x.HasValue ? Tuple.Create(1.0, x.Value) : Tuple.Create(0.0, 3.14159)), true); |
||||
|
AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
||||
|
AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, 14); |
||||
|
AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 9); |
||||
|
AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 9); |
||||
|
Assert.AreEqual(stats.Minimum, min); |
||||
|
Assert.AreEqual(stats.Maximum, max); |
||||
|
Assert.AreEqual(stats.Count, count); |
||||
|
Assert.AreEqual(stats.TotalWeight, count); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// <c>IEnumerable</c> <c>Nullable</c> Double Low Accuracy.
|
||||
|
/// </summary>
|
||||
|
/// <param name="dataSet">Dataset name.</param>
|
||||
|
/// <param name="digits">Digits count.</param>
|
||||
|
/// <param name="skewness">Skewness value.</param>
|
||||
|
/// <param name="kurtosis">Kurtosis value.</param>
|
||||
|
/// <param name="median">Median value.</param>
|
||||
|
/// <param name="min">Min value.</param>
|
||||
|
/// <param name="max">Max value.</param>
|
||||
|
/// <param name="count">Count value.</param>
|
||||
|
[TestCase("lottery", 14, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
||||
|
[TestCase("lew", 14, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
||||
|
[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
||||
|
[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
||||
|
[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
||||
|
[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
||||
|
[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
||||
|
[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
||||
|
public void IEnumerableZeroWeightTupleLowAccuracy(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
||||
|
{ |
||||
|
var data = _data[dataSet]; |
||||
|
var stats = new WeightedDescriptiveStatistics(data.DataWithNulls.Select(x => x.HasValue ? Tuple.Create(1.0, x.Value) : Tuple.Create(0.0, 3.14159)), false); |
||||
|
AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14); |
||||
|
AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
||||
|
AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 7); |
||||
|
AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 7); |
||||
|
Assert.AreEqual(stats.Minimum, min); |
||||
|
Assert.AreEqual(stats.Maximum, max); |
||||
|
Assert.AreEqual(stats.Count, count); |
||||
|
Assert.AreEqual(stats.TotalWeight, count); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void ShortSequences() |
||||
|
{ |
||||
|
var stats5 = new WeightedDescriptiveStatistics(new Tuple<double, double>[0]); |
||||
|
Assert.That(stats5.Skewness, Is.NaN); |
||||
|
Assert.That(stats5.Kurtosis, Is.NaN); |
||||
|
|
||||
|
var stats6 = new WeightedDescriptiveStatistics(new[] { Tuple.Create(1.0, 1.0) }); |
||||
|
Assert.That(stats6.Skewness, Is.NaN); |
||||
|
Assert.That(stats6.Kurtosis, Is.NaN); |
||||
|
|
||||
|
var stats7 = new WeightedDescriptiveStatistics(new[] { Tuple.Create(1.0, 1.0), Tuple.Create(1.0, 2.0) }); |
||||
|
Assert.That(stats7.Skewness, Is.NaN); |
||||
|
Assert.That(stats7.Kurtosis, Is.NaN); |
||||
|
|
||||
|
var stats8 = new WeightedDescriptiveStatistics(new[] { Tuple.Create(1.0, 1.0), Tuple.Create(1.0, 2.0), Tuple.Create(1.0, -3.0) }); |
||||
|
Assert.That(stats8.Skewness, Is.Not.NaN); |
||||
|
Assert.That(stats8.Kurtosis, Is.NaN); |
||||
|
|
||||
|
var stats9 = new WeightedDescriptiveStatistics(new[] { Tuple.Create(1.0, 1.0), Tuple.Create(1.0, 2.0), Tuple.Create(1.0, -3.0), Tuple.Create(1.0, -4.0) }); |
||||
|
Assert.That(stats9.Skewness, Is.Not.NaN); |
||||
|
Assert.That(stats9.Kurtosis, Is.Not.NaN); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void NegativeWeightsThrow() |
||||
|
{ |
||||
|
Assert.That(() => new WeightedDescriptiveStatistics(new[] { Tuple.Create(-1.0, 1.0) }), Throws.TypeOf<ArgumentOutOfRangeException>()); |
||||
|
} |
||||
|
|
||||
|
[Test] |
||||
|
public void ZeroVarianceSequence() |
||||
|
{ |
||||
|
var stats2 = new WeightedDescriptiveStatistics(new[] { Tuple.Create(1.0, 2.0), Tuple.Create(1.0, 2.0), Tuple.Create(1.0, 2.0), Tuple.Create(1.0, 2.0) }); |
||||
|
Assert.That(stats2.Skewness, Is.NaN); |
||||
|
Assert.That(stats2.Kurtosis, Is.NaN); |
||||
|
} |
||||
|
|
||||
|
#if NET5_0_OR_GREATER
|
||||
|
/// <summary>
|
||||
|
/// <c>IEnumerable</c> Double.
|
||||
|
/// </summary>
|
||||
|
/// <param name="dataSet">Dataset name.</param>
|
||||
|
/// <param name="digits">Digits count.</param>
|
||||
|
/// <param name="skewness">Skewness value.</param>
|
||||
|
/// <param name="kurtosis">Kurtosis value.</param>
|
||||
|
/// <param name="median">Median value.</param>
|
||||
|
/// <param name="min">Min value.</param>
|
||||
|
/// <param name="max">Max value.</param>
|
||||
|
/// <param name="count">Count value.</param>
|
||||
|
[TestCase("lottery", 12, -0.09333165310779, -1.19256091074856, 522.5, 4, 999, 218)] |
||||
|
[TestCase("lew", 12, -0.050606638756334, -1.49604979214447, -162, -579, 300, 200)] |
||||
|
[TestCase("mavro", 11, 0.64492948110824, -0.82052379677456, 2.0018, 2.0013, 2.0027, 50)] |
||||
|
[TestCase("michelso", 11, -0.0185388637725746, 0.33968459842539, 299.85, 299.62, 300.07, 100)] |
||||
|
[TestCase("numacc1", 15, 0, double.NaN, 10000002, 10000001, 10000003, 3)] |
||||
|
[TestCase("numacc2", 13, 0, -2.003003003003, 1.2, 1.1, 1.3, 1001)] |
||||
|
[TestCase("numacc3", 9, 0, -2.003003003003, 1000000.2, 1000000.1, 1000000.3, 1001)] |
||||
|
[TestCase("numacc4", 7, 0, -2.00300300299913, 10000000.2, 10000000.1, 10000000.3, 1001)] |
||||
|
[TestCase("meixner", 8, -0.016649617280859657, 0.8171318629552635, -0.002042931016531602, -4.825626912281697, 5.3018298664184913, 10000)] |
||||
|
public void JsonDeserializationTest(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count) |
||||
|
{ |
||||
|
var data = _data[dataSet]; |
||||
|
var serialize = new WeightedDescriptiveStatistics(data.Data.Select(x => (1.0, x)), false); |
||||
|
var jsonSerializerOptions = new JsonSerializerOptions |
||||
|
{ |
||||
|
NumberHandling = JsonNumberHandling.AllowNamedFloatingPointLiterals, |
||||
|
}; |
||||
|
var json = JsonSerializer.Serialize(serialize, jsonSerializerOptions); |
||||
|
var stats = JsonSerializer.Deserialize<DescriptiveStatistics>(json, jsonSerializerOptions); |
||||
|
Assert.NotNull(stats); |
||||
|
AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 10); |
||||
|
AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits); |
||||
|
AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 8); |
||||
|
AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 8); |
||||
|
Assert.AreEqual(stats.Minimum, min); |
||||
|
Assert.AreEqual(stats.Maximum, max); |
||||
|
Assert.AreEqual(stats.Count, count); |
||||
|
Assert.AreEqual(stats.TotalWeight, count); |
||||
|
} |
||||
|
#endif
|
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,411 @@ |
|||||
|
// <copyright file="DescriptiveStatistics.cs" company="Math.NET">
|
||||
|
// Math.NET Numerics, part of the Math.NET Project
|
||||
|
// http://numerics.mathdotnet.com
|
||||
|
// http://github.com/mathnet/mathnet-numerics
|
||||
|
//
|
||||
|
// Copyright (c) 2009-2015 Math.NET
|
||||
|
//
|
||||
|
// Permission is hereby granted, free of charge, to any person
|
||||
|
// obtaining a copy of this software and associated documentation
|
||||
|
// files (the "Software"), to deal in the Software without
|
||||
|
// restriction, including without limitation the rights to use,
|
||||
|
// copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
|
// copies of the Software, and to permit persons to whom the
|
||||
|
// Software is furnished to do so, subject to the following
|
||||
|
// conditions:
|
||||
|
//
|
||||
|
// The above copyright notice and this permission notice shall be
|
||||
|
// included in all copies or substantial portions of the Software.
|
||||
|
//
|
||||
|
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
|
// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
||||
|
// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
|
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
||||
|
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
|
||||
|
// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
||||
|
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
||||
|
// OTHER DEALINGS IN THE SOFTWARE.
|
||||
|
// </copyright>
|
||||
|
|
||||
|
using System; |
||||
|
using System.Collections.Generic; |
||||
|
using System.Runtime.Serialization; |
||||
|
using System.Linq; |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
using System.Text.Json.Serialization; |
||||
|
#endif
|
||||
|
|
||||
|
namespace MathNet.Numerics.Statistics |
||||
|
{ |
||||
|
/// <summary>
|
||||
|
/// Computes the basic statistics of data set. The class meets the
|
||||
|
/// NIST standard of accuracy for mean, variance, and standard deviation
|
||||
|
/// (the only statistics they provide exact values for) and exceeds them
|
||||
|
/// in increased accuracy mode.
|
||||
|
/// Recommendation: consider to use RunningWeightedStatistics instead.
|
||||
|
/// </summary>
|
||||
|
/// <remarks>
|
||||
|
/// This type declares a DataContract for out of the box ephemeral serialization
|
||||
|
/// with engines like DataContractSerializer, Protocol Buffers and FsPickler,
|
||||
|
/// but does not guarantee any compatibility between versions.
|
||||
|
/// It is not recommended to rely on this mechanism for durable persistence.
|
||||
|
/// </remarks>
|
||||
|
[DataContract(Namespace = "urn:MathNet/Numerics")] |
||||
|
public class WeightedDescriptiveStatistics |
||||
|
{ |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the <see cref="WeightedDescriptiveStatistics"/> class.
|
||||
|
/// </summary>
|
||||
|
/// <param name="data">The sample data.</param>
|
||||
|
/// <param name="increasedAccuracy">
|
||||
|
/// If set to <c>true</c>, increased accuracy mode used.
|
||||
|
/// Increased accuracy mode uses <see cref="decimal"/> types for internal calculations.
|
||||
|
/// </param>
|
||||
|
/// <remarks>
|
||||
|
/// Don't use increased accuracy for data sets containing large values (in absolute value).
|
||||
|
/// This may cause the calculations to overflow.
|
||||
|
/// </remarks>
|
||||
|
public WeightedDescriptiveStatistics(IEnumerable<Tuple<double, double>> data, bool increasedAccuracy = false) |
||||
|
{ |
||||
|
if (data == null) |
||||
|
{ |
||||
|
throw new ArgumentNullException(nameof(data)); |
||||
|
} |
||||
|
|
||||
|
var data2 = data.Select(x => (x.Item1, x.Item2)); |
||||
|
if (increasedAccuracy) |
||||
|
ComputeDecimal(data2); |
||||
|
else |
||||
|
Compute(data2); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the <see cref="WeightedDescriptiveStatistics"/> class.
|
||||
|
/// </summary>
|
||||
|
/// <param name="data">The sample data.</param>
|
||||
|
/// <param name="increasedAccuracy">
|
||||
|
/// If set to <c>true</c>, increased accuracy mode used.
|
||||
|
/// Increased accuracy mode uses <see cref="decimal"/> types for internal calculations.
|
||||
|
/// </param>
|
||||
|
/// <remarks>
|
||||
|
/// Don't use increased accuracy for data sets containing large values (in absolute value).
|
||||
|
/// This may cause the calculations to overflow.
|
||||
|
/// </remarks>
|
||||
|
public WeightedDescriptiveStatistics(IEnumerable<(double, double)> data, bool increasedAccuracy = false) |
||||
|
{ |
||||
|
if (data == null) |
||||
|
{ |
||||
|
throw new ArgumentNullException(nameof(data)); |
||||
|
} |
||||
|
|
||||
|
if (increasedAccuracy) |
||||
|
ComputeDecimal(data); |
||||
|
else |
||||
|
Compute(data); |
||||
|
} |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
/// <summary>
|
||||
|
/// Initializes a new instance of the <see cref="DescriptiveStatistics"/> class.
|
||||
|
/// </summary>
|
||||
|
/// <remarks>
|
||||
|
/// Used for Json serialization
|
||||
|
/// </remarks>
|
||||
|
public DescriptiveStatistics() |
||||
|
{ |
||||
|
} |
||||
|
#endif
|
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the size of the sample.
|
||||
|
/// </summary>
|
||||
|
/// <value>The size of the sample.</value>
|
||||
|
[DataMember(Order = 1)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public long Count { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the sample mean.
|
||||
|
/// </summary>
|
||||
|
/// <value>The sample mean.</value>
|
||||
|
[DataMember(Order = 2)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Mean { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer).
|
||||
|
/// </summary>
|
||||
|
/// <value>The sample variance.</value>
|
||||
|
[DataMember(Order = 3)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Variance { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the unbiased population standard deviation (on a dataset of size N will use an N-1 normalizer).
|
||||
|
/// </summary>
|
||||
|
/// <value>The sample standard deviation.</value>
|
||||
|
[DataMember(Order = 4)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double StandardDeviation { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the unbiased estimator of the population skewness.
|
||||
|
/// </summary>
|
||||
|
/// <value>The sample skewness.</value>
|
||||
|
/// <remarks>Returns zero if <see cref="Count"/> is less than three. </remarks>
|
||||
|
[DataMember(Order = 5)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Skewness { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the unbiased estimator of the population excess kurtosis using the G_2 estimator.
|
||||
|
/// </summary>
|
||||
|
/// <value>The sample kurtosis.</value>
|
||||
|
/// <remarks>Returns zero if <see cref="Count"/> is less than four. </remarks>
|
||||
|
[DataMember(Order = 6)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Kurtosis { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the maximum sample value.
|
||||
|
/// </summary>
|
||||
|
/// <value>The maximum sample value.</value>
|
||||
|
[DataMember(Order = 7)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Maximum { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the minimum sample value.
|
||||
|
/// </summary>
|
||||
|
/// <value>The minimum sample value.</value>
|
||||
|
[DataMember(Order = 8)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double Minimum { get; private set; } |
||||
|
|
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Gets the total weight. When used with unweighted data, returns the number of samples.
|
||||
|
/// </summary>
|
||||
|
/// <value>The total weight.</value>
|
||||
|
[DataMember(Order = 9)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double TotalWeight { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// The Kish's effective sample size https://en.wikipedia.org/wiki/Effective_sample_size
|
||||
|
/// </summary>
|
||||
|
/// <value>The Kish's effective sample size.</value>
|
||||
|
[DataMember(Order = 10)] |
||||
|
#if NET5_0_OR_GREATER
|
||||
|
[JsonInclude] |
||||
|
#endif
|
||||
|
public double EffectiveSampleSize { get; private set; } |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes descriptive statistics from a stream of data values and reliability weights.
|
||||
|
/// </summary>
|
||||
|
/// <param name="data">A sequence of datapoints.</param>
|
||||
|
void Compute(IEnumerable<(double, double)> data) |
||||
|
{ |
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|
double mean = 0; |
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|
double variance = 0; |
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|
double skewness = 0; |
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|
double kurtosis = 0; |
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|
double minimum = double.PositiveInfinity; |
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|
double maximum = double.NegativeInfinity; |
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|
long n = 0; |
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|
double totalWeight = 0; |
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|
double den = 0; |
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|
|
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|
double V2 = 0; |
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|
double V3 = 0; |
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|
double V4 = 0; |
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|
|
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|
foreach (var (w, xi) in data) |
||||
|
{ |
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|
if (w < 0) |
||||
|
{ |
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|
throw new ArgumentOutOfRangeException(nameof(data), w, "Expected non-negative weighting of sample"); |
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|
} |
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|
else if (w > 0) |
||||
|
{ |
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|
++n; |
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|
double delta = xi - mean; |
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|
double prevWeight = totalWeight; |
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|
totalWeight += w; |
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|
V2 += w * w; |
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|
V3 += w * w * w; |
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|
V4 += w * w * w * w; |
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|
|
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|
den += w * (2.0 * prevWeight - den) / totalWeight; |
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|
|
||||
|
double scaleDelta = delta * w / totalWeight; |
||||
|
double scaleDeltaSqr = scaleDelta * scaleDelta; |
||||
|
double tmpDelta = delta * scaleDelta * prevWeight; |
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|
double r = prevWeight / w; |
||||
|
|
||||
|
mean += scaleDelta; |
||||
|
|
||||
|
kurtosis += tmpDelta * scaleDeltaSqr * (r * r - r + 1.0) |
||||
|
+ 6.0 * scaleDeltaSqr * variance - 4.0 * scaleDelta * skewness; |
||||
|
|
||||
|
skewness += tmpDelta * scaleDelta * (r - 1.0) - 3.0 * scaleDelta * variance; |
||||
|
variance += tmpDelta; |
||||
|
|
||||
|
if (minimum > xi) |
||||
|
{ |
||||
|
minimum = xi; |
||||
|
} |
||||
|
|
||||
|
if (maximum < xi) |
||||
|
{ |
||||
|
maximum = xi; |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
SetStatisticsWeighted(mean, variance, skewness, kurtosis, minimum, maximum, n, totalWeight, den, V2, V3, V4); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Computes descriptive statistics from a stream of data values and reliability weights.
|
||||
|
/// </summary>
|
||||
|
/// <param name="data">A sequence of datapoints.</param>
|
||||
|
void ComputeDecimal(IEnumerable<(double, double)> data) |
||||
|
{ |
||||
|
decimal mean = 0; |
||||
|
decimal variance = 0; |
||||
|
decimal skewness = 0; |
||||
|
decimal kurtosis = 0; |
||||
|
decimal minimum = decimal.MaxValue; |
||||
|
decimal maximum = decimal.MinValue; |
||||
|
decimal totalWeight = 0; |
||||
|
long n = 0; |
||||
|
decimal den = 0; |
||||
|
|
||||
|
decimal V2 = 0; |
||||
|
decimal V3 = 0; |
||||
|
decimal V4 = 0; |
||||
|
|
||||
|
foreach (var (w, x) in data) |
||||
|
{ |
||||
|
if (w < 0) |
||||
|
{ |
||||
|
throw new ArgumentOutOfRangeException(nameof(data), w, "Expected non-negative weighting of sample"); |
||||
|
} |
||||
|
else if (w > 0) |
||||
|
{ |
||||
|
|
||||
|
decimal xi = (decimal)x; |
||||
|
decimal decW = (decimal)w; |
||||
|
++n; |
||||
|
decimal delta = xi - mean; |
||||
|
decimal prevWeight = totalWeight; |
||||
|
totalWeight += decW; |
||||
|
V2 += decW * decW; |
||||
|
V3 += decW * decW * decW; |
||||
|
V4 += decW * decW * decW * decW; |
||||
|
den += decW * (2.0m * prevWeight - den) / totalWeight; |
||||
|
decimal scaleDelta = delta * decW / totalWeight; |
||||
|
decimal scaleDeltaSqr = scaleDelta * scaleDelta; |
||||
|
decimal tmpDelta = delta * scaleDelta * prevWeight; |
||||
|
decimal r = prevWeight / decW; |
||||
|
|
||||
|
mean += scaleDelta; |
||||
|
|
||||
|
kurtosis += tmpDelta * scaleDeltaSqr * (r * r - r + 1.0m) |
||||
|
+ 6.0m * scaleDeltaSqr * variance - 4.0m * scaleDelta * skewness; |
||||
|
|
||||
|
skewness += tmpDelta * scaleDelta * (r - 1.0m) - 3.0m * scaleDelta * variance; |
||||
|
variance += tmpDelta; |
||||
|
|
||||
|
if (minimum > xi) |
||||
|
{ |
||||
|
minimum = xi; |
||||
|
} |
||||
|
|
||||
|
if (maximum < xi) |
||||
|
{ |
||||
|
maximum = xi; |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
SetStatisticsWeighted((double)mean, (double)variance, (double)skewness, (double)kurtosis, (double)minimum, (double)maximum, n, (double)totalWeight, (double)den, (double)V2, (double)V3, (double)V4); |
||||
|
} |
||||
|
|
||||
|
/// <summary>
|
||||
|
/// Internal use. Method use for setting the statistics.
|
||||
|
/// </summary>
|
||||
|
/// <param name="mean">For setting Mean.</param>
|
||||
|
/// <param name="variance">For setting Variance.</param>
|
||||
|
/// <param name="skewness">For setting Skewness.</param>
|
||||
|
/// <param name="kurtosis">For setting Kurtosis.</param>
|
||||
|
/// <param name="minimum">For setting Minimum.</param>
|
||||
|
/// <param name="maximum">For setting Maximum.</param>
|
||||
|
/// <param name="n">For setting Count.</param>
|
||||
|
void SetStatisticsWeighted(double mean, double variance, double skewness, double kurtosis, double minimum, double maximum, long n, double w1, double den, double w2, double w3, double w4) |
||||
|
{ |
||||
|
Mean = mean; |
||||
|
Count = n; |
||||
|
TotalWeight = w1; |
||||
|
EffectiveSampleSize = w1 * w1 / w2; |
||||
|
Minimum = double.NaN; |
||||
|
Maximum = double.NaN; |
||||
|
Variance = double.NaN; |
||||
|
StandardDeviation = double.NaN; |
||||
|
Skewness = double.NaN; |
||||
|
Kurtosis = double.NaN; |
||||
|
|
||||
|
if (n > 0) |
||||
|
{ |
||||
|
Minimum = minimum; |
||||
|
Maximum = maximum; |
||||
|
|
||||
|
if (n > 1) |
||||
|
{ |
||||
|
Variance = variance / den; |
||||
|
StandardDeviation = Math.Sqrt(Variance); |
||||
|
} |
||||
|
|
||||
|
if (Variance != 0) |
||||
|
{ |
||||
|
if (n > 2) |
||||
|
{ |
||||
|
var skewDen = (w1 * (w1 * w1 - 3.0 * w2) + 2.0 * w3) / (w1 * w1); |
||||
|
Skewness = skewness / (skewDen * Variance * StandardDeviation); |
||||
|
} |
||||
|
|
||||
|
if (n > 3) |
||||
|
{ |
||||
|
double p2 = w1 * w1; |
||||
|
double p4 = p2 * p2; |
||||
|
double w2p2 = w2 * w2; |
||||
|
double poly = p4 - 6.0 * p2 * w2 + 8.0 * w1 * w3 + 3.0 * w2p2 - 6.0 * w4; |
||||
|
double a = p4 - 4.0 * w1 * w3 + 3.0 * w2p2; |
||||
|
double b = 3.0 * (p4 - 2.0 * p2 * w2 + 4.0 * w1 * w3 - 3.0 * w2p2); |
||||
|
Kurtosis = (a * w1 * kurtosis / (variance * variance) - b) * (den / (w1 * poly)); |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
} |
||||
Loading…
Reference in new issue