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Statistics: more tests to verify consistency between implementations

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Christoph Ruegg 13 years ago
parent
c56cc4986d
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c568c2fe86
2 changed files with 56 additions and 31 deletions
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  1. 1
      src/Numerics/Statistics/DescriptiveStatistics.cs
  2. 86
      src/UnitTests/StatisticsTests/StatisticsTests.cs

1
src/Numerics/Statistics/DescriptiveStatistics.cs
View File

@ -39,6 +39,7 @@ namespace MathNet.Numerics.Statistics
/// (the only statistics they provide exact values for) and exceeds them
/// in increased accuracy mode.
/// </summary>
/// <remarks>Consider to use RunningStatistics instead.</remarks>
public class DescriptiveStatistics
{
/// <summary>

86
src/UnitTests/StatisticsTests/StatisticsTests.cs
View File

@ -49,16 +49,17 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
public class StatisticsTests
{
readonly IDictionary<string, StatTestData> _data = new Dictionary<string, StatTestData>
{
{"lottery", new StatTestData("./data/NIST/Lottery.dat")},
{"lew", new StatTestData("./data/NIST/Lew.dat")},
{"mavro", new StatTestData("./data/NIST/Mavro.dat")},
{"michelso", new StatTestData("./data/NIST/Michelso.dat")},
{"numacc1", new StatTestData("./data/NIST/NumAcc1.dat")},
{"numacc2", new StatTestData("./data/NIST/NumAcc2.dat")},
{"numacc3", new StatTestData("./data/NIST/NumAcc3.dat")},
{"numacc4", new StatTestData("./data/NIST/NumAcc4.dat")}
};
{
{ "lottery", new StatTestData("./data/NIST/Lottery.dat") },
{ "lew", new StatTestData("./data/NIST/Lew.dat") },
{ "mavro", new StatTestData("./data/NIST/Mavro.dat") },
{ "michelso", new StatTestData("./data/NIST/Michelso.dat") },
{ "numacc1", new StatTestData("./data/NIST/NumAcc1.dat") },
{ "numacc2", new StatTestData("./data/NIST/NumAcc2.dat") },
{ "numacc3", new StatTestData("./data/NIST/NumAcc3.dat") },
{ "numacc4", new StatTestData("./data/NIST/NumAcc4.dat") },
{ "meixner", new StatTestData("./data/NIST/Meixner.dat") }
};
[Test]
public void ThrowsOnNullData()
@ -257,7 +258,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
[Test]
public void MinimumMaximumOnShortSequence()
{
var samples = new[] {-1.0, 5, 0, -3, 10, -0.5, 4};
var samples = new[] { -1.0, 5, 0, -3, 10, -0.5, 4 };
Assert.That(Statistics.Minimum(samples), Is.EqualTo(-3), "Min");
Assert.That(Statistics.Maximum(samples), Is.EqualTo(10), "Max");
Assert.That(ArrayStatistics.Minimum(samples), Is.EqualTo(-3), "Min");
@ -331,7 +332,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=1)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{0,0},{1,0}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.EmpiricalInvCDF(samples, tau), 1e-14);
Assert.AreEqual(expected, Statistics.EmpiricalInvCDFFunc(samples)(tau), 1e-14);
@ -360,7 +361,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=2)
// Mathematica: Not Supported
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R2), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R2)(tau), 1e-14);
@ -385,7 +386,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=3)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{1/2,0},{0,0}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R3), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R3)(tau), 1e-14);
@ -412,7 +413,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=4)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{0,0},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R4), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R4)(tau), 1e-14);
@ -439,7 +440,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=5)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{1/2,0},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R5), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R5)(tau), 1e-14);
@ -466,7 +467,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=6)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{0,1},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R6), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R6)(tau), 1e-14);
@ -493,7 +494,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=7)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{1,-1},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R7), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R7)(tau), 1e-14);
@ -519,7 +520,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
{
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=8)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{1/3,1/3},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.Quantile(samples, tau), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R8), 1e-14);
@ -527,7 +528,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
Assert.AreEqual(expected, ArrayStatistics.QuantileInplace(samples, tau), 1e-14);
Assert.AreEqual(expected, ArrayStatistics.QuantileCustomInplace(samples, tau, QuantileDefinition.Median), 1e-14);
Assert.AreEqual(expected, ArrayStatistics.QuantileCustomInplace(samples, tau, 1 / 3d, 1 / 3d, 0d, 1d), 1e-14);
Assert.AreEqual(expected, ArrayStatistics.QuantileCustomInplace(samples, tau, 1/3d, 1/3d, 0d, 1d), 1e-14);
Array.Sort(samples);
Assert.AreEqual(expected, SortedArrayStatistics.Quantile(samples, tau), 1e-14);
@ -548,7 +549,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
{
// R: quantile(c(-1,5,0,-3,10,-0.5,4,0.2,1,6),probs=c(0,1,0.5,0.2,0.7,0.01,0.99,0.52,0.325),type=9)
// Mathematica: Quantile[{-1,5,0,-3,10,-1/2,4,1/5,1,6},{0,1,1/2,1/5,7/10,1/100,99/100,13/25,13/40},{{3/8,1/4},{0,1}}]
var samples = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var samples = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(expected, Statistics.QuantileCustom(samples, tau, QuantileDefinition.R9), 1e-14);
Assert.AreEqual(expected, Statistics.QuantileCustomFunc(samples, QuantileDefinition.R9)(tau), 1e-14);
@ -746,7 +747,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
{
// R: median(c(-1,5,0,-3,10,-0.5,4,0.2,1,6))
// Mathematica: Median[{-1,5,0,-3,10,-1/2,4,1/5,1,6}]
var even = new[] {-1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6};
var even = new[] { -1, 5, 0, -3, 10, -0.5, 4, 0.2, 1, 6 };
Assert.AreEqual(0.6d, Statistics.Median(even), 1e-14);
Assert.AreEqual(0.6d, ArrayStatistics.MedianInplace(even), 1e-14);
Array.Sort(even);
@ -835,16 +836,39 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
[TestCase("mavro")]
[TestCase("michelso")]
[TestCase("numacc1")]
[TestCase("numacc2")]
[TestCase("meixner")]
public void ArrayStatisticsConsistentWithStreamimgStatistics(string dataSet)
{
var data = _data[dataSet];
AssertHelpers.AlmostEqualRelative(ArrayStatistics.Mean(data.Data), StreamingStatistics.Mean(data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.Variance(data.Data), StreamingStatistics.Variance(data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.StandardDeviation(data.Data), StreamingStatistics.StandardDeviation(data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.PopulationVariance(data.Data), StreamingStatistics.PopulationVariance(data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.PopulationStandardDeviation(data.Data), StreamingStatistics.PopulationStandardDeviation(data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.Covariance(data.Data, data.Data), StreamingStatistics.Covariance(data.Data, data.Data), 10);
AssertHelpers.AlmostEqualRelative(ArrayStatistics.PopulationCovariance(data.Data, data.Data), StreamingStatistics.PopulationCovariance(data.Data, data.Data), 10);
Assert.That(ArrayStatistics.Mean(data.Data), Is.EqualTo(StreamingStatistics.Mean(data.Data)).Within(1e-15), "Mean");
Assert.That(ArrayStatistics.Variance(data.Data), Is.EqualTo(StreamingStatistics.Variance(data.Data)).Within(1e-15), "Variance");
Assert.That(ArrayStatistics.StandardDeviation(data.Data), Is.EqualTo(StreamingStatistics.StandardDeviation(data.Data)).Within(1e-15), "StandardDeviation");
Assert.That(ArrayStatistics.PopulationVariance(data.Data), Is.EqualTo(StreamingStatistics.PopulationVariance(data.Data)).Within(1e-15), "PopulationVariance");
Assert.That(ArrayStatistics.PopulationStandardDeviation(data.Data), Is.EqualTo(StreamingStatistics.PopulationStandardDeviation(data.Data)).Within(1e-15), "PopulationStandardDeviation");
Assert.That(ArrayStatistics.Covariance(data.Data, data.Data), Is.EqualTo(StreamingStatistics.Covariance(data.Data, data.Data)).Within(1e-10), "Covariance");
Assert.That(ArrayStatistics.PopulationCovariance(data.Data, data.Data), Is.EqualTo(StreamingStatistics.PopulationCovariance(data.Data, data.Data)).Within(1e-10), "PopulationCovariance");
}
[TestCase("lottery")]
[TestCase("lew")]
[TestCase("mavro")]
[TestCase("michelso")]
[TestCase("numacc1")]
[TestCase("numacc2")]
[TestCase("meixner")]
public void RunningStatisticsConsistentWithDescriptiveStatistics(string dataSet)
{
var data = _data[dataSet];
var running = new RunningStatistics(data.Data);
var descriptive = new DescriptiveStatistics(data.Data);
Assert.That(running.Minimum, Is.EqualTo(descriptive.Minimum), "Minimum");
Assert.That(running.Maximum, Is.EqualTo(descriptive.Maximum), "Maximum");
Assert.That(running.Mean, Is.EqualTo(descriptive.Mean).Within(1e-15), "Mean");
Assert.That(running.Variance, Is.EqualTo(descriptive.Variance).Within(1e-15), "Variance");
Assert.That(running.StandardDeviation, Is.EqualTo(descriptive.StandardDeviation).Within(1e-15), "StandardDeviation");
Assert.That(running.Skewness, Is.EqualTo(descriptive.Skewness).Within(1e-15), "Skewness");
Assert.That(running.Kurtosis, Is.EqualTo(descriptive.Kurtosis).Within(1e-14), "Kurtosis");
}
[Test]
@ -956,11 +980,11 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
{
Assert.That(Statistics.Median(new[] { 2.0, double.NegativeInfinity, double.PositiveInfinity }), Is.EqualTo(2.0));
Assert.That(Statistics.Median(new[] { 2.0, double.NegativeInfinity, 3.0, double.PositiveInfinity }), Is.EqualTo(2.5));
Assert.That(ArrayStatistics.MedianInplace(new[] { 2.0, double.NegativeInfinity, double.PositiveInfinity}), Is.EqualTo(2.0));
Assert.That(ArrayStatistics.MedianInplace(new[] { 2.0, double.NegativeInfinity, double.PositiveInfinity }), Is.EqualTo(2.0));
Assert.That(ArrayStatistics.MedianInplace(new[] { double.NegativeInfinity, 2.0, double.PositiveInfinity }), Is.EqualTo(2.0));
Assert.That(ArrayStatistics.MedianInplace(new[] { double.NegativeInfinity, double.PositiveInfinity, 2.0 }), Is.EqualTo(2.0));
Assert.That(ArrayStatistics.MedianInplace(new[] { double.NegativeInfinity, 2.0, 3.0, double.PositiveInfinity }), Is.EqualTo(2.5));
Assert.That(ArrayStatistics.MedianInplace(new[] { double.NegativeInfinity, 2.0, double.PositiveInfinity, 3.0, }), Is.EqualTo(2.5));
Assert.That(ArrayStatistics.MedianInplace(new[] { double.NegativeInfinity, 2.0, double.PositiveInfinity, 3.0, }), Is.EqualTo(2.5));
Assert.That(SortedArrayStatistics.Median(new[] { double.NegativeInfinity, 2.0, double.PositiveInfinity }), Is.EqualTo(2.0));
Assert.That(SortedArrayStatistics.Median(new[] { double.NegativeInfinity, 2.0, 3.0, double.PositiveInfinity }), Is.EqualTo(2.5));
}

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