From 870e1d3d98318745d0552c7a6a6f5dada90d7c82 Mon Sep 17 00:00:00 2001 From: Thomas Neidhart Date: Sun, 26 Apr 2015 21:10:01 +0200 Subject: [PATCH] [MATH-1197] 2-sample KS statistic was wrong in case of ties. --- src/changes/changes.xml | 6 +- .../stat/inference/KolmogorovSmirnovTest.java | 45 ++++++++-- .../inference/KolmogorovSmirnovTestTest.java | 83 ++++++++++++++++--- 3 files changed, 116 insertions(+), 18 deletions(-) diff --git a/src/changes/changes.xml b/src/changes/changes.xml index 609835d4a..02a4406d8 100644 --- a/src/changes/changes.xml +++ b/src/changes/changes.xml @@ -50,7 +50,11 @@ If the output is not quite correct, check for invisible trailing spaces! Commons Math Release Notes - + + + Computation of 2-sample Kolmogoriv-Smirnov statistic in case of ties + was not correct. + Kolmogorov-Smirnov test @@ -429,7 +455,7 @@ public class KolmogorovSmirnovTest { return 1; } if (exact) { - return exactK(d,n); + return exactK(d, n); } if (n <= 140) { return roundedK(d, n); @@ -834,8 +860,13 @@ public class KolmogorovSmirnovTest { * @throws TooManyIterationsException if the series does not converge */ public double ksSum(double t, double tolerance, int maxIterations) { + if (t == 0.0) { + return 1.0; + } + // TODO: for small t (say less than 1), the alternative expansion in part 3 of [1] // from class javadoc should be used. + final double x = -2 * t * t; int sign = -1; long i = 1; diff --git a/src/test/java/org/apache/commons/math3/stat/inference/KolmogorovSmirnovTestTest.java b/src/test/java/org/apache/commons/math3/stat/inference/KolmogorovSmirnovTestTest.java index 9ac5c7cf5..56e4c5853 100644 --- a/src/test/java/org/apache/commons/math3/stat/inference/KolmogorovSmirnovTestTest.java +++ b/src/test/java/org/apache/commons/math3/stat/inference/KolmogorovSmirnovTestTest.java @@ -26,7 +26,7 @@ import org.junit.Test; /** * Test cases for {@link KolmogorovSmirnovTest}. - * + * * @since 3.3 */ public class KolmogorovSmirnovTestTest { @@ -220,7 +220,7 @@ public class KolmogorovSmirnovTestTest { } } } - + @Test public void testPelzGoodApproximation() { KolmogorovSmirnovTest ksTest = new KolmogorovSmirnovTest(); @@ -236,7 +236,7 @@ public class KolmogorovSmirnovTestTest { 0.9999999999999877, 0.9999999999999191, 0.9999999999999254, 0.9999999999998178, 0.9999999999917788, 0.9999999999998556, 0.9999999999992014, 0.9999999999988859, 0.9999999999999325, 0.9999999999821726 }; - + final double tol = 10e-15; int k = 0; for (int i = 0; i < 6; i++) { @@ -254,8 +254,8 @@ public class KolmogorovSmirnovTestTest { Assert.assertEquals(0.0319983962391632, test.kolmogorovSmirnovTest(gaussian, gaussian2), TOLERANCE); Assert.assertEquals(0.202352941176471, test.kolmogorovSmirnovStatistic(gaussian, gaussian2), TOLERANCE); } - - /** + + /** * MATH-1181 * Verify that large sample method is selected for sample product > Integer.MAX_VALUE * (integer overflow in sample product) @@ -269,7 +269,7 @@ public class KolmogorovSmirnovTestTest { final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest(); Assert.assertFalse(Double.isNaN(test.kolmogorovSmirnovTest(x, y))); } - + /** * Verifies that Monte Carlo simulation gives results close to exact p values. This test is a @@ -302,15 +302,78 @@ public class KolmogorovSmirnovTestTest { } } + @Test + public void testTwoSampleWithManyTies() { + // MATH-1197 + final double[] x = { + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, + 3.181199, 3.181199, 3.181199, 3.181199, 3.181199, 3.181199, + 3.723539, 3.723539, 3.723539, 3.723539, 4.383482, 4.383482, + 4.383482, 4.383482, 5.320671, 5.320671, 5.320671, 5.717284, + 6.964001, 7.352165, 8.710510, 8.710510, 8.710510, 8.710510, + 8.710510, 8.710510, 9.539004, 9.539004, 10.720619, 17.726077, + 17.726077, 17.726077, 17.726077, 22.053875, 23.799144, 27.355308, + 30.584960, 30.584960, 30.584960, 30.584960, 30.751808 + }; + + final double[] y = { + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 0.000000, 0.000000, 2.202653, 2.202653, 2.202653, + 2.202653, 2.202653, 2.202653, 2.202653, 2.202653, 3.061758, + 3.723539, 5.628420, 5.628420, 5.628420, 5.628420, 5.628420, + 6.916982, 6.916982, 6.916982, 10.178538, 10.178538, 10.178538, + 10.178538, 10.178538 + }; + + final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest(); + + Assert.assertEquals(0.0640394088, test.kolmogorovSmirnovStatistic(x, y), 1e-6); + Assert.assertEquals(0.9792777290, test.kolmogorovSmirnovTest(x, y), 1e-6); + + } + /** * Verifies the inequality exactP(criticalValue, n, m, true) < alpha < exactP(criticalValue, n, * m, false). - * + * * Note that the validity of this check depends on the fact that alpha lies strictly between two * attained values of the distribution and that criticalValue is one of the attained values. The * critical value table (reference below) uses attained values. This test therefore also * verifies that criticalValue is attained. - * + * * @param n first sample size * @param m second sample size * @param criticalValue critical value @@ -324,7 +387,7 @@ public class KolmogorovSmirnovTestTest { /** * Verifies that approximateP(criticalValue, n, m) is within epsilon of alpha. - * + * * @param n first sample size * @param m second sample size * @param criticalValue critical value (from table) @@ -335,5 +398,5 @@ public class KolmogorovSmirnovTestTest { final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest(); Assert.assertEquals(alpha, test.approximateP(criticalValue, n, m), epsilon); } - + } \ No newline at end of file