[commons-math] 03/05: MATH-1431: Avoid NaN in case of empty bin.

[erans]

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commit 9d4d838638c7ee0c6f89c8061e89ed08171ee304
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Mon May 31 03:37:22 2021 +0200

    MATH-1431: Avoid NaN in case of empty bin.
    
    Thanks to Artem Onuchin.
    
    Closes #79.
---
 .../legacy/distribution/EmpiricalDistribution.java |  3 ++-
 .../distribution/EmpiricalDistributionTest.java    | 23 +++++++++++++++++++++-
 src/changes/changes.xml                            |  3 +++
 3 files changed, 27 insertions(+), 2 deletions(-)

diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
index 041f21f..4a697bb 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
@@ -743,7 +743,8 @@ public class EmpiricalDistribution extends 
AbstractRealDistribution
      * @return within-bin kernel parameterized by bStats
      */
     protected ContinuousDistribution getKernel(SummaryStatistics bStats) {
-        if (bStats.getN() == 1 || bStats.getVariance() == 0) {
+        if (bStats.getN() <= 1 ||
+            bStats.getVariance() == 0) {
             return new ConstantContinuousDistribution(bStats.getMean());
         } else {
             return new NormalDistribution(bStats.getMean(), 
bStats.getStandardDeviation());
diff --git 
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
 
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
index dfdfdd9..5e9e650 100644
--- 
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
+++ 
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
@@ -24,10 +24,13 @@ import java.net.URL;
 import java.util.ArrayList;
 import java.util.Arrays;
 
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.simple.RandomSource;
 import org.apache.commons.statistics.distribution.ContinuousDistribution;
 import 
org.apache.commons.statistics.distribution.ConstantContinuousDistribution;
 import 
org.apache.commons.statistics.distribution.UniformContinuousDistribution;
 import org.apache.commons.statistics.distribution.NormalDistribution;
+import org.apache.commons.statistics.distribution.ExponentialDistribution;
 import org.apache.commons.math4.legacy.TestUtils;
 import org.apache.commons.math4.legacy.analysis.UnivariateFunction;
 import 
org.apache.commons.math4.legacy.analysis.integration.BaseAbstractUnivariateIntegrator;
@@ -35,7 +38,6 @@ import 
org.apache.commons.math4.legacy.analysis.integration.IterativeLegendreGau
 import org.apache.commons.math4.legacy.exception.MathIllegalStateException;
 import org.apache.commons.math4.legacy.exception.NullArgumentException;
 import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException;
-import org.apache.commons.rng.simple.RandomSource;
 import org.apache.commons.math4.legacy.stat.descriptive.SummaryStatistics;
 import org.apache.commons.math4.legacy.util.FastMath;
 import org.junit.Assert;
@@ -657,6 +659,25 @@ public final class EmpiricalDistributionTest extends 
RealDistributionAbstractTes
         Assert.assertEquals(9.0, dist.inverseCumulativeProbability(0.6), tol);
     }
 
+    @Test
+    public void testMath1431() {
+        final UniformRandomProvider rng = 
RandomSource.create(RandomSource.WELL_19937_C, 1000);
+        final ContinuousDistribution.Sampler exponentialDistributionSampler
+            = new ExponentialDistribution(0.05).createSampler(rng);
+        final double[] empiricalDataPoints = new double[3000];
+        for (int i = 0; i < empiricalDataPoints.length; i++) {
+            empiricalDataPoints[i] = exponentialDistributionSampler.sample();
+        }
+
+        final EmpiricalDistribution testDistribution = new 
EmpiricalDistribution(100);
+        testDistribution.load(empiricalDataPoints);
+
+        for (int i = 0; i < 1000; i++) {
+            final double point = rng.nextDouble();
+            final double cdf = testDistribution.cumulativeProbability(point);
+            Assert.assertFalse("point: " + point, Double.isNaN(cdf));
+        }
+   }
 
     /**
      * Empirical distribution using a constant smoothing kernel.
diff --git a/src/changes/changes.xml b/src/changes/changes.xml
index c8912d5..68f4c05 100644
--- a/src/changes/changes.xml
+++ b/src/changes/changes.xml
@@ -86,6 +86,9 @@ Caveat:
  nightmare was one of the main reasons for creating more focused
  components.]
 ">
+      <action dev="erans" type="fix" issue="MATH-1431" due-to="Artem Onuchin">
+        "EmpiricalDistribution" handles empty bin.
+      </action>
       <action dev="erans" type="update" issue="MATH-1582">
         Transforms codes moved into a dedicated maven module.
       </action>