Merge b132e42 into 60bcdd4

apache · Feb 2, 2018 · d2d4445 · d2d4445
2 parents 60bcdd4 + b132e42
commit d2d4445
Show file tree

Hide file tree

Showing 2 changed files with 26 additions and 1 deletion.
diff --git a/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java b/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java
@@ -738,7 +738,7 @@ private double cumBinP(int binIndex) {
      * @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/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java
@@ -24,10 +24,12 @@
 import java.util.ArrayList;
 import java.util.Arrays;
 
+import org.apache.commons.rng.RestorableUniformRandomProvider;
 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.TestUtils;
 import org.apache.commons.math4.analysis.UnivariateFunction;
 import org.apache.commons.math4.analysis.integration.BaseAbstractUnivariateIntegrator;
@@ -528,6 +530,29 @@ public void testKernelOverrideUniform() {
         Assert.assertEquals(9.0, dist.inverseCumulativeProbability(0.6), tol);
     }
 
+    @Test
+    public void testVerySkewedCase() {
+
+        final RestorableUniformRandomProvider randomProvider = RandomSource.create(RandomSource.WELL_19937_C, 1000);
+
+        // data we load into EmpiricalDistribution
+        final ContinuousDistribution.Sampler exponentialDistributionSampler = new ExponentialDistribution(0.05)
+                .createSampler(randomProvider);
+        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 = randomProvider.nextDouble();
+            final double cdf = testDistribution.cumulativeProbability(point);
+            Assert.assertFalse("point: " + point, Double.isNaN(cdf));
+        }
+
+    }
 
     /**
      * Empirical distribution using a constant smoothing kernel.