Changed inference classes to pass null RandomGenerators to distribution constructors. JIRA: MATH-1154.

src/changes/changes.xml

@@ -73,6 +73,11 @@ Users are encouraged to upgrade to this version as this release not
   2. A few methods in the FastMath class are in fact slower that their
   counterpart in either Math or StrictMath (cf. MATH-740 and MATH-901).
 ">
+      <action dev="psteitz" type="add" issue="MATH-1154" >
+        Changed classes in the inference package that instantiate distributions to
+        pass null RandomGenerators to avoid initialization overhead for the default
+        generator.
+      </action>
       <action dev="luc" type="add" issue="MATH-1156" >
         Added all Java 8 StrictMath methods to FastMath, so FastMath remains compatible
         with newer Java versions.

src/main/java/org/apache/commons/math3/stat/inference/BinomialTest.java

@@ -119,7 +119,8 @@ public class BinomialTest {
             throw new NullArgumentException();
         }
 
-        final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
         switch (alternativeHypothesis) {
         case GREATER_THAN:
             return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);

src/main/java/org/apache/commons/math3/stat/inference/ChiSquareTest.java

@@ -155,8 +155,9 @@ public class ChiSquareTest {
         throws NotPositiveException, NotStrictlyPositiveException,
         DimensionMismatchException, MaxCountExceededException {
 
-        ChiSquaredDistribution distribution =
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
-            new ChiSquaredDistribution(expected.length - 1.0);
+        final ChiSquaredDistribution distribution =
+            new ChiSquaredDistribution(null, expected.length - 1.0);
         return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
     }
 
@@ -311,8 +312,8 @@ public class ChiSquareTest {
 
         checkArray(counts);
         double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
-        ChiSquaredDistribution distribution;
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
-        distribution = new ChiSquaredDistribution(df);
+        final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df);
         return 1 - distribution.cumulativeProbability(chiSquare(counts));
 
     }
@@ -507,8 +508,9 @@ public class ChiSquareTest {
         throws DimensionMismatchException, NotPositiveException, ZeroException,
         MaxCountExceededException {
 
-        ChiSquaredDistribution distribution;
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
-        distribution = new ChiSquaredDistribution((double) observed1.length - 1);
+        final ChiSquaredDistribution distribution =
+                new ChiSquaredDistribution(null, (double) observed1.length - 1);
         return 1 - distribution.cumulativeProbability(
                 chiSquareDataSetsComparison(observed1, observed2));
 

src/main/java/org/apache/commons/math3/stat/inference/GTest.java

@@ -152,10 +152,10 @@ public class GTest {
             throws NotPositiveException, NotStrictlyPositiveException,
             DimensionMismatchException, MaxCountExceededException {
 
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution(expected.length - 1.0);
+                new ChiSquaredDistribution(null, expected.length - 1.0);
-        return 1.0 - distribution.cumulativeProbability(
+        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
-                g(expected, observed));
     }
 
     /**
@@ -183,10 +183,10 @@ public class GTest {
             throws NotPositiveException, NotStrictlyPositiveException,
             DimensionMismatchException, MaxCountExceededException {
 
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution(expected.length - 2.0);
+                new ChiSquaredDistribution(null, expected.length - 2.0);
-        return 1.0 - distribution.cumulativeProbability(
+        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
-                g(expected, observed));
     }
 
     /**
@@ -472,8 +472,10 @@ public class GTest {
             final long[] observed2)
             throws DimensionMismatchException, NotPositiveException, ZeroException,
             MaxCountExceededException {
-        final ChiSquaredDistribution distribution = new ChiSquaredDistribution(
+
-                (double) observed1.length - 1);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final ChiSquaredDistribution distribution =
+                new ChiSquaredDistribution(null, (double) observed1.length - 1);
         return 1 - distribution.cumulativeProbability(
                 gDataSetsComparison(observed1, observed2));
     }

src/main/java/org/apache/commons/math3/stat/inference/MannWhitneyUTest.java

@@ -181,7 +181,8 @@ public class MannWhitneyUTest {
         final double z = (Umin - EU) / FastMath.sqrt(VarU);
 
         // No try-catch or advertised exception because args are valid
-        final NormalDistribution standardNormal = new NormalDistribution(0, 1);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);
 
         return 2 * standardNormal.cumulativeProbability(z);
     }

src/main/java/org/apache/commons/math3/stat/inference/OneWayAnova.java

@@ -124,9 +124,10 @@ public class OneWayAnova {
         throws NullArgumentException, DimensionMismatchException,
         ConvergenceException, MaxCountExceededException {
 
-        AnovaStats a = anovaStats(categoryData);
+        final AnovaStats a = anovaStats(categoryData);
         // No try-catch or advertised exception because args are valid
-        FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
         return 1.0 - fdist.cumulativeProbability(a.F);
 
     }
@@ -167,7 +168,8 @@ public class OneWayAnova {
                ConvergenceException, MaxCountExceededException {
 
         final AnovaStats a = anovaStats(categoryData, allowOneElementData);
-        final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
         return 1.0 - fdist.cumulativeProbability(a.F);
 
     }

src/main/java/org/apache/commons/math3/stat/inference/TTest.java

@@ -1056,8 +1056,9 @@ public class TTest {
                            final double v, final double n)
         throws MaxCountExceededException, MathIllegalArgumentException {
 
-        double t = FastMath.abs(t(m, mu, v, n));
+        final double t = FastMath.abs(t(m, mu, v, n));
-        TDistribution distribution = new TDistribution(n - 1);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final TDistribution distribution = new TDistribution(null, n - 1);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }
@@ -1086,7 +1087,8 @@ public class TTest {
 
         final double t = FastMath.abs(t(m1, m2, v1, v2, n1, n2));
         final double degreesOfFreedom = df(v1, v2, n1, n2);
-        TDistribution distribution = new TDistribution(degreesOfFreedom);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }
@@ -1115,7 +1117,8 @@ public class TTest {
 
         final double t = FastMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
         final double degreesOfFreedom = n1 + n2 - 2;
-        TDistribution distribution = new TDistribution(degreesOfFreedom);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }

src/main/java/org/apache/commons/math3/stat/inference/WilcoxonSignedRankTest.java

@@ -253,7 +253,8 @@ public class WilcoxonSignedRankTest {
         final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);
 
         // No try-catch or advertised exception because args are valid
-        final NormalDistribution standardNormal = new NormalDistribution(0, 1);
+        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
+        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);
 
         return 2*standardNormal.cumulativeProbability(z);
     }