MATH-1600: NormalizedRandomGenerator as "DoubleSupplier

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/CorrelatedRandomVectorGenerator.java

@@ -166,7 +166,7 @@ public RealMatrix getRootMatrix() {
     public double[] get() {
         // generate uncorrelated vector
         for (int i = 0; i < normalized.length; ++i) {
-            normalized[i] = generator.nextNormalizedDouble();
+            normalized[i] = generator.getAsDouble();
         }
 
         // compute correlated vector

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/NormalizedRandomGenerator.java

@@ -17,21 +17,19 @@
 
 package org.apache.commons.math4.legacy.random;
 
+import java.util.function.DoubleSupplier;
+
 /**
  * This interface represent a normalized random generator for
  * scalars.
  * Normalized generator provide null mean and unit standard deviation scalars.
+ *  Supply a random scalar with null mean and unit standard deviation.
+ *  <p>This supplier does <strong>not</strong> specify the shape of the
+ *  distribution, it is the implementing class that provides it. The
+ *  only contract here is to generate numbers with null mean and unit
+ *  standard deviation.</p>
  * @since 1.2
  */
-public interface NormalizedRandomGenerator {
-
-  /** Generate a random scalar with null mean and unit standard deviation.
-   * <p>This method does <strong>not</strong> specify the shape of the
-   * distribution, it is the implementing class that provides it. The
-   * only contract here is to generate numbers with null mean and unit
-   * standard deviation.</p>
-   * @return a random scalar with null mean and unit standard deviation
-   */
-  double nextNormalizedDouble();
+public interface NormalizedRandomGenerator extends DoubleSupplier {
 
 }

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/StableRandomGenerator.java

@@ -85,7 +85,7 @@ public StableRandomGenerator(final UniformRandomProvider generator,
      * @return a random scalar with zero location and unit scale
      */
     @Override
-    public double nextNormalizedDouble() {
+    public double getAsDouble() {
         // we need 2 uniform random numbers to calculate omega and phi
         double omega = -AccurateMath.log(generator.nextDouble());
         double phi = AccurateMath.PI * (generator.nextDouble() - 0.5);

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/random/UniformRandomGenerator.java

@@ -52,7 +52,7 @@ public UniformRandomGenerator(UniformRandomProvider generator) {
      * @return a random scalar in the range \( [-\sqrt{3}, +\sqrt{3}] \).
      */
     @Override
-    public double nextNormalizedDouble() {
+    public double getAsDouble() {
         return SQRT3 * (2 * generator.nextDouble() - 1);
     }
 }

commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/CorrelatedRandomVectorGeneratorTest.java

@@ -200,7 +200,7 @@ private NormalizedRandomGenerator gaussianRandom(final UniformRandomProvider rng
         return new NormalizedRandomGenerator() {
             /** {@inheritDoc} */
             @Override
-            public double nextNormalizedDouble() {
+            public double getAsDouble() {
                 return n.sample();
             }
         };

commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/StableRandomGeneratorTest.java

@@ -38,12 +38,12 @@ public class StableRandomGeneratorTest {
      * TODO: verify that tolerance this wide is really OK
      */
     @Test
-    public void testNextDouble() {
+    public void testGetAsDouble() {
         StableRandomGenerator generator = new StableRandomGenerator(rg, 1.3,
                 0.1);
         double[] sample = new double[2 * sampleSize];
         for (int i = 0; i < sample.length; ++i) {
-            sample[i] = generator.nextNormalizedDouble();
+            sample[i] = generator.getAsDouble();
         }
         Assert.assertEquals(0.0, StatUtils.mean(sample), 0.3);
     }
@@ -57,7 +57,7 @@ public void testGaussianCase() {
 
         double[] sample = new double[sampleSize];
         for (int i = 0; i < sample.length; ++i) {
-            sample[i] = generator.nextNormalizedDouble();
+            sample[i] = generator.getAsDouble();
         }
         Assert.assertEquals(0.0, StatUtils.mean(sample), 0.02);
         Assert.assertEquals(1.0, StatUtils.variance(sample), 0.02);
@@ -72,7 +72,7 @@ public void testCauchyCase() {
         DescriptiveStatistics summary = new DescriptiveStatistics();
 
         for (int i = 0; i < sampleSize; ++i) {
-            double sample = generator.nextNormalizedDouble();
+            double sample = generator.getAsDouble();
             summary.addValue(sample);
         }
 

commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/random/UniformRandomGeneratorTest.java

@@ -32,7 +32,7 @@ public void testMeanAndStandardDeviation() {
         UniformRandomGenerator generator = new UniformRandomGenerator(rg);
         double[] sample = new double[10000];
         for (int i = 0; i < sample.length; ++i) {
-            sample[i] = generator.nextNormalizedDouble();
+            sample[i] = generator.getAsDouble();
         }
         Assert.assertEquals(0.0, StatUtils.mean(sample), 0.07);
         Assert.assertEquals(1.0, StatUtils.variance(sample), 0.02);

commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/regression/GLSMultipleLinearRegressionTest.java

@@ -310,7 +310,7 @@ private NormalizedRandomGenerator gaussianRandom(final UniformRandomProvider rng
         return new NormalizedRandomGenerator() {
             /** {@inheritDoc} */
             @Override
-            public double nextNormalizedDouble() {
+            public double getAsDouble() {
                 return n.sample();
             }
         };