[commons-math] 01/02: Unneeded dependency (unit test).

[erans]

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in repository https://gitbox.apache.org/repos/asf/commons-math.git

commit dd73a1ba803174a4c7ae0c82c165d0f2fabfb622
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Sat Jun 5 14:49:01 2021 +0200

    Unneeded dependency (unit test).
---
 .../math4/legacy/filter/KalmanFilterTest.java      | 30 +++++++++++++++-------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git 
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/filter/KalmanFilterTest.java
 
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/filter/KalmanFilterTest.java
index 0dbba2c..ae3c592 100644
--- 
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/filter/KalmanFilterTest.java
+++ 
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/filter/KalmanFilterTest.java
@@ -14,17 +14,18 @@
 
 package org.apache.commons.math4.legacy.filter;
 
-import org.apache.commons.statistics.distribution.ContinuousDistribution;
-import org.apache.commons.statistics.distribution.NormalDistribution;
+import org.apache.commons.rng.simple.RandomSource;
+import 
org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
+import org.apache.commons.rng.sampling.distribution.GaussianSampler;
+import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
+import org.apache.commons.numbers.core.Precision;
 import org.apache.commons.math4.legacy.linear.Array2DRowRealMatrix;
 import org.apache.commons.math4.legacy.linear.ArrayRealVector;
 import org.apache.commons.math4.legacy.linear.MatrixDimensionMismatchException;
 import org.apache.commons.math4.legacy.linear.MatrixUtils;
 import org.apache.commons.math4.legacy.linear.RealMatrix;
 import org.apache.commons.math4.legacy.linear.RealVector;
-import org.apache.commons.rng.simple.RandomSource;
 import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
-import org.apache.commons.numbers.core.Precision;
 import org.junit.Assert;
 import org.junit.Test;
 
@@ -121,7 +122,7 @@ public class KalmanFilterTest {
         RealVector pNoise = new ArrayRealVector(1);
         RealVector mNoise = new ArrayRealVector(1);
 
-        final ContinuousDistribution.Sampler rand = new NormalDistribution(0, 
1).createSampler(RandomSource.create(RandomSource.WELL_19937_C));
+        final ContinuousSampler rand = createGaussianSampler(0, 1);
 
         // iterate 60 steps
         for (int i = 0; i < 60; i++) {
@@ -210,7 +211,7 @@ public class KalmanFilterTest {
         double[] expectedInitialState = new double[] { 0.0, 0.0 };
         assertVectorEquals(expectedInitialState, filter.getStateEstimation());
 
-        final ContinuousDistribution.Sampler rand = new NormalDistribution(0, 
1).createSampler(RandomSource.create(RandomSource.WELL_19937_C));
+        final ContinuousSampler rand = createGaussianSampler(0, 1);
 
         RealVector tmpPNoise = new ArrayRealVector(
                 new double[] { AccurateMath.pow(dt, 2d) / 2d, dt });
@@ -387,7 +388,7 @@ public class KalmanFilterTest {
         final MeasurementModel mm = new DefaultMeasurementModel(H, R);
         final KalmanFilter filter = new KalmanFilter(pm, mm);
 
-        final ContinuousDistribution.Sampler dist = new NormalDistribution(0, 
measurementNoise).createSampler(RandomSource.create(RandomSource.WELL_19937_C));
+        final ContinuousSampler rand = createGaussianSampler(0, 
measurementNoise);
 
         for (int i = 0; i < iterations; i++) {
             // get the "real" cannonball position
@@ -395,8 +396,8 @@ public class KalmanFilterTest {
             double y = cannonball.getY();
 
             // apply measurement noise to current cannonball position
-            double nx = x + dist.sample();
-            double ny = y + dist.sample();
+            double nx = x + rand.sample();
+            double ny = y + rand.sample();
 
             cannonball.step();
 
@@ -439,4 +440,15 @@ public class KalmanFilterTest {
             }
         }
     }
+
+    /**
+     * @param mu Mean
+     * @param sigma Standard deviation.
+     * @return a sampler that follows the N(mu,sigma) distribution.
+     */
+    private ContinuousSampler createGaussianSampler(double mu,
+                                                    double sigma) {
+        return 
GaussianSampler.of(ZigguratNormalizedGaussianSampler.of(RandomSource.create(RandomSource.JSF_64)),
+                                  mu, sigma);
+    }
 }