Apache
/
commons-math
mirror of https://github.com/apache/commons-math.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

MATH-1411: Relaxing tolerance. 

Unit test now succeeds reasonably often, even when using a random seed.
This commit is contained in:
Gilles Sadowski 2021-05-28 19:16:54 +02:00
parent 2977d26a0f
commit bab2a8b991
2 changed files with 6 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/LevenbergMarquardtOptimizerTest.java
View File

@ -252,11 +252,9 @@ public class LevenbergMarquardtOptimizerTest
final double ySigma = 15; final double ySigma = 15;
final double radius = 111.111; final double radius = 111.111;
// The test is extremely sensitive to the seed. // The test is extremely sensitive to the seed.
final long seed = 59321761419L;
final RandomCirclePointGenerator factory final RandomCirclePointGenerator factory
= new RandomCirclePointGenerator(xCenter, yCenter, radius, = new RandomCirclePointGenerator(xCenter, yCenter, radius,
xSigma, ySigma, xSigma, ySigma);
seed);
final CircleProblem circle = new CircleProblem(xSigma, ySigma); final CircleProblem circle = new CircleProblem(xSigma, ySigma);
final int numPoints = 10; final int numPoints = 10;
@ -276,8 +274,8 @@ public class LevenbergMarquardtOptimizerTest
final double[] asymptoticStandardErrorFound = optimum.getSigma(1e-14).toArray(); final double[] asymptoticStandardErrorFound = optimum.getSigma(1e-14).toArray();
// Check that the parameters are found within the assumed error bars. // Check that the parameters are found within the assumed error bars.
Assert.assertEquals(xCenter, paramFound[0], asymptoticStandardErrorFound[0]); Assert.assertEquals(xCenter, paramFound[0], 1.5 * asymptoticStandardErrorFound[0]);
Assert.assertEquals(yCenter, paramFound[1], asymptoticStandardErrorFound[1]); Assert.assertEquals(yCenter, paramFound[1], 1.5 * asymptoticStandardErrorFound[1]);
Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]); Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]);
} }
@ -289,11 +287,9 @@ public class LevenbergMarquardtOptimizerTest
final double xSigma = 10; final double xSigma = 10;
final double ySigma = 15; final double ySigma = 15;
final double radius = 111.111; final double radius = 111.111;
final long seed = 3456789L;
final RandomCirclePointGenerator factory final RandomCirclePointGenerator factory
= new RandomCirclePointGenerator(xCenter, yCenter, radius, = new RandomCirclePointGenerator(xCenter, yCenter, radius,
xSigma, ySigma, xSigma, ySigma);
seed);
final CircleProblem circle = new CircleProblem(xSigma, ySigma); final CircleProblem circle = new CircleProblem(xSigma, ySigma);
final int numPoints = 10; final int numPoints = 10;

5
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/fitting/leastsquares/RandomCirclePointGenerator.java
View File

@ -50,9 +50,8 @@ public class RandomCirclePointGenerator {
double y, double y,
double radius, double radius,
double xSigma, double xSigma,
double ySigma, double ySigma) {
long seed) { final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_44497_B);
final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_44497_B, seed);
this.radius = radius; this.radius = radius;
cX = new NormalDistribution(x, xSigma).createSampler(rng); cX = new NormalDistribution(x, xSigma).createSampler(rng);
cY = new NormalDistribution(y, ySigma).createSampler(rng); cY = new NormalDistribution(y, ySigma).createSampler(rng);