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MATH-1552: Fiddling with setup and expectations (unit tests).

This commit is contained in:
Gilles Sadowski 2021-06-05 23:48:27 +02:00
parent 4075e87c76
commit df927f0b67
3 changed files with 75 additions and 65 deletions
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56
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/OptimTestUtils.java
View File

@ -20,6 +20,7 @@ import java.util.Arrays;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.simple.RandomSource;
import org.apache.commons.rng.sampling.distribution.MarsagliaNormalizedGaussianSampler;
import org.apache.commons.rng.sampling.distribution.ContinuousUniformSampler;
import org.apache.commons.math4.legacy.analysis.MultivariateFunction;
import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
@ -213,29 +214,24 @@ class OptimTestUtils {
}
static class Ackley implements MultivariateFunction {
private double axisratio;
Ackley(double axra) {
axisratio = axra;
}
public Ackley() {
this(1);
}
private static final double A = 20;
private static final double B = 0.2;
private static final double C = 2 * Math.PI;
@Override
public double value(double[] x) {
double f = 0;
double res2 = 0;
double fac = 0;
for (int i = 0; i < x.length; ++i) {
fac = AccurateMath.pow(axisratio, (i - 1.) / (x.length - 1.));
f += fac * fac * x[i] * x[i];
res2 += AccurateMath.cos(2. * AccurateMath.PI * fac * x[i]);
final int dim = x.length;
double acc1 = 0;
double acc2 = 0;
for (int i = 0; i < dim; i++) {
final double v = x[i];
acc1 += v * v;
acc2 += Math.cos(C * v);
}
f = (20. - 20. * AccurateMath.exp(-0.2 * AccurateMath.sqrt(f / x.length))
+ AccurateMath.exp(1.) - AccurateMath.exp(res2 / x.length));
return f;
acc1 = -B * Math.sqrt(acc1 / dim);
acc2 /= dim;
return -A * Math.exp(acc1) - Math.exp(acc2) + A + Math.E;
}
}
@ -334,11 +330,29 @@ class OptimTestUtils {
}
static double[] point(int n, double value) {
double[] ds = new double[n];
final double[] ds = new double[n];
Arrays.fill(ds, value);
return ds;
}
/**
* @param n Dimension.
* @param value Value.
* @param jitter Random noise to add to {@code value}.
*/
static double[] point(int n,
double value,
double jitter) {
final ContinuousUniformSampler s = new ContinuousUniformSampler(rng(),
-jitter,
jitter);
final double[] ds = new double[n];
for (int i = 0; i < n; i++) {
ds[i] = value + s.sample();
}
return ds;
}
/** Creates a RNG instance. */
static UniformRandomProvider rng() {
return RandomSource.create(RandomSource.MWC_256);

28
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
View File

@ -272,7 +272,7 @@ public class SimplexOptimizerMultiDirectionalTest {
doTest(new OptimTestUtils.DiffPow(),
OptimTestUtils.point(DIM, 1.0),
GoalType.MINIMIZE,
614,
1000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-14);
}
@ -280,33 +280,31 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testSsDiffPow() {
doTest(new OptimTestUtils.SsDiffPow(),
OptimTestUtils.point(DIM / 2, 1.0),
OptimTestUtils.point(DIM / 2, 1.0, 1e-1),
GoalType.MINIMIZE,
656,
5000,
new PointValuePair(OptimTestUtils.point(DIM / 2, 0.0), 0.0),
1e-15);
1e-3);
}
@Ignore
@Test
public void testAckley() {
doTest(new OptimTestUtils.Ackley(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
587,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
0);
7500,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0d),
1e-11);
}
@Ignore
@Test
public void testRastrigin() {
doTest(new OptimTestUtils.Rastrigin(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
535,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
0);
5000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0d),
1e-6);
}
/**
@ -329,7 +327,7 @@ public class SimplexOptimizerMultiDirectionalTest {
new ObjectiveFunction(func),
goal,
new InitialGuess(startPoint),
new MultiDirectionalSimplex(dim, 0.1));
new MultiDirectionalSimplex(dim, 1));
final double dist = MathArrays.distance(expected.getPoint(),
result.getPoint());
Assert.assertEquals(0d, dist, tol);

56
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
View File

@ -270,7 +270,7 @@ public class SimplexOptimizerNelderMeadTest {
doTest(new OptimTestUtils.Rosen(),
OptimTestUtils.point(DIM, 0.1),
GoalType.MINIMIZE,
11975,
9078,
new PointValuePair(OptimTestUtils.point(DIM, 1.0), 0.0),
1e-6);
}
@ -279,18 +279,18 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testEllipse() {
doTest(new OptimTestUtils.Elli(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 2e-1),
GoalType.MINIMIZE,
7184,
12000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-14);
1e-6);
}
@Ignore
@Test
public void testElliRotated() {
doTest(new OptimTestUtils.ElliRotated(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
7467,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
@ -300,9 +300,9 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testCigar() {
doTest(new OptimTestUtils.Cigar(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 2e-1),
GoalType.MINIMIZE,
9160,
7000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-6);
}
@ -311,7 +311,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testTwoAxes() {
doTest(new OptimTestUtils.TwoAxes(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
3451,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
@ -322,20 +322,19 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testCigTab() {
doTest(new OptimTestUtils.CigTab(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
7454,
7000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-14);
1e-4);
}
@Ignore
@Test
public void testSphere() {
doTest(new OptimTestUtils.Sphere(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
3881,
3000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-6);
}
@ -344,9 +343,9 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testTablet() {
doTest(new OptimTestUtils.Tablet(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 1e-1),
GoalType.MINIMIZE,
6639,
10000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-14);
}
@ -355,42 +354,41 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testDiffPow() {
doTest(new OptimTestUtils.DiffPow(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 2e-1),
GoalType.MINIMIZE,
4105,
7000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
1e-14);
1e-6);
}
@Ignore
@Test
public void testSsDiffPow() {
doTest(new OptimTestUtils.SsDiffPow(),
OptimTestUtils.point(DIM / 2, 1.0),
OptimTestUtils.point(DIM / 2, 1.0, 2e-1),
GoalType.MINIMIZE,
3990,
4000,
new PointValuePair(OptimTestUtils.point(DIM / 2, 0.0), 0.0),
1e-15);
1e-3);
}
@Ignore
@Test
public void testAckley() {
doTest(new OptimTestUtils.Ackley(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 2e-1),
GoalType.MINIMIZE,
2849,
10000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
0);
1e-11);
}
@Ignore
@Test
public void testRastrigin() {
doTest(new OptimTestUtils.Rastrigin(),
OptimTestUtils.point(DIM, 1.0),
OptimTestUtils.point(DIM, 1.0, 2e-1),
GoalType.MINIMIZE,
2166,
10000,
new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
0);
}
@ -415,7 +413,7 @@ public class SimplexOptimizerNelderMeadTest {
new ObjectiveFunction(func),
goal,
new InitialGuess(startPoint),
new NelderMeadSimplex(dim, 0.1));
new NelderMeadSimplex(dim, 1));
final double dist = MathArrays.distance(expected.getPoint(),
result.getPoint());
Assert.assertEquals(0d, dist, tol);