This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 3d2b2107b58ffc316c849da43734506b7fe04254
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Tue Jul 28 23:23:24 2020 +0200
Add unit tests.
Failing tests are disabled.
---
.../SimplexOptimizerMultiDirectionalTest.java | 233 ++++++++++++++-------
.../noderiv/SimplexOptimizerNelderMeadTest.java | 223 ++++++++++++++------
2 files changed, 315 insertions(+), 141 deletions(-)
diff --git
a/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
b/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
index 11da025..cf108df 100644
---
a/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
+++
b/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
@@ -30,14 +30,18 @@ import
org.apache.commons.math4.optim.nonlinear.scalar.noderiv.MultiDirectionalS
import
org.apache.commons.math4.optim.nonlinear.scalar.noderiv.NelderMeadSimplex;
import
org.apache.commons.math4.optim.nonlinear.scalar.noderiv.SimplexOptimizer;
import org.apache.commons.math4.util.FastMath;
+import org.apache.commons.math4.util.MathArrays;
import org.junit.Assert;
import org.junit.Test;
+import org.junit.Ignore;
public class SimplexOptimizerMultiDirectionalTest {
+ private static final int DIM = 13;
+
@Test(expected=MathUnsupportedOperationException.class)
public void testBoundsUnsupported() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
@@ -51,7 +55,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
@@ -72,7 +76,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
@@ -93,7 +97,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
@@ -114,7 +118,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(new
SimpleValueChecker(1e-15, 1e-30));
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
@@ -134,18 +138,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testRosenbrock() {
- MultivariateFunction rosenbrock
- = new MultivariateFunction() {
- @Override
- public double value(double[] x) {
- ++count;
- double a = x[1] - x[0] * x[0];
- double b = 1.0 - x[0];
- return 100 * a * a + b * b;
- }
- };
-
- count = 0;
+ final OptimTestUtils.Rosenbrock rosenbrock = new
OptimTestUtils.Rosenbrock();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -156,8 +149,6 @@ public class SimplexOptimizerMultiDirectionalTest {
{ -1.2, 1.0 },
{ 0.9, 1.2 },
{ 3.5, -2.3 } }));
-
- Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 50);
Assert.assertTrue(optimizer.getEvaluations() < 100);
Assert.assertTrue(optimum.getValue() > 1e-2);
@@ -165,20 +156,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testPowell() {
- MultivariateFunction powell
- = new MultivariateFunction() {
- @Override
- public double value(double[] x) {
- ++count;
- double a = x[0] + 10 * x[1];
- double b = x[2] - x[3];
- double c = x[1] - 2 * x[2];
- double d = x[0] - x[3];
- return a * a + 5 * b * b + c * c * c * c + 10 * d * d
* d * d;
- }
- };
-
- count = 0;
+ final OptimTestUtils.Powell powell = new OptimTestUtils.Powell();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
PointValuePair optimum
= optimizer.optimize(new MaxEval(1000),
@@ -186,7 +164,6 @@ public class SimplexOptimizerMultiDirectionalTest {
GoalType.MINIMIZE,
new InitialGuess(new double[] { 3, -1, 0, 1
}),
new MultiDirectionalSimplex(4));
- Assert.assertEquals(count, optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 800);
Assert.assertTrue(optimizer.getEvaluations() < 900);
Assert.assertTrue(optimum.getValue() > 1e-2);
@@ -197,7 +174,7 @@ public class SimplexOptimizerMultiDirectionalTest {
// fails because MultiDirectional.iterateSimplex is looping forever
// the while(true) should be replaced with a convergence check
SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14);
- final Gaussian2D function = new Gaussian2D(0, 0, 1);
+ final OptimTestUtils.Gaussian2D function = new
OptimTestUtils.Gaussian2D(0, 0, 1);
PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
new
ObjectiveFunction(function),
GoalType.MAXIMIZE,
@@ -214,50 +191,152 @@ public class SimplexOptimizerMultiDirectionalTest {
Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
}
- private static class FourExtrema implements MultivariateFunction {
- // The following function has 4 local extrema.
- final double xM = -3.841947088256863675365;
- final double yM = -1.391745200270734924416;
- final double xP = 0.2286682237349059125691;
- final double yP = -yM;
- final double valueXmYm = 0.2373295333134216789769; // Local maximum.
- final double valueXmYp = -valueXmYm; // Local minimum.
- final double valueXpYm = -0.7290400707055187115322; // Global minimum.
- final double valueXpYp = -valueXpYm; // Global maximum.
-
- @Override
- public double value(double[] variables) {
- final double x = variables[0];
- final double y = variables[1];
- return (x == 0 || y == 0) ? 0 :
- FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) *
FastMath.atan(y) / (x * y);
- }
+ @Test
+ public void testRosen() {
+ doTest(new OptimTestUtils.Rosen(),
+ OptimTestUtils.point(DIM, 0.1),
+ GoalType.MINIMIZE,
+ 183861,
+ new PointValuePair(OptimTestUtils.point(DIM, 1.0), 0.0),
+ 1e-4);
+ }
+
+ @Test
+ public void testEllipse() {
+ doTest(new OptimTestUtils.Elli(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 873,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ //@Ignore
+ @Test
+ public void testElliRotated() {
+ doTest(new OptimTestUtils.ElliRotated(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 873,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testCigar() {
+ doTest(new OptimTestUtils.Cigar(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 925,
+ new PointValuePair(OptimTestUtils.point(DIM,0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testTwoAxes() {
+ doTest(new OptimTestUtils.TwoAxes(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 1159,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testCigTab() {
+ doTest(new OptimTestUtils.CigTab(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 795,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testSphere() {
+ doTest(new OptimTestUtils.Sphere(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 665,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testTablet() {
+ doTest(new OptimTestUtils.Tablet(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 873,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testDiffPow() {
+ doTest(new OptimTestUtils.DiffPow(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 614,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Test
+ public void testSsDiffPow() {
+ doTest(new OptimTestUtils.SsDiffPow(),
+ OptimTestUtils.point(DIM / 2, 1.0),
+ GoalType.MINIMIZE,
+ 656,
+ new PointValuePair(OptimTestUtils.point(DIM / 2, 0.0), 0.0),
+ 1e-15);
+ }
+
+ @Ignore
+ @Test
+ public void testAckley() {
+ doTest(new OptimTestUtils.Ackley(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 587,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 0);
}
- private static class Gaussian2D implements MultivariateFunction {
- private final double[] maximumPosition;
- private final double std;
-
- public Gaussian2D(double xOpt, double yOpt, double std) {
- maximumPosition = new double[] { xOpt, yOpt };
- this.std = std;
- }
-
- public double getMaximum() {
- return value(maximumPosition);
- }
-
- public double[] getMaximumPosition() {
- return maximumPosition.clone();
- }
-
- @Override
- public double value(double[] point) {
- final double x = point[0], y = point[1];
- final double twoS2 = 2.0 * std * std;
- return 1.0 / (twoS2 * FastMath.PI) * FastMath.exp(-(x * x + y * y)
/ twoS2);
- }
+ @Ignore
+ @Test
+ public void testRastrigin() {
+ doTest(new OptimTestUtils.Rastrigin(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 535,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 0);
}
- private int count;
+ /**
+ * @param func Function to optimize.
+ * @param startPoint Starting point.
+ * @param goal Minimization or maximization.
+ * @param maxEvaluations Maximum number of evaluations.
+ * @param expected Expected optimum.
+ * @param tol Tolerance for checking that the optimum is correct.
+ */
+ private void doTest(MultivariateFunction func,
+ double[] startPoint,
+ GoalType goal,
+ int maxEvaluations,
+ PointValuePair expected,
+ double tol) {
+ final int dim = startPoint.length;
+ final SimplexOptimizer optim = new SimplexOptimizer(1e-10, 1e-12);
+ final PointValuePair result = optim.optimize(new
MaxEval(maxEvaluations),
+ new
ObjectiveFunction(func),
+ goal,
+ new
InitialGuess(startPoint),
+ new
MultiDirectionalSimplex(dim, 0.1));
+ final double dist = MathArrays.distance(expected.getPoint(),
+ result.getPoint());
+ Assert.assertEquals(0d, dist, tol);
+ }
}
diff --git
a/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
b/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
index 0f5067d..921f69b 100644
---
a/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
+++
b/src/test/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
@@ -34,14 +34,18 @@ import
org.apache.commons.math4.optim.nonlinear.scalar.ObjectiveFunction;
import
org.apache.commons.math4.optim.nonlinear.scalar.noderiv.NelderMeadSimplex;
import
org.apache.commons.math4.optim.nonlinear.scalar.noderiv.SimplexOptimizer;
import org.apache.commons.math4.util.FastMath;
+import org.apache.commons.math4.util.MathArrays;
import org.junit.Assert;
import org.junit.Test;
+import org.junit.Ignore;
public class SimplexOptimizerNelderMeadTest {
+ private static final int DIM = 13;
+
@Test(expected=MathUnsupportedOperationException.class)
public void testBoundsUnsupported() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
@@ -55,7 +59,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -76,7 +80,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -97,7 +101,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -118,7 +122,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
- final FourExtrema fourExtrema = new FourExtrema();
+ final OptimTestUtils.FourExtrema fourExtrema = new
OptimTestUtils.FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -139,7 +143,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testRosenbrock() {
- Rosenbrock rosenbrock = new Rosenbrock();
+ OptimTestUtils.Rosenbrock rosenbrock = new OptimTestUtils.Rosenbrock();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
@@ -150,8 +154,6 @@ public class SimplexOptimizerNelderMeadTest {
{ -1.2, 1 },
{ 0.9, 1.2 },
{ 3.5, -2.3 } }));
-
- Assert.assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 40);
Assert.assertTrue(optimizer.getEvaluations() < 50);
Assert.assertTrue(optimum.getValue() < 8e-4);
@@ -159,7 +161,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test
public void testPowell() {
- Powell powell = new Powell();
+ OptimTestUtils.Powell powell = new OptimTestUtils.Powell();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
@@ -167,7 +169,6 @@ public class SimplexOptimizerNelderMeadTest {
GoalType.MINIMIZE,
new InitialGuess(new double[] { 3, -1, 0, 1 }),
new NelderMeadSimplex(4));
- Assert.assertEquals(powell.getCount(), optimizer.getEvaluations());
Assert.assertTrue(optimizer.getEvaluations() > 110);
Assert.assertTrue(optimizer.getEvaluations() < 130);
Assert.assertTrue(optimum.getValue() < 2e-3);
@@ -258,7 +259,7 @@ public class SimplexOptimizerNelderMeadTest {
@Test(expected=TooManyEvaluationsException.class)
public void testMaxIterations() {
- Powell powell = new Powell();
+ OptimTestUtils.Powell powell = new OptimTestUtils.Powell();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
optimizer.optimize(new MaxEval(20),
new ObjectiveFunction(powell),
@@ -267,65 +268,159 @@ public class SimplexOptimizerNelderMeadTest {
new NelderMeadSimplex(4));
}
- private static class FourExtrema implements MultivariateFunction {
- // The following function has 4 local extrema.
- final double xM = -3.841947088256863675365;
- final double yM = -1.391745200270734924416;
- final double xP = 0.2286682237349059125691;
- final double yP = -yM;
- final double valueXmYm = 0.2373295333134216789769; // Local maximum.
- final double valueXmYp = -valueXmYm; // Local minimum.
- final double valueXpYm = -0.7290400707055187115322; // Global minimum.
- final double valueXpYp = -valueXpYm; // Global maximum.
-
- @Override
- public double value(double[] variables) {
- final double x = variables[0];
- final double y = variables[1];
- return (x == 0 || y == 0) ? 0 :
- FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) *
FastMath.atan(y) / (x * y);
- }
+ @Test
+ public void testRosen() {
+ doTest(new OptimTestUtils.Rosen(),
+ OptimTestUtils.point(DIM, 0.1),
+ GoalType.MINIMIZE,
+ 11975,
+ new PointValuePair(OptimTestUtils.point(DIM, 1.0), 0.0),
+ 1e-6);
}
- private static class Rosenbrock implements MultivariateFunction {
- private int count;
+ @Ignore
+ @Test
+ public void testEllipse() {
+ doTest(new OptimTestUtils.Elli(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 7184,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Ignore
+ @Test
+ public void testElliRotated() {
+ doTest(new OptimTestUtils.ElliRotated(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 7467,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
- public Rosenbrock() {
- count = 0;
- }
+ @Test
+ public void testCigar() {
+ doTest(new OptimTestUtils.Cigar(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 9160,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-6);
+ }
- @Override
- public double value(double[] x) {
- ++count;
- double a = x[1] - x[0] * x[0];
- double b = 1.0 - x[0];
- return 100 * a * a + b * b;
- }
+ @Ignore
+ @Test
+ public void testTwoAxes() {
+ doTest(new OptimTestUtils.TwoAxes(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 3451,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Ignore
+ @Test
+ public void testCigTab() {
+ doTest(new OptimTestUtils.CigTab(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 7454,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Ignore
+ @Test
+ public void testSphere() {
+ doTest(new OptimTestUtils.Sphere(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 3881,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-6);
+ }
- public int getCount() {
- return count;
- }
+ @Ignore
+ @Test
+ public void testTablet() {
+ doTest(new OptimTestUtils.Tablet(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 6639,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Ignore
+ @Test
+ public void testDiffPow() {
+ doTest(new OptimTestUtils.DiffPow(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 4105,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 1e-14);
+ }
+
+ @Ignore
+ @Test
+ public void testSsDiffPow() {
+ doTest(new OptimTestUtils.SsDiffPow(),
+ OptimTestUtils.point(DIM / 2, 1.0),
+ GoalType.MINIMIZE,
+ 3990,
+ new PointValuePair(OptimTestUtils.point(DIM / 2, 0.0), 0.0),
+ 1e-15);
+ }
+
+ @Ignore
+ @Test
+ public void testAckley() {
+ doTest(new OptimTestUtils.Ackley(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 2849,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 0);
+ }
+
+ @Ignore
+ @Test
+ public void testRastrigin() {
+ doTest(new OptimTestUtils.Rastrigin(),
+ OptimTestUtils.point(DIM, 1.0),
+ GoalType.MINIMIZE,
+ 2166,
+ new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0),
+ 0);
}
- private static class Powell implements MultivariateFunction {
- private int count;
-
- public Powell() {
- count = 0;
- }
-
- @Override
- public double value(double[] x) {
- ++count;
- double a = x[0] + 10 * x[1];
- double b = x[2] - x[3];
- double c = x[1] - 2 * x[2];
- double d = x[0] - x[3];
- return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d;
- }
-
- public int getCount() {
- return count;
- }
+ /**
+ * @param func Function to optimize.
+ * @param startPoint Starting point.
+ * @param goal Minimization or maximization.
+ * @param maxEvaluations Maximum number of evaluations.
+ * @param expected Expected optimum.
+ * @param tol Tolerance for checking that the optimum is correct.
+ */
+ private void doTest(MultivariateFunction func,
+ double[] startPoint,
+ GoalType goal,
+ int maxEvaluations,
+ PointValuePair expected,
+ double tol) {
+ final int dim = startPoint.length;
+ final SimplexOptimizer optim = new SimplexOptimizer(1e-13, 1e-14);
+ final PointValuePair result = optim.optimize(new
MaxEval(maxEvaluations),
+ new
ObjectiveFunction(func),
+ goal,
+ new
InitialGuess(startPoint),
+ new
NelderMeadSimplex(dim, 0.1));
+ final double dist = MathArrays.distance(expected.getPoint(),
+ result.getPoint());
+ Assert.assertEquals(0d, dist, tol);
}
}
