org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction Java Examples
The following examples show how to use
org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction.
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Example #1
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 180);
Assert.assertTrue(optimizer.getEvaluations() < 220);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #2
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { -3.0, 0.0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #3
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #4
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { -3.0, 0.0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #5
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
}
Example #6
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares2() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2, -3 }, new double[] { 10, 0.1 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1e-6);
}
Example #7
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares1() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 3e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 4e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1.0e-6);
}
Example #8
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #9
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares1() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 3e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 4e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1.0e-6);
}
Example #10
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMath283() {
// 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);
PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
new ObjectiveFunction(function),
GoalType.MAXIMIZE,
new InitialGuess(function.getMaximumPosition()),
new MultiDirectionalSimplex(2));
final double EPSILON = 1e-5;
final double expectedMaximum = function.getMaximum();
final double actualMaximum = estimate.getValue();
Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON);
final double[] expectedPosition = function.getMaximumPosition();
final double[] actualPosition = estimate.getPoint();
Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON );
Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
}
Example #11
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param fLineTol Tolerance (relative error on the objective function)
* for the internal line search algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double fLineTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
Assert.assertTrue(optim.getIterations() > 0);
}
Example #12
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 1e-5);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 3e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #13
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMath283() {
// 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);
PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
new ObjectiveFunction(function),
GoalType.MAXIMIZE,
new InitialGuess(function.getMaximumPosition()),
new MultiDirectionalSimplex(2));
final double EPSILON = 1e-5;
final double expectedMaximum = function.getMaximum();
final double actualMaximum = estimate.getValue();
Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON);
final double[] expectedPosition = function.getMaximumPosition();
final double[] actualPosition = estimate.getPoint();
Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON );
Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
}
Example #14
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #15
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #16
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares2() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2, -3 }, new double[] { 10, 0.1 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1e-6);
}
Example #17
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testRosenbrock() {
Rosenbrock rosenbrock = new Rosenbrock();
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(rosenbrock),
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.2, 1 }),
new NelderMeadSimplex(new double[][] {
{ -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);
}
Example #18
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares2() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2, -3 }, new double[] { 10, 0.1 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1e-6);
}
Example #19
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 5e-6);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 6e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 1e-11);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #20
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 2e-7);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 2e-5);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 6e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #21
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares2() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2, -3 }, new double[] { 10, 0.1 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1e-6);
}
Example #22
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param fLineTol Tolerance (relative error on the objective function)
* for the internal line search algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double fLineTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
Assert.assertTrue(optim.getIterations() > 0);
}
Example #23
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 1e-5);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 3e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #24
| Source File: SimplexOptimizerNelderMeadTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testLeastSquares1() {
final RealMatrix factors
= new Array2DRowRealMatrix(new double[][] {
{ 1, 0 },
{ 0, 1 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
PointValuePair optimum =
optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(ls),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 10, 10 }),
new NelderMeadSimplex(2));
Assert.assertEquals( 2, optimum.getPointRef()[0], 3e-5);
Assert.assertEquals(-3, optimum.getPointRef()[1], 4e-4);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 80);
Assert.assertTrue(optimum.getValue() < 1.0e-6);
}
Example #25
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMath283() {
// 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);
PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
new ObjectiveFunction(function),
GoalType.MAXIMIZE,
new InitialGuess(function.getMaximumPosition()),
new MultiDirectionalSimplex(2));
final double EPSILON = 1e-5;
final double expectedMaximum = function.getMaximum();
final double actualMaximum = estimate.getValue();
Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON);
final double[] expectedPosition = function.getMaximumPosition();
final double[] actualPosition = estimate.getPoint();
Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON );
Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
}
Example #26
| Source File: PowellOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* @param func Function to optimize.
* @param optimum Expected optimum.
* @param init Starting point.
* @param goal Minimization or maximization.
* @param fTol Tolerance (relative error on the objective function) for
* "Powell" algorithm.
* @param fLineTol Tolerance (relative error on the objective function)
* for the internal line search algorithm.
* @param pointTol Tolerance for checking that the optimum is correct.
*/
private void doTest(MultivariateFunction func,
double[] optimum,
double[] init,
GoalType goal,
double fTol,
double fLineTol,
double pointTol) {
final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
fLineTol, Math.ulp(1d));
final PointValuePair result = optim.optimize(new MaxEval(1000),
new ObjectiveFunction(func),
goal,
new InitialGuess(init));
final double[] point = result.getPoint();
for (int i = 0, dim = optimum.length; i < dim; i++) {
Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
optimum[i], point[i], pointTol);
}
Assert.assertTrue(optim.getIterations() > 0);
}
Example #27
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 180);
Assert.assertTrue(optimizer.getEvaluations() < 220);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #28
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MAXIMIZE,
new InitialGuess(new double[] { -3.0, 0.0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #29
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 1, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
Example #30
| Source File: SimplexOptimizerMultiDirectionalTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
final FourExtrema fourExtrema = new FourExtrema();
final PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
new ObjectiveFunction(fourExtrema),
GoalType.MINIMIZE,
new InitialGuess(new double[] { -3, 0 }),
new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
// Check that the number of iterations is updated (MATH-949).
Assert.assertTrue(optimizer.getIterations() > 0);
}
