org.apache.commons.math3.optim.InitialGuess Java Examples
The following examples show how to use
org.apache.commons.math3.optim.InitialGuess.
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Example #1
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMoreEstimatedParametersSimple() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 3.0, 2.0, 0.0, 0.0 },
{ 0.0, 1.0, -1.0, 1.0 },
{ 2.0, 0.0, 1.0, 0.0 }
}, new double[] { 7.0, 3.0, 5.0 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 7, 6, 5, 4 }));
Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
}
Example #2
| 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 #3
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testColumnsPermutation() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
new double[] { 4.0, 6.0, 1.0 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6),
1e-3, 1e-3, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
Assert.assertEquals(0.0, optimum.getValue(), 1.0e-10);
}
Example #4
| Source File: LevenbergMarquardtOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Override
@Test(expected=SingularMatrixException.class)
public void testNonInvertible() {
/*
* Overrides the method from parent class, since the default singularity
* threshold (1e-14) does not trigger the expected exception.
*/
LinearProblem problem = new LinearProblem(new double[][] {
{ 1, 2, -3 },
{ 2, 1, 3 },
{ -3, 0, -9 }
}, new double[] { 1, 1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1, 1 }),
new InitialGuess(new double[] { 0, 0, 0 }));
Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);
optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
}
Example #5
| 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 #6
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testQRColumnsPermutation() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
new double[] { 4, 6, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
}
Example #7
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=ConvergenceException.class)
public void testNonInvertible() throws Exception {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1, 2, -3 },
{ 2, 1, 3 },
{ -3, 0, -9 }
}, new double[] { 1, 1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1, 1 }),
new InitialGuess(new double[] { 0, 0, 0 }));
}
Example #8
| 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 #9
| 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 #10
| 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 #11
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testOneSet() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1, 0, 0 },
{ -1, 1, 0 },
{ 0, -1, 1 }
}, new double[] { 1, 1, 1});
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1, 1 }),
new InitialGuess(new double[] { 0, 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
}
Example #12
| 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 #13
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFittingGoodInit() {
CircleVectorial circle = new CircleVectorial();
double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0);
double[] weights = new double[points.length];
Arrays.fill(weights, 2);
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(target),
new Weight(weights),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
}
Example #14
| Source File: MultivariateFunctionMappingAdapterTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testOptimumOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
Example #15
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testNoDependency() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 2, 0, 0, 0, 0, 0 },
{ 0, 2, 0, 0, 0, 0 },
{ 0, 0, 2, 0, 0, 0 },
{ 0, 0, 0, 2, 0, 0 },
{ 0, 0, 0, 0, 2, 0 },
{ 0, 0, 0, 0, 0, 2 }
}, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6),
1e-3, 1e-3, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
for (int i = 0; i < problem.target.length; ++i) {
Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
}
}
Example #16
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testNonInversible() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1, 2, -3 },
{ 2, 1, 3 },
{ -3, 0, -9 }
}, new double[] { 1, 1, 1 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6),
1e-3, 1e-3, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0, 0 }));
Assert.assertTrue(optimum.getValue() > 0.5);
}
Example #17
| 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 #18
| Source File: MultivariateFunctionMappingAdapterTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testStartSimplexInsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
Example #19
| Source File: GaussNewtonOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
optimizer.optimize(new MaxEval(100),
circle.getModelFunction(),
circle.getModelFunctionJacobian(),
new Target(new double[] { 0, 0, 0, 0, 0 }),
new Weight(new double[] { 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 98.680, 47.345 }));
}
Example #20
| 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 #21
| Source File: MultivariateFunctionPenaltyAdapterTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testStartSimplexOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionPenaltyAdapter wrapped
= new MultivariateFunctionPenaltyAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[] { 1.0, 0.5 });
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(new double[] { -1.5, 4.0 }));
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);
}
Example #22
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
LinearProblem problem
= new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
new double[] { -1, 1 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1 }),
new InitialGuess(new double[] { 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(new double[] { 1 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0, 0 }));
}
Example #23
| Source File: AbstractLeastSquaresOptimizerAbstractTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testNoDependency() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 2, 0, 0, 0, 0, 0 },
{ 0, 2, 0, 0, 0, 0 },
{ 0, 0, 2, 0, 0, 0 },
{ 0, 0, 0, 2, 0, 0 },
{ 0, 0, 0, 0, 2, 0 },
{ 0, 0, 0, 0, 0, 2 }
}, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
AbstractLeastSquaresOptimizer optimizer = createOptimizer();
PointVectorValuePair optimum =
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
for (int i = 0; i < problem.target.length; ++i) {
Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
}
}
Example #24
| 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 #25
| Source File: MultivariateFunctionMappingAdapterTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testStartSimplexInsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
Example #26
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testOneSet() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1, 0, 0 },
{ -1, 1, 0 },
{ 0, -1, 1 }
}, new double[] { 1, 1, 1});
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6),
1e-3, 1e-3, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 0, 0, 0 }));
Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
}
Example #27
| 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 #28
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testMoreEstimatedParametersUnsorted() {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 1.0, 1.0, 1.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 1.0, -1.0 },
{ 0.0, 0.0, -1.0, 1.0, 0.0, 1.0 },
{ 0.0, 0.0, 0.0, -1.0, 1.0, 0.0 }
}, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-6, 1e-6),
1e-3, 1e-3, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
}
Example #29
| 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 #30
| Source File: MultivariateFunctionMappingAdapterTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testOptimumOutsideRange() {
final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, 1.0, 3.0, 2.0, 3.0);
final MultivariateFunctionMappingAdapter wrapped
= new MultivariateFunctionMappingAdapter(biQuadratic,
biQuadratic.getLower(),
biQuadratic.getUpper());
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
final AbstractSimplex simplex = new NelderMeadSimplex(new double[][] {
wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
});
final PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
new ObjectiveFunction(wrapped),
simplex,
GoalType.MINIMIZE,
new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());
Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);
}
