Java Code Examples for org.apache.commons.math3.optim.PointValuePair#getPointRef()
The following examples show how to use
org.apache.commons.math3.optim.PointValuePair#getPointRef() .
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Example 1
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFitting() {
CircleScalar problem = new CircleScalar();
problem.addPoint( 30.0, 68.0);
problem.addPoint( 50.0, -6.0);
problem.addPoint(110.0, -20.0);
problem.addPoint( 35.0, 15.0);
problem.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-30, 1e-30),
1e-15, 1e-13, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.960161753, problem.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-7);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-6);
}
Example 2
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFitting() {
CircleScalar problem = new CircleScalar();
problem.addPoint( 30.0, 68.0);
problem.addPoint( 50.0, -6.0);
problem.addPoint(110.0, -20.0);
problem.addPoint( 35.0, 15.0);
problem.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-30, 1e-30),
new BrentSolver(1e-15, 1e-13));
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.960161753, problem.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
}
Example 3
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFitting() {
CircleScalar problem = new CircleScalar();
problem.addPoint( 30.0, 68.0);
problem.addPoint( 50.0, -6.0);
problem.addPoint(110.0, -20.0);
problem.addPoint( 35.0, 15.0);
problem.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-30, 1e-30),
new BrentSolver(1e-15, 1e-13));
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.960161753, problem.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
}
Example 4
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFitting() {
CircleScalar problem = new CircleScalar();
problem.addPoint( 30.0, 68.0);
problem.addPoint( 50.0, -6.0);
problem.addPoint(110.0, -20.0);
problem.addPoint( 35.0, 15.0);
problem.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-30, 1e-30),
new BrentSolver(1e-15, 1e-13));
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.960161753, problem.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
}
Example 5
| Source File: NonLinearConjugateGradientOptimizerTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
@Test
public void testCircleFitting() {
CircleScalar problem = new CircleScalar();
problem.addPoint( 30.0, 68.0);
problem.addPoint( 50.0, -6.0);
problem.addPoint(110.0, -20.0);
problem.addPoint( 35.0, 15.0);
problem.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-30, 1e-30),
1e-15, 1e-13, 1);
PointValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getObjectiveFunction(),
problem.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertEquals(69.960161753, problem.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-7);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-6);
}
Example 6
| Source File: AbstractSimplex.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Evaluate all the non-evaluated points of the simplex.
*
* @param evaluationFunction Evaluation function.
* @param comparator Comparator to use to sort simplex vertices from best to worst.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
public void evaluate(final MultivariateFunction evaluationFunction,
final Comparator<PointValuePair> comparator) {
// Evaluate the objective function at all non-evaluated simplex points.
for (int i = 0; i < simplex.length; i++) {
final PointValuePair vertex = simplex[i];
final double[] point = vertex.getPointRef();
if (Double.isNaN(vertex.getValue())) {
simplex[i] = new PointValuePair(point, evaluationFunction.value(point), false);
}
}
// Sort the simplex from best to worst.
Arrays.sort(simplex, comparator);
}
Example 7
| Source File: AbstractSimplex.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Evaluate all the non-evaluated points of the simplex.
*
* @param evaluationFunction Evaluation function.
* @param comparator Comparator to use to sort simplex vertices from best to worst.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
public void evaluate(final MultivariateFunction evaluationFunction,
final Comparator<PointValuePair> comparator) {
// Evaluate the objective function at all non-evaluated simplex points.
for (int i = 0; i < simplex.length; i++) {
final PointValuePair vertex = simplex[i];
final double[] point = vertex.getPointRef();
if (Double.isNaN(vertex.getValue())) {
simplex[i] = new PointValuePair(point, evaluationFunction.value(point), false);
}
}
// Sort the simplex from best to worst.
Arrays.sort(simplex, comparator);
}
Example 8
| Source File: MultiStartMultivariateOptimizerTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
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);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
GradientMultivariateOptimizer underlying
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-10, 1e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
new double[] { 10, 10 },
new GaussianRandomGenerator(g));
MultiStartMultivariateOptimizer optimizer
= new MultiStartMultivariateOptimizer(underlying, 10, generator);
PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
circle.getObjectiveFunction(),
circle.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Assert.assertEquals(200, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
for (PointValuePair o : optima) {
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertEquals(69.960161753, circle.getRadius(center), 1e-8);
Assert.assertEquals(96.075902096, center.getX(), 1e-8);
Assert.assertEquals(48.135167894, center.getY(), 1e-8);
}
Assert.assertTrue(optimizer.getEvaluations() > 70);
Assert.assertTrue(optimizer.getEvaluations() < 90);
Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
}
Example 9
| Source File: AbstractSimplex.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Evaluate all the non-evaluated points of the simplex.
*
* @param evaluationFunction Evaluation function.
* @param comparator Comparator to use to sort simplex vertices from best to worst.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
public void evaluate(final MultivariateFunction evaluationFunction,
final Comparator<PointValuePair> comparator) {
// Evaluate the objective function at all non-evaluated simplex points.
for (int i = 0; i < simplex.length; i++) {
final PointValuePair vertex = simplex[i];
final double[] point = vertex.getPointRef();
if (Double.isNaN(vertex.getValue())) {
simplex[i] = new PointValuePair(point, evaluationFunction.value(point), false);
}
}
// Sort the simplex from best to worst.
Arrays.sort(simplex, comparator);
}
Example 10
| Source File: MultiStartMultivariateOptimizerTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
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);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
GradientMultivariateOptimizer underlying
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-10, 1e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
new double[] { 10, 10 },
new GaussianRandomGenerator(g));
MultiStartMultivariateOptimizer optimizer
= new MultiStartMultivariateOptimizer(underlying, 10, generator);
PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
circle.getObjectiveFunction(),
circle.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Assert.assertEquals(200, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
for (PointValuePair o : optima) {
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertEquals(69.960161753, circle.getRadius(center), 1e-8);
Assert.assertEquals(96.075902096, center.getX(), 1e-8);
Assert.assertEquals(48.135167894, center.getY(), 1e-8);
}
Assert.assertTrue(optimizer.getEvaluations() > 70);
Assert.assertTrue(optimizer.getEvaluations() < 90);
Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
}
Example 11
| Source File: AbstractSimplex.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Evaluate all the non-evaluated points of the simplex.
*
* @param evaluationFunction Evaluation function.
* @param comparator Comparator to use to sort simplex vertices from best to worst.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
public void evaluate(final MultivariateFunction evaluationFunction,
final Comparator<PointValuePair> comparator) {
// Evaluate the objective function at all non-evaluated simplex points.
for (int i = 0; i < simplex.length; i++) {
final PointValuePair vertex = simplex[i];
final double[] point = vertex.getPointRef();
if (Double.isNaN(vertex.getValue())) {
simplex[i] = new PointValuePair(point, evaluationFunction.value(point), false);
}
}
// Sort the simplex from best to worst.
Arrays.sort(simplex, comparator);
}
Example 12
| Source File: MultiStartMultivariateOptimizerTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
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);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
GradientMultivariateOptimizer underlying
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-10, 1e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
new double[] { 10, 10 },
new GaussianRandomGenerator(g));
MultiStartMultivariateOptimizer optimizer
= new MultiStartMultivariateOptimizer(underlying, 10, generator);
PointValuePair optimum
= optimizer.optimize(new MaxEval(200),
circle.getObjectiveFunction(),
circle.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Assert.assertEquals(200, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
for (PointValuePair o : optima) {
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertEquals(69.960161753, circle.getRadius(center), 1e-8);
Assert.assertEquals(96.075902096, center.getX(), 1e-8);
Assert.assertEquals(48.135167894, center.getY(), 1e-8);
}
Assert.assertTrue(optimizer.getEvaluations() > 70);
Assert.assertTrue(optimizer.getEvaluations() < 90);
Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
}
Example 13
| Source File: AbstractSimplex.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Evaluate all the non-evaluated points of the simplex.
*
* @param evaluationFunction Evaluation function.
* @param comparator Comparator to use to sort simplex vertices from best to worst.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
*/
public void evaluate(final MultivariateFunction evaluationFunction,
final Comparator<PointValuePair> comparator) {
// Evaluate the objective function at all non-evaluated simplex points.
for (int i = 0; i < simplex.length; i++) {
final PointValuePair vertex = simplex[i];
final double[] point = vertex.getPointRef();
if (Double.isNaN(vertex.getValue())) {
simplex[i] = new PointValuePair(point, evaluationFunction.value(point), false);
}
}
// Sort the simplex from best to worst.
Arrays.sort(simplex, comparator);
}
Example 14
| Source File: MultiStartMultivariateOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
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);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
GradientMultivariateOptimizer underlying
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-10, 1e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
new double[] { 10, 10 },
new GaussianRandomGenerator(g));
int nbStarts = 10;
MultiStartMultivariateOptimizer optimizer
= new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
PointValuePair optimum
= optimizer.optimize(new MaxEval(1000),
circle.getObjectiveFunction(),
circle.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Assert.assertEquals(1000, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
Assert.assertEquals(nbStarts, optima.length);
for (PointValuePair o : optima) {
// we check the results of all intermediate restarts here (there are 10 such results)
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertTrue(69.9592 < circle.getRadius(center));
Assert.assertTrue(69.9602 > circle.getRadius(center));
Assert.assertTrue(96.0745 < center.getX());
Assert.assertTrue(96.0762 > center.getX());
Assert.assertTrue(48.1344 < center.getY());
Assert.assertTrue(48.1354 > center.getY());
}
Assert.assertTrue(optimizer.getEvaluations() > 850);
Assert.assertTrue(optimizer.getEvaluations() < 900);
Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
}
Example 15
| Source File: MultiStartMultivariateOptimizerTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
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);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
GradientMultivariateOptimizer underlying
= new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
new SimpleValueChecker(1e-10, 1e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
new double[] { 10, 10 },
new GaussianRandomGenerator(g));
int nbStarts = 10;
MultiStartMultivariateOptimizer optimizer
= new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
PointValuePair optimum
= optimizer.optimize(new MaxEval(1000),
circle.getObjectiveFunction(),
circle.getObjectiveFunctionGradient(),
GoalType.MINIMIZE,
new InitialGuess(new double[] { 98.680, 47.345 }));
Assert.assertEquals(1000, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
Assert.assertEquals(nbStarts, optima.length);
for (PointValuePair o : optima) {
// we check the results of all intermediate restarts here (there are 10 such results)
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertTrue(69.9592 < circle.getRadius(center));
Assert.assertTrue(69.9602 > circle.getRadius(center));
Assert.assertTrue(96.0745 < center.getX());
Assert.assertTrue(96.0762 > center.getX());
Assert.assertTrue(48.1344 < center.getY());
Assert.assertTrue(48.1354 > center.getY());
}
Assert.assertTrue(optimizer.getEvaluations() > 850);
Assert.assertTrue(optimizer.getEvaluations() < 900);
Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
}
