Examples of CircleScalar

Examples of org.apache.commons.math3.optim.nonlinear.scalar.gradient.CircleScalar

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import org.junit.Test;

public class MultiStartMultivariateOptimizerTest {
    @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());
        }
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Examples of org.apache.commons.math3.optim.nonlinear.scalar.gradient.CircleScalar

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import org.junit.Test;

public class MultiStartMultivariateOptimizerTest {
    @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);
View Full Code Here

Examples of org.apache.commons.math3.optimization.general.CircleScalar

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@Deprecated
public class MultivariateDifferentiableMultiStartOptimizerTest {

    @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
        MultivariateDifferentiableOptimizer underlying =
                new MultivariateDifferentiableOptimizer() {

            private final NonLinearConjugateGradientOptimizer cg =
                    new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                            new SimpleValueChecker(1.0e-10, 1.0e-10));
            public PointValuePair optimize(int maxEval,
                                           MultivariateDifferentiableFunction f,
                                           GoalType goalType,
                                           double[] startPoint) {
                return cg.optimize(maxEval, f, goalType, startPoint);
            }

            public int getMaxEvaluations() {
                return cg.getMaxEvaluations();
            }

            public int getEvaluations() {
                return cg.getEvaluations();
            }

            public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
                return cg.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(new double[] { 50.0, 50.0 }, new double[] { 10.0, 10.0 },
                                                  new GaussianRandomGenerator(g));
        MultivariateDifferentiableMultiStartOptimizer optimizer =
            new MultivariateDifferentiableMultiStartOptimizer(underlying, 10, generator);
        PointValuePair optimum =
            optimizer.optimize(200, circle, GoalType.MINIMIZE, 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), 1.0e-8);
            Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
            Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
        }
        Assert.assertTrue(optimizer.getEvaluations() > 70);
        Assert.assertTrue(optimizer.getEvaluations() < 90);
View Full Code Here

Examples of org.apache.commons.math3.optimization.general.CircleScalar

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public class MultivariateDifferentiableMultiStartOptimizerTest {

    @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
        MultivariateDifferentiableOptimizer underlying =
                new MultivariateDifferentiableOptimizer() {

            private final NonLinearConjugateGradientOptimizer cg =
                    new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                            new SimpleValueChecker(1.0e-10, 1.0e-10));
            public PointValuePair optimize(int maxEval,
                                           MultivariateDifferentiableFunction f,
                                           GoalType goalType,
                                           double[] startPoint) {
                return cg.optimize(maxEval, f, goalType, startPoint);
            }

            public int getMaxEvaluations() {
                return cg.getMaxEvaluations();
            }

            public int getEvaluations() {
                return cg.getEvaluations();
            }

            public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
                return cg.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(new double[] { 50.0, 50.0 }, new double[] { 10.0, 10.0 },
                                                  new GaussianRandomGenerator(g));
        MultivariateDifferentiableMultiStartOptimizer optimizer =
            new MultivariateDifferentiableMultiStartOptimizer(underlying, 10, generator);
        PointValuePair optimum =
            optimizer.optimize(200, circle, GoalType.MINIMIZE, 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), 1.0e-8);
            Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
            Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
        }
        Assert.assertTrue(optimizer.getEvaluations() > 70);
        Assert.assertTrue(optimizer.getEvaluations() < 90);
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