Package org.apache.commons.math3.analysis

Examples of org.apache.commons.math3.analysis.SinFunction

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        }
    }

    @Test
    public void testMinEndpoints() {
        UnivariateFunction f = new SinFunction();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-8, 1e-14);

        // endpoint is minimum
        double result = optimizer.optimize(50, f, GoalType.MINIMIZE, 3 * Math.PI / 2, 5).getPoint();
        Assert.assertEquals(3 * Math.PI / 2, result, 1e-6);
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public class UnivariateMultiStartOptimizerTest {

    @Test
    public void testSinMin() {
        UnivariateFunction f = new SinFunction();
        UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(44428400075l);
        UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
            new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 10, g);
        optimizer.optimize(300, f, GoalType.MINIMIZE, -100.0, 100.0);
        UnivariatePointValuePair[] optima = optimizer.getOptima();
        for (int i = 1; i < optima.length; ++i) {
            double d = (optima[i].getPoint() - optima[i-1].getPoint()) / (2 * FastMath.PI);
            Assert.assertTrue(FastMath.abs(d - FastMath.rint(d)) < 1.0e-8);
            Assert.assertEquals(-1.0, f.value(optima[i].getPoint()), 1.0e-10);
            Assert.assertEquals(f.value(optima[i].getPoint()), optima[i].getValue(), 1.0e-10);
        }
        Assert.assertTrue(optimizer.getEvaluations() > 200);
        Assert.assertTrue(optimizer.getEvaluations() < 300);
    }
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    /**
     *
     */
    @Test
    public void testSinZero() {
        DifferentiableUnivariateFunction f = new SinFunction();
        double result;

        NewtonSolver solver = new NewtonSolver();
        result = solver.solve(100, f, 3, 4);
        Assert.assertEquals(result, FastMath.PI, solver.getAbsoluteAccuracy());
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    @Test
    public void testSinZero() {
        // The sinus function is behaved well around the root at pi. The second
        // order derivative is zero, which means linear approximating methods
        // still converge quadratically.
        UnivariateFunction f = new SinFunction();
        double result;
        UnivariateSolver solver = getSolver();

        result = solver.solve(100, f, 3, 4);
        //System.out.println(
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        Assert.assertEquals(5.0, result, 0.0);
    }

    @Test
    public void testBadEndpoints() {
        UnivariateFunction f = new SinFunction();
        UnivariateSolver solver = getSolver();
        try {  // bad interval
            solver.solve(100, f, 1, -1);
            Assert.fail("Expecting NumberIsTooLargeException - bad interval");
        } catch (NumberIsTooLargeException ex) {
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        }
    }

    @Test
    public void testSolutionLeftSide() {
        UnivariateFunction f = new SinFunction();
        UnivariateSolver solver = getSolver();
        double left = -1.5;
        double right = 0.05;
        for(int i = 0; i < 10; i++) {
            // Test whether the allowed solutions are taken into account.
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        }
    }

    @Test
    public void testSolutionRightSide() {
        UnivariateFunction f = new SinFunction();
        UnivariateSolver solver = getSolver();
        double left = -1.5;
        double right = 0.05;
        for(int i = 0; i < 10; i++) {
            // Test whether the allowed solutions are taken into account.
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            right += 0.3;
        }
    }
    @Test
    public void testSolutionBelowSide() {
        UnivariateFunction f = new SinFunction();
        UnivariateSolver solver = getSolver();
        double left = -1.5;
        double right = 0.05;
        for(int i = 0; i < 10; i++) {
            // Test whether the allowed solutions are taken into account.
            double solution = getSolution(solver, 100, f, left, right, AllowedSolution.BELOW_SIDE);
            if (!Double.isNaN(solution)) {
                Assert.assertTrue(f.value(solution) <= 0.0);
            }

            // Prepare for next test.
            left -= 0.1;
            right += 0.3;
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        }
    }

    @Test
    public void testSolutionAboveSide() {
        UnivariateFunction f = new SinFunction();
        UnivariateSolver solver = getSolver();
        double left = -1.5;
        double right = 0.05;
        for(int i = 0; i < 10; i++) {
            // Test whether the allowed solutions are taken into account.
            double solution = getSolution(solver, 100, f, left, right, AllowedSolution.ABOVE_SIDE);
            if (!Double.isNaN(solution)) {
                Assert.assertTrue(f.value(solution) >= 0.0);
            }

            // Prepare for next test.
            left -= 0.1;
            right += 0.3;
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    /**
     * Test of integrator for the sine function.
     */
    @Test
    public void testSinFunction() {
        UnivariateFunction f = new SinFunction();
        UnivariateIntegrator integrator = new TrapezoidIntegrator();
        double min, max, expected, result, tolerance;

        min = 0; max = FastMath.PI; expected = 2;
        tolerance = FastMath.abs(expected * integrator.getRelativeAccuracy());
 
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Related Classes of org.apache.commons.math3.analysis.SinFunction

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.integration.RombergIntegratorTest
  • org.apache.commons.math3.analysis.integration.SimpsonIntegratorTest
  • org.apache.commons.math3.analysis.integration.TrapezoidIntegratorTest
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
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