Examples of org.apache.commons.math.optimization.VectorialPointValuePair

Package org.apache.commons.math.optimization

Examples of org.apache.commons.math.optimization.VectorialPointValuePair

org.apache.commons.math.optimization.VectorialPointValuePair
This class holds a point and the vectorial value of an objective function at this point.
This is a simple immutable container.
@see RealPointValuePair @see org.apache.commons.math.analysis.MultivariateVectorialFunction @version $Revision: 980981 $ $Date: 2010-07-31 00:03:04 +0200 (sam. 31 juil. 2010) $ @since 2.0


    public void testTrivial() throws FunctionEvaluationException, OptimizationException {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1 }, new double[] { 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        try {
            optimizer.guessParametersErrors();
            fail("an exception should have been thrown");
        } catch (OptimizationException ee) {
            // expected behavior
        } catch (Exception e) {
            fail("wrong exception caught");
        }
        assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
    }

View Full Code Here

        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 });


        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
        assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
        assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
        assertEquals(1.0, optimum.getValue()[2], 1.0e-10);


    }

View Full Code Here

                { 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 });
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        for (int i = 0; i < problem.target.length; ++i) {
            assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
        }
    }

View Full Code Here

                {  1,  0, 0 },
                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
        assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);


    }

View Full Code Here

                {  0,  0,   0,  0, epsilon, 1 },
                {  0,  0,   0,  0,       1, 1 }
        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});


        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
        assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
        assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
        assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
        assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);


    }

View Full Code Here

                {  7.0, 5.0,  6.0,  5.0 },
                {  8.0, 6.0, 10.0,  9.0 },
                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum1 =
            optimizer.optimize(problem1, problem1.target, new double[] { 1, 1, 1, 1 },
                               new double[] { 0, 1, 2, 3 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);
        assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10);
        assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10);
        assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10);


        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        VectorialPointValuePair optimum2 =
            optimizer.optimize(problem2, problem2.target, new double[] { 1, 1, 1, 1 },
                               new double[] { 0, 1, 2, 3 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8);
        assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8);
        assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8);
        assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8);


    }

View Full Code Here

                { 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 });


        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 2, 2, 2, 2, 2, 2 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(3.0, optimum.getPointRef()[2], 1.0e-10);
        assertEquals(4.0, optimum.getPointRef()[3], 1.0e-10);
        assertEquals(5.0, optimum.getPointRef()[4], 1.0e-10);
        assertEquals(6.0, optimum.getPointRef()[5], 1.0e-10);


    }

View Full Code Here

                { 1.0, -1.0 },
                { 1.0,  3.0 }
        }, new double[] { 3.0, 1.0, 5.0 });


        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 },
                               new double[] { 1, 1 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(2.0, optimum.getPointRef()[0], 1.0e-10);
        assertEquals(1.0, optimum.getPointRef()[1], 1.0e-10);


    }

View Full Code Here

    public void testInconsistentSizes() throws FunctionEvaluationException, OptimizationException {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();


        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
        assertEquals(+1, optimum.getPoint()[1], 1.0e-10);


        try {
            optimizer.optimize(problem, problem.target,
                               new double[] { 1 },
                               new double[] { 0, 0 });

View Full Code Here

        circle.addPoint( 50.0,  -6.0);
        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        VectorialPointValuePair optimum =
            optimizer.optimize(circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 98.680, 47.345 });
        assertTrue(optimizer.getEvaluations() < 10);
        assertTrue(optimizer.getJacobianEvaluations() < 10);
        double rms = optimizer.getRMS();
        assertEquals(1.768262623567235,  Math.sqrt(circle.getN()) * rms,  1.0e-10);
        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        assertEquals(69.96016176931406, circle.getRadius(center), 1.0e-10);
        assertEquals(96.07590211815305, center.x,      1.0e-10);
        assertEquals(48.13516790438953, center.y,      1.0e-10);
        double[][] cov = optimizer.getCovariances();
        assertEquals(1.839, cov[0][0], 0.001);

View Full Code Here

0 1 2 3 4 5 6 7 8

TOP

Related Classes of org.apache.commons.math.optimization.VectorialPointValuePair

newExamples.archive.Exoplanet

newExamples.FittingRun

org.apache.commons.math.optimization.fitting.CurveFitter

org.apache.commons.math.optimization.general.GaussNewtonOptimizer

org.apache.commons.math.optimization.general.GaussNewtonOptimizerTest

org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer

org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizerTest

org.apache.commons.math.optimization.general.MinpackTest

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.