Package org.apache.commons.math3.optim.nonlinear.vector

Examples of org.apache.commons.math3.optim.nonlinear.vector.Target

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        try {
            PointVectorValuePair optimum
                = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
                                     function.getModelFunction(),
                                     function.getModelFunctionJacobian(),
                                     new Target(function.getTarget()),
                                     new Weight(function.getWeight()),
                                     new InitialGuess(function.getStartPoint()));
            Assert.assertFalse(exceptionExpected);
            function.checkTheoreticalMinCost(optimizer.getRMS());
            function.checkTheoreticalMinParams(optimum);
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        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();
        final PointVectorValuePair optimum = optimizer.optimize(new MaxEval(100),
                                                                circle.getModelFunction(),
                                                                circle.getModelFunctionJacobian(),
                                                                new Target(circle.target()),
                                                                new Weight(circle.weight()),
                                                                new InitialGuess(init));

        final double[] paramFound = optimum.getPoint();
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            = dataset.getLeastSquaresProblem();

        optimizer.optimize(new MaxEval(1),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           new Target(y),
                           new Weight(w),
                           new InitialGuess(a));
        final double expected = dataset.getResidualSumOfSquares();
        final double actual = optimizer.getChiSquare();
        Assert.assertEquals(dataset.getName(), expected, actual,
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            = dataset.getLeastSquaresProblem();

        optimizer.optimize(new MaxEval(1),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           new Target(y),
                           new Weight(w),
                           new InitialGuess(a));

        final double expected = FastMath
            .sqrt(dataset.getResidualSumOfSquares() /
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(1),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(y),
                                 new Weight(w),
                                 new InitialGuess(a));

        final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
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            // covariance matrix).
            final PointVectorValuePair optimum
                = optim.optimize(new MaxEval(Integer.MAX_VALUE),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(problem.target()),
                                 new Weight(problem.weight()),
                                 new InitialGuess(init));
            final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);

            // Accumulate statistics.
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        final double[] w = problem.weight();

        optim.optimize(new MaxEval(Integer.MAX_VALUE),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       new Target(t),
                       new Weight(w),
                       new InitialGuess(params));

        return optim.getChiSquare() / (t.length - params.length);
    }
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                                                  orthoTolerance,
                                                  Precision.SAFE_MIN);
            optimizer.optimize(new MaxEval(maxCostEval),
                               problem,
                               problemJacobian,
                               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 }));
            Assert.assertTrue(!shouldFail);
        } catch (DimensionMismatchException ee) {
            Assert.assertTrue(shouldFail);
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(dataPoints[1]),
                                 new Weight(weights),
                                 new InitialGuess(new double[] { 10, 900, 80, 27, 225 }));

        final double[] solution = optimum.getPoint();
        final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };
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        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 }));
    }
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Related Classes of org.apache.commons.math3.optim.nonlinear.vector.Target

  • 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.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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