Example usage for org.apache.commons.math3.fitting.leastsquares LeastSquaresBuilder LeastSquaresBuilder

Introduction

In this page you can find the example usage for org.apache.commons.math3.fitting.leastsquares LeastSquaresBuilder LeastSquaresBuilder.

Prototype

LeastSquaresBuilder

Source Link

Usage

From source file:org.orekit.utils.SecularAndHarmonic.java

/** Fit parameters.
 * @see #getFittedParameters()/*from w w  w.jav  a  2 s.c o m*/
 */
public void fit() {

    final AbstractCurveFitter fitter = new AbstractCurveFitter() {
        /** {@inheritDoc} */
        @Override
        protected LeastSquaresProblem getProblem(final Collection<WeightedObservedPoint> observations) {
            // Prepare least-squares problem.
            final int len = observations.size();
            final double[] target = new double[len];
            final double[] weights = new double[len];

            int i = 0;
            for (final WeightedObservedPoint obs : observations) {
                target[i] = obs.getY();
                weights[i] = obs.getWeight();
                ++i;
            }

            final AbstractCurveFitter.TheoreticalValuesFunction model = new AbstractCurveFitter.TheoreticalValuesFunction(
                    new LocalParametricFunction(), observations);

            // build a new least squares problem set up to fit a secular and harmonic curve to the observed points
            return new LeastSquaresBuilder().maxEvaluations(Integer.MAX_VALUE).maxIterations(Integer.MAX_VALUE)
                    .start(fitted).target(target).weight(new DiagonalMatrix(weights))
                    .model(model.getModelFunction(), model.getModelFunctionJacobian()).build();

        }
    };

    fitted = fitter.fit(observedPoints);

}

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From source file:uk.ac.diamond.scisoft.analysis.optimize.ApacheOptimizer.java

/**
 * create a multivariateJacobianFunction from MVF and MMF (using builder?)
 * /*  www  . j  a  v a 2 s  .  c o m*/
 */
private void internalLeastSquaresOptimize() {
    LeastSquaresOptimizer opt = createLeastSquaresOptimizer();

    try {

        LeastSquaresBuilder builder = new LeastSquaresBuilder().model(createJacobianFunction())
                .target(data.getData()).start(getParameterValues()).lazyEvaluation(false)
                .maxEvaluations(MAX_EVAL).maxIterations(MAX_ITER);

        builder.checker(new EvaluationRmsChecker(REL_TOL, ABS_TOL));

        if (weight != null) {
            builder.weight(MatrixUtils.createRealDiagonalMatrix(weight.getData()));
        }

        // TODO add checker, validator
        LeastSquaresProblem problem = builder.build();

        Optimum result = opt.optimize(problem);

        RealVector res = result.getPoint();
        setParameterValues(
                res instanceof ArrayRealVector ? ((ArrayRealVector) res).getDataRef() : res.toArray());
        try {
            RealVector err = result.getSigma(1e-14);

            //            sqrt(S / (n - m) * C[i][i]);
            double c = result.getCost();
            int n = data.getSize();
            int m = getParameterValues().length;

            double[] s = err instanceof ArrayRealVector ? ((ArrayRealVector) err).getDataRef() : err.toArray();

            errors = new double[s.length];

            for (int i = 0; i < errors.length; i++)
                errors[i] = Math.sqrt(((c * c) / ((n - m)) * (s[i] * s[i])));

        } catch (SingularMatrixException e) {
            logger.warn("Could not find errors as covariance matrix was singular");
        }

        logger.trace("Residual: {} from {}", result.getRMS(), Math.sqrt(calculateResidual()));
    } catch (Exception e) {
        logger.error("Problem with least squares optimizer", e);
        throw new IllegalArgumentException("Problem with least squares optimizer");
    }
}

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