Examples of org.apache.commons.math3.optimization.LeastSquaresConverter

Package org.apache.commons.math3.optimization

Examples of org.apache.commons.math3.optimization.LeastSquaresConverter

org.apache.commons.math3.linear.Array2DRowRealMatrix
This class converts {@link MultivariateVectorFunction vectorialobjective functions} to {@link MultivariateFunction scalar objective functions}when the goal is to minimize them.
This class is mostly used when the vectorial objective function represents a theoretical result computed from a point set applied to a model and the models point must be adjusted to fit the theoretical result to some reference observations. The observations may be obtained for example from physical measurements whether the model is built from theoretical considerations.

This class computes a possibly weighted squared sum of the residuals, which is a scalar value. The residuals are the difference between the theoretical model (i.e. the output of the vectorial objective function) and the observations. The class implements the {@link MultivariateFunction} interface and can therefore beminimized by any optimizer supporting scalar objectives functions.This is one way to perform a least square estimation. There are other ways to do this without using this converter, as some optimization algorithms directly support vectorial objective functions.

This class support combination of residuals with or without weights and correlations.
@see MultivariateFunction @see MultivariateVectorFunction @deprecated As of 3.1 (to be removed in 4.0). @since 2.0

        if (lo >= hi) {
            throw new NumberIsTooLargeException(lo, hi, false);
        }
        if (init < lo ||
            init > hi) {
            throw new OutOfRangeException(init, lo, hi);
        }


        lower = lo;
        upper = hi;
        start = init;

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    @Override
    public double inverseCumulativeProbability(double p)
        throws OutOfRangeException {
        if (p < 0 || p > 1) {
            throw new OutOfRangeException(p, 0, 1);
        }
        if (p == 0) {
            return a;
        }
        if (p == 1) {

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        /**
         * {@inheritDoc}
         * @throws TooManyEvaluationsException.
         */
        public void trigger(int max) {
            throw new TooManyEvaluationsException(max);
        }

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        /**
         * {@inheritDoc}
         * @throws TooManyIterationsException.
         */
        public void trigger(int max) {
            throw new TooManyIterationsException(max);
        }

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                                              double threshold) {
        super(wrong, threshold, false);
        this.index = index;
        this.threshold = threshold;


        final ExceptionContext context = getContext();
        context.addMessage(LocalizedFormats.NOT_POSITIVE_DEFINITE_MATRIX);
        context.addMessage(LocalizedFormats.ARRAY_ELEMENT, wrong, index);
    }

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            final double t = x.dotProduct(z);
            final double epsa = (s + MACH_PREC) * CBRT_MACH_PREC;
            if (FastMath.abs(s - t) > epsa) {
                final NonSelfAdjointOperatorException e;
                e = new NonSelfAdjointOperatorException();
                final ExceptionContext context = e.getContext();
                context.setValue(SymmLQ.OPERATOR, l);
                context.setValue(SymmLQ.VECTOR1, x);
                context.setValue(SymmLQ.VECTOR2, y);
                context.setValue(SymmLQ.THRESHOLD, Double.valueOf(epsa));
                throw e;
            }
        }

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         */
        private static void throwNPDLOException(final RealLinearOperator l,
            final RealVector v) throws NonPositiveDefiniteOperatorException {
            final NonPositiveDefiniteOperatorException e;
            e = new NonPositiveDefiniteOperatorException();
            final ExceptionContext context = e.getContext();
            context.setValue(OPERATOR, l);
            context.setValue(VECTOR, v);
            throw e;
        }

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            // build the P matrix elements from Taylor series formulas
            final BigFraction[] pI = pData[i];
            final int factor = -(i + 1);
            int aj = factor;
            for (int j = 0; j < pI.length; ++j) {
                pI[j] = new BigFraction(aj * (j + 2));
                aj *= factor;
            }
        }


        return new Array2DRowFieldMatrix<BigFraction>(pData, false);

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      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);

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        }


        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

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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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