Uses of Class
com.opengamma.strata.math.impl.statistics.leastsquare.GeneralizedLeastSquareResults
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Packages that use GeneralizedLeastSquareResults Package Description com.opengamma.strata.math.impl.interpolation com.opengamma.strata.math.impl.statistics.leastsquare -
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Uses of GeneralizedLeastSquareResults in com.opengamma.strata.math.impl.interpolation
Methods in com.opengamma.strata.math.impl.interpolation that return GeneralizedLeastSquareResults Modifier and Type Method Description GeneralizedLeastSquareResults<double[]>PSplineFitter. solve(List<double[]> x, List<Double> y, List<Double> sigma, double[] xa, double[] xb, int[] nKnots, int[] degree, double[] lambda, int[] differenceOrder)Given a set of data {x_i ,y_i} where each x_i is a vector and the y_i are scalars, we wish to find a function (represented by B-splines) that fits the data while maintaining smoothness in each direction.GeneralizedLeastSquareResults<Double>PSplineFitter. solve(List<Double> x, List<Double> y, List<Double> sigma, double xa, double xb, int nKnots, int degree, double lambda, int differenceOrder)Fits a curve to x-y data. -
Uses of GeneralizedLeastSquareResults in com.opengamma.strata.math.impl.statistics.leastsquare
Methods in com.opengamma.strata.math.impl.statistics.leastsquare that return GeneralizedLeastSquareResults Modifier and Type Method Description <T> GeneralizedLeastSquareResults<T>GeneralizedLeastSquare. solve(List<T> x, List<Double> y, List<Double> sigma, List<Function<T,Double>> basisFunctions)<T> GeneralizedLeastSquareResults<T>GeneralizedLeastSquare. solve(List<T> x, List<Double> y, List<Double> sigma, List<Function<T,Double>> basisFunctions, double lambda, int differenceOrder)Generalised least square with penalty on (higher-order) finite differences of weights.<T> GeneralizedLeastSquareResults<T>GeneralizedLeastSquare. solve(List<T> x, List<Double> y, List<Double> sigma, List<Function<T,Double>> basisFunctions, int[] sizes, double[] lambda, int[] differenceOrder)Specialist method used mainly for solving multidimensional P-spline problems where the basis functions (B-splines) span a N-dimension space, and the weights sit on an N-dimension grid and are treated as a N-order tensor rather than a vector, so k-order differencing is done for each tensor index while varying the other indices.<T> GeneralizedLeastSquareResults<T>GeneralizedLeastSquare. solve(T[] x, double[] y, double[] sigma, List<Function<T,Double>> basisFunctions)<T> GeneralizedLeastSquareResults<T>GeneralizedLeastSquare. solve(T[] x, double[] y, double[] sigma, List<Function<T,Double>> basisFunctions, double lambda, int differenceOrder)Generalised least square with penalty on (higher-order) finite differences of weights.
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