Uses of Class
com.opengamma.strata.math.impl.statistics.leastsquare.GeneralizedLeastSquareResults

Packages that use GeneralizedLeastSquareResults

Package	Description
com.opengamma.strata.math.impl.interpolation
com.opengamma.strata.math.impl.statistics.leastsquare

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 and 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 and 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.