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

Packages that use LeastSquareResults

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

Uses of LeastSquareResults in com.opengamma.strata.math.impl.statistics.leastsquare

Subclasses of LeastSquareResults in com.opengamma.strata.math.impl.statistics.leastsquare

Modifier and Type	Class and Description
class	GeneralizedLeastSquareResults<T> Generalized least square calculator.
class	LeastSquareResultsWithTransform Container for the results of a least square (minimum chi-square) fit, where some model (with a set of parameters), is calibrated to a data set, but the model parameters are first transformed to some fitting parameters (usually to impose some constants).
class	LeastSquareWithPenaltyResults Hold for results of NonLinearLeastSquareWithPenalty.

Methods in com.opengamma.strata.math.impl.statistics.leastsquare that return LeastSquareResults

Modifier and Type	Method and Description
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, DoubleArray sigma, ParameterizedFunction<Double,DoubleArray,Double> func, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity is not available but an array of measurements errors is.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, DoubleArray sigma, ParameterizedFunction<Double,DoubleArray,Double> func, ParameterizedFunction<Double,DoubleArray,DoubleArray> grad, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity and measurement errors are available.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, double sigma, ParameterizedFunction<Double,DoubleArray,Double> func, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity is not available but a measurement error is.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, double sigma, ParameterizedFunction<Double,DoubleArray,Double> func, ParameterizedFunction<Double,DoubleArray,DoubleArray> grad, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity and a single measurement error are available.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, DoubleArray sigma, Function<DoubleArray,DoubleArray> func, DoubleArray startPos) Use this when the model is given as a function of its parameters only (i.e.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, DoubleArray sigma, Function<DoubleArray,DoubleArray> func, DoubleArray startPos, DoubleArray maxJumps) Use this when the model is given as a function of its parameters only (i.e.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, DoubleArray sigma, Function<DoubleArray,DoubleArray> func, Function<DoubleArray,DoubleMatrix> jac, DoubleArray startPos) Use this when the model is given as a function of its parameters only (i.e.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, DoubleArray sigma, Function<DoubleArray,DoubleArray> func, Function<DoubleArray,DoubleMatrix> jac, DoubleArray startPos, DoubleArray maxJumps) Use this when the model is given as a function of its parameters only (i.e.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, DoubleArray sigma, Function<DoubleArray,DoubleArray> func, Function<DoubleArray,DoubleMatrix> jac, DoubleArray startPos, Function<DoubleArray,Boolean> constraints, DoubleArray maxJumps) Use this when the model is given as a function of its parameters only (i.e.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, ParameterizedFunction<Double,DoubleArray,Double> func, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity is not available.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray x, DoubleArray y, ParameterizedFunction<Double,DoubleArray,Double> func, ParameterizedFunction<Double,DoubleArray,DoubleArray> grad, DoubleArray startPos) Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity.
LeastSquareResults	NonLinearLeastSquare.solve(DoubleArray observedValues, Function<DoubleArray,DoubleArray> func, DoubleArray startPos) Use this when the model is given as a function of its parameters only (i.e.

Constructors in com.opengamma.strata.math.impl.statistics.leastsquare with parameters of type LeastSquareResults

Constructor and Description
LeastSquareResults(LeastSquareResults from)
LeastSquareResultsWithTransform(LeastSquareResults transformedFitResult)
LeastSquareResultsWithTransform(LeastSquareResults transformedFitResult, NonLinearParameterTransforms transform)