Uses of Class
com.opengamma.strata.math.impl.function.ParameterizedFunction

Packages that use ParameterizedFunction

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

Uses of ParameterizedFunction in com.opengamma.strata.math.impl.function

Subclasses of ParameterizedFunction in com.opengamma.strata.math.impl.function

Modifier and Type	Class and Description
class	ParameterizedCurve A parameterised curve that gives the both the curve (the function y=f(x) where x and y are scalars) and the curve sensitivity (dy/dp where p is one of the parameters) for given parameters.
class	ParameterizedSurface A parameterised surface that gives the both the surface (the function z=f(xy) where xy is a 2D point and z is a scalar) and the surface sensitivity (dz/dp where p is one of the parameters) for given parameters.

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

Methods in com.opengamma.strata.math.impl.statistics.leastsquare with parameters of type ParameterizedFunction

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, 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 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 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 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.