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

Class NonLinearLeastSquare

java.lang.Object

com.opengamma.strata.math.impl.statistics.leastsquare.NonLinearLeastSquare

public class NonLinearLeastSquare
extends Object

Non linear least square calculator.

Constructor Summary

Constructors

Constructor	Description
NonLinearLeastSquare()
NonLinearLeastSquare(Decomposition<?> decomposition, MatrixAlgebra algebra, double eps)

Method Summary

Modifier and Type	Method	Description
DoubleMatrix	calInverseJacobian(DoubleArray sigma, Function<DoubleArray,DoubleArray> func, Function<DoubleArray,DoubleMatrix> jac, DoubleArray originalSolution)	the inverse-Jacobian where the i-j entry is the sensitivity of the ith (fitted) parameter (a_i) to the jth data point (y_j).
LeastSquareResults	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	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	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	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	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	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	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	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	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	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	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	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.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

NonLinearLeastSquare

public NonLinearLeastSquare()

NonLinearLeastSquare

public NonLinearLeastSquare(Decomposition<?> decomposition,
                            MatrixAlgebra algebra,
                            double eps)

Method Detail

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

func - The model in ParameterizedFunction form (i.e. takes measurement points and a set of parameters and returns a model value)

startPos - Initial value of the parameters

Returns:

A LeastSquareResults object

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

sigma - y Set of measurement errors

func - The model in ParameterizedFunction form (i.e. takes measurement points and a set of parameters and returns a model value)

startPos - Initial value of the parameters

Returns:

A LeastSquareResults object

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

sigma - Set of measurement errors

func - The model in ParameterizedFunction form (i.e. takes measurement points and a set of parameters and returns a model value)

startPos - Initial value of the parameters

Returns:

A LeastSquareResults object

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

func - The model in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model value)

grad - The model parameter sensitivities in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model parameter sensitivities)

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

sigma - Measurement errors

func - The model in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model value)

grad - The model parameter sensitivities in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model parameter sensitivities)

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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.

Parameters:

x - Set of measurement points

y - Set of measurement values

sigma - Set of measurement errors

func - The model in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model value)

grad - The model parameter sensitivities in ParameterizedFunction form (i.e. takes a measurement points and a set of parameters and returns a model parameter sensitivities)

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is not available

Parameters:

observedValues - Set of measurement values

func - The model as a function of its parameters only

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is not available

Parameters:

observedValues - Set of measurement values

sigma - Set of measurement errors

func - The model as a function of its parameters only

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is not available

Parameters:

observedValues - Set of measurement values

sigma - Set of measurement errors

func - The model as a function of its parameters only

startPos - Initial value of the parameters

maxJumps - A vector containing the maximum absolute allowed step in a particular direction in each iteration. Can be null, in which case no constant on the step size is applied.

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is available

Parameters:

observedValues - Set of measurement values

sigma - Set of measurement errors

func - The model as a function of its parameters only

jac - The model sensitivity to its parameters (i.e. the Jacobian matrix) as a function of its parameters only

startPos - Initial value of the parameters

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is available

Parameters:

observedValues - Set of measurement values

sigma - Set of measurement errors

func - The model as a function of its parameters only

jac - The model sensitivity to its parameters (i.e. the Jacobian matrix) as a function of its parameters only

startPos - Initial value of the parameters

maxJumps - A vector containing the maximum absolute allowed step in a particular direction in each iteration. Can be null, in which case on constant on the step size is applied.

Returns:

value of the fitted parameters

solve

public LeastSquareResults 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. a function that takes a set of parameters and return a set of model values, so the measurement points are already known to the function), and analytic parameter sensitivity is available

Parameters:

observedValues - Set of measurement values

sigma - Set of measurement errors

func - The model as a function of its parameters only

jac - The model sensitivity to its parameters (i.e. the Jacobian matrix) as a function of its parameters only

startPos - Initial value of the parameters

constraints - A function that returns true if the trial point is within the constraints of the model

maxJumps - A vector containing the maximum absolute allowed step in a particular direction in each iteration. Can be null, in which case on constant on the step size is applied.

Returns:

value of the fitted parameters

calInverseJacobian

public DoubleMatrix calInverseJacobian(DoubleArray sigma,
                                       Function<DoubleArray,DoubleArray> func,
                                       Function<DoubleArray,DoubleMatrix> jac,
                                       DoubleArray originalSolution)

the inverse-Jacobian where the i-j entry is the sensitivity of the ith (fitted) parameter (a_i) to the jth data point (y_j).

Parameters:

sigma - Set of measurement errors

func - The model as a function of its parameters only

jac - The model sensitivity to its parameters (i.e. the Jacobian matrix) as a function of its parameters only

originalSolution - The value of the parameters at a converged solution

Returns:

inverse-Jacobian