org.hipparchus.optim.nonlinear.vector.leastsquares

Class LeastSquaresBuilder

java.lang.Object

org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresBuilder

public class LeastSquaresBuilder
extends Object

A mutable builder for LeastSquaresProblems.

See Also:

LeastSquaresFactory

Constructor Summary

Constructors

Constructor and Description
LeastSquaresBuilder()

Method Summary

Modifier and Type	Method and Description
LeastSquaresProblem	build() Construct a LeastSquaresProblem from the data in this builder.
LeastSquaresBuilder	checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker) Configure the convergence checker.
LeastSquaresBuilder	checkerPair(ConvergenceChecker<PointVectorValuePair> newChecker) Configure the convergence checker.
LeastSquaresBuilder	lazyEvaluation(boolean newValue) Configure whether evaluation will be lazy or not.
LeastSquaresBuilder	maxEvaluations(int newMaxEvaluations) Configure the max evaluations.
LeastSquaresBuilder	maxIterations(int newMaxIterations) Configure the max iterations.
LeastSquaresBuilder	model(MultivariateJacobianFunction newModel) Configure the model function.
LeastSquaresBuilder	model(MultivariateVectorFunction value, MultivariateMatrixFunction jacobian) Configure the model function.
LeastSquaresBuilder	parameterValidator(ParameterValidator newValidator) Configure the validator of the model parameters.
LeastSquaresBuilder	start(double[] newStart) Configure the initial guess.
LeastSquaresBuilder	start(RealVector newStart) Configure the initial guess.
LeastSquaresBuilder	target(double[] newTarget) Configure the observed data.
LeastSquaresBuilder	target(RealVector newTarget) Configure the observed data.
LeastSquaresBuilder	weight(RealMatrix newWeight) Configure the weight matrix.

Methods inherited from class java.lang.Object

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

Constructor Detail

LeastSquaresBuilder

public LeastSquaresBuilder()

Method Detail

build

public LeastSquaresProblem build()

Construct a LeastSquaresProblem from the data in this builder.

Returns:

a new LeastSquaresProblem.

maxEvaluations

public LeastSquaresBuilder maxEvaluations(int newMaxEvaluations)

Configure the max evaluations.

Parameters:

newMaxEvaluations - the maximum number of evaluations permitted.

Returns:

this

maxIterations

public LeastSquaresBuilder maxIterations(int newMaxIterations)

Configure the max iterations.

Parameters:

newMaxIterations - the maximum number of iterations permitted.

Returns:

this

checker

public LeastSquaresBuilder checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker)

Configure the convergence checker.

Parameters:

newChecker - the convergence checker.

Returns:

this

checkerPair

public LeastSquaresBuilder checkerPair(ConvergenceChecker<PointVectorValuePair> newChecker)

Configure the convergence checker.

This function is an overloaded version of checker(ConvergenceChecker).

Parameters:

newChecker - the convergence checker.

Returns:

this

model

public LeastSquaresBuilder model(MultivariateVectorFunction value,
                                 MultivariateMatrixFunction jacobian)

Configure the model function.

Parameters:

value - the model function value

jacobian - the Jacobian of value

Returns:

this

model

public LeastSquaresBuilder model(MultivariateJacobianFunction newModel)

Configure the model function.

Parameters:

newModel - the model function value and Jacobian

Returns:

this

target

public LeastSquaresBuilder target(RealVector newTarget)

Configure the observed data.

Parameters:

newTarget - the observed data.

Returns:

this

target

public LeastSquaresBuilder target(double[] newTarget)

Configure the observed data.

Parameters:

newTarget - the observed data.

Returns:

this

start

public LeastSquaresBuilder start(RealVector newStart)

Configure the initial guess.

Parameters:

newStart - the initial guess.

Returns:

this

start

public LeastSquaresBuilder start(double[] newStart)

Configure the initial guess.

Parameters:

newStart - the initial guess.

Returns:

this

weight

public LeastSquaresBuilder weight(RealMatrix newWeight)

Configure the weight matrix.

Parameters:

newWeight - the weight matrix

Returns:

this

lazyEvaluation

public LeastSquaresBuilder lazyEvaluation(boolean newValue)

Configure whether evaluation will be lazy or not.

Parameters:

newValue - Whether to perform lazy evaluation.

Returns:

this object.

parameterValidator

public LeastSquaresBuilder parameterValidator(ParameterValidator newValidator)

Configure the validator of the model parameters.

Parameters:

newValidator - Parameter validator.

Returns:

this object.