Package org.hipparchus.optim.nonlinear.vector.leastsquares

Class LeastSquaresFactory

java.lang.Object

org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresFactory

public class LeastSquaresFactory
extends Object

A Factory for creating LeastSquaresProblems.

Method Summary

Modifier and Type	Method	Description
static LeastSquaresProblem	countEvaluations(LeastSquaresProblem problem, Incrementor counter)	Count the evaluations of a particular problem.
static LeastSquaresProblem	create(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	create(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static ConvergenceChecker<LeastSquaresProblem.Evaluation>	evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker)	View a convergence checker specified for a PointVectorValuePair as one specified for an LeastSquaresProblem.Evaluation.
static MultivariateJacobianFunction	model(MultivariateVectorFunction value, MultivariateMatrixFunction jacobian)	Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.
static LeastSquaresProblem	weightDiagonal(LeastSquaresProblem problem, RealVector weights)	Apply a diagonal weight matrix to the LeastSquaresProblem.
static LeastSquaresProblem	weightMatrix(LeastSquaresProblem problem, RealMatrix weights)	Apply a dense weight matrix to the LeastSquaresProblem.

Methods inherited from class java.lang.Object

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

Method Details

create

public static LeastSquaresProblem create(MultivariateJacobianFunction model,
 RealVector observed,
 RealVector start,
 RealMatrix weight,
 ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
 int maxEvaluations,
 int maxIterations,
 boolean lazyEvaluation,
 ParameterValidator paramValidator)

Create a LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

Parameters:

model - the model function. Produces the computed values.

observed - the observed (target) values

start - the initial guess.

weight - the weight matrix

checker - convergence checker

maxEvaluations - the maximum number of times to evaluate the model

maxIterations - the maximum number to times to iterate in the algorithm

lazyEvaluation - Whether the call to LeastSquaresProblem.evaluate(RealVector) will defer the evaluation until access to the value is requested.

paramValidator - Model parameters validator.

Returns:

the specified General Least Squares problem.

create

public static LeastSquaresProblem create(MultivariateJacobianFunction model,
 RealVector observed,
 RealVector start,
 ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
 int maxEvaluations,
 int maxIterations)

Create a LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

Parameters:

model - the model function. Produces the computed values.

observed - the observed (target) values

start - the initial guess.

checker - convergence checker

maxEvaluations - the maximum number of times to evaluate the model

maxIterations - the maximum number to times to iterate in the algorithm

Returns:

the specified General Least Squares problem.

create

public static LeastSquaresProblem create(MultivariateJacobianFunction model,
 RealVector observed,
 RealVector start,
 RealMatrix weight,
 ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
 int maxEvaluations,
 int maxIterations)

Create a LeastSquaresProblem from the given elements.

Parameters:

model - the model function. Produces the computed values.

observed - the observed (target) values

start - the initial guess.

weight - the weight matrix

checker - convergence checker

maxEvaluations - the maximum number of times to evaluate the model

maxIterations - the maximum number to times to iterate in the algorithm

Returns:

the specified General Least Squares problem.

create

public static LeastSquaresProblem create(MultivariateVectorFunction model,
 MultivariateMatrixFunction jacobian,
 double[] observed,
 double[] start,
 RealMatrix weight,
 ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
 int maxEvaluations,
 int maxIterations)

Create a LeastSquaresProblem from the given elements.

This factory method is provided for continuity with previous interfaces. Newer applications should use create(MultivariateJacobianFunction, RealVector, RealVector, ConvergenceChecker, int, int), or create(MultivariateJacobianFunction, RealVector, RealVector, RealMatrix, ConvergenceChecker, int, int).

Parameters:

model - the model function. Produces the computed values.

jacobian - the jacobian of the model with respect to the parameters

observed - the observed (target) values

start - the initial guess.

weight - the weight matrix

checker - convergence checker

maxEvaluations - the maximum number of times to evaluate the model

maxIterations - the maximum number to times to iterate in the algorithm

Returns:

the specified General Least Squares problem.

weightMatrix

public static LeastSquaresProblem weightMatrix(LeastSquaresProblem problem,
 RealMatrix weights)

Apply a dense weight matrix to the LeastSquaresProblem.

Parameters:

problem - the unweighted problem

weights - the matrix of weights

Returns:

a new LeastSquaresProblem with the weights applied. The original problem is not modified.

weightDiagonal

public static LeastSquaresProblem weightDiagonal(LeastSquaresProblem problem,
 RealVector weights)

Apply a diagonal weight matrix to the LeastSquaresProblem.

Parameters:

problem - the unweighted problem

weights - the diagonal of the weight matrix

Returns:

a new LeastSquaresProblem with the weights applied. The original problem is not modified.

countEvaluations

public static LeastSquaresProblem countEvaluations(LeastSquaresProblem problem,
 Incrementor counter)

Count the evaluations of a particular problem. The counter will be incremented every time LeastSquaresProblem.evaluate(RealVector) is called on the returned problem.

Parameters:

problem - the problem to track.

counter - the counter to increment.

Returns:

a least squares problem that tracks evaluations

evaluationChecker

public static ConvergenceChecker<LeastSquaresProblem.Evaluation> evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker)

View a convergence checker specified for a PointVectorValuePair as one specified for an LeastSquaresProblem.Evaluation.

Parameters:

checker - the convergence checker to adapt.

Returns:

a convergence checker that delegates to checker.

model

public static MultivariateJacobianFunction model(MultivariateVectorFunction value,
 MultivariateMatrixFunction jacobian)

Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.

Parameters:

value - the vector value function

jacobian - the Jacobian function

Returns:

a function that computes both at the same time