Modifier and Type	Class and Description
private static class	LeastSquaresFactory.LocalLeastSquaresProblem A private, "field" immutable (not "real" immutable) implementation of `LeastSquaresProblem`.
private static class	LeastSquaresFactory.LocalValueAndJacobianFunction Combine a `MultivariateVectorFunction` with a `MultivariateMatrixFunction` to produce a `MultivariateJacobianFunction`.

Access	Constructor and Description
private	LeastSquaresFactory() Prevent instantiation.

Method Summary

Modifier and Type	Method and Description
public static LeastSquaresProblem	Returns: a least squares problem that tracks evaluations countEvaluations(final LeastSquaresProblem the problem to track. problem, final Incrementor the counter to increment. counter) Deprecated as a consequence of `Incrementor` being deprecated. Count the evaluations of a particular problem.
public static LeastSquaresProblem	Returns: the specified General Least Squares problem. create(final MultivariateJacobianFunction the model function. Produces the computed values. model, final RealVector the observed (target) values observed, final RealVector the initial guess. start, final RealMatrix the weight matrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker checker, final int the maximum number of times to evaluate the model maxEvaluations, final int the maximum number to times to iterate in the algorithm maxIterations, final boolean Whether the call to `Evaluation#evaluate(RealVector)` will defer the evaluation until access to the value is requested. lazyEvaluation, final ParameterValidator Model parameters validator. paramValidator) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements.
public static LeastSquaresProblem	Returns: the specified General Least Squares problem. create(final MultivariateJacobianFunction the model function. Produces the computed values. model, final RealVector the observed (target) values observed, final RealVector the initial guess. start, final ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker checker, final int the maximum number of times to evaluate the model maxEvaluations, final int the maximum number to times to iterate in the algorithm maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements.
public static LeastSquaresProblem	Returns: the specified General Least Squares problem. create(final MultivariateJacobianFunction the model function. Produces the computed values. model, final RealVector the observed (target) values observed, final RealVector the initial guess. start, final RealMatrix the weight matrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker checker, final int the maximum number of times to evaluate the model maxEvaluations, final int the maximum number to times to iterate in the algorithm maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements.
public static LeastSquaresProblem	Returns: the specified General Least Squares problem. create(final MultivariateVectorFunction the model function. Produces the computed values. model, final MultivariateMatrixFunction the jacobian of the model with respect to the parameters jacobian, final double[] the observed (target) values observed, final double[] the initial guess. start, final RealMatrix the weight matrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker checker, final int the maximum number of times to evaluate the model maxEvaluations, final int the maximum number to times to iterate in the algorithm maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements.
public static ConvergenceChecker<LeastSquaresProblem.Evaluation>	Returns: a convergence checker that delegates to `checker`. evaluationChecker(final ConvergenceChecker<PointVectorValuePair> the convergence checker to adapt. checker) View a convergence checker specified for a `PointVectorValuePair` as one specified for an `Evaluation`.
public static MultivariateJacobianFunction	Returns: a function that computes both at the same time model(final MultivariateVectorFunction the vector value function value, final MultivariateMatrixFunction the Jacobian function jacobian) Combine a `MultivariateVectorFunction` with a `MultivariateMatrixFunction` to produce a `MultivariateJacobianFunction`.
private static RealMatrix	Returns: the square-root of the weight matrix. squareRoot(final RealMatrix Symmetric, positive-definite (weight) matrix. m) Computes the square-root of the weight matrix.
public static LeastSquaresProblem	Returns: a new `LeastSquaresProblem` with the weights applied. The original `problem` is not modified. weightDiagonal(final LeastSquaresProblem the unweighted problem problem, final RealVector the diagonal of the weight matrix weights) Apply a diagonal weight matrix to the `LeastSquaresProblem`.
public static LeastSquaresProblem	Returns: a new `LeastSquaresProblem` with the weights applied. The original `problem` is not modified. weightMatrix(final LeastSquaresProblem the unweighted problem problem, final RealMatrix the matrix of weights weights) Apply a dense weight matrix to the `LeastSquaresProblem`.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Constructor Detail

LeastSquaresFactory	back to summary
private LeastSquaresFactory() Prevent instantiation.

Method Detail

countEvaluations back to summary

countEvaluations	back to summary
public static LeastSquaresProblem countEvaluations(final LeastSquaresProblem problem, final Incrementor counter) Deprecated as a consequence of `Incrementor` being deprecated. See corresponding docs for further information. 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:LeastSquaresProblem the problem to track. counter:Incrementor the counter to increment. Returns:LeastSquaresProblem a least squares problem that tracks evaluations

public static LeastSquaresProblem countEvaluations(final LeastSquaresProblem problem, final Incrementor counter)

Deprecated

as a consequence of Incrementor being deprecated. See corresponding docs for further information.

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:LeastSquaresProblem: the problem to track.
counter:Incrementor: the counter to increment.

Returns:LeastSquaresProblem

a least squares problem that tracks evaluations

create back to summary

create	back to summary
public static LeastSquaresProblem create(final MultivariateJacobianFunction model, final RealVector observed, final RealVector start, final RealMatrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations, final boolean lazyEvaluation, final ParameterValidator paramValidator) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements. There will be no weights applied (unit weights). Parameters model:MultivariateJacobianFunction the model function. Produces the computed values. observed:RealVector the observed (target) values start:RealVector the initial guess. weight:RealMatrix the weight matrix checker:ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker maxEvaluations:int the maximum number of times to evaluate the model maxIterations:int the maximum number to times to iterate in the algorithm lazyEvaluation:boolean Whether the call to `Evaluation#evaluate(RealVector)` will defer the evaluation until access to the value is requested. paramValidator:ParameterValidator Model parameters validator. Returns:LeastSquaresProblem the specified General Least Squares problem. Since 3.4

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

Create a org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

Parameters

model:MultivariateJacobianFunction: the model function. Produces the computed values.
observed:RealVector: the observed (target) values
start:RealVector: the initial guess.
weight:RealMatrix: the weight matrix
checker:ConvergenceChecker<LeastSquaresProblem.Evaluation>: convergence checker
maxEvaluations:int: the maximum number of times to evaluate the model
maxIterations:int: the maximum number to times to iterate in the algorithm
lazyEvaluation:boolean: Whether the call to Evaluation#evaluate(RealVector) will defer the evaluation until access to the value is requested.
paramValidator:ParameterValidator: Model parameters validator.

Returns:LeastSquaresProblem

the specified General Least Squares problem.

Since

3.4

create back to summary

create	back to summary
public static LeastSquaresProblem create(final MultivariateJacobianFunction model, final RealVector observed, final RealVector start, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements. There will be no weights applied (unit weights). Parameters model:MultivariateJacobianFunction the model function. Produces the computed values. observed:RealVector the observed (target) values start:RealVector the initial guess. checker:ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker maxEvaluations:int the maximum number of times to evaluate the model maxIterations:int the maximum number to times to iterate in the algorithm Returns:LeastSquaresProblem the specified General Least Squares problem.

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

Create a org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

Parameters

model:MultivariateJacobianFunction: the model function. Produces the computed values.
observed:RealVector: the observed (target) values
start:RealVector: the initial guess.
checker:ConvergenceChecker<LeastSquaresProblem.Evaluation>: convergence checker
maxEvaluations:int: the maximum number of times to evaluate the model
maxIterations:int: the maximum number to times to iterate in the algorithm

Returns:LeastSquaresProblem

the specified General Least Squares problem.

create back to summary

create	back to summary
public static LeastSquaresProblem create(final MultivariateJacobianFunction model, final RealVector observed, final RealVector start, final RealMatrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem` from the given elements. Parameters model:MultivariateJacobianFunction the model function. Produces the computed values. observed:RealVector the observed (target) values start:RealVector the initial guess. weight:RealMatrix the weight matrix checker:ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker maxEvaluations:int the maximum number of times to evaluate the model maxIterations:int the maximum number to times to iterate in the algorithm Returns:LeastSquaresProblem the specified General Least Squares problem.

Create a org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem from the given elements.

Parameters

model:MultivariateJacobianFunction: the model function. Produces the computed values.
observed:RealVector: the observed (target) values
start:RealVector: the initial guess.
weight:RealMatrix: the weight matrix
checker:ConvergenceChecker<LeastSquaresProblem.Evaluation>: convergence checker
maxEvaluations:int: the maximum number of times to evaluate the model
maxIterations:int: the maximum number to times to iterate in the algorithm

Returns:LeastSquaresProblem

the specified General Least Squares problem.

create back to summary

create	back to summary
public static LeastSquaresProblem create(final MultivariateVectorFunction model, final MultivariateMatrixFunction jacobian, final double[] observed, final double[] start, final RealMatrix weight, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations) Create a `org.apache.commons.math3.fitting.leastsquares.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:MultivariateVectorFunction the model function. Produces the computed values. jacobian:MultivariateMatrixFunction the jacobian of the model with respect to the parameters observed:double[] the observed (target) values start:double[] the initial guess. weight:RealMatrix the weight matrix checker:ConvergenceChecker<LeastSquaresProblem.Evaluation> convergence checker maxEvaluations:int the maximum number of times to evaluate the model maxIterations:int the maximum number to times to iterate in the algorithm Returns:LeastSquaresProblem the specified General Least Squares problem.

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

Create a org.apache.commons.math3.fitting.leastsquares.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:MultivariateVectorFunction: the model function. Produces the computed values.
jacobian:MultivariateMatrixFunction: the jacobian of the model with respect to the parameters
observed:double[]: the observed (target) values
start:double[]: the initial guess.
weight:RealMatrix: the weight matrix
checker:ConvergenceChecker<LeastSquaresProblem.Evaluation>: convergence checker
maxEvaluations:int: the maximum number of times to evaluate the model
maxIterations:int: the maximum number to times to iterate in the algorithm

Returns:LeastSquaresProblem

the specified General Least Squares problem.

evaluationChecker back to summary

evaluationChecker	back to summary
public static ConvergenceChecker<LeastSquaresProblem.Evaluation> evaluationChecker(final ConvergenceChecker<PointVectorValuePair> checker) View a convergence checker specified for a `PointVectorValuePair` as one specified for an `Evaluation`. Parameters checker:ConvergenceChecker<PointVectorValuePair> the convergence checker to adapt. Returns:ConvergenceChecker<LeastSquaresProblem.Evaluation> a convergence checker that delegates to `checker`.

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

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

Parameters

checker:ConvergenceChecker<PointVectorValuePair>: the convergence checker to adapt.

Returns:ConvergenceChecker<LeastSquaresProblem.Evaluation>

a convergence checker that delegates to checker.

model back to summary

model	back to summary
public static MultivariateJacobianFunction model(final MultivariateVectorFunction value, final MultivariateMatrixFunction jacobian) Combine a `MultivariateVectorFunction` with a `MultivariateMatrixFunction` to produce a `MultivariateJacobianFunction`. Parameters value:MultivariateVectorFunction the vector value function jacobian:MultivariateMatrixFunction the Jacobian function Returns:MultivariateJacobianFunction a function that computes both at the same time

public static MultivariateJacobianFunction model(final MultivariateVectorFunction value, final MultivariateMatrixFunction jacobian)

Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.

Parameters

value:MultivariateVectorFunction: the vector value function
jacobian:MultivariateMatrixFunction: the Jacobian function

Returns:MultivariateJacobianFunction

a function that computes both at the same time

squareRoot	back to summary
private static RealMatrix squareRoot(final RealMatrix m) Computes the square-root of the weight matrix. Parameters m:RealMatrix Symmetric, positive-definite (weight) matrix. Returns:RealMatrix the square-root of the weight matrix.

squareRoot

back to summary

private static RealMatrix squareRoot(final RealMatrix m)

Computes the square-root of the weight matrix.

Parameters

m:RealMatrix: Symmetric, positive-definite (weight) matrix.

Returns:RealMatrix

the square-root of the weight matrix.

weightDiagonal back to summary

weightDiagonal	back to summary
public static LeastSquaresProblem weightDiagonal(final LeastSquaresProblem problem, final RealVector weights) Apply a diagonal weight matrix to the `LeastSquaresProblem`. Parameters problem:LeastSquaresProblem the unweighted problem weights:RealVector the diagonal of the weight matrix Returns:LeastSquaresProblem a new `LeastSquaresProblem` with the weights applied. The original `problem` is not modified.

public static LeastSquaresProblem weightDiagonal(final LeastSquaresProblem problem, final RealVector weights)

Apply a diagonal weight matrix to the LeastSquaresProblem.

Parameters

problem:LeastSquaresProblem: the unweighted problem
weights:RealVector: the diagonal of the weight matrix

Returns:LeastSquaresProblem

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

weightMatrix back to summary

weightMatrix	back to summary
public static LeastSquaresProblem weightMatrix(final LeastSquaresProblem problem, final RealMatrix weights) Apply a dense weight matrix to the `LeastSquaresProblem`. Parameters problem:LeastSquaresProblem the unweighted problem weights:RealMatrix the matrix of weights Returns:LeastSquaresProblem a new `LeastSquaresProblem` with the weights applied. The original `problem` is not modified.

public static LeastSquaresProblem weightMatrix(final LeastSquaresProblem problem, final RealMatrix weights)

Apply a dense weight matrix to the LeastSquaresProblem.

Parameters

problem:LeastSquaresProblem: the unweighted problem
weights:RealMatrix: the matrix of weights

Returns:LeastSquaresProblem

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

org.apache.commons.math3.fitting.leastsquares back to summary

private Class LeastSquaresFactory.LocalLeastSquaresProblem

Class Inheritance

extends AbstractOptimizationProblem<LeastSquaresProblem.Evaluation>
implements LeastSquaresProblem

java.lang.Object
org.apache.commons.math3.optim.AbstractOptimizationProblem
org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.LocalLeastSquaresProblem

All Implemented Interfaces

org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem, org.apache.commons.math3.optim.OptimizationProblem

A private, "field" immutable (not "real" immutable) implementation of LeastSquaresProblem.

Since: 3.3

Nested and Inner Type Summary

Modifier and Type	Class and Description
private static class	LeastSquaresFactory.LocalLeastSquaresProblem.LazyUnweightedEvaluation Container with the model lazy evaluation at a particular point.
private static class	LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation Container with the model evaluation at a particular point.

Field Summary

Modifier and Type	Field and Description
private final boolean	lazyEvaluation Whether to use lazy evaluation.
private final MultivariateJacobianFunction	model Model function.
private final ParameterValidator	paramValidator Model parameters validator.
private final RealVector	start Initial guess.
private final RealVector	target Target values for the model function at optimum.

Constructor Summary

Access Constructor and Description

Access	Constructor and Description
pack-priv	LocalLeastSquaresProblem(final MultivariateJacobianFunction the model function model, final RealVector the observed data target, final RealVector the initial guess start, final ConvergenceChecker<LeastSquaresProblem.Evaluation> the convergence checker checker, final int the allowed evaluations maxEvaluations, final int the allowed iterations maxIterations, final boolean Whether the call to `Evaluation#evaluate(RealVector)` will defer the evaluation until access to the value is requested. lazyEvaluation, final ParameterValidator Model parameters validator. paramValidator) Create a `LeastSquaresProblem` from the given data.

pack-priv

LocalLeastSquaresProblem(final MultivariateJacobianFunction

the model function

model, final RealVector

the observed data

target, final RealVector

the initial guess

start, final ConvergenceChecker<LeastSquaresProblem.Evaluation>

the convergence checker

checker, final int

the allowed evaluations

maxEvaluations, final int

the allowed iterations

maxIterations, final boolean

Whether the call to Evaluation#evaluate(RealVector) will defer the evaluation until access to the value is requested.

lazyEvaluation, final ParameterValidator

Model parameters validator.

paramValidator)

Create a LeastSquaresProblem from the given data.

Method Summary

Modifier and Type	Method and Description
public LeastSquaresProblem.Evaluation	evaluate(final RealVector the parameter values. point) Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.evaluate. Evaluate the model at the specified point.
public int	getObservationSize() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getObservationSize. Get the number of observations (rows in the Jacobian) in this problem.
public int	getParameterSize() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getParameterSize. Get the number of parameters (columns in the Jacobian) in this problem.
public RealVector	getStart() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getStart. Gets the initial guess.

Inherited from org.apache.commons.math3.optim.AbstractOptimizationProblem:: getConvergenceChecker getEvaluationCounter getIterationCounter

Field Detail

lazyEvaluation	back to summary
private final boolean lazyEvaluation Whether to use lazy evaluation.

model	back to summary
private final MultivariateJacobianFunction model Model function.

paramValidator	back to summary
private final ParameterValidator paramValidator Model parameters validator.

start	back to summary
private final RealVector start Initial guess.

target	back to summary
private final RealVector target Target values for the model function at optimum.

Constructor Detail

LocalLeastSquaresProblem back to summary

LocalLeastSquaresProblem	back to summary
pack-priv LocalLeastSquaresProblem(final MultivariateJacobianFunction model, final RealVector target, final RealVector start, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations, final boolean lazyEvaluation, final ParameterValidator paramValidator) Create a `LeastSquaresProblem` from the given data. Parameters model:MultivariateJacobianFunction the model function target:RealVector the observed data start:RealVector the initial guess checker:ConvergenceChecker<LeastSquaresProblem.Evaluation> the convergence checker maxEvaluations:int the allowed evaluations maxIterations:int the allowed iterations lazyEvaluation:boolean Whether the call to `Evaluation#evaluate(RealVector)` will defer the evaluation until access to the value is requested. paramValidator:ParameterValidator Model parameters validator.

pack-priv LocalLeastSquaresProblem(final MultivariateJacobianFunction model, final RealVector target, final RealVector start, final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, final int maxEvaluations, final int maxIterations, final boolean lazyEvaluation, final ParameterValidator paramValidator)

Create a LeastSquaresProblem from the given data.

Parameters

model:MultivariateJacobianFunction: the model function
target:RealVector: the observed data
start:RealVector: the initial guess
checker:ConvergenceChecker<LeastSquaresProblem.Evaluation>: the convergence checker
maxEvaluations:int: the allowed evaluations
maxIterations:int: the allowed iterations
lazyEvaluation:boolean: Whether the call to Evaluation#evaluate(RealVector) will defer the evaluation until access to the value is requested.
paramValidator:ParameterValidator: Model parameters validator.

Method Detail

evaluate	back to summary
public LeastSquaresProblem.Evaluation evaluate(final RealVector point) Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.evaluate. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.evaluate. Evaluate the model at the specified point. Parameters point:RealVector the parameter values. Returns:LeastSquaresProblem.Evaluation the model's value and derivative at the given point.

evaluate

back to summary

public LeastSquaresProblem.Evaluation evaluate(final RealVector point)

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.evaluate.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.evaluate.

Evaluate the model at the specified point.

Parameters

point:RealVector: the parameter values.

Returns:LeastSquaresProblem.Evaluation

the model's value and derivative at the given point.

getObservationSize	back to summary
public int getObservationSize() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getObservationSize. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getObservationSize. Get the number of observations (rows in the Jacobian) in this problem. Returns:int the number of scalar observations

getObservationSize

back to summary

public int getObservationSize()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getObservationSize.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getObservationSize.

Get the number of observations (rows in the Jacobian) in this problem.

Returns:int: the number of scalar observations

getParameterSize	back to summary
public int getParameterSize() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getParameterSize. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getParameterSize. Get the number of parameters (columns in the Jacobian) in this problem. Returns:int the number of scalar parameters

getParameterSize

back to summary

public int getParameterSize()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getParameterSize.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getParameterSize.

Get the number of parameters (columns in the Jacobian) in this problem.

Returns:int: the number of scalar parameters

getStart	back to summary
public RealVector getStart() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getStart. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getStart. Gets the initial guess. Returns:RealVector the initial guess values.

getStart

back to summary

public RealVector getStart()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getStart.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.getStart.

Gets the initial guess.

Returns:RealVector: the initial guess values.

org.apache.commons.math3.fitting.leastsquares back to summary

private Class LeastSquaresFactory.LocalLeastSquaresProblem.LazyUnweightedEvaluation

Class Inheritance

extends AbstractEvaluation

java.lang.Object
org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation
org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.LocalLeastSquaresProblem.LazyUnweightedEvaluation

Container with the model lazy evaluation at a particular point.

Field Summary

Modifier and Type	Field and Description
private final ValueAndJacobianFunction	model Model and Jacobian functions.
private final RealVector	point Point of evaluation.
private final RealVector	target Target values for the model function at optimum.

Constructor Summary

Access	Constructor and Description
private	LazyUnweightedEvaluation(final ValueAndJacobianFunction the model function model, final RealVector the observed values target, final RealVector the abscissa point) Create an `Evaluation` with no weights.

Method Summary

Modifier and Type	Method and Description
public RealMatrix	getJacobian() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Get the weighted Jacobian matrix.
public RealVector	getPoint() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Get the abscissa (independent variables) of this evaluation.
public RealVector	getResiduals() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Get the weighted residuals.

Inherited from org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation:: getCost getCovariances getRMS getSigma

Field Detail

model	back to summary
private final ValueAndJacobianFunction model Model and Jacobian functions.

point	back to summary
private final RealVector point Point of evaluation.

target	back to summary
private final RealVector target Target values for the model function at optimum.

Constructor Detail

LazyUnweightedEvaluation back to summary

LazyUnweightedEvaluation	back to summary
private LazyUnweightedEvaluation(final ValueAndJacobianFunction model, final RealVector target, final RealVector point) Create an `Evaluation` with no weights. Parameters model:ValueAndJacobianFunction the model function target:RealVector the observed values point:RealVector the abscissa

private LazyUnweightedEvaluation(final ValueAndJacobianFunction model, final RealVector target, final RealVector point)

Create an Evaluation with no weights.

Parameters

model:ValueAndJacobianFunction: the model function
target:RealVector: the observed values
point:RealVector: the abscissa

Method Detail

getJacobian	back to summary
public RealMatrix getJacobian() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Get the weighted Jacobian matrix. Returns:RealMatrix the weighted Jacobian: W^1/2 J.

getJacobian

back to summary

public RealMatrix getJacobian()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian.

Get the weighted Jacobian matrix.

Returns:RealMatrix: the weighted Jacobian: W^1/2 J.

getPoint back to summary

getPoint	back to summary
public RealVector getPoint() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Get the abscissa (independent variables) of this evaluation. Returns:RealVector the point provided to `evaluate(RealVector)`.

public RealVector getPoint()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint.

Get the abscissa (independent variables) of this evaluation.

Returns:RealVector: the point provided to evaluate(RealVector).

getResiduals	back to summary
public RealVector getResiduals() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Get the weighted residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function. The raw residuals are then multiplied by the square root of the weight matrix. Returns:RealVector the weighted residuals: W^1/2 K.

getResiduals

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public RealVector getResiduals()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals.

Get the weighted residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function. The raw residuals are then multiplied by the square root of the weight matrix.

Returns:RealVector: the weighted residuals: W^1/2 K.

org.apache.commons.math3.fitting.leastsquares back to summary

private Class LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation

Class Inheritance

extends AbstractEvaluation

java.lang.Object
org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation
org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation

Container with the model evaluation at a particular point.

Field Summary

Modifier and Type	Field and Description
private final RealMatrix	jacobian Derivative at point.
private final RealVector	point Point of evaluation.
private final RealVector	residuals Computed residuals.

Constructor Summary

Access	Constructor and Description
private	UnweightedEvaluation(final RealVector the computed function values values, final RealMatrix the computed function Jacobian jacobian, final RealVector the observed values target, final RealVector the abscissa point) Create an `Evaluation` with no weights.

Method Summary

Modifier and Type	Method and Description
public RealMatrix	getJacobian() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Get the weighted Jacobian matrix.
public RealVector	getPoint() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Get the abscissa (independent variables) of this evaluation.
public RealVector	getResiduals() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Get the weighted residuals.

Inherited from org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation:: getCost getCovariances getRMS getSigma

Field Detail

jacobian	back to summary
private final RealMatrix jacobian Derivative at point.

point	back to summary
private final RealVector point Point of evaluation.

residuals	back to summary
private final RealVector residuals Computed residuals.

Constructor Detail

UnweightedEvaluation back to summary

UnweightedEvaluation	back to summary
private UnweightedEvaluation(final RealVector values, final RealMatrix jacobian, final RealVector target, final RealVector point) Create an `Evaluation` with no weights. Parameters values:RealVector the computed function values jacobian:RealMatrix the computed function Jacobian target:RealVector the observed values point:RealVector the abscissa

private UnweightedEvaluation(final RealVector values, final RealMatrix jacobian, final RealVector target, final RealVector point)

Create an Evaluation with no weights.

Parameters

values:RealVector: the computed function values
jacobian:RealMatrix: the computed function Jacobian
target:RealVector: the observed values
point:RealVector: the abscissa

Method Detail

getJacobian	back to summary
public RealMatrix getJacobian() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian. Get the weighted Jacobian matrix. Returns:RealMatrix the weighted Jacobian: W^1/2 J.

getJacobian

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public RealMatrix getJacobian()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getJacobian.

Get the weighted Jacobian matrix.

Returns:RealMatrix: the weighted Jacobian: W^1/2 J.

getPoint back to summary

getPoint	back to summary
public RealVector getPoint() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint. Get the abscissa (independent variables) of this evaluation. Returns:RealVector the point provided to `evaluate(RealVector)`.

public RealVector getPoint()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getPoint.

Get the abscissa (independent variables) of this evaluation.

Returns:RealVector: the point provided to evaluate(RealVector).

getResiduals	back to summary
public RealVector getResiduals() Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals. Get the weighted residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function. The raw residuals are then multiplied by the square root of the weight matrix. Returns:RealVector the weighted residuals: W^1/2 K.

getResiduals

back to summary

public RealVector getResiduals()

Implements org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals.

Doc from org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation.getResiduals.

Returns:RealVector: the weighted residuals: W^1/2 K.

org.apache.commons.math3.fitting.leastsquares back to summary

private Class LeastSquaresFactory.LocalValueAndJacobianFunction

Class Inheritance

extends Object
implements ValueAndJacobianFunction

java.lang.Object
org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.LocalValueAndJacobianFunction

All Implemented Interfaces

org.apache.commons.math3.fitting.leastsquares.ValueAndJacobianFunction, org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction

Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.

Field Summary

Modifier and Type	Field and Description
private final MultivariateMatrixFunction	jacobian Model's Jacobian.
private final MultivariateVectorFunction	value Model.

Constructor Summary

Access	Constructor and Description
pack-priv	LocalValueAndJacobianFunction(final MultivariateVectorFunction Model function. value, final MultivariateMatrixFunction Model's Jacobian function. jacobian)

Method Summary

Modifier and Type	Method and Description
public RealMatrix	computeJacobian(final double[] Point. params) Implements org.apache.commons.math3.fitting.leastsquares.ValueAndJacobianFunction.computeJacobian. Compute the Jacobian.
public RealVector	computeValue(final double[] Point. params) Implements org.apache.commons.math3.fitting.leastsquares.ValueAndJacobianFunction.computeValue. Compute the value.
public Pair<RealVector, RealMatrix>	value(final RealVector the abscissae point) Implements org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction.value. Compute the function value and its Jacobian.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

jacobian	back to summary
private final MultivariateMatrixFunction jacobian Model's Jacobian.

value	back to summary
private final MultivariateVectorFunction value Model.

Constructor Detail

LocalValueAndJacobianFunction	back to summary
pack-priv LocalValueAndJacobianFunction(final MultivariateVectorFunction value, final MultivariateMatrixFunction jacobian) Parameters value:MultivariateVectorFunction Model function. jacobian:MultivariateMatrixFunction Model's Jacobian function.