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

Class AbstractEvaluation

java.lang.Object

org.hipparchus.optim.nonlinear.vector.leastsquares.AbstractEvaluation

All Implemented Interfaces:

LeastSquaresProblem.Evaluation

public abstract class AbstractEvaluation
extends Object
implements LeastSquaresProblem.Evaluation

An implementation of LeastSquaresProblem.Evaluation that is designed for extension. All of the methods implemented here use the methods that are left unimplemented.

TODO cache results?

Method Summary

Modifier and Type	Method	Description
double	getChiSquare()	Get the sum of the squares of the residuals.
double	getCost()	Get the cost.
RealMatrix	getCovariances(double threshold)	Get the covariance matrix of the optimized parameters.
double	getReducedChiSquare(int numberOfFittedParameters)	Get the reduced chi-square.
double	getRMS()	Get the normalized cost.
RealVector	getSigma(double covarianceSingularityThreshold)	Get an estimate of the standard deviation of the parameters.

Methods inherited from interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation

getJacobian, getPoint, getResiduals

Methods inherited from class java.lang.Object

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

Method Detail

getCovariances

public RealMatrix getCovariances(double threshold)

Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of the JTJ matrix, where J is the Jacobian matrix. The threshold parameter is a way for the caller to specify that the result of this computation should be considered meaningless, and thus trigger an exception.

Specified by:

getCovariances in interface LeastSquaresProblem.Evaluation

Parameters:

threshold - Singularity threshold.

Returns:

the covariance matrix.

getSigma

public RealVector getSigma(double covarianceSingularityThreshold)

Get an estimate of the standard deviation of the parameters. The returned values are the square root of the diagonal coefficients of the covariance matrix, sd(a[i]) ~= sqrt(C[i][i]), where a[i] is the optimized value of the i-th parameter, and C is the covariance matrix.

Specified by:

getSigma in interface LeastSquaresProblem.Evaluation

Parameters:

covarianceSingularityThreshold - Singularity threshold (see computeCovariances).

Returns:

an estimate of the standard deviation of the optimized parameters

getRMS

public double getRMS()

Get the normalized cost. It is the square-root of the sum of squared of the residuals, divided by the number of measurements.

Specified by:

getRMS in interface LeastSquaresProblem.Evaluation

Returns:

the cost.

getCost

public double getCost()

Get the cost. It is the square-root of the objective function.

Specified by:

getCost in interface LeastSquaresProblem.Evaluation

Returns:

the cost.

See Also:

LeastSquaresProblem.Evaluation.getResiduals(), LeastSquaresProblem.Evaluation.getChiSquare()

getChiSquare

public double getChiSquare()

Get the sum of the squares of the residuals.

Specified by:

getChiSquare in interface LeastSquaresProblem.Evaluation

Returns:

the cost.

See Also:

LeastSquaresProblem.Evaluation.getResiduals(), LeastSquaresProblem.Evaluation.getCost()

getReducedChiSquare

public double getReducedChiSquare(int numberOfFittedParameters)

Get the reduced chi-square.

Specified by:

getReducedChiSquare in interface LeastSquaresProblem.Evaluation

Parameters:

numberOfFittedParameters - Number of fitted parameters.

Returns:

the sum of the squares of the residuals divided by the number of degrees of freedom.