Package org.apache.commons.math4.legacy.fitting.leastsquares

Interface LeastSquaresProblem.Evaluation

All Known Subinterfaces:

LeastSquaresOptimizer.Optimum

All Known Implementing Classes:

AbstractEvaluation

Enclosing interface:

LeastSquaresProblem

public static interface LeastSquaresProblem.Evaluation

An evaluation of a LeastSquaresProblem at a particular point. This class also computes several quantities derived from the value and its Jacobian.

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.
RealMatrix	getJacobian()	Get the weighted Jacobian matrix.
RealVector	getPoint()	Get the abscissa (independent variables) of this evaluation.
double	getReducedChiSquare(int n)	Get the reduced chi-square.
RealVector	getResiduals()	Get the weighted residuals.
double	getRMS()	Get the normalized cost.
RealVector	getSigma(double covarianceSingularityThreshold)	Get an estimate of the standard deviation of the parameters.

Method Detail

getCovariances

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.

Parameters:

threshold - Singularity threshold.

Returns:

the covariance matrix.

Throws:

SingularMatrixException - if the covariance matrix cannot be computed (singular problem).

getSigma

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.

Parameters:

covarianceSingularityThreshold - Singularity threshold (see computeCovariances).

Returns:

an estimate of the standard deviation of the optimized parameters

Throws:

SingularMatrixException - if the covariance matrix cannot be computed.

getRMS

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.

Returns:

the cost.

getJacobian

RealMatrix getJacobian()

Get the weighted Jacobian matrix.

Returns:

the weighted Jacobian: W^1/2 J.

Throws:

DimensionMismatchException - if the Jacobian dimension does not match problem dimension.

getCost

double getCost()

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

Returns:

the cost.

See Also:

getResiduals(), getChiSquare()

getChiSquare

double getChiSquare()

Get the sum of the squares of the residuals.

Returns:

the cost.

See Also:

getResiduals(), getCost()

getReducedChiSquare

double getReducedChiSquare(int n)

Get the reduced chi-square.

Parameters:

n - Number of fitted parameters.

Returns:

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

getResiduals

RealVector 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:

the weighted residuals: W^1/2 K.

Throws:

DimensionMismatchException - if the residuals have the wrong length.

getPoint

RealVector getPoint()

Get the abscissa (independent variables) of this evaluation.

Returns:

the point provided to LeastSquaresProblem.evaluate(RealVector).