org.apache.commons.math3.fitting.leastsquares
public abstract class AbstractEvaluation extends Object implements LeastSquaresProblem.Evaluation
LeastSquaresProblem.Evaluation that is designed for extension. All of the
methods implemented here use the methods that are left unimplemented.
TODO cache results?| Modifier and Type | Method and Description |
|---|---|
double |
getCost()
Get the cost.
|
RealMatrix |
getCovariances(double threshold)
Get the covariance matrix of the optimized parameters.
|
double |
getRMS()
Get the normalized cost.
|
RealVector |
getSigma(double covarianceSingularityThreshold)
Get an estimate of the standard deviation of the parameters.
|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetJacobian, getPoint, getResidualspublic RealMatrix getCovariances(double threshold)
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.getCovariances in interface LeastSquaresProblem.Evaluationthreshold - Singularity threshold.public RealVector getSigma(double covarianceSingularityThreshold)
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.getSigma in interface LeastSquaresProblem.EvaluationcovarianceSingularityThreshold - Singularity threshold (see computeCovariances).public double getRMS()
getRMS in interface LeastSquaresProblem.Evaluationpublic double getCost()
getCost in interface LeastSquaresProblem.EvaluationLeastSquaresProblem.Evaluation.getResiduals()Copyright © 2003–2015 The Apache Software Foundation. All rights reserved.