Class LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation
java.lang.Object
org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation
org.apache.commons.math3.fitting.leastsquares.LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation
- All Implemented Interfaces:
LeastSquaresProblem.Evaluation
- Enclosing class:
LeastSquaresFactory.LocalLeastSquaresProblem
private static class LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation
extends AbstractEvaluation
Container with the model evaluation at a particular point.
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Field Summary
FieldsModifier and TypeFieldDescriptionprivate final RealMatrix
Derivative at point.private final RealVector
Point of evaluation.private final RealVector
Computed residuals. -
Constructor Summary
ConstructorsModifierConstructorDescriptionprivate
UnweightedEvaluation
(RealVector values, RealMatrix jacobian, RealVector target, RealVector point) Create anLeastSquaresProblem.Evaluation
with no weights. -
Method Summary
Modifier and TypeMethodDescriptionGet the weighted Jacobian matrix.getPoint()
Get the abscissa (independent variables) of this evaluation.Get the weighted residuals.Methods inherited from class org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation
getCost, getCovariances, getRMS, getSigma
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Field Details
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point
Point of evaluation. -
jacobian
Derivative at point. -
residuals
Computed residuals.
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Constructor Details
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UnweightedEvaluation
private UnweightedEvaluation(RealVector values, RealMatrix jacobian, RealVector target, RealVector point) Create anLeastSquaresProblem.Evaluation
with no weights.- Parameters:
values
- the computed function valuesjacobian
- the computed function Jacobiantarget
- the observed valuespoint
- the abscissa
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Method Details
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getJacobian
Get the weighted Jacobian matrix.- Returns:
- the weighted Jacobian: W1/2 J.
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getPoint
Get the abscissa (independent variables) of this evaluation.- Returns:
- the point provided to
LeastSquaresProblem.evaluate(RealVector)
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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: W1/2 K.
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