Uses of Interface
org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation
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Packages that use LeastSquaresProblem.Evaluation Package Description org.apache.commons.math3.fitting.leastsquares This package provides algorithms that minimize the residuals between observations and model values. -
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Uses of LeastSquaresProblem.Evaluation in org.apache.commons.math3.fitting.leastsquares
Subinterfaces of LeastSquaresProblem.Evaluation in org.apache.commons.math3.fitting.leastsquares Modifier and Type Interface Description static interface
LeastSquaresOptimizer.Optimum
The optimum found by the optimizer.Classes in org.apache.commons.math3.fitting.leastsquares that implement LeastSquaresProblem.Evaluation Modifier and Type Class Description class
AbstractEvaluation
An implementation ofLeastSquaresProblem.Evaluation
that is designed for extension.(package private) class
DenseWeightedEvaluation
Applies a dense weight matrix to an evaluation.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.(package private) class
OptimumImpl
A pedantic implementation ofLeastSquaresOptimizer.Optimum
.Fields in org.apache.commons.math3.fitting.leastsquares declared as LeastSquaresProblem.Evaluation Modifier and Type Field Description private LeastSquaresProblem.Evaluation
DenseWeightedEvaluation. unweighted
the unweighted evaluationprivate LeastSquaresProblem.Evaluation
OptimumImpl. value
abscissa and ordinateFields in org.apache.commons.math3.fitting.leastsquares with type parameters of type LeastSquaresProblem.Evaluation Modifier and Type Field Description private ConvergenceChecker<LeastSquaresProblem.Evaluation>
LeastSquaresBuilder. checker
convergence checkerMethods in org.apache.commons.math3.fitting.leastsquares that return LeastSquaresProblem.Evaluation Modifier and Type Method Description LeastSquaresProblem.Evaluation
LeastSquaresAdapter. evaluate(RealVector point)
Evaluate the model at the specified point.LeastSquaresProblem.Evaluation
LeastSquaresFactory.LocalLeastSquaresProblem. evaluate(RealVector point)
Evaluate the model at the specified point.LeastSquaresProblem.Evaluation
LeastSquaresProblem. evaluate(RealVector point)
Evaluate the model at the specified point.Methods in org.apache.commons.math3.fitting.leastsquares that return types with arguments of type LeastSquaresProblem.Evaluation Modifier and Type Method Description static ConvergenceChecker<LeastSquaresProblem.Evaluation>
LeastSquaresFactory. evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker)
View a convergence checker specified for aPointVectorValuePair
as one specified for anLeastSquaresProblem.Evaluation
.ConvergenceChecker<LeastSquaresProblem.Evaluation>
LeastSquaresAdapter. getConvergenceChecker()
Gets the convergence checker.Methods in org.apache.commons.math3.fitting.leastsquares with parameters of type LeastSquaresProblem.Evaluation Modifier and Type Method Description boolean
EvaluationRmsChecker. converged(int iteration, LeastSquaresProblem.Evaluation previous, LeastSquaresProblem.Evaluation current)
Check if the optimization algorithm has converged.Method parameters in org.apache.commons.math3.fitting.leastsquares with type arguments of type LeastSquaresProblem.Evaluation Modifier and Type Method Description LeastSquaresBuilder
LeastSquaresBuilder. checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker)
Configure the convergence checker.static LeastSquaresProblem
LeastSquaresFactory. create(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)
Create aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory. create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)
Create aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory. create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator)
Create aLeastSquaresProblem
from the given elements.static LeastSquaresProblem
LeastSquaresFactory. create(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)
Create aLeastSquaresProblem
from the given elements.Constructors in org.apache.commons.math3.fitting.leastsquares with parameters of type LeastSquaresProblem.Evaluation Constructor Description DenseWeightedEvaluation(LeastSquaresProblem.Evaluation unweighted, RealMatrix weightSqrt)
Create a weighted evaluation from an unweighted one.OptimumImpl(LeastSquaresProblem.Evaluation value, int evaluations, int iterations)
Construct an optimum from an evaluation and the values of the counters.Constructor parameters in org.apache.commons.math3.fitting.leastsquares with type arguments of type LeastSquaresProblem.Evaluation Constructor Description LocalLeastSquaresProblem(MultivariateJacobianFunction model, RealVector target, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator)
Create aLeastSquaresProblem
from the given data.
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