Uses of Interface
org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation
Packages that use LeastSquaresProblem.Evaluation
Package
Description
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.leastsquaresModifier and TypeInterfaceDescriptionstatic interfaceThe optimum found by the optimizer.Classes in org.apache.commons.math3.fitting.leastsquares that implement LeastSquaresProblem.EvaluationModifier and TypeClassDescriptionclassAn implementation ofLeastSquaresProblem.Evaluationthat is designed for extension.(package private) classApplies a dense weight matrix to an evaluation.private static classContainer with the model lazy evaluation at a particular point.private static classContainer with the model evaluation at a particular point.(package private) classA pedantic implementation ofLeastSquaresOptimizer.Optimum.Subclasses with type arguments of type LeastSquaresProblem.Evaluation in org.apache.commons.math3.fitting.leastsquaresModifier and TypeClassDescriptionprivate static classA private, "field" immutable (not "real" immutable) implementation ofLeastSquaresProblem.Subinterfaces with type arguments of type LeastSquaresProblem.Evaluation in org.apache.commons.math3.fitting.leastsquaresModifier and TypeInterfaceDescriptioninterfaceThe data necessary to define a non-linear least squares problem.Classes in org.apache.commons.math3.fitting.leastsquares that implement interfaces with type arguments of type LeastSquaresProblem.EvaluationModifier and TypeClassDescriptionclassCheck if an optimization has converged based on the change in computed RMS.Fields in org.apache.commons.math3.fitting.leastsquares declared as LeastSquaresProblem.EvaluationModifier and TypeFieldDescriptionprivate final LeastSquaresProblem.EvaluationDenseWeightedEvaluation.unweightedthe unweighted evaluationprivate final LeastSquaresProblem.EvaluationOptimumImpl.valueabscissa and ordinateFields in org.apache.commons.math3.fitting.leastsquares with type parameters of type LeastSquaresProblem.EvaluationMethods in org.apache.commons.math3.fitting.leastsquares that return LeastSquaresProblem.EvaluationModifier and TypeMethodDescriptionLeastSquaresAdapter.evaluate(RealVector point) Evaluate the model at the specified point.LeastSquaresFactory.LocalLeastSquaresProblem.evaluate(RealVector point) Evaluate the model at the specified point.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.EvaluationModifier and TypeMethodDescriptionLeastSquaresFactory.evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker) View a convergence checker specified for aPointVectorValuePairas one specified for anLeastSquaresProblem.Evaluation.LeastSquaresAdapter.getConvergenceChecker()Gets the convergence checker.Methods in org.apache.commons.math3.fitting.leastsquares with parameters of type LeastSquaresProblem.EvaluationModifier and TypeMethodDescriptionbooleanEvaluationRmsChecker.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.EvaluationModifier and TypeMethodDescriptionLeastSquaresBuilder.checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker) Configure the convergence checker.static LeastSquaresProblemLeastSquaresFactory.create(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblemfrom the given elements.static LeastSquaresProblemLeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblemfrom the given elements.static LeastSquaresProblemLeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator) Create aLeastSquaresProblemfrom the given elements.static LeastSquaresProblemLeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblemfrom the given elements.Constructors in org.apache.commons.math3.fitting.leastsquares with parameters of type LeastSquaresProblem.EvaluationModifierConstructorDescription(package private)DenseWeightedEvaluation(LeastSquaresProblem.Evaluation unweighted, RealMatrix weightSqrt) Create a weighted evaluation from an unweighted one.(package private)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.EvaluationModifierConstructorDescription(package private)LocalLeastSquaresProblem(MultivariateJacobianFunction model, RealVector target, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator) Create aLeastSquaresProblemfrom the given data.