Package | Description |
---|---|
org.apache.commons.math3.fitting.leastsquares |
This package provides algorithms that minimize the residuals
between observations and model values.
|
Modifier and Type | Interface and Description |
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static interface |
LeastSquaresOptimizer.Optimum
The optimum found by the optimizer.
|
Modifier and Type | Class and Description |
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class |
AbstractEvaluation
An implementation of
LeastSquaresProblem.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 of
LeastSquaresOptimizer.Optimum . |
Modifier and Type | Field and Description |
---|---|
private LeastSquaresProblem.Evaluation |
DenseWeightedEvaluation.unweighted
the unweighted evaluation
|
private LeastSquaresProblem.Evaluation |
OptimumImpl.value
abscissa and ordinate
|
Modifier and Type | Field and Description |
---|---|
private ConvergenceChecker<LeastSquaresProblem.Evaluation> |
LeastSquaresBuilder.checker
convergence checker
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and Description |
---|---|
static ConvergenceChecker<LeastSquaresProblem.Evaluation> |
LeastSquaresFactory.evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker)
View a convergence checker specified for a
PointVectorValuePair as one
specified for an LeastSquaresProblem.Evaluation . |
ConvergenceChecker<LeastSquaresProblem.Evaluation> |
LeastSquaresAdapter.getConvergenceChecker()
Gets the convergence checker.
|
Modifier and Type | Method and Description |
---|---|
boolean |
EvaluationRmsChecker.converged(int iteration,
LeastSquaresProblem.Evaluation previous,
LeastSquaresProblem.Evaluation current)
Check if the optimization algorithm has converged.
|
Modifier and Type | Method and Description |
---|---|
LeastSquaresBuilder |
LeastSquaresBuilder.checker(ConvergenceChecker<LeastSquaresProblem.Evaluation> newChecker)
Configure the convergence checker.
|
static LeastSquaresProblem |
LeastSquaresFactory.create(MultivariateJacobianFunction model,
RealVector observed,
RealVector start,
ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
int maxEvaluations,
int maxIterations)
Create a
LeastSquaresProblem
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 a
LeastSquaresProblem
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 a
LeastSquaresProblem
from the given elements. |
static LeastSquaresProblem |
LeastSquaresFactory.create(MultivariateVectorFunction model,
MultivariateMatrixFunction jacobian,
double[] observed,
double[] start,
RealMatrix weight,
ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
int maxEvaluations,
int maxIterations)
Create a
LeastSquaresProblem
from the given elements. |
Constructor and 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 and Description |
---|
LocalLeastSquaresProblem(MultivariateJacobianFunction model,
RealVector target,
RealVector start,
ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
int maxEvaluations,
int maxIterations,
boolean lazyEvaluation,
ParameterValidator paramValidator)
Create a
LeastSquaresProblem from the given data. |