Package org.apache.commons.math3.fitting.leastsquares

Class OptimumImpl

java.lang.Object

org.apache.commons.math3.fitting.leastsquares.OptimumImpl

All Implemented Interfaces:

LeastSquaresOptimizer.Optimum, LeastSquaresProblem.Evaluation

class OptimumImpl
extends Object
implements LeastSquaresOptimizer.Optimum

A pedantic implementation of LeastSquaresOptimizer.Optimum.

Since:

3.3

Field Summary

Fields

Modifier and Type	Field	Description
private final int	evaluations	number of evaluations to compute this optimum
private final int	iterations	number of iterations to compute this optimum
private final LeastSquaresProblem.Evaluation	value	abscissa and ordinate

Constructor Summary

Constructors

Constructor	Description
OptimumImpl(LeastSquaresProblem.Evaluation value, int evaluations, int iterations)	Construct an optimum from an evaluation and the values of the counters.

Method Summary

Modifier and Type	Method	Description
double	getCost()	Get the cost.
RealMatrix	getCovariances(double threshold)	Get the covariance matrix of the optimized parameters.
int	getEvaluations()	Get the number of times the model was evaluated in order to produce this optimum.
int	getIterations()	Get the number of times the algorithm iterated in order to produce this optimum.
RealMatrix	getJacobian()	Get the weighted Jacobian matrix.
RealVector	getPoint()	Get the abscissa (independent variables) of this evaluation.
RealVector	getResiduals()	Get the weighted residuals.
double	getRMS()	Get the normalized cost.
RealVector	getSigma(double covarianceSingularityThreshold)	Get an estimate of the standard deviation of the parameters.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

value

private final LeastSquaresProblem.Evaluation value

abscissa and ordinate

evaluations

private final int evaluations

number of evaluations to compute this optimum

iterations

private final int iterations

number of iterations to compute this optimum

Constructor Details

OptimumImpl

OptimumImpl(LeastSquaresProblem.Evaluation value,
 int evaluations,
 int iterations)

Construct an optimum from an evaluation and the values of the counters.

Parameters:

value - the function value

evaluations - number of times the function was evaluated

iterations - number of iterations of the algorithm

Method Details

getEvaluations

public int getEvaluations()

Get the number of times the model was evaluated in order to produce this optimum.

Specified by:

getEvaluations in interface LeastSquaresOptimizer.Optimum

Returns:

the number of model (objective) function evaluations

getIterations

public int getIterations()

Get the number of times the algorithm iterated in order to produce this optimum. In general least squares it is common to have one evaluation per iterations.

Specified by:

getIterations in interface LeastSquaresOptimizer.Optimum

Returns:

the number of iterations

getCovariances

public RealMatrix getCovariances(double threshold)

Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of the 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.

Specified by:

getCovariances in interface LeastSquaresProblem.Evaluation

Parameters:

threshold - Singularity threshold.

Returns:

the covariance matrix.

getSigma

public RealVector getSigma(double covarianceSingularityThreshold)

Get an estimate of the standard deviation of the parameters. The returned values are the square root of the diagonal coefficients of the covariance matrix, 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.

Specified by:

getSigma in interface LeastSquaresProblem.Evaluation

Parameters:

covarianceSingularityThreshold - Singularity threshold (see computeCovariances).

Returns:

an estimate of the standard deviation of the optimized parameters

getRMS

public double getRMS()

Get the normalized cost. It is the square-root of the sum of squared of the residuals, divided by the number of measurements.

Specified by:

getRMS in interface LeastSquaresProblem.Evaluation

Returns:

the cost.

getJacobian

public RealMatrix getJacobian()

Get the weighted Jacobian matrix.

Specified by:

getJacobian in interface LeastSquaresProblem.Evaluation

Returns:

the weighted Jacobian: W^1/2 J.

getCost

public double getCost()

Get the cost.

Specified by:

getCost in interface LeastSquaresProblem.Evaluation

Returns:

the cost.

See Also:

• LeastSquaresProblem.Evaluation.getResiduals()

getResiduals

public RealVector 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.

Specified by:

getResiduals in interface LeastSquaresProblem.Evaluation

Returns:

the weighted residuals: W^1/2 K.

getPoint

public RealVector getPoint()

Get the abscissa (independent variables) of this evaluation.

Specified by:

getPoint in interface LeastSquaresProblem.Evaluation

Returns:

the point provided to LeastSquaresProblem.evaluate(RealVector).