Package org.apache.commons.math.estimation

Class GaussNewtonEstimator

java.lang.Object

org.apache.commons.math.estimation.AbstractEstimator

org.apache.commons.math.estimation.GaussNewtonEstimator

All Implemented Interfaces:

java.io.Serializable, Estimator

@Deprecated
public class GaussNewtonEstimator
extends AbstractEstimator
implements java.io.Serializable

Deprecated.

as of 2.0, everything in package org.apache.commons.math.estimation has been deprecated and replaced by package org.apache.commons.math.optimization.general

This class implements a solver for estimation problems.

This class solves estimation problems using a weighted least squares criterion on the measurement residuals. It uses a Gauss-Newton algorithm.

Since:

1.2

See Also:

Serialized Form

Field Summary

Fields inherited from class org.apache.commons.math.estimation.AbstractEstimator

DEFAULT_MAX_COST_EVALUATIONS

Constructor Summary

Constructors

Constructor	Description
GaussNewtonEstimator()	Deprecated. Simple constructor with default settings.
GaussNewtonEstimator(int maxCostEval, double convergence, double steadyStateThreshold)	Deprecated. Simple constructor.

Method Summary

Modifier and Type	Method	Description
void	estimate(EstimationProblem problem)	Deprecated. Solve an estimation problem using a least squares criterion.
void	setConvergence(double convergence)	Deprecated. Set the convergence criterion threshold.
void	setSteadyStateThreshold(double steadyStateThreshold)	Deprecated. Set the steady state detection threshold.

Methods inherited from class org.apache.commons.math.estimation.AbstractEstimator

getChiSquare, getCostEvaluations, getCovariances, getJacobianEvaluations, getRMS, guessParametersErrors, setMaxCostEval

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussNewtonEstimator

public GaussNewtonEstimator()

Deprecated.

Simple constructor with default settings.

The estimator is built with default values for all settings.

See Also:

DEFAULT_STEADY_STATE_THRESHOLD, DEFAULT_CONVERGENCE, AbstractEstimator.DEFAULT_MAX_COST_EVALUATIONS

GaussNewtonEstimator

public GaussNewtonEstimator(int maxCostEval,
                            double convergence,
                            double steadyStateThreshold)

Deprecated.

Simple constructor.

This constructor builds an estimator and stores its convergence characteristics.

An estimator is considered to have converged whenever either the criterion goes below a physical threshold under which improvements are considered useless or when the algorithm is unable to improve it (even if it is still high). The first condition that is met stops the iterations.

The fact an estimator has converged does not mean that the model accurately fits the measurements. It only means no better solution can be found, it does not mean this one is good. Such an analysis is left to the caller.

If neither conditions are fulfilled before a given number of iterations, the algorithm is considered to have failed and an EstimationException is thrown.

Parameters:

maxCostEval - maximal number of cost evaluations allowed

convergence - criterion threshold below which we do not need to improve the criterion anymore

steadyStateThreshold - steady state detection threshold, the problem has converged has reached a steady state if FastMath.abs(Jn - Jn-1) < Jn × convergence, where Jn and Jn-1 are the current and preceding criterion values (square sum of the weighted residuals of considered measurements).

Method Detail

setConvergence

public void setConvergence(double convergence)

Deprecated.

Set the convergence criterion threshold.

Parameters:

convergence - criterion threshold below which we do not need to improve the criterion anymore

setSteadyStateThreshold

public void setSteadyStateThreshold(double steadyStateThreshold)

Deprecated.

Set the steady state detection threshold.

The problem has converged has reached a steady state if FastMath.abs(Jn - Jn-1) < Jn × convergence, where Jn and Jn-1 are the current and preceding criterion values (square sum of the weighted residuals of considered measurements).

Parameters:

steadyStateThreshold - steady state detection threshold

estimate

public void estimate(EstimationProblem problem)
              throws EstimationException

Deprecated.

Solve an estimation problem using a least squares criterion.

This method set the unbound parameters of the given problem starting from their current values through several iterations. At each step, the unbound parameters are changed in order to minimize a weighted least square criterion based on the measurements of the problem.

The iterations are stopped either when the criterion goes below a physical threshold under which improvement are considered useless or when the algorithm is unable to improve it (even if it is still high). The first condition that is met stops the iterations. If the convergence it not reached before the maximum number of iterations, an EstimationException is thrown.

Specified by:

estimate in interface Estimator

Specified by:

estimate in class AbstractEstimator

Parameters:

problem - estimation problem to solve

Throws:

EstimationException - if the problem cannot be solved

See Also:

EstimationProblem