## 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:
  Serializable, Estimator

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

    @Deprecated
    public class GaussNewtonEstimator

extends AbstractEstimator
implements Serializable

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
Version:
  $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $
See Also:
  Serialized Form

Field Summary
`private double` `convergence` Deprecated. Threshold for cost convergence.
`private static double` `DEFAULT_CONVERGENCE` Deprecated. Default threshold for cost convergence.
`private static double` `DEFAULT_STEADY_STATE_THRESHOLD` Deprecated. Default threshold for cost steady state detection.
`private static long` `serialVersionUID` Deprecated. Serializable version identifier
`private double` `steadyStateThreshold` Deprecated. Threshold for cost steady state detection.

Fields inherited from class org.apache.commons.math.estimation.AbstractEstimator
`cols, cost, DEFAULT_MAX_COST_EVALUATIONS, jacobian, measurements, parameters, residuals, rows`

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

Method Summary
`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, incrementJacobianEvaluationsCounter, initializeEstimate, setMaxCostEval, updateJacobian, updateResidualsAndCost`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### serialVersionUID

    private static final long serialVersionUID

  Deprecated.
  Serializable version identifier

  See Also:
    Constant Field Values

### DEFAULT_STEADY_STATE_THRESHOLD

    private static final double DEFAULT_STEADY_STATE_THRESHOLD

  Deprecated.
  Default threshold for cost steady state detection.

  See Also:
    Constant Field Values

### DEFAULT_CONVERGENCE

    private static final double DEFAULT_CONVERGENCE

  Deprecated.
  Default threshold for cost convergence.

  See Also:
    Constant Field Values

### steadyStateThreshold

    private double steadyStateThreshold

  Deprecated.
  Threshold for cost steady state detection.

### convergence

    private double convergence

  Deprecated.
  Threshold for cost convergence.

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`

Copyright (c) 2003-2013 Apache Software Foundation