Package com.irurueta.numerical.polynomials.estimators

Class LMedSPolynomialRobustEstimator

java.lang.Object

com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator

com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator

public class LMedSPolynomialRobustEstimator
extends PolynomialRobustEstimator

Finds the best polynomial using LMedS algorithm.

Field Summary

Fields

Modifier and Type	Field	Description
static final double	DEFAULT_STOP_THRESHOLD	Default value to be used for stop threshold.
static final double	MIN_STOP_THRESHOLD	Minimum value that can be set as stop threshold.
private double	stopThreshold	Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.

Fields inherited from class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator

confidence, DEFAULT_CONFIDENCE, DEFAULT_MAX_ITERATIONS, DEFAULT_PROGRESS_DELTA, DEFAULT_ROBUST_METHOD, DEFAULT_USE_GEOMETRIC_DISTANCE, evaluations, listener, locked, MAX_CONFIDENCE, MAX_PROGRESS_DELTA, maxIterations, MIN_CONFIDENCE, MIN_ITERATIONS, MIN_PROGRESS_DELTA, polynomialEstimator, progressDelta, useGeometricDistance

Constructor Summary

Constructors

Constructor	Description
LMedSPolynomialRobustEstimator()	Constructor.
LMedSPolynomialRobustEstimator(int degree)	Constructor.
LMedSPolynomialRobustEstimator(int degree, PolynomialRobustEstimatorListener listener)	Constructor.
LMedSPolynomialRobustEstimator(int degree, List<PolynomialEvaluation> evaluations)	Constructor.
LMedSPolynomialRobustEstimator(int degree, List<PolynomialEvaluation> evaluations, PolynomialRobustEstimatorListener listener)	Constructor.
LMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener listener)	Constructor.
LMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations)	Constructor.
LMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations, PolynomialRobustEstimatorListener listener)	Constructor.

Method Summary

Modifier and Type	Method	Description
Polynomial	estimate()	Estimates polynomial.
RobustEstimatorMethod	getMethod()	Returns method being used for robust estimation.
double	getStopThreshold()	Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
void	setStopThreshold(double stopThreshold)	Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.

Methods inherited from class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator

create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, getAlgebraicDistance, getAlgebraicDistance, getAlgebraicDistance, getAlgebraicDistance, getAlgebraicDistance, getConfidence, getDegree, getDistance, getEvaluations, getGeometricDistance, getGeometricOrAlgebraicDistance, getListener, getMaxIterations, getMinNumberOfEvaluations, getProgressDelta, getQualityScores, isGeometricDistanceUsed, isLocked, isReady, setConfidence, setDegree, setEvaluations, setGeometricDistanceUsed, setListener, setMaxIterations, setProgressDelta, setQualityScores

Methods inherited from class java.lang.Object

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

Field Details

DEFAULT_STOP_THRESHOLD

public static final double DEFAULT_STOP_THRESHOLD

Default value to be used for stop threshold. Stop threshold can be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solutions is found that generates a threshold below this value, the algorithm will stop. Threshold will be used to compare either algebraic or geometric distance of estimated polynomial respect each provided evaluation.

See Also:

• Constant Field Values

MIN_STOP_THRESHOLD

public static final double MIN_STOP_THRESHOLD

Minimum value that can be set as stop threshold. Threshold must be strictly greater than 0.0.

See Also:

• Constant Field Values

stopThreshold

private double stopThreshold

Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in case where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the sopt threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.

Constructor Details

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator()

Constructor.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(int degree)

Constructor.

Parameters:

degree - degree of polynomial to be estimated.

Throws:

IllegalArgumentException - if provided degree is less than 1.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations)

Constructor.

Parameters:

evaluations - collections of polynomial evaluations.

Throws:

IllegalArgumentException - if provided number of evaluations is less than the required minimum.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener listener)

Constructor.

Parameters:

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(int degree,
 List<PolynomialEvaluation> evaluations)

Constructor.

Parameters:

degree - degree of polynomial to be estimated.

evaluations - collection of polynomial evaluations.

Throws:

IllegalArgumentException - if provided degree is less than 1 or if provided number of evaluations is less than the required minimum for provided degree.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(int degree,
 PolynomialRobustEstimatorListener listener)

Constructor.

Parameters:

degree - degree of polynomial to be estimated.

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

Throws:

IllegalArgumentException - if provided degree is less than 1.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations,
 PolynomialRobustEstimatorListener listener)

Constructor.

Parameters:

evaluations - collection of polynomial evaluations.

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

Throws:

IllegalArgumentException - if provided number of evaluations is less than the required minimum.

LMedSPolynomialRobustEstimator

public LMedSPolynomialRobustEstimator(int degree,
 List<PolynomialEvaluation> evaluations,
 PolynomialRobustEstimatorListener listener)

Constructor.

Parameters:

degree - degree of polynomial to be estimated.

evaluations - collection of polynomial evaluations.

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

Throws:

IllegalArgumentException - if provided degree is less than 1 or if provided number of evaluations is less than the required minimum for provided degree.

Method Details

getStopThreshold

public double getStopThreshold()

Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.

Returns:

stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.

setStopThreshold

public void setStopThreshold(double stopThreshold)
                      throws LockedException

Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results

Parameters:

stopThreshold - stop threshold to stop the algorithm prematurely when a certain accuracy has been reached

Throws:

IllegalArgumentException - if provided value is zero or negative

LockedException - if robust estimator is locked because an estimation is already in progress

estimate

public Polynomial estimate()
                    throws LockedException,
NotReadyException,
RobustEstimatorException

Estimates polynomial.

Specified by:

estimate in class PolynomialRobustEstimator

Returns:

estimated polynomial.

Throws:

LockedException - if robust estimator is locked because an estimation is already in progress.

NotReadyException - if provided input data is not enough to start the estimation.

RobustEstimatorException - if estimation fails for any other reason (i.e. numerical instability, no solution available, etc).

getMethod

public RobustEstimatorMethod getMethod()

Returns method being used for robust estimation.

Specified by:

getMethod in class PolynomialRobustEstimator

Returns:

method being used for robust estimation.