Package com.irurueta.numerical.polynomials.estimators

Class PROMedSPolynomialRobustEstimator

java.lang.Object

com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator

com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator

public class PROMedSPolynomialRobustEstimator
extends PolynomialRobustEstimator

Finds the best polynomial using PROMedS 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 allowed stop threshold value
private double[]	qualityScores	Quality scores corresponding to each provided polynomial evaluation.
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
PROMedSPolynomialRobustEstimator()	Constructor.
PROMedSPolynomialRobustEstimator(int degree)	Constructor.
PROMedSPolynomialRobustEstimator(int degree, PolynomialRobustEstimatorListener listener)	Constructor.
PROMedSPolynomialRobustEstimator(int degree, List<PolynomialEvaluation> evaluations)	Constructor.
PROMedSPolynomialRobustEstimator(int degree, List<PolynomialEvaluation> evaluations, PolynomialRobustEstimatorListener listener)	Constructor.
PROMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener listener)	Constructor.
PROMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations)	Constructor.
PROMedSPolynomialRobustEstimator(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[]	getQualityScores()	Returns quality scores corresponding to each provided point.
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.
private void	internalSetQualityScores(double[] qualityScores)	Sets quality scores corresponding to each provided polynomial evaluation.
boolean	isReady()	Indicates if estimator is ready to start the polynomial estimation.
void	setQualityScores(double[] qualityScores)	Sets quality scores corresponding to each provided point.
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, isGeometricDistanceUsed, isLocked, setConfidence, setDegree, setEvaluations, setGeometricDistanceUsed, setListener, setMaxIterations, setProgressDelta

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 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

See Also:

• Constant Field Values

MIN_STOP_THRESHOLD

public static final double MIN_STOP_THRESHOLD

Minimum allowed stop threshold value

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 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

qualityScores

private double[] qualityScores

Quality scores corresponding to each provided polynomial evaluation. The larger the score value the better the quality of the sample.

Constructor Details

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator()

Constructor.

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(int degree)

Constructor.

Parameters:

degree - degree of polynomial to be estimated.

Throws:

IllegalArgumentException - if provided degree is less than 1.

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(List<PolynomialEvaluation> evaluations)

Constructor.

Parameters:

evaluations - collection of polynomial evaluations.

Throws:

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

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener listener)

Constructor.

Parameters:

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

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(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.

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(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.

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(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.

PROMedSPolynomialRobustEstimator

public PROMedSPolynomialRobustEstimator(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

getQualityScores

public double[] getQualityScores()

Returns quality scores corresponding to each provided point. The larger the score value the better the quality of the sampled point

Overrides:

getQualityScores in class PolynomialRobustEstimator

Returns:

quality scores corresponding to each point

setQualityScores

public void setQualityScores(double[] qualityScores)
                      throws LockedException

Sets quality scores corresponding to each provided point. The larger the score value the better the quality of the sampled point.

Overrides:

setQualityScores in class PolynomialRobustEstimator

Parameters:

qualityScores - quality scores corresponding to each point.

Throws:

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

IllegalArgumentException - if provided quality scores length is smaller than required minimum size.

isReady

public boolean isReady()

Indicates if estimator is ready to start the polynomial estimation. This is true when input data (i.e. polynomial evaluations and quality scores) are provided and enough data is available.

Overrides:

isReady in class PolynomialRobustEstimator

Returns:

true if estimator is ready, false otherwise

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.

internalSetQualityScores

private void internalSetQualityScores(double[] qualityScores)

Sets quality scores corresponding to each provided polynomial evaluation. This method is used internally and does not check whether instance is locked or not

Parameters:

qualityScores - quality scores to be set

Throws:

IllegalArgumentException - if provided quality scores length is smaller than required minimum size.