Uses of Class
com.irurueta.numerical.LockedException

Packages that use LockedException

Package	Description
com.irurueta.numerical	This library contains packages for:
com.irurueta.numerical.optimization
com.irurueta.numerical.polynomials.estimators
com.irurueta.numerical.robust	This package contains robust estimators that can be used to discard outliers for cases where a model of the data is known (i.e.
com.irurueta.numerical.roots	This package contains classes to find function roots.

Uses of LockedException in com.irurueta.numerical

Methods in com.irurueta.numerical that throw LockedException

Modifier and Type	Method	Description
double	AccurateMaximumLikelihoodEstimator.estimate()	Starts the estimation of the most likely value contained within provided input data array.
double	HistogramMaximumLikelihoodEstimator.estimate()	Starts the estimation of the most likely value contained within provided input data array.
abstract double	MaximumLikelihoodEstimator.estimate()	Starts the estimation of the most likely value contained within provided input data array.
void	MaximumLikelihoodEstimator.setGaussianSigma(double gaussianSigma)	Sets Gaussian sigma to be used on each sample when aggregating Gaussian functions centered at each input data sample value.
void	AccurateMaximumLikelihoodEstimator.setHistogramInitialSolutionUsed(boolean used)	Sets boolean that indicates that an initial coarse solution will be computed first by using an internal HistogramMaximumLikelihoodEstimator in order to initialize the internal BrentSingleOptimizer to obtain a more accurate solution.
void	MaximumLikelihoodEstimator.setInputData(double[] inputData)	Sets array containing input data to be used to find the most likely value.
void	MaximumLikelihoodEstimator.setInputData(double[] inputData, double minValue, double maxValue)	Sets array containing input data to be used to find the most likely value along with the minimum and maximum values assumed to be contained in it.
void	MaximumLikelihoodEstimator.setMinMaxValues(double minValue, double maxValue)	Sets minimum and maximum value assumed to be found in input data array.
void	HistogramMaximumLikelihoodEstimator.setNumberOfBins(int numberOfBins)	Sets number of bins to be used on the histogram.

Uses of LockedException in com.irurueta.numerical.optimization

Methods in com.irurueta.numerical.optimization that throw LockedException

Modifier and Type	Method	Description
void	BracketedSingleOptimizer.computeBracket()	Computes a bracket of values using the whole range of possible values as an initial guess.
void	BracketedSingleOptimizer.computeBracket(double minEvalPoint)	Computes a bracket of values using provided value as a starting point, and assuming that bracket finishes at Double.MAX_VALUE.
void	BracketedSingleOptimizer.computeBracket(double minEvalPoint, double middleEvalPoint)	Computes a bracket of values using provided values as a starting point.
void	BracketedSingleOptimizer.evaluateBracket()	Computes function evaluations at provided or estimated bracket locations.
void	BrentSingleOptimizer.minimize()	This function estimates a function minimum within provided or computed bracket of values.
void	ConjugateGradientMultiOptimizer.minimize()	This function estimates a function minimum.
void	DerivativeBrentSingleOptimizer.minimize()	This function estimates a function minimum within provided or computed bracket of values.
void	DerivativeConjugateGradientMultiOptimizer.minimize()	This function estimates a function minimum.
void	GoldenSingleOptimizer.minimize()	This function estimates a function minimum within provided or computed bracket of values.
void	Optimizer.minimize()	This function estimates a function minimum.
void	PowellMultiOptimizer.minimize()	This function estimates a function minimum.
void	QuasiNewtonMultiOptimizer.minimize()	This function estimates a function minimum.
void	SimplexMultiOptimizer.minimize()	This function estimates a function minimum.
void	BracketedSingleOptimizer.setBracket(double minEvalPoint, double middleEvalPoint, double maxEvalPoint)	Sets a bracket of values to later search for a minimum.
void	DerivativeBrentSingleOptimizer.setDerivativeListener(SingleDimensionFunctionEvaluatorListener derivativeListener)	Sets derivative listener that gets function derivative.
void	ConjugateGradientMultiOptimizer.setGradientListener(GradientFunctionEvaluatorListener gradientListener)	Sets gradient listener in charge of obtaining gradient values for the function to be evaluated.
void	DerivativeLineMultiOptimizer.setGradientListener(GradientFunctionEvaluatorListener gradientListener)	Sets gradient listener.
void	QuasiNewtonMultiOptimizer.setGradientListener(GradientFunctionEvaluatorListener gradientListener)	Sets gradient listener in charge of obtaining gradient values for the function to be evaluated.
void	MultiOptimizer.setListener(MultiDimensionFunctionEvaluatorListener listener)	Sets listener to evaluate a multidimensional function.
void	SingleOptimizer.setListener(SingleDimensionFunctionEvaluatorListener listener)	Sets listener.
void	Optimizer.setOnIterationCompletedListener(OnIterationCompletedListener iterationCompletedListener)	Sets listener to handle minimization events.
void	PowellMultiOptimizer.setPointAndDirections(double[] point, com.irurueta.algebra.Matrix directions)	Sets start point and set of directions to start looking for minimum.
void	SimplexMultiOptimizer.setSimplex(double[] startPoint, double delta)	Sets a simplex defined as a central point and a set of surrounding points at distance delta.
void	SimplexMultiOptimizer.setSimplex(double[] startPoint, double[] deltas)	Sets a simplex defined as a central point and a set of surrounding points at their corresponding distance deltas[i], where "i" corresponds to one position of provided array of distances.
void	SimplexMultiOptimizer.setSimplex(com.irurueta.algebra.Matrix simplex)	Sets simplex as a matrix containing on each row a point of the simplex.
void	ConjugateGradientMultiOptimizer.setStartPoint(double[] point)	Sets start point where local minimum is searched nearby.
void	DerivativeConjugateGradientMultiOptimizer.setStartPoint(double[] point)	Sets start point where local minimum is searched nearby.
void	PowellMultiOptimizer.setStartPoint(double[] startPoint)	Sets start point where local minimum is searched nearby.
void	QuasiNewtonMultiOptimizer.setStartPoint(double[] startPoint)	Sets start point where algorithm will be started.
void	DerivativeLineMultiOptimizer.setStartPointAndDirection(double[] point, double[] direction)	Internal method to set start point and direction to start the search for a local minimum.
void	LineMultiOptimizer.setStartPointAndDirection(double[] point, double[] direction)	Internal method to set start point and direction to start the search for a local minimum.
void	BrentSingleOptimizer.setTolerance(double tolerance)	Sets algorithm's tolerance.
void	ConjugateGradientMultiOptimizer.setTolerance(double tolerance)	Sets tolerance or accuracy to be expected on estimated local minimum.
void	DerivativeBrentSingleOptimizer.setTolerance(double tolerance)	Sets tolerance value.
void	DerivativeConjugateGradientMultiOptimizer.setTolerance(double tolerance)	Sets tolerance or accuracy to be expected on estimated local minimum.
void	GoldenSingleOptimizer.setTolerance(double tolerance)	Sets algorithm's tolerance.
void	PowellMultiOptimizer.setTolerance(double tolerance)	Sets tolerance or accuracy to be expected on estimated local minimum.
void	QuasiNewtonMultiOptimizer.setTolerance(double tolerance)	Sets tolerance or accuracy to be expected on estimated local minimum.
void	SimplexMultiOptimizer.setTolerance(double tolerance)	Sets tolerance or accuracy to be expected on estimated local minimum.
void	ConjugateGradientMultiOptimizer.setUsePolakRibiere(boolean useIt)	Sets boolean indicating whether Polak-Ribiere method or Fletcher-Reeves method is used.
void	DerivativeConjugateGradientMultiOptimizer.setUsePolakRibiere(boolean useIt)	Sets boolean indicating whether Polak-Ribiere method or Fletcher-Reeves method is used.

Uses of LockedException in com.irurueta.numerical.polynomials.estimators

Methods in com.irurueta.numerical.polynomials.estimators that throw LockedException

Modifier and Type	Method	Description
Polynomial	LMedSPolynomialRobustEstimator.estimate()	Estimates polynomial.
Polynomial	LMSEPolynomialEstimator.estimate()	Estimates a polynomial based on provided evaluations.
Polynomial	MSACPolynomialRobustEstimator.estimate()	Estimates polynomial.
abstract Polynomial	PolynomialEstimator.estimate()	Estimates a polynomial based on provided evaluations.
abstract Polynomial	PolynomialRobustEstimator.estimate()	Estimates polynomial.
Polynomial	PROMedSPolynomialRobustEstimator.estimate()	Estimates polynomial.
Polynomial	PROSACPolynomialRobustEstimator.estimate()	Estimates polynomial.
Polynomial	RANSACPolynomialRobustEstimator.estimate()	Estimates polynomial.
Polynomial	WeightedPolynomialEstimator.estimate()	Estimates a polynomial based on provided evaluations.
void	PolynomialRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%).
void	PolynomialEstimator.setDegree(int degree)	Sets degree of polynomial to be estimated.
void	PolynomialRobustEstimator.setDegree(int degree)	Sets degree of polynomial to be estimated.
void	PolynomialEstimator.setDegreeAndEvaluations(int degree, List<PolynomialEvaluation> evaluations)	Sets degree of polynomial to be estimated and collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of specified degree.
void	WeightedPolynomialEstimator.setDegreeEvaluationsAndWeights(int degree, List<PolynomialEvaluation> evaluations, double[] weights)	Sets degree of polynomial to be estimated and collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of specified degree.
void	PolynomialEstimator.setEvaluations(List<PolynomialEvaluation> evaluations)	Sets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
void	PolynomialRobustEstimator.setEvaluations(List<PolynomialEvaluation> evaluations)	Sets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
void	WeightedPolynomialEstimator.setEvaluationsAndWeights(List<PolynomialEvaluation> evaluations, double[] weights)	Sets collection of polynomial evaluations along with their corresponding weights.
void	PolynomialRobustEstimator.setGeometricDistanceUsed(boolean geometricDistanceUsed)	Specifies whether geometric distance will be used to find outliers or algebraic distance will be used instead.
void	PolynomialEstimator.setListener(PolynomialEstimatorListener listener)	Sets listener to be notified of events such as when estimation starts, ends or estimation progress changes.
void	LMSEPolynomialEstimator.setLMSESolutionAllowed(boolean allowed)	Specified if an LMSE (Least Mean Square Error) solution is allowed if more evaluations than the required minimum are provided.
void	WeightedPolynomialEstimator.setMaxEvaluations(int maxEvaluations)	Sets maximum number of evaluations to be weighted and taken into account.
void	PolynomialRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	PolynomialRobustEstimator.setProgressDelta(float progressDelta)	Sets amount of progress variation before notifying a progress change during estimation.
void	PolynomialRobustEstimator.setQualityScores(double[] qualityScores)	Sets quality scores corresponding to each polynomial evaluation.
void	PROMedSPolynomialRobustEstimator.setQualityScores(double[] qualityScores)	Sets quality scores corresponding to each provided point.
void	PROSACPolynomialRobustEstimator.setQualityScores(double[] qualityScores)	Sets quality scores corresponding to each provided point.
void	WeightedPolynomialEstimator.setSortWeightsEnabled(boolean sortWeights)	Specifies whether weights are sorted by so that largest weighted evaluations are used first.
void	LMedSPolynomialRobustEstimator.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.
void	PROMedSPolynomialRobustEstimator.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.
void	MSACPolynomialRobustEstimator.setThreshold(double threshold)	Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
void	PROSACPolynomialRobustEstimator.setThreshold(double threshold)	Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
void	RANSACPolynomialRobustEstimator.setThreshold(double threshold)	Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.

Uses of LockedException in com.irurueta.numerical.robust

Methods in com.irurueta.numerical.robust that throw LockedException

Modifier and Type	Method	Description
T	LMedSRobustEstimator.estimate()	Robustly estimates an instance of T.
T	MSACRobustEstimator.estimate()	Robustly estimates an instance of T.
T	PROMedSRobustEstimator.estimate()	Robustly estimates an instance of T.
T	PROSACRobustEstimator.estimate()	Robustly estimates an instance of T.
T	RANSACRobustEstimator.estimate()	Robustly estimates an instance of T.
abstract T	RobustEstimator.estimate()	Robustly estimates an instance of T.
void	PROMedSRobustEstimator.setBeta(double beta)	Sets beta, which is the probability that a match is declared inlier by mistake, i.e.
void	PROSACRobustEstimator.setBeta(double beta)	Sets beta, which is the probability that a match is declared inlier by mistake, i.e.
void	PROSACRobustEstimator.setComputeAndKeepInliersEnabled(boolean computeAndKeepInliers)	Specifies whether inliers must be computed and kept.
void	RANSACRobustEstimator.setComputeAndKeepInliersEnabled(boolean computeAndKeepInliers)	Specifies whether inliers must be computed and kept.
void	PROSACRobustEstimator.setComputeAndKeepResidualsEnabled(boolean computeAndKeepResiduals)	Specifies whether residuals must be computed and kept.
void	RANSACRobustEstimator.setComputeAndKeepResidualsEnabled(boolean computeAndKeepResiduals)	Specifies whether residuals must be computed and kept.
void	LMedSRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
void	MSACRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
void	PROMedSRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
void	PROSACRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
void	RANSACRobustEstimator.setConfidence(double confidence)	Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
void	PROMedSRobustEstimator.setEta0(double eta0)	Sets eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
void	PROSACRobustEstimator.setEta0(double eta0)	Sets eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
void	LMedSRobustEstimator.setInlierFactor(double inlierFactor)	Sets factor to normalize or adjust threshold to determine inliers.
void	PROMedSRobustEstimator.setInlierFactor(double inlierFactor)	Sets factor to normalize or adjust threshold to determine inliers.
void	RobustEstimator.setListener(RobustEstimatorListener<T> listener)	Sets listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
void	LMedSRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	MSACRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	PROMedSRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	PROSACRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	RANSACRobustEstimator.setMaxIterations(int maxIterations)	Sets maximum allowed number of iterations.
void	PROMedSRobustEstimator.setMaxOutliersProportion(double maxOutliersProportion)	Sets maximum allowed outliers proportion in the input data.
void	PROSACRobustEstimator.setMaxOutliersProportion(double maxOutliersProportion)	Sets maximum allowed outliers proportion in the input data.
void	RobustEstimator.setProgressDelta(float progressDelta)	Sets amount of progress variation before notifying a progress change during estimation.
void	LMedSRobustEstimator.setStopThreshold(double stopThreshold)	Sets threshold to be used to keep the algorithm iterating in case that best threshold is not small enough.
void	PROMedSRobustEstimator.setStopThresholdEnabled(boolean stopThresholdEnabled)	Sets boolean indicating whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.
void	PROMedSRobustEstimator.setUseInlierThresholds(boolean useInlierThresholds)	Sets flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.

Uses of LockedException in com.irurueta.numerical.roots

Methods in com.irurueta.numerical.roots that throw LockedException

Modifier and Type	Method	Description
void	BracketedSingleRootEstimator.computeBracket()	Starting at zero, this method expands the range (i.e.
void	BracketedSingleRootEstimator.computeBracket(double point)	Starting from provided point, this method expands the range (i.e.
void	BracketedSingleRootEstimator.computeBracket(double minEvalPoint, double maxEvalPoint)	Starting from provided minimum and maximum values, this method expands the range (i.e.
void	BisectionSingleRootEstimator.estimate()	Estimates a single root of the provided single dimension function contained within a given bracket of values.
void	BrentSingleRootEstimator.estimate()	Estimates a local root for a given single dimension function being evaluated by provided listener.
void	FalsePositionSingleRootEstimator.estimate()	Estimates a single root of the provided single dimension function contained within a given bracket of values.
void	FirstDegreePolynomialRootsEstimator.estimate()	Estimates the root of provided polynomial.
void	LaguerrePolynomialRootsEstimator.estimate()	Estimates the roots of provided polynomial.
void	NewtonRaphsonSingleRootEstimator.estimate()	Estimates a local root for a given single dimension function being evaluated by provided listener.
void	RidderSingleRootEstimator.estimate()	Estimates a local root for a given single dimension function being evaluated by provided listener.
void	RootEstimator.estimate()	Estimates the root or roots for a given function.
void	SafeNewtonRaphsonSingleRootEstimator.estimate()	Estimates a local root for a given single dimension function being evaluated by provided listener.
void	SecantSingleRootEstimator.estimate()	Estimates a local root for a given single dimension function being evaluated by provided listener.
void	SecondDegreePolynomialRootsEstimator.estimate()	Estimates the roots of provided polynomial.
void	ThirdDegreePolynomialRootsEstimator.estimate()	Estimates the roots of provided polynomial.
void	BracketedSingleRootEstimator.setBracket(double minEvalPoint, double maxEvalPoint)	Sets the bracket of values (i.e.
void	DerivativeSingleRootEstimator.setDerivativeListener(SingleDimensionFunctionEvaluatorListener derivativeListener)	Sets derivative listener to evaluate a function's derivative.
void	SingleRootEstimator.setListener(SingleDimensionFunctionEvaluatorListener listener)	Sets listener that evaluates a single dimension function in order to find its root.
void	LaguerrePolynomialRootsEstimator.setPolishRootsEnabled(boolean enable)	Sets boolean indicating whether roots will be refined after an initial estimation.
void	FirstDegreePolynomialRootsEstimator.setPolynomialParameters(double[] polyParams)	Set array of first degree polynomial parameters.
void	PolynomialRootsEstimator.setPolynomialParameters(com.irurueta.algebra.Complex[] polyParams)	Sets parameters of a polynomial, taking into account that a polynomial of degree n is defined as: p(x) = a0 * x^n + a1 * x^(n - 1) + ...
void	SecondDegreePolynomialRootsEstimator.setPolynomialParameters(double[] polyParams)	Set array of second degree polynomial parameters.
void	ThirdDegreePolynomialRootsEstimator.setPolynomialParameters(double[] polyParams)	Set array of third degree polynomial parameters.
void	BisectionSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance to find a root.
void	BrentSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance value.
void	FalsePositionSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance to find a root.
void	NewtonRaphsonSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance value.
void	RidderSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance value.
void	SafeNewtonRaphsonSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance value.
void	SecantSingleRootEstimator.setTolerance(double tolerance)	Sets tolerance value.